User login
Immunotherapy may benefit relapsed HSCT recipients
Photo from Business Wire
Results of a phase 1 study suggest that repeated doses of the immunotherapy drug ipilimumab is a feasible treatment option for patients with hematologic diseases who relapse after allogeneic hematopoietic stem cell transplant (HSCT).
Seven of the 28 patients studied responded to the treatment, but immune-mediated toxic effects and graft-vs-host disease (GVHD) occurred as well.
These results were published in NEJM.
Ipilimumab, which is already approved to treat unresectable or metastatic melanoma, works by blocking the immune checkpoint CTLA-4. Blockade of CTLA-4 has been shown to augment T-cell activation and proliferation.
“We believe [,in the case of relapse after HSCT,] the donor immune cells are present but can’t recognize the tumor cells because of inhibitory signals that disguise them,” said study author Matthew Davids, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“By blocking the checkpoint, you allow the donor cells to see the cancer cells.”
Dr Davids and his colleagues tested this theory in 28 patients who had relapsed after allogeneic HSCT. The patients had acute myeloid leukemia (AML, n=12), Hodgkin lymphoma (n=7), non-Hodgkin lymphoma (n=4), myelodysplastic syndromes (MDS, n=2), multiple myeloma (n=1), myeloproliferative neoplasm (n=1), or acute lymphoblastic leukemia (n=1).
Patients had received a median of 3 prior treatment regimens, excluding HSCT (range, 1 to 14), and 20 patients (71%) had received treatment for relapse after transplant. Eight patients (29%) previously had grade 1/2 acute GVHD, and 16 (57%) previously had chronic GVHD.
The median time from transplant to initial treatment with ipilimumab was 675 days (range, 198 to 1830), and the median time from relapse to initial treatment with ipilimumab was 97 days (range, 0 to
1415).
Patients received induction therapy with ipilimumab at a dose of 3 mg/kg or 10 mg/kg every 3 weeks for a total of 4 doses. Those who had a clinical benefit received additional doses every 12 weeks for up to 60 weeks.
Safety
Five patients discontinued ipilimumab due to dose-limiting toxic effects. Four of these patients had GVHD, and 1 had severe immune-related adverse events.
Dose-limiting GVHD presented as chronic GVHD of the liver in 3 patients and acute GVHD of the gut in 1 patient.
Immune-related adverse events included death (n=1), pneumonitis (2 grade 2 events, 1 grade 4 event), colitis (1 grade 3 event), immune thrombocytopenia (1 grade 2 event), and diarrhea (1 grade 2 event).
Efficacy
There were no responses in patients who received ipilimumab at 3 mg/kg. Among the 22 patients who received ipilimumab at 10 mg/kg, 5 had a complete response, and 2 had a partial response.
Six other patients did not qualify as having responses but had a decrease in their tumor burden. Altogether, ipilimumab reduced tumor burden in 59% of patients.
The complete responses occurred in 4 patients with extramedullary AML and 1 patient with MDS developing into AML. Two of the AML patients remained in complete response at 12 and 15 months, and the patient with MDS remained in complete response at 16 months.
At a median follow-up of 15 months (range, 8 to 27), the median duration of response had not been reached. Responses were associated with in situ infiltration of cytotoxic CD8+ T cells, decreased activation of regulatory T cells, and expansion of subpopulations of effector T cells.
The 1-year overall survival rate was 49%.
The investigators said these encouraging results have set the stage for larger trials of checkpoint blockade in this patient population. Further research is planned to determine whether immunotherapy drugs could be given to high-risk patients to prevent relapse.
Photo from Business Wire
Results of a phase 1 study suggest that repeated doses of the immunotherapy drug ipilimumab is a feasible treatment option for patients with hematologic diseases who relapse after allogeneic hematopoietic stem cell transplant (HSCT).
Seven of the 28 patients studied responded to the treatment, but immune-mediated toxic effects and graft-vs-host disease (GVHD) occurred as well.
These results were published in NEJM.
Ipilimumab, which is already approved to treat unresectable or metastatic melanoma, works by blocking the immune checkpoint CTLA-4. Blockade of CTLA-4 has been shown to augment T-cell activation and proliferation.
“We believe [,in the case of relapse after HSCT,] the donor immune cells are present but can’t recognize the tumor cells because of inhibitory signals that disguise them,” said study author Matthew Davids, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“By blocking the checkpoint, you allow the donor cells to see the cancer cells.”
Dr Davids and his colleagues tested this theory in 28 patients who had relapsed after allogeneic HSCT. The patients had acute myeloid leukemia (AML, n=12), Hodgkin lymphoma (n=7), non-Hodgkin lymphoma (n=4), myelodysplastic syndromes (MDS, n=2), multiple myeloma (n=1), myeloproliferative neoplasm (n=1), or acute lymphoblastic leukemia (n=1).
Patients had received a median of 3 prior treatment regimens, excluding HSCT (range, 1 to 14), and 20 patients (71%) had received treatment for relapse after transplant. Eight patients (29%) previously had grade 1/2 acute GVHD, and 16 (57%) previously had chronic GVHD.
The median time from transplant to initial treatment with ipilimumab was 675 days (range, 198 to 1830), and the median time from relapse to initial treatment with ipilimumab was 97 days (range, 0 to
1415).
Patients received induction therapy with ipilimumab at a dose of 3 mg/kg or 10 mg/kg every 3 weeks for a total of 4 doses. Those who had a clinical benefit received additional doses every 12 weeks for up to 60 weeks.
Safety
Five patients discontinued ipilimumab due to dose-limiting toxic effects. Four of these patients had GVHD, and 1 had severe immune-related adverse events.
Dose-limiting GVHD presented as chronic GVHD of the liver in 3 patients and acute GVHD of the gut in 1 patient.
Immune-related adverse events included death (n=1), pneumonitis (2 grade 2 events, 1 grade 4 event), colitis (1 grade 3 event), immune thrombocytopenia (1 grade 2 event), and diarrhea (1 grade 2 event).
Efficacy
There were no responses in patients who received ipilimumab at 3 mg/kg. Among the 22 patients who received ipilimumab at 10 mg/kg, 5 had a complete response, and 2 had a partial response.
Six other patients did not qualify as having responses but had a decrease in their tumor burden. Altogether, ipilimumab reduced tumor burden in 59% of patients.
The complete responses occurred in 4 patients with extramedullary AML and 1 patient with MDS developing into AML. Two of the AML patients remained in complete response at 12 and 15 months, and the patient with MDS remained in complete response at 16 months.
At a median follow-up of 15 months (range, 8 to 27), the median duration of response had not been reached. Responses were associated with in situ infiltration of cytotoxic CD8+ T cells, decreased activation of regulatory T cells, and expansion of subpopulations of effector T cells.
The 1-year overall survival rate was 49%.
The investigators said these encouraging results have set the stage for larger trials of checkpoint blockade in this patient population. Further research is planned to determine whether immunotherapy drugs could be given to high-risk patients to prevent relapse.
Photo from Business Wire
Results of a phase 1 study suggest that repeated doses of the immunotherapy drug ipilimumab is a feasible treatment option for patients with hematologic diseases who relapse after allogeneic hematopoietic stem cell transplant (HSCT).
Seven of the 28 patients studied responded to the treatment, but immune-mediated toxic effects and graft-vs-host disease (GVHD) occurred as well.
These results were published in NEJM.
Ipilimumab, which is already approved to treat unresectable or metastatic melanoma, works by blocking the immune checkpoint CTLA-4. Blockade of CTLA-4 has been shown to augment T-cell activation and proliferation.
“We believe [,in the case of relapse after HSCT,] the donor immune cells are present but can’t recognize the tumor cells because of inhibitory signals that disguise them,” said study author Matthew Davids, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“By blocking the checkpoint, you allow the donor cells to see the cancer cells.”
Dr Davids and his colleagues tested this theory in 28 patients who had relapsed after allogeneic HSCT. The patients had acute myeloid leukemia (AML, n=12), Hodgkin lymphoma (n=7), non-Hodgkin lymphoma (n=4), myelodysplastic syndromes (MDS, n=2), multiple myeloma (n=1), myeloproliferative neoplasm (n=1), or acute lymphoblastic leukemia (n=1).
Patients had received a median of 3 prior treatment regimens, excluding HSCT (range, 1 to 14), and 20 patients (71%) had received treatment for relapse after transplant. Eight patients (29%) previously had grade 1/2 acute GVHD, and 16 (57%) previously had chronic GVHD.
The median time from transplant to initial treatment with ipilimumab was 675 days (range, 198 to 1830), and the median time from relapse to initial treatment with ipilimumab was 97 days (range, 0 to
1415).
Patients received induction therapy with ipilimumab at a dose of 3 mg/kg or 10 mg/kg every 3 weeks for a total of 4 doses. Those who had a clinical benefit received additional doses every 12 weeks for up to 60 weeks.
Safety
Five patients discontinued ipilimumab due to dose-limiting toxic effects. Four of these patients had GVHD, and 1 had severe immune-related adverse events.
Dose-limiting GVHD presented as chronic GVHD of the liver in 3 patients and acute GVHD of the gut in 1 patient.
Immune-related adverse events included death (n=1), pneumonitis (2 grade 2 events, 1 grade 4 event), colitis (1 grade 3 event), immune thrombocytopenia (1 grade 2 event), and diarrhea (1 grade 2 event).
Efficacy
There were no responses in patients who received ipilimumab at 3 mg/kg. Among the 22 patients who received ipilimumab at 10 mg/kg, 5 had a complete response, and 2 had a partial response.
Six other patients did not qualify as having responses but had a decrease in their tumor burden. Altogether, ipilimumab reduced tumor burden in 59% of patients.
The complete responses occurred in 4 patients with extramedullary AML and 1 patient with MDS developing into AML. Two of the AML patients remained in complete response at 12 and 15 months, and the patient with MDS remained in complete response at 16 months.
At a median follow-up of 15 months (range, 8 to 27), the median duration of response had not been reached. Responses were associated with in situ infiltration of cytotoxic CD8+ T cells, decreased activation of regulatory T cells, and expansion of subpopulations of effector T cells.
The 1-year overall survival rate was 49%.
The investigators said these encouraging results have set the stage for larger trials of checkpoint blockade in this patient population. Further research is planned to determine whether immunotherapy drugs could be given to high-risk patients to prevent relapse.
Pushing the Limits
Deciding when a hospitalized child's vital signs are acceptably within range and when they should generate alerts, alarms, and escalations of care is critically important yet surprisingly complicated. Many patients in the hospital who are recovering appropriately exhibit vital signs that fall outside normal ranges for well children. In a technology‐focused hospital environment, these out‐of‐range vital signs often generate alerts in the electronic health record (EHR) and alarms on physiologic monitors that can disrupt patients' sleep, generate concern in parents, lead to unnecessary testing and treatment by physicians, interrupt nurses during important patient care tasks, and lead to alarm fatigue. It is this last area, the problem of alarm fatigue, that Goel and colleagues[1] have used to frame the rationale and results of their study reported in this issue of the Journal of Hospital Medicine.
Goel and colleagues correctly point out that physiologic monitor alarm rates are high in children's hospitals, and alarms warranting intervention or action are rare.[2, 3, 4, 5, 6] Few studies have rigorously examined interventions to reduce unnecessary hospital physiologic monitor alarms, especially in pediatric settings. Of all the potential interventions, widening parameters has the most face validity: if you set wide enough alarm parameters, fewer alarms will be triggered. However, it comes with a potential safety tradeoff of missed actionable alarms.
Before EHR data became widely available for research, normal (or perhaps more appropriate for the hospital setting, expected) vital sign ranges were defined using expert opinion. The first publication describing the distribution of EHR‐documented vital signs in hospitalized children was published in 2013.[7] Goel and colleagues have built upon this prior work in their article, in which they present percentiles of EHR‐documented heart rate (HR) and respiratory rate (RR) developed using data from more than 7000 children hospitalized at an academic children's hospital. In a separate validation dataset, they then compared the performance of their proposed physiologic monitor alarm parametersthe 5th and 95th percentiles for HR and RR from this studyto the 2004 National Institutes of Health (NIH) vital sign reference ranges[8] that were the basis of default alarm parameters at their hospital. They also compared their percentiles to the 2013 study.[7]
The 2 main findings of Goel and colleagues' study were: (1) using their separate validation dataset, 55.6% fewer HR and RR observations were out of range based on their newly developed percentiles as compared to the NIH vital sign reference ranges; and (2) the HR and RR percentiles they developed were very similar to those reported in the 2013 study,[7] which used data from 2 other institutions, externally validating their findings.
The team then pushed the data a step further in a safety analysis and evaluated the sensitivity of the 5th and 95th percentiles for HR and RR from this study for detecting deterioration in 148 patients in the 12 hours before either a rapid response team activation or a cardiorespiratory arrest. The overall sensitivity for having either a HR or RR value out of range was 93% for Goel and colleagues' percentiles and 97% for the NIH ranges. Goel and colleagues concluded that using the 5th and 95th HR and RR percentiles provides a potentially safe means by which to modify physiologic bedside monitor alarm limits.
There are 2 important limitations to this work. The first is that the study uses EHR‐documented data to estimate the performance of new physiologic monitor settings. Although there are few published reports of differences between nurse‐charted vital signs and monitor data, those that do exist suggest that nurse charting favors more stable vital signs,[9, 10] even when charting oxygen saturation in patients with true, prolonged desaturation.[9] We agree with the authors of 1 report, who speculated that nurses recognize that temporary changes in vital signs are untypical for that patient and might choose to ignore them and either await a period of stability or make an educated estimate for that hour.[9] When using Goel and colleagues' 5th and 95th percentiles as alarm parameters, the expected scenario is that monitors will generate alarms for 10% of HR values and 10% of RR values. Because of the differences between nurse‐charted vital signs and monitor data, the monitors will probably generate many more alarms.
The second limitation is the approach Goel and colleagues took in performing a safety analysis using chart review. Unfortunately, it is nearly impossible for a retrospective chart review to form the basis of a convincing scientific argument for the safety of different alarm parameters. It requires balancing the complex and sometimes competing nurse‐level, patient‐level, and alarm‐level factors that determine nurse response time to alarms. It is possible to do prospectively, and we hope Goel's team will follow up this article with a description of the implementation and safety of these parameters in clinical practice.
In addition, the clinical implications of HR and RR at the 95th percentile might be considered less immediately life threatening than HR and RR at the 5th percentile, even though statistically they are equally abnormal. When choosing percentile‐based alarm parameters, statistical symmetry might be less important than the potential immediate consequences of missing bradycardia or bradypnea. It would be reasonable to consider setting high HR and RR at the 99th percentile or higher, because elevated HR or RR alone is rarely immediately actionable, and set the low HR and RR at the 5th or 10th percentile.
Despite these caveats, should the percentiles proposed by Goel and colleagues be used to inform pediatric vital sign clinical decision support throughout the world? When faced with the alternative of using vital sign parameters that are not based on data from hospitalized children, these percentiles offer a clear advantage, especially for hospitals similar to Goel's. The most obvious immediate use for these percentiles is to improve noninterruptive[11] vital sign clinical decision support in the EHR, the actual source of the data in this study.
The question of whether to implement Goel's 5th and 95th percentiles as physiologic monitor alarm parameters is more complex. In contrast to EHR decision support, there are much clearer downstream consequences of sounding unnecessary alarms as well as failing to sound important alarms for a child in extremis. Because their percentiles are not based on monitor data, the projected number of alarms generated at different percentile thresholds cannot be accurately estimated, although using their 5th and 95th percentiles should result in fewer alarms than the NIH parameters.
In conclusion, the work by Goel and colleagues represents an important contribution to knowledge about the ranges of expected vital signs in hospitalized children. Their findings can be immediately used to guide EHR decision support. Their percentiles are also relevant to physiologic monitor alarm parameters, although the performance and safety of using the 5th and 95th percentiles remain in question. Hospitals aiming to implement these data‐driven parameters should first evaluate the performance of different percentiles from this article using data obtained from their own monitor system and, if proceeding with clinical implementation, pilot the parameters to accurately gauge alarm rates and assess safety before spreading hospital wide.
Disclosures
Dr. Bonafide is supported by a Mentored Patient‐Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Brady is supported by a Patient‐Centered Outcomes Research Mentored Clinical Investigator Award from the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. The funding organizations had no role in the design, preparation, review, or approval of this article; nor the decision to submit the article for publication. The authors have no financial relationships relevant to this article or conflicts of interest to disclose.
- Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children. J Hosp Med. 2016;11(12):817–823. , , , et al.
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- What are we missing? Arrhythmia detection in the pediatric intensive care unit. J Pediatr. 2013;163(2):511–514. , , , , .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619. , .
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131:e1150–e1157. , , , , , .
- NIH Clinical Center. Pediatric services: age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc. nih.gov/ccc/pedweb/pedsstaff/age.html. Published November 1, 2004. Accessed June 9, 2016.
- A comparison of oxygen saturation data in inpatients with low oxygen saturation using automated continuous monitoring and intermittent manual data charting. Anesth Analg. 2014;118(2):326–331. , , , , .
- Comparison of nurse and computer charting of physiological variables in an intensive care unit. Int J Clin Monit Comput. 1996;13(4):235–241. , , , .
- Drug‐drug interactions that should be non‐interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2013;20(3):489–493. , , , et al.
Deciding when a hospitalized child's vital signs are acceptably within range and when they should generate alerts, alarms, and escalations of care is critically important yet surprisingly complicated. Many patients in the hospital who are recovering appropriately exhibit vital signs that fall outside normal ranges for well children. In a technology‐focused hospital environment, these out‐of‐range vital signs often generate alerts in the electronic health record (EHR) and alarms on physiologic monitors that can disrupt patients' sleep, generate concern in parents, lead to unnecessary testing and treatment by physicians, interrupt nurses during important patient care tasks, and lead to alarm fatigue. It is this last area, the problem of alarm fatigue, that Goel and colleagues[1] have used to frame the rationale and results of their study reported in this issue of the Journal of Hospital Medicine.
Goel and colleagues correctly point out that physiologic monitor alarm rates are high in children's hospitals, and alarms warranting intervention or action are rare.[2, 3, 4, 5, 6] Few studies have rigorously examined interventions to reduce unnecessary hospital physiologic monitor alarms, especially in pediatric settings. Of all the potential interventions, widening parameters has the most face validity: if you set wide enough alarm parameters, fewer alarms will be triggered. However, it comes with a potential safety tradeoff of missed actionable alarms.
Before EHR data became widely available for research, normal (or perhaps more appropriate for the hospital setting, expected) vital sign ranges were defined using expert opinion. The first publication describing the distribution of EHR‐documented vital signs in hospitalized children was published in 2013.[7] Goel and colleagues have built upon this prior work in their article, in which they present percentiles of EHR‐documented heart rate (HR) and respiratory rate (RR) developed using data from more than 7000 children hospitalized at an academic children's hospital. In a separate validation dataset, they then compared the performance of their proposed physiologic monitor alarm parametersthe 5th and 95th percentiles for HR and RR from this studyto the 2004 National Institutes of Health (NIH) vital sign reference ranges[8] that were the basis of default alarm parameters at their hospital. They also compared their percentiles to the 2013 study.[7]
The 2 main findings of Goel and colleagues' study were: (1) using their separate validation dataset, 55.6% fewer HR and RR observations were out of range based on their newly developed percentiles as compared to the NIH vital sign reference ranges; and (2) the HR and RR percentiles they developed were very similar to those reported in the 2013 study,[7] which used data from 2 other institutions, externally validating their findings.
The team then pushed the data a step further in a safety analysis and evaluated the sensitivity of the 5th and 95th percentiles for HR and RR from this study for detecting deterioration in 148 patients in the 12 hours before either a rapid response team activation or a cardiorespiratory arrest. The overall sensitivity for having either a HR or RR value out of range was 93% for Goel and colleagues' percentiles and 97% for the NIH ranges. Goel and colleagues concluded that using the 5th and 95th HR and RR percentiles provides a potentially safe means by which to modify physiologic bedside monitor alarm limits.
There are 2 important limitations to this work. The first is that the study uses EHR‐documented data to estimate the performance of new physiologic monitor settings. Although there are few published reports of differences between nurse‐charted vital signs and monitor data, those that do exist suggest that nurse charting favors more stable vital signs,[9, 10] even when charting oxygen saturation in patients with true, prolonged desaturation.[9] We agree with the authors of 1 report, who speculated that nurses recognize that temporary changes in vital signs are untypical for that patient and might choose to ignore them and either await a period of stability or make an educated estimate for that hour.[9] When using Goel and colleagues' 5th and 95th percentiles as alarm parameters, the expected scenario is that monitors will generate alarms for 10% of HR values and 10% of RR values. Because of the differences between nurse‐charted vital signs and monitor data, the monitors will probably generate many more alarms.
The second limitation is the approach Goel and colleagues took in performing a safety analysis using chart review. Unfortunately, it is nearly impossible for a retrospective chart review to form the basis of a convincing scientific argument for the safety of different alarm parameters. It requires balancing the complex and sometimes competing nurse‐level, patient‐level, and alarm‐level factors that determine nurse response time to alarms. It is possible to do prospectively, and we hope Goel's team will follow up this article with a description of the implementation and safety of these parameters in clinical practice.
In addition, the clinical implications of HR and RR at the 95th percentile might be considered less immediately life threatening than HR and RR at the 5th percentile, even though statistically they are equally abnormal. When choosing percentile‐based alarm parameters, statistical symmetry might be less important than the potential immediate consequences of missing bradycardia or bradypnea. It would be reasonable to consider setting high HR and RR at the 99th percentile or higher, because elevated HR or RR alone is rarely immediately actionable, and set the low HR and RR at the 5th or 10th percentile.
Despite these caveats, should the percentiles proposed by Goel and colleagues be used to inform pediatric vital sign clinical decision support throughout the world? When faced with the alternative of using vital sign parameters that are not based on data from hospitalized children, these percentiles offer a clear advantage, especially for hospitals similar to Goel's. The most obvious immediate use for these percentiles is to improve noninterruptive[11] vital sign clinical decision support in the EHR, the actual source of the data in this study.
The question of whether to implement Goel's 5th and 95th percentiles as physiologic monitor alarm parameters is more complex. In contrast to EHR decision support, there are much clearer downstream consequences of sounding unnecessary alarms as well as failing to sound important alarms for a child in extremis. Because their percentiles are not based on monitor data, the projected number of alarms generated at different percentile thresholds cannot be accurately estimated, although using their 5th and 95th percentiles should result in fewer alarms than the NIH parameters.
In conclusion, the work by Goel and colleagues represents an important contribution to knowledge about the ranges of expected vital signs in hospitalized children. Their findings can be immediately used to guide EHR decision support. Their percentiles are also relevant to physiologic monitor alarm parameters, although the performance and safety of using the 5th and 95th percentiles remain in question. Hospitals aiming to implement these data‐driven parameters should first evaluate the performance of different percentiles from this article using data obtained from their own monitor system and, if proceeding with clinical implementation, pilot the parameters to accurately gauge alarm rates and assess safety before spreading hospital wide.
Disclosures
Dr. Bonafide is supported by a Mentored Patient‐Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Brady is supported by a Patient‐Centered Outcomes Research Mentored Clinical Investigator Award from the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. The funding organizations had no role in the design, preparation, review, or approval of this article; nor the decision to submit the article for publication. The authors have no financial relationships relevant to this article or conflicts of interest to disclose.
Deciding when a hospitalized child's vital signs are acceptably within range and when they should generate alerts, alarms, and escalations of care is critically important yet surprisingly complicated. Many patients in the hospital who are recovering appropriately exhibit vital signs that fall outside normal ranges for well children. In a technology‐focused hospital environment, these out‐of‐range vital signs often generate alerts in the electronic health record (EHR) and alarms on physiologic monitors that can disrupt patients' sleep, generate concern in parents, lead to unnecessary testing and treatment by physicians, interrupt nurses during important patient care tasks, and lead to alarm fatigue. It is this last area, the problem of alarm fatigue, that Goel and colleagues[1] have used to frame the rationale and results of their study reported in this issue of the Journal of Hospital Medicine.
Goel and colleagues correctly point out that physiologic monitor alarm rates are high in children's hospitals, and alarms warranting intervention or action are rare.[2, 3, 4, 5, 6] Few studies have rigorously examined interventions to reduce unnecessary hospital physiologic monitor alarms, especially in pediatric settings. Of all the potential interventions, widening parameters has the most face validity: if you set wide enough alarm parameters, fewer alarms will be triggered. However, it comes with a potential safety tradeoff of missed actionable alarms.
Before EHR data became widely available for research, normal (or perhaps more appropriate for the hospital setting, expected) vital sign ranges were defined using expert opinion. The first publication describing the distribution of EHR‐documented vital signs in hospitalized children was published in 2013.[7] Goel and colleagues have built upon this prior work in their article, in which they present percentiles of EHR‐documented heart rate (HR) and respiratory rate (RR) developed using data from more than 7000 children hospitalized at an academic children's hospital. In a separate validation dataset, they then compared the performance of their proposed physiologic monitor alarm parametersthe 5th and 95th percentiles for HR and RR from this studyto the 2004 National Institutes of Health (NIH) vital sign reference ranges[8] that were the basis of default alarm parameters at their hospital. They also compared their percentiles to the 2013 study.[7]
The 2 main findings of Goel and colleagues' study were: (1) using their separate validation dataset, 55.6% fewer HR and RR observations were out of range based on their newly developed percentiles as compared to the NIH vital sign reference ranges; and (2) the HR and RR percentiles they developed were very similar to those reported in the 2013 study,[7] which used data from 2 other institutions, externally validating their findings.
The team then pushed the data a step further in a safety analysis and evaluated the sensitivity of the 5th and 95th percentiles for HR and RR from this study for detecting deterioration in 148 patients in the 12 hours before either a rapid response team activation or a cardiorespiratory arrest. The overall sensitivity for having either a HR or RR value out of range was 93% for Goel and colleagues' percentiles and 97% for the NIH ranges. Goel and colleagues concluded that using the 5th and 95th HR and RR percentiles provides a potentially safe means by which to modify physiologic bedside monitor alarm limits.
There are 2 important limitations to this work. The first is that the study uses EHR‐documented data to estimate the performance of new physiologic monitor settings. Although there are few published reports of differences between nurse‐charted vital signs and monitor data, those that do exist suggest that nurse charting favors more stable vital signs,[9, 10] even when charting oxygen saturation in patients with true, prolonged desaturation.[9] We agree with the authors of 1 report, who speculated that nurses recognize that temporary changes in vital signs are untypical for that patient and might choose to ignore them and either await a period of stability or make an educated estimate for that hour.[9] When using Goel and colleagues' 5th and 95th percentiles as alarm parameters, the expected scenario is that monitors will generate alarms for 10% of HR values and 10% of RR values. Because of the differences between nurse‐charted vital signs and monitor data, the monitors will probably generate many more alarms.
The second limitation is the approach Goel and colleagues took in performing a safety analysis using chart review. Unfortunately, it is nearly impossible for a retrospective chart review to form the basis of a convincing scientific argument for the safety of different alarm parameters. It requires balancing the complex and sometimes competing nurse‐level, patient‐level, and alarm‐level factors that determine nurse response time to alarms. It is possible to do prospectively, and we hope Goel's team will follow up this article with a description of the implementation and safety of these parameters in clinical practice.
In addition, the clinical implications of HR and RR at the 95th percentile might be considered less immediately life threatening than HR and RR at the 5th percentile, even though statistically they are equally abnormal. When choosing percentile‐based alarm parameters, statistical symmetry might be less important than the potential immediate consequences of missing bradycardia or bradypnea. It would be reasonable to consider setting high HR and RR at the 99th percentile or higher, because elevated HR or RR alone is rarely immediately actionable, and set the low HR and RR at the 5th or 10th percentile.
Despite these caveats, should the percentiles proposed by Goel and colleagues be used to inform pediatric vital sign clinical decision support throughout the world? When faced with the alternative of using vital sign parameters that are not based on data from hospitalized children, these percentiles offer a clear advantage, especially for hospitals similar to Goel's. The most obvious immediate use for these percentiles is to improve noninterruptive[11] vital sign clinical decision support in the EHR, the actual source of the data in this study.
The question of whether to implement Goel's 5th and 95th percentiles as physiologic monitor alarm parameters is more complex. In contrast to EHR decision support, there are much clearer downstream consequences of sounding unnecessary alarms as well as failing to sound important alarms for a child in extremis. Because their percentiles are not based on monitor data, the projected number of alarms generated at different percentile thresholds cannot be accurately estimated, although using their 5th and 95th percentiles should result in fewer alarms than the NIH parameters.
In conclusion, the work by Goel and colleagues represents an important contribution to knowledge about the ranges of expected vital signs in hospitalized children. Their findings can be immediately used to guide EHR decision support. Their percentiles are also relevant to physiologic monitor alarm parameters, although the performance and safety of using the 5th and 95th percentiles remain in question. Hospitals aiming to implement these data‐driven parameters should first evaluate the performance of different percentiles from this article using data obtained from their own monitor system and, if proceeding with clinical implementation, pilot the parameters to accurately gauge alarm rates and assess safety before spreading hospital wide.
Disclosures
Dr. Bonafide is supported by a Mentored Patient‐Oriented Research Career Development Award from the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Brady is supported by a Patient‐Centered Outcomes Research Mentored Clinical Investigator Award from the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. The funding organizations had no role in the design, preparation, review, or approval of this article; nor the decision to submit the article for publication. The authors have no financial relationships relevant to this article or conflicts of interest to disclose.
- Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children. J Hosp Med. 2016;11(12):817–823. , , , et al.
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- What are we missing? Arrhythmia detection in the pediatric intensive care unit. J Pediatr. 2013;163(2):511–514. , , , , .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619. , .
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131:e1150–e1157. , , , , , .
- NIH Clinical Center. Pediatric services: age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc. nih.gov/ccc/pedweb/pedsstaff/age.html. Published November 1, 2004. Accessed June 9, 2016.
- A comparison of oxygen saturation data in inpatients with low oxygen saturation using automated continuous monitoring and intermittent manual data charting. Anesth Analg. 2014;118(2):326–331. , , , , .
- Comparison of nurse and computer charting of physiological variables in an intensive care unit. Int J Clin Monit Comput. 1996;13(4):235–241. , , , .
- Drug‐drug interactions that should be non‐interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2013;20(3):489–493. , , , et al.
- Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children. J Hosp Med. 2016;11(12):817–823. , , , et al.
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- What are we missing? Arrhythmia detection in the pediatric intensive care unit. J Pediatr. 2013;163(2):511–514. , , , , .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619. , .
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131:e1150–e1157. , , , , , .
- NIH Clinical Center. Pediatric services: age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc. nih.gov/ccc/pedweb/pedsstaff/age.html. Published November 1, 2004. Accessed June 9, 2016.
- A comparison of oxygen saturation data in inpatients with low oxygen saturation using automated continuous monitoring and intermittent manual data charting. Anesth Analg. 2014;118(2):326–331. , , , , .
- Comparison of nurse and computer charting of physiological variables in an intensive care unit. Int J Clin Monit Comput. 1996;13(4):235–241. , , , .
- Drug‐drug interactions that should be non‐interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2013;20(3):489–493. , , , et al.
Data‐Driven Pediatric Alarm Limits
The management of alarms in the hospital setting is a significant patient safety issue. In 2013, the Joint Commission issued Sentinel Event Alert #50 to draw attention to the fact that tens of thousands of alarms occur daily throughout individual hospitals, and 85% to 99% are false or not clinically actionable.[1] These alarms, designed to be a safety net in patient care, have the unintended consequence of causing provider desensitization, also known as alarm fatigue, which contributes to adverse events as severe as patient mortality.[1, 2] For this reason, a 2014 Joint Commission National Patient Safety Goal urged hospitals to prioritize alarm system safety and to develop policies and procedures to manage alarms and alarm fatigue.[3]
Multiple efforts have been made to address alarm fatigue in hospitalized adults. Studies have quantified the frequency and types of medical device alarms,[4, 5, 6, 7, 8, 9] and some proposed solutions to decrease excess alarms.[10, 11, 12, 13, 14, 15] One such solution is to change alarm limit settings, an intervention shown to be efficacious in the literature.[5, 6, 16, 17] Although no adverse patient outcomes are reported in these studies, none of them included a formal safety evaluation to evaluate whether alarm rate reduction occurred at the expense of clinically significant alarms.
Specific to pediatrics, frameworks to address alarm fatigue have been proposed,[18] and the relationship between nurse response time and frequency of exposure to nonactionable alarms has been reported.[19] However, efforts to address alarm fatigue in the pediatric setting are less well studied overall, and there is little guidance regarding optimization of pediatric alarm parameters. Although multiple established reference ranges exist for pediatric vital signs,[20, 21, 22] a systematic review in 2011 found that only 2 of 5 published heart rate (HR) and 6 respiratory rate (RR) guidelines cited any references, and even these had weak underpinning evidence.[23] Consequently, ranges defining normal pediatric vital signs are derived either from small sample observational data in healthy outpatient children or consensus opinion. In a 2013 study by Bonafide et al.,[24] charted vital sign data from hospitalized children were used to develop percentile curves for HR and RR, and from these it was estimated that 54% of vital sign measurements in hospitalized children are out of range using currently accepted normal vital sign parameters.[24] Although these calculated vital sign parameters were not implemented clinically, they called into question reference ranges that are currently widely accepted and used as parameters for electronic health record (EHR) alerts, early warning scoring systems, and physiologic monitor alarms.
With the goal of safely decreasing the number of out‐of‐range vital sign measurements that result from current, often nonevidence‐based pediatric vital sign reference ranges, we used data from noncritically ill pediatric inpatients to derive HR and RR percentile charts for hospitalized children. In anticipation of local implementation of these data‐driven vital sign ranges as physiologic monitor parameters, we performed a retrospective safety analysis by evaluating the effect of data‐driven alarm limit modification on identification of cardiorespiratory arrests (CRA) and rapid response team (RRT) activations.
METHODS
We performed a cross‐sectional study of children less than 18 years of age hospitalized on general medical and surgical units at Lucile Packard Children's Hospital Stanford, a 311‐bed quaternary‐care academic hospital with a full complement of pediatric medical and surgical subspecialties and transplant programs. During the study period, the hospital used the Cerner EHR (Millennium; Cerner, Kansas City, MO) and Philips IntelliVue bedside monitors (Koninklijke Philips N.V., Amsterdam, the Netherlands). The Stanford University Institutional Review Board approved this study.
Establishing Data‐Driven HR and RR Parameters
Vital sign documentation in the EHR at our institution is performed primarily by nurses and facilitated by bedside monitor biomedical device integration. We extracted vital signs data from the institution's EHR for all general medical and surgical patients discharged between January 1, 2013 and May 3, 2014. To be most conservative in the definition of normal vital sign ranges for pediatric inpatients, we excluded critically ill children (those who spent any part of their hospitalization in an intensive care unit [ICU]). Physiologically implausible vital sign values were excluded as per the methods of Bonafide et al.[24] The data were separated into 2 different sets: a training set (patients discharged between January 1, 2013 and December 31, 2013) and a test set for validation (patients discharged between January 1, 2014 and May 3, 2014). To avoid oversampling from both particular time periods and individual patients in the training set, we randomly selected 1 HR and RR pair from each 4‐hour interval during a hospitalization, and then randomly sampled a maximum of 10 HR and RR pairs per patient. Using these vital sign measurements, we calculated age‐stratified 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for both HR and RR.
Based on a combination of expert opinion and local consensus from our Medical Executive and Patient Safety Committees, we selected the 5th and 95th percentile values as proposed data‐driven parameter limits and compared them to the 5th and 95th percentile values generated in the 2013 study[24] and to the 2004 National Institutes of Health (NIH)adapted vital sign reference ranges currently used at our hospital.[25] Using 1 randomly selected HR and RR pair from every 4‐hour interval in the validation set, we compared the proportion of out‐of‐range HR and RR observations with the proposed 5th and 95th percentile data‐driven parameters versus the current NIH reference ranges. We also calculated average differences between our data‐driven 5th and 95th percentile values and the calculated HR and RR values in the 2013 study.[24]
Safety Analysis
To assess the safety of the newly created 5th and 95th percentile HR and RR parameters prior to clinical adoption, we retrospectively reviewed data associated with all RRT and CRA events on the hospital's medical/surgical units from March 4, 2013 until March 3, 2014. The RRT/CRA event data were obtained from logs kept by the hospital's code committee. We excluded events that lacked a documented patient identifier, occurred in locations other than the acute medical/surgical units, or occurred in patients >18 years old. The resulting charts were manually reviewed to determine the date and time of RRT or CRA event activation. Because evidence exists that hospitalized pediatric patients with CRA show signs of vital sign decompensation as early as 12 hours prior to the event,[26, 27, 28, 29] we extracted all EHR‐charted HR and RR data in the 12 hours preceding RRT and CRA events from the institution's clinical data warehouse for analysis, excluding patients without charted vital sign data in this time period. The sets of patients with any out‐of‐range HR or RR measurements in the 12‐hours prior to an event were compared according to the current NIH reference ranges[25] versus data‐driven parameters. Additionally, manual chart review was performed to assess the reason for code or RRT activation, and to determine the role that out‐of‐range vital signs played in alerting clinical staff of patient decompensation.
Statistical Analysis
All analysis was performed using R statistical package software (version 0.98.1062 for Mac OS X 10_9_5; The R Foundation for Statistical Computing, Vienna, Austria) with an SQL database (MySQL 2015; Oracle Corp., Redwood City, CA).
RESULTS
Data‐Driven HR and RR Parameters
We established a training set of 62,508 vital sign measurements for 7202 unique patients to calculate 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for HR and RR among the 14 age groups (see Supporting Information, Appendix 1, in the online version of this article). Figures 1 and 2 compare the proposed data‐driven vital sign ranges with (1) our current HR and RR reference ranges and (2) the 5th and 95th percentile values created in the similar 2013 study.[24] The greatest difference between our study and the 2013 study was across data‐driven 95th percentile RR parameters, which were an average of 4.8 points lower in our study.


Our validation set consisted of 82,993 vital sign measurements for 2287 unique patients. Application of data‐driven HR and RR 5th and 95th percentile limits resulted in 24,045 (55.6%) fewer out‐of‐range measurements compared to current NIH reference ranges (19,240 vs 43,285). Forty‐five percent fewer HR values and 61% fewer RR values were considered out of range using the proposed data‐driven parameters (see Supporting Information, Appendix 2, in the online version of this article).
Safety
Of the 218 unique out‐of‐ICU RRT and CRA events logged from March 4, 2013 to March 3, 2014, 63 patients were excluded from analysis: 10 lacked identifying information, 33 occurred outside of medical/surgical units, and 20 occurred in patients >18 years of age. The remaining 155 patient charts were reviewed. Seven patients were subsequently excluded because they lacked EHR‐documented vital signs data in the 12 hours prior to RRT or CRA team activation, yielding a cohort of 148 patients (128 RRT events, 20 CRA events).
Table 1 describes the analysis of vital signs in the 12 hours leading up to the 148 RRT and CRA events. All 121 patients with out‐of‐range HR values using NIH reference ranges also had out‐of‐range HR values with the proposed data‐driven parameters; an additional 8 patients had low HR values using the data‐driven parameters. Of the 137 patients with an out‐of‐range RR value using NIH reference ranges, 33 (24.1%) were not considered out of range by the data‐driven parameters. Of these, 28 had high RR and 5 had low RR according to NIH reference ranges.
No. Patients With HR Out of Range* | No. Patients With RR Out of Range* | No. Patients With HR or RR Out of Range* | |
---|---|---|---|
| |||
NIH ranges | 121 | 137 | 144 |
Data‐driven ranges | 129 | 104 | 138 |
Difference (causal threshold) | +8 (low HR) | 28 (high RR), 5 (low RR) | +2 (low HR), 8 (high RR) |
After evaluating out‐of‐range HR and RR individually, the 148 RRT and CRA events were analyzed for either out‐of‐range HR values or RR values. In doing so, 144 (97.3%) patients had either HR or RR measurements that were considered out of range using our current NIH reference ranges. One hundred thirty‐eight (93.2%) had either HR or RR measurements that were considered out of range with the proposed parameters. One hundred thirty‐six (94.4%) of the 144 patients with out‐of‐range HR or RR measurements according to NIH reference ranges were also considered out of range using proposed parameters. The data‐driven parameters identified 2 additional patients with low HR who did not have out‐of‐range HR or RR values using the current NIH reference ranges. Manual chart review of the RRT/CRA events in the 8 patients who had normal HR or RR using the data‐driven parameters revealed that RRT or CRA team interventions occurred for clinical indications that did not rely upon HR or RR measurement (eg, laboratory testing abnormalities, desaturation events) (Table 2).
Indication for event | Patient Age |
---|---|
| |
1. Desaturation and apnea | 10 months |
2. Hyperammonemia (abnormal lab result) | 5 years |
3. Acute hematemesis | 16 years |
4. Lightheadedness, feeling faint | 17 years |
5. Desaturation with significant oxygen requirement | 17 years |
6. Desaturation with significant oxygen requirement | 17 years |
7. Patient stated difficulty breathing | 18 years |
8. Difficulty breathing (anaphylactic shock)* | 18 years |
DISCUSSION
This is the first published study to analyze the safety of implementing data‐driven HR and RR parameters in hospitalized children. Based on retrospective analysis of a 12‐month cohort of patients requiring RRT or CRA team activation, our data‐driven HR and RR parameters were at least as safe as the NIH‐published reference ranges employed at our children's hospital. In addition to maintaining sensitivity to RRT and CRA events, the data‐driven parameters resulted in an estimated 55.6% fewer out‐of‐range measurements among medical/surgical pediatric inpatients.
Improper alarm settings are 1 of 4 major contributing factors to reported alarm‐related events,[1] and data‐driven HR and RR parameters provide a means by which to address the Joint Commission Sentinel Event Alert[1] and National Patient Safety Goal[3] regarding alarm management safety for hospitalized pediatric patients. Our results suggest that this evidence‐based approach may reduce the frequency of false alarms (thereby mitigating alarm fatigue), and should be studied prospectively for implementation in the clinical setting.
The selection of percentile values to define the new data‐driven parameter ranges involved various considerations. In an effort to minimize alarm fatigue, we considered using the 1st and 99th percentile values. However, our Medical Executive and Patient Safety Committees determined that the 99th percentile values for HR and RR for many of the age groups exceeded those that would raise clinical concern. A more conservative approach, applying the 5th and 95th percentile values, was deemed clinically appropriate and consistent with recommendations from the only other study to calculate data‐driven HR and RR parameters for hospitalized children.[24]
When taken in total, Bonafide et al.'s 2013 study demonstrated that up to 54% of vital sign values were abnormal according to textbook reference ranges.[24] Similarly, we estimated 55.6% fewer out‐of‐range HR and RR measurements with our data‐driven parameters. Although our 5th and 95th HR percentile and 5th percentile RR values are strikingly similar to those developed in the 2013 study,[24] the difference in 95th percentile RR values between the studies was potentially clinically significant, with our data‐driven upper RR values being 4.8 breaths per minute lower (more conservative) on average. Bonafide et al. transformed the RR values to fit a normal distribution, which might account for this difference. Ultimately, our safety analysis demonstrated that 24% fewer patients were considered out of range for high RR prior to RRT/CRA events with the data‐driven parameters compared to NIH norms. Even fewer RRT/CRA patients would have been considered out of range per Bonafide's less conservative 95% RR limits.
Importantly, all 8 patients in our safety analysis without abnormal vital sign measurements in the 12 hours preceding their clinical events according to the proposed data‐driven parameters (but identified as having high RR per current reference ranges) had RRT or CRA events triggered due to other significant clinical manifestations or vital sign abnormalities (eg, hypoxia). This finding is supported by the literature, which suggests that RRTs are rarely activated due to single vital sign abnormality alone. Prior analysis of RRT activations in our pediatric hospital demonstrated that only approximately 10% of RRTs were activated primarily on the basis of HR or RR vital sign abnormalities (5.6% tachycardia, 2.8% tachypnea, 1.4% bradycardia), whereas 36% were activated due to respiratory distress.[30] The clinical relevance of high RR in isolation is questionable given a recent pediatric study that raised all RR limits and decreased alarm frequency without adverse patient safety consequences.[31] Our results suggest that modifying HR and RR alarm parameters using data‐driven 5th and 95th percentile limits to decrease alarm frequency does not pose additional safety risk related to identification of RRT and CRA events. We encourage continued work toward development of multivariate or smart alarms that analyze multiple simultaneous vital sign measurements and trends to determine whether an alarm should be triggered.[32, 33]
The ability to demonstrate the safety of data‐driven HR and RR parameters is a precursor to hospital‐wide implementation. We believe it is crucial to perform a safety analysis prior to implementation due to the role vital signs play in clinical assessment and detection of patient deterioration.[30, 34, 35, 36, 37] Though a few studies have shown that modification of alarm parameters decreases alarm frequency,[5, 6, 10, 16, 17] to our knowledge no formal safety evaluations have ever been published. This study provides the first published safety evaluation of data‐driven HR and RR parameters.
By decreasing the quantity of out‐of‐range vital sign values while preserving the ability to detect patient deterioration, data‐driven vital sign alarm limits have the potential to decrease false monitor alarms, alarm‐generated noise, and alarm fatigue. Future work includes prospectively studying the impact of adoption of data‐driven vital sign parameters on monitor alarm burden and monitoring the safety of the changes. Additional safety analysis could include comparing the sensitivity and specificity of early warning score systems when data‐driven vital sign ranges are substituted for traditional physiologic parameters. Further personalization of vital sign parameters will involve incorporating patient‐specific characteristics (eg, demographics, diagnoses) into the data‐driven analysis to further decrease alarm burden while enhancing patient safety. Ultimately, using a patient's own physiologic data to define highly personalized vital sign parameter limits represents a truly precision approach, and could revolutionize the way hospitalized patients are monitored.
Numerous relevant issues are not yet addressed in this initial, single‐institution study. First, although the biomedical device integration facilitated the direct import of monitor data into the EHR (decreasing transcription errors), our analysis was performed using EHR‐charted data. As such, the effect on bedside monitor alarms was not directly evaluated in our study, including those due to technical alarms or patient artifact. Second, our overall sample size for the training set was quite large; however, in some cases the number of patients per age category was limited. Third, although we evaluated the identification of severe deterioration leading to RRT or CRA events, the sensitivity of the new limits to the need for other interventions (eg, fluid bolus for dehydration or escalation of respiratory support for asthma exacerbation) or unplanned transfers to the ICU was not assessed. Fourth, the analysis was retrospective, and so the impact of data‐driven alarm limits on length of stay and readmission could not be defined. Fifth, excluding all vital sign measurements from patients who spent any time in the ICU setting decreased the amount of data available for analysis. However, excluding sicker patients probably resulted in narrower data‐driven HR and RR ranges, leading to more conservative proposed parameters that are more likely to identify patient decompensation in our safety analysis. Finally, this was a single‐site study. We believe our data‐driven limits are applicable to other tertiary or quaternary care facilities given the similarity to those generated in a study performed in a comparable setting,[24] but generalizability to other settings may be limited if the local population is sufficiently different. Furthermore, because institutional policies (eg, indications for care escalation) differ, individual institutions should determine whether our analysis is applicable to their setting or if local safety evaluation is necessary.
CONCLUSION
A large proportion of HR and RR values for hospitalized children at our institution are out of range according to current vital sign reference ranges. Our new data‐driven alarm parameters for hospitalized children provide a potentially safe means by which to modify physiologic bedside monitor alarm limits, a first step toward customization of alarm limit settings in an effort to mitigate alarm fatigue.
Acknowledgements
The authors thank Debby Huang and Joshua Glandorf in the Information Services Department at Stanford Children's Health for assistance with data acquisition. No compensation was received for their contributions.
Disclosures: All authors gave approval of the final manuscript version submitted for publication and agreed to be accountable for all aspects of the work. Dr. Veena V. Goel conceptualized and designed the study; collected, managed, analyzed and interpreted the data; prepared and reviewed the initial manuscript; and approved the final manuscript as submitted. Ms. Sarah F. Poole contributed to the design of the study and performed the primary data analysis for the study. Ms. Poole critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Goel and Ms. Poole had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Paul J. Sharek and Dr. Jonathan P. Palma contributed to the study design and data interpretation. Drs. Sharek and Palma critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Terry S. Platchek, Dr. Natalie M. Pageler, and Dr. Christopher A. Longhurst contributed to the study design. Drs. Platchek, Pageler, and Longhurst critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Ms. Poole is supported by the Stanford Biosciences Graduate Program through a Fulbright New Zealand Science and Innovation Graduate Award and through the J.R. Templin Trust Scholarship. The authors report no conflicts of interest.
- The Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3. Available at: https://www.jointcommission.org/sea_issue_50/. Accessed October 12, 2013.
- Alarm fatigue” a factor in 2d death: UMass hospital cited for violations. The Boston Globe. September 21, 2011. Available at: https://www.bostonglobe.com/2011/09/20/umass/qSOhm8dYmmaq4uTHZb7FNM/story.html. Accessed December 19, 2014 . “
- The Joint Commission. Alarm system safety. Available at: https://www.jointcommission.org/assets/1/18/R3_Report_Issue_5_12_2_13_Final.pdf. Published December 11, 2013. Accessed October 12, 2013.
- ALARMED: adverse events in low‐risk patients with chest pain receiving continuous electrocardiographic monitoring in the emergency department. A pilot study. Am J Emerg Med. 2006;24(1):62–67. , , , , .
- Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19(1):28–34; quiz 35. , .
- Physiologic monitoring alarm load on medical/surgical floors of a community hospital. Biomed Instrum Technol. 2011;(suppl):29–36. , , .
- Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25(12):1360–1366. , , , , , .
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144. , , , et al.
- Alarm fatigue. Nurs Clin North Am. 2012;47(3):375–382. .
- Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277. .
- Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual. 2014;29(1):9–18. , , , .
- An evidence‐based approach to reduce nuisance alarms and alarm fatigue. Biomed Instrum Technol. 2011;(suppl):46–52. .
- Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PLoS One. 2014;9(10):e110274. , , , et al.
- Effect of altering alarm settings: a randomized controlled study. Biomed Instrum Technol. 2015;49(3):214–222. , , , , , .
- Alarm limit settings for early warning systems to identify at‐risk patients. J Adv Nurs. 2009;65(9):1844–1852. , , , , .
- A framework for reducing alarm fatigue on pediatric inpatient units. Hosp Pediatr. 2015;5(3):160–163. , .
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- The Johns Hopkins Hospital, , . The Harriet Lane Handbook. 20th ed. Philadelphia, PA: Elsevier Saunders; 2014.
- Nelson Textbook of Pediatrics. 19th ed. Philadelphia, PA.: Elsevier Saunders; 2011. , .
- Pediatric assessment. In: Pediatric Advanced Life Support: Provider Manual. Dallas, TX: American Heart Association; 2006:9–16. , , , .
- Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. Lancet. 2011;377(9770):1011–1018. , , , et al.
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131(4):e1150–e1157. , , , , , .
- National Institutes of Health. Age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc.nih.gov/ccc/pedweb/pedsstaff/age.html. Accessed July 26, 2015.
- Guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Part 9: pediatric basic life support. The American Heart Association in collaboration with the International Liaison Committee on Resuscitation. Circulation. 2000;102(8 suppl):I253–I290.
- Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary‐care hospital. Med J Aust. 1999;171(1):22–25. , , , , , .
- Duration of life‐threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–1634. , , , et al.
- Pediatric cardiopulmonary resuscitation: a collective review. Ann Emerg Med. 1999;33(2):195–205. , .
- Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):2267–2274. , , , et al.
- A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694. , , , et al.
- Collection of annotated data in a clinical validation study for alarm algorithms in intensive care—a methodologic framework. J Crit Care. 2010;25(1):128–135. , , , et al.
- Making ICU alarms meaningful: a comparison of traditional vs. trend‐based algorithms. Proc AMIA Symp. 1999:379–383. , , .
- Implementation of a medical emergency team in a large pediatric teaching hospital prevents respiratory and cardiopulmonary arrests outside the intensive care unit. Pediatr Crit Care Med. 2007;8(3):236–246; quiz 247. , , , et al.
- Centile‐based Early Warning Scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):969–970. .
- Centile‐based early warning scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):1013–1018. , , , , , .
- Reduction of paediatric in‐patient cardiac arrest and death with a medical emergency team: preliminary results. Arch Dis Child. 2005;90(11):1148–1152. , , , , .
The management of alarms in the hospital setting is a significant patient safety issue. In 2013, the Joint Commission issued Sentinel Event Alert #50 to draw attention to the fact that tens of thousands of alarms occur daily throughout individual hospitals, and 85% to 99% are false or not clinically actionable.[1] These alarms, designed to be a safety net in patient care, have the unintended consequence of causing provider desensitization, also known as alarm fatigue, which contributes to adverse events as severe as patient mortality.[1, 2] For this reason, a 2014 Joint Commission National Patient Safety Goal urged hospitals to prioritize alarm system safety and to develop policies and procedures to manage alarms and alarm fatigue.[3]
Multiple efforts have been made to address alarm fatigue in hospitalized adults. Studies have quantified the frequency and types of medical device alarms,[4, 5, 6, 7, 8, 9] and some proposed solutions to decrease excess alarms.[10, 11, 12, 13, 14, 15] One such solution is to change alarm limit settings, an intervention shown to be efficacious in the literature.[5, 6, 16, 17] Although no adverse patient outcomes are reported in these studies, none of them included a formal safety evaluation to evaluate whether alarm rate reduction occurred at the expense of clinically significant alarms.
Specific to pediatrics, frameworks to address alarm fatigue have been proposed,[18] and the relationship between nurse response time and frequency of exposure to nonactionable alarms has been reported.[19] However, efforts to address alarm fatigue in the pediatric setting are less well studied overall, and there is little guidance regarding optimization of pediatric alarm parameters. Although multiple established reference ranges exist for pediatric vital signs,[20, 21, 22] a systematic review in 2011 found that only 2 of 5 published heart rate (HR) and 6 respiratory rate (RR) guidelines cited any references, and even these had weak underpinning evidence.[23] Consequently, ranges defining normal pediatric vital signs are derived either from small sample observational data in healthy outpatient children or consensus opinion. In a 2013 study by Bonafide et al.,[24] charted vital sign data from hospitalized children were used to develop percentile curves for HR and RR, and from these it was estimated that 54% of vital sign measurements in hospitalized children are out of range using currently accepted normal vital sign parameters.[24] Although these calculated vital sign parameters were not implemented clinically, they called into question reference ranges that are currently widely accepted and used as parameters for electronic health record (EHR) alerts, early warning scoring systems, and physiologic monitor alarms.
With the goal of safely decreasing the number of out‐of‐range vital sign measurements that result from current, often nonevidence‐based pediatric vital sign reference ranges, we used data from noncritically ill pediatric inpatients to derive HR and RR percentile charts for hospitalized children. In anticipation of local implementation of these data‐driven vital sign ranges as physiologic monitor parameters, we performed a retrospective safety analysis by evaluating the effect of data‐driven alarm limit modification on identification of cardiorespiratory arrests (CRA) and rapid response team (RRT) activations.
METHODS
We performed a cross‐sectional study of children less than 18 years of age hospitalized on general medical and surgical units at Lucile Packard Children's Hospital Stanford, a 311‐bed quaternary‐care academic hospital with a full complement of pediatric medical and surgical subspecialties and transplant programs. During the study period, the hospital used the Cerner EHR (Millennium; Cerner, Kansas City, MO) and Philips IntelliVue bedside monitors (Koninklijke Philips N.V., Amsterdam, the Netherlands). The Stanford University Institutional Review Board approved this study.
Establishing Data‐Driven HR and RR Parameters
Vital sign documentation in the EHR at our institution is performed primarily by nurses and facilitated by bedside monitor biomedical device integration. We extracted vital signs data from the institution's EHR for all general medical and surgical patients discharged between January 1, 2013 and May 3, 2014. To be most conservative in the definition of normal vital sign ranges for pediatric inpatients, we excluded critically ill children (those who spent any part of their hospitalization in an intensive care unit [ICU]). Physiologically implausible vital sign values were excluded as per the methods of Bonafide et al.[24] The data were separated into 2 different sets: a training set (patients discharged between January 1, 2013 and December 31, 2013) and a test set for validation (patients discharged between January 1, 2014 and May 3, 2014). To avoid oversampling from both particular time periods and individual patients in the training set, we randomly selected 1 HR and RR pair from each 4‐hour interval during a hospitalization, and then randomly sampled a maximum of 10 HR and RR pairs per patient. Using these vital sign measurements, we calculated age‐stratified 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for both HR and RR.
Based on a combination of expert opinion and local consensus from our Medical Executive and Patient Safety Committees, we selected the 5th and 95th percentile values as proposed data‐driven parameter limits and compared them to the 5th and 95th percentile values generated in the 2013 study[24] and to the 2004 National Institutes of Health (NIH)adapted vital sign reference ranges currently used at our hospital.[25] Using 1 randomly selected HR and RR pair from every 4‐hour interval in the validation set, we compared the proportion of out‐of‐range HR and RR observations with the proposed 5th and 95th percentile data‐driven parameters versus the current NIH reference ranges. We also calculated average differences between our data‐driven 5th and 95th percentile values and the calculated HR and RR values in the 2013 study.[24]
Safety Analysis
To assess the safety of the newly created 5th and 95th percentile HR and RR parameters prior to clinical adoption, we retrospectively reviewed data associated with all RRT and CRA events on the hospital's medical/surgical units from March 4, 2013 until March 3, 2014. The RRT/CRA event data were obtained from logs kept by the hospital's code committee. We excluded events that lacked a documented patient identifier, occurred in locations other than the acute medical/surgical units, or occurred in patients >18 years old. The resulting charts were manually reviewed to determine the date and time of RRT or CRA event activation. Because evidence exists that hospitalized pediatric patients with CRA show signs of vital sign decompensation as early as 12 hours prior to the event,[26, 27, 28, 29] we extracted all EHR‐charted HR and RR data in the 12 hours preceding RRT and CRA events from the institution's clinical data warehouse for analysis, excluding patients without charted vital sign data in this time period. The sets of patients with any out‐of‐range HR or RR measurements in the 12‐hours prior to an event were compared according to the current NIH reference ranges[25] versus data‐driven parameters. Additionally, manual chart review was performed to assess the reason for code or RRT activation, and to determine the role that out‐of‐range vital signs played in alerting clinical staff of patient decompensation.
Statistical Analysis
All analysis was performed using R statistical package software (version 0.98.1062 for Mac OS X 10_9_5; The R Foundation for Statistical Computing, Vienna, Austria) with an SQL database (MySQL 2015; Oracle Corp., Redwood City, CA).
RESULTS
Data‐Driven HR and RR Parameters
We established a training set of 62,508 vital sign measurements for 7202 unique patients to calculate 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for HR and RR among the 14 age groups (see Supporting Information, Appendix 1, in the online version of this article). Figures 1 and 2 compare the proposed data‐driven vital sign ranges with (1) our current HR and RR reference ranges and (2) the 5th and 95th percentile values created in the similar 2013 study.[24] The greatest difference between our study and the 2013 study was across data‐driven 95th percentile RR parameters, which were an average of 4.8 points lower in our study.


Our validation set consisted of 82,993 vital sign measurements for 2287 unique patients. Application of data‐driven HR and RR 5th and 95th percentile limits resulted in 24,045 (55.6%) fewer out‐of‐range measurements compared to current NIH reference ranges (19,240 vs 43,285). Forty‐five percent fewer HR values and 61% fewer RR values were considered out of range using the proposed data‐driven parameters (see Supporting Information, Appendix 2, in the online version of this article).
Safety
Of the 218 unique out‐of‐ICU RRT and CRA events logged from March 4, 2013 to March 3, 2014, 63 patients were excluded from analysis: 10 lacked identifying information, 33 occurred outside of medical/surgical units, and 20 occurred in patients >18 years of age. The remaining 155 patient charts were reviewed. Seven patients were subsequently excluded because they lacked EHR‐documented vital signs data in the 12 hours prior to RRT or CRA team activation, yielding a cohort of 148 patients (128 RRT events, 20 CRA events).
Table 1 describes the analysis of vital signs in the 12 hours leading up to the 148 RRT and CRA events. All 121 patients with out‐of‐range HR values using NIH reference ranges also had out‐of‐range HR values with the proposed data‐driven parameters; an additional 8 patients had low HR values using the data‐driven parameters. Of the 137 patients with an out‐of‐range RR value using NIH reference ranges, 33 (24.1%) were not considered out of range by the data‐driven parameters. Of these, 28 had high RR and 5 had low RR according to NIH reference ranges.
No. Patients With HR Out of Range* | No. Patients With RR Out of Range* | No. Patients With HR or RR Out of Range* | |
---|---|---|---|
| |||
NIH ranges | 121 | 137 | 144 |
Data‐driven ranges | 129 | 104 | 138 |
Difference (causal threshold) | +8 (low HR) | 28 (high RR), 5 (low RR) | +2 (low HR), 8 (high RR) |
After evaluating out‐of‐range HR and RR individually, the 148 RRT and CRA events were analyzed for either out‐of‐range HR values or RR values. In doing so, 144 (97.3%) patients had either HR or RR measurements that were considered out of range using our current NIH reference ranges. One hundred thirty‐eight (93.2%) had either HR or RR measurements that were considered out of range with the proposed parameters. One hundred thirty‐six (94.4%) of the 144 patients with out‐of‐range HR or RR measurements according to NIH reference ranges were also considered out of range using proposed parameters. The data‐driven parameters identified 2 additional patients with low HR who did not have out‐of‐range HR or RR values using the current NIH reference ranges. Manual chart review of the RRT/CRA events in the 8 patients who had normal HR or RR using the data‐driven parameters revealed that RRT or CRA team interventions occurred for clinical indications that did not rely upon HR or RR measurement (eg, laboratory testing abnormalities, desaturation events) (Table 2).
Indication for event | Patient Age |
---|---|
| |
1. Desaturation and apnea | 10 months |
2. Hyperammonemia (abnormal lab result) | 5 years |
3. Acute hematemesis | 16 years |
4. Lightheadedness, feeling faint | 17 years |
5. Desaturation with significant oxygen requirement | 17 years |
6. Desaturation with significant oxygen requirement | 17 years |
7. Patient stated difficulty breathing | 18 years |
8. Difficulty breathing (anaphylactic shock)* | 18 years |
DISCUSSION
This is the first published study to analyze the safety of implementing data‐driven HR and RR parameters in hospitalized children. Based on retrospective analysis of a 12‐month cohort of patients requiring RRT or CRA team activation, our data‐driven HR and RR parameters were at least as safe as the NIH‐published reference ranges employed at our children's hospital. In addition to maintaining sensitivity to RRT and CRA events, the data‐driven parameters resulted in an estimated 55.6% fewer out‐of‐range measurements among medical/surgical pediatric inpatients.
Improper alarm settings are 1 of 4 major contributing factors to reported alarm‐related events,[1] and data‐driven HR and RR parameters provide a means by which to address the Joint Commission Sentinel Event Alert[1] and National Patient Safety Goal[3] regarding alarm management safety for hospitalized pediatric patients. Our results suggest that this evidence‐based approach may reduce the frequency of false alarms (thereby mitigating alarm fatigue), and should be studied prospectively for implementation in the clinical setting.
The selection of percentile values to define the new data‐driven parameter ranges involved various considerations. In an effort to minimize alarm fatigue, we considered using the 1st and 99th percentile values. However, our Medical Executive and Patient Safety Committees determined that the 99th percentile values for HR and RR for many of the age groups exceeded those that would raise clinical concern. A more conservative approach, applying the 5th and 95th percentile values, was deemed clinically appropriate and consistent with recommendations from the only other study to calculate data‐driven HR and RR parameters for hospitalized children.[24]
When taken in total, Bonafide et al.'s 2013 study demonstrated that up to 54% of vital sign values were abnormal according to textbook reference ranges.[24] Similarly, we estimated 55.6% fewer out‐of‐range HR and RR measurements with our data‐driven parameters. Although our 5th and 95th HR percentile and 5th percentile RR values are strikingly similar to those developed in the 2013 study,[24] the difference in 95th percentile RR values between the studies was potentially clinically significant, with our data‐driven upper RR values being 4.8 breaths per minute lower (more conservative) on average. Bonafide et al. transformed the RR values to fit a normal distribution, which might account for this difference. Ultimately, our safety analysis demonstrated that 24% fewer patients were considered out of range for high RR prior to RRT/CRA events with the data‐driven parameters compared to NIH norms. Even fewer RRT/CRA patients would have been considered out of range per Bonafide's less conservative 95% RR limits.
Importantly, all 8 patients in our safety analysis without abnormal vital sign measurements in the 12 hours preceding their clinical events according to the proposed data‐driven parameters (but identified as having high RR per current reference ranges) had RRT or CRA events triggered due to other significant clinical manifestations or vital sign abnormalities (eg, hypoxia). This finding is supported by the literature, which suggests that RRTs are rarely activated due to single vital sign abnormality alone. Prior analysis of RRT activations in our pediatric hospital demonstrated that only approximately 10% of RRTs were activated primarily on the basis of HR or RR vital sign abnormalities (5.6% tachycardia, 2.8% tachypnea, 1.4% bradycardia), whereas 36% were activated due to respiratory distress.[30] The clinical relevance of high RR in isolation is questionable given a recent pediatric study that raised all RR limits and decreased alarm frequency without adverse patient safety consequences.[31] Our results suggest that modifying HR and RR alarm parameters using data‐driven 5th and 95th percentile limits to decrease alarm frequency does not pose additional safety risk related to identification of RRT and CRA events. We encourage continued work toward development of multivariate or smart alarms that analyze multiple simultaneous vital sign measurements and trends to determine whether an alarm should be triggered.[32, 33]
The ability to demonstrate the safety of data‐driven HR and RR parameters is a precursor to hospital‐wide implementation. We believe it is crucial to perform a safety analysis prior to implementation due to the role vital signs play in clinical assessment and detection of patient deterioration.[30, 34, 35, 36, 37] Though a few studies have shown that modification of alarm parameters decreases alarm frequency,[5, 6, 10, 16, 17] to our knowledge no formal safety evaluations have ever been published. This study provides the first published safety evaluation of data‐driven HR and RR parameters.
By decreasing the quantity of out‐of‐range vital sign values while preserving the ability to detect patient deterioration, data‐driven vital sign alarm limits have the potential to decrease false monitor alarms, alarm‐generated noise, and alarm fatigue. Future work includes prospectively studying the impact of adoption of data‐driven vital sign parameters on monitor alarm burden and monitoring the safety of the changes. Additional safety analysis could include comparing the sensitivity and specificity of early warning score systems when data‐driven vital sign ranges are substituted for traditional physiologic parameters. Further personalization of vital sign parameters will involve incorporating patient‐specific characteristics (eg, demographics, diagnoses) into the data‐driven analysis to further decrease alarm burden while enhancing patient safety. Ultimately, using a patient's own physiologic data to define highly personalized vital sign parameter limits represents a truly precision approach, and could revolutionize the way hospitalized patients are monitored.
Numerous relevant issues are not yet addressed in this initial, single‐institution study. First, although the biomedical device integration facilitated the direct import of monitor data into the EHR (decreasing transcription errors), our analysis was performed using EHR‐charted data. As such, the effect on bedside monitor alarms was not directly evaluated in our study, including those due to technical alarms or patient artifact. Second, our overall sample size for the training set was quite large; however, in some cases the number of patients per age category was limited. Third, although we evaluated the identification of severe deterioration leading to RRT or CRA events, the sensitivity of the new limits to the need for other interventions (eg, fluid bolus for dehydration or escalation of respiratory support for asthma exacerbation) or unplanned transfers to the ICU was not assessed. Fourth, the analysis was retrospective, and so the impact of data‐driven alarm limits on length of stay and readmission could not be defined. Fifth, excluding all vital sign measurements from patients who spent any time in the ICU setting decreased the amount of data available for analysis. However, excluding sicker patients probably resulted in narrower data‐driven HR and RR ranges, leading to more conservative proposed parameters that are more likely to identify patient decompensation in our safety analysis. Finally, this was a single‐site study. We believe our data‐driven limits are applicable to other tertiary or quaternary care facilities given the similarity to those generated in a study performed in a comparable setting,[24] but generalizability to other settings may be limited if the local population is sufficiently different. Furthermore, because institutional policies (eg, indications for care escalation) differ, individual institutions should determine whether our analysis is applicable to their setting or if local safety evaluation is necessary.
CONCLUSION
A large proportion of HR and RR values for hospitalized children at our institution are out of range according to current vital sign reference ranges. Our new data‐driven alarm parameters for hospitalized children provide a potentially safe means by which to modify physiologic bedside monitor alarm limits, a first step toward customization of alarm limit settings in an effort to mitigate alarm fatigue.
Acknowledgements
The authors thank Debby Huang and Joshua Glandorf in the Information Services Department at Stanford Children's Health for assistance with data acquisition. No compensation was received for their contributions.
Disclosures: All authors gave approval of the final manuscript version submitted for publication and agreed to be accountable for all aspects of the work. Dr. Veena V. Goel conceptualized and designed the study; collected, managed, analyzed and interpreted the data; prepared and reviewed the initial manuscript; and approved the final manuscript as submitted. Ms. Sarah F. Poole contributed to the design of the study and performed the primary data analysis for the study. Ms. Poole critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Goel and Ms. Poole had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Paul J. Sharek and Dr. Jonathan P. Palma contributed to the study design and data interpretation. Drs. Sharek and Palma critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Terry S. Platchek, Dr. Natalie M. Pageler, and Dr. Christopher A. Longhurst contributed to the study design. Drs. Platchek, Pageler, and Longhurst critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Ms. Poole is supported by the Stanford Biosciences Graduate Program through a Fulbright New Zealand Science and Innovation Graduate Award and through the J.R. Templin Trust Scholarship. The authors report no conflicts of interest.
The management of alarms in the hospital setting is a significant patient safety issue. In 2013, the Joint Commission issued Sentinel Event Alert #50 to draw attention to the fact that tens of thousands of alarms occur daily throughout individual hospitals, and 85% to 99% are false or not clinically actionable.[1] These alarms, designed to be a safety net in patient care, have the unintended consequence of causing provider desensitization, also known as alarm fatigue, which contributes to adverse events as severe as patient mortality.[1, 2] For this reason, a 2014 Joint Commission National Patient Safety Goal urged hospitals to prioritize alarm system safety and to develop policies and procedures to manage alarms and alarm fatigue.[3]
Multiple efforts have been made to address alarm fatigue in hospitalized adults. Studies have quantified the frequency and types of medical device alarms,[4, 5, 6, 7, 8, 9] and some proposed solutions to decrease excess alarms.[10, 11, 12, 13, 14, 15] One such solution is to change alarm limit settings, an intervention shown to be efficacious in the literature.[5, 6, 16, 17] Although no adverse patient outcomes are reported in these studies, none of them included a formal safety evaluation to evaluate whether alarm rate reduction occurred at the expense of clinically significant alarms.
Specific to pediatrics, frameworks to address alarm fatigue have been proposed,[18] and the relationship between nurse response time and frequency of exposure to nonactionable alarms has been reported.[19] However, efforts to address alarm fatigue in the pediatric setting are less well studied overall, and there is little guidance regarding optimization of pediatric alarm parameters. Although multiple established reference ranges exist for pediatric vital signs,[20, 21, 22] a systematic review in 2011 found that only 2 of 5 published heart rate (HR) and 6 respiratory rate (RR) guidelines cited any references, and even these had weak underpinning evidence.[23] Consequently, ranges defining normal pediatric vital signs are derived either from small sample observational data in healthy outpatient children or consensus opinion. In a 2013 study by Bonafide et al.,[24] charted vital sign data from hospitalized children were used to develop percentile curves for HR and RR, and from these it was estimated that 54% of vital sign measurements in hospitalized children are out of range using currently accepted normal vital sign parameters.[24] Although these calculated vital sign parameters were not implemented clinically, they called into question reference ranges that are currently widely accepted and used as parameters for electronic health record (EHR) alerts, early warning scoring systems, and physiologic monitor alarms.
With the goal of safely decreasing the number of out‐of‐range vital sign measurements that result from current, often nonevidence‐based pediatric vital sign reference ranges, we used data from noncritically ill pediatric inpatients to derive HR and RR percentile charts for hospitalized children. In anticipation of local implementation of these data‐driven vital sign ranges as physiologic monitor parameters, we performed a retrospective safety analysis by evaluating the effect of data‐driven alarm limit modification on identification of cardiorespiratory arrests (CRA) and rapid response team (RRT) activations.
METHODS
We performed a cross‐sectional study of children less than 18 years of age hospitalized on general medical and surgical units at Lucile Packard Children's Hospital Stanford, a 311‐bed quaternary‐care academic hospital with a full complement of pediatric medical and surgical subspecialties and transplant programs. During the study period, the hospital used the Cerner EHR (Millennium; Cerner, Kansas City, MO) and Philips IntelliVue bedside monitors (Koninklijke Philips N.V., Amsterdam, the Netherlands). The Stanford University Institutional Review Board approved this study.
Establishing Data‐Driven HR and RR Parameters
Vital sign documentation in the EHR at our institution is performed primarily by nurses and facilitated by bedside monitor biomedical device integration. We extracted vital signs data from the institution's EHR for all general medical and surgical patients discharged between January 1, 2013 and May 3, 2014. To be most conservative in the definition of normal vital sign ranges for pediatric inpatients, we excluded critically ill children (those who spent any part of their hospitalization in an intensive care unit [ICU]). Physiologically implausible vital sign values were excluded as per the methods of Bonafide et al.[24] The data were separated into 2 different sets: a training set (patients discharged between January 1, 2013 and December 31, 2013) and a test set for validation (patients discharged between January 1, 2014 and May 3, 2014). To avoid oversampling from both particular time periods and individual patients in the training set, we randomly selected 1 HR and RR pair from each 4‐hour interval during a hospitalization, and then randomly sampled a maximum of 10 HR and RR pairs per patient. Using these vital sign measurements, we calculated age‐stratified 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for both HR and RR.
Based on a combination of expert opinion and local consensus from our Medical Executive and Patient Safety Committees, we selected the 5th and 95th percentile values as proposed data‐driven parameter limits and compared them to the 5th and 95th percentile values generated in the 2013 study[24] and to the 2004 National Institutes of Health (NIH)adapted vital sign reference ranges currently used at our hospital.[25] Using 1 randomly selected HR and RR pair from every 4‐hour interval in the validation set, we compared the proportion of out‐of‐range HR and RR observations with the proposed 5th and 95th percentile data‐driven parameters versus the current NIH reference ranges. We also calculated average differences between our data‐driven 5th and 95th percentile values and the calculated HR and RR values in the 2013 study.[24]
Safety Analysis
To assess the safety of the newly created 5th and 95th percentile HR and RR parameters prior to clinical adoption, we retrospectively reviewed data associated with all RRT and CRA events on the hospital's medical/surgical units from March 4, 2013 until March 3, 2014. The RRT/CRA event data were obtained from logs kept by the hospital's code committee. We excluded events that lacked a documented patient identifier, occurred in locations other than the acute medical/surgical units, or occurred in patients >18 years old. The resulting charts were manually reviewed to determine the date and time of RRT or CRA event activation. Because evidence exists that hospitalized pediatric patients with CRA show signs of vital sign decompensation as early as 12 hours prior to the event,[26, 27, 28, 29] we extracted all EHR‐charted HR and RR data in the 12 hours preceding RRT and CRA events from the institution's clinical data warehouse for analysis, excluding patients without charted vital sign data in this time period. The sets of patients with any out‐of‐range HR or RR measurements in the 12‐hours prior to an event were compared according to the current NIH reference ranges[25] versus data‐driven parameters. Additionally, manual chart review was performed to assess the reason for code or RRT activation, and to determine the role that out‐of‐range vital signs played in alerting clinical staff of patient decompensation.
Statistical Analysis
All analysis was performed using R statistical package software (version 0.98.1062 for Mac OS X 10_9_5; The R Foundation for Statistical Computing, Vienna, Austria) with an SQL database (MySQL 2015; Oracle Corp., Redwood City, CA).
RESULTS
Data‐Driven HR and RR Parameters
We established a training set of 62,508 vital sign measurements for 7202 unique patients to calculate 1st, 5th, 10th, 50th, 90th, 95th, and 99th percentiles for HR and RR among the 14 age groups (see Supporting Information, Appendix 1, in the online version of this article). Figures 1 and 2 compare the proposed data‐driven vital sign ranges with (1) our current HR and RR reference ranges and (2) the 5th and 95th percentile values created in the similar 2013 study.[24] The greatest difference between our study and the 2013 study was across data‐driven 95th percentile RR parameters, which were an average of 4.8 points lower in our study.


Our validation set consisted of 82,993 vital sign measurements for 2287 unique patients. Application of data‐driven HR and RR 5th and 95th percentile limits resulted in 24,045 (55.6%) fewer out‐of‐range measurements compared to current NIH reference ranges (19,240 vs 43,285). Forty‐five percent fewer HR values and 61% fewer RR values were considered out of range using the proposed data‐driven parameters (see Supporting Information, Appendix 2, in the online version of this article).
Safety
Of the 218 unique out‐of‐ICU RRT and CRA events logged from March 4, 2013 to March 3, 2014, 63 patients were excluded from analysis: 10 lacked identifying information, 33 occurred outside of medical/surgical units, and 20 occurred in patients >18 years of age. The remaining 155 patient charts were reviewed. Seven patients were subsequently excluded because they lacked EHR‐documented vital signs data in the 12 hours prior to RRT or CRA team activation, yielding a cohort of 148 patients (128 RRT events, 20 CRA events).
Table 1 describes the analysis of vital signs in the 12 hours leading up to the 148 RRT and CRA events. All 121 patients with out‐of‐range HR values using NIH reference ranges also had out‐of‐range HR values with the proposed data‐driven parameters; an additional 8 patients had low HR values using the data‐driven parameters. Of the 137 patients with an out‐of‐range RR value using NIH reference ranges, 33 (24.1%) were not considered out of range by the data‐driven parameters. Of these, 28 had high RR and 5 had low RR according to NIH reference ranges.
No. Patients With HR Out of Range* | No. Patients With RR Out of Range* | No. Patients With HR or RR Out of Range* | |
---|---|---|---|
| |||
NIH ranges | 121 | 137 | 144 |
Data‐driven ranges | 129 | 104 | 138 |
Difference (causal threshold) | +8 (low HR) | 28 (high RR), 5 (low RR) | +2 (low HR), 8 (high RR) |
After evaluating out‐of‐range HR and RR individually, the 148 RRT and CRA events were analyzed for either out‐of‐range HR values or RR values. In doing so, 144 (97.3%) patients had either HR or RR measurements that were considered out of range using our current NIH reference ranges. One hundred thirty‐eight (93.2%) had either HR or RR measurements that were considered out of range with the proposed parameters. One hundred thirty‐six (94.4%) of the 144 patients with out‐of‐range HR or RR measurements according to NIH reference ranges were also considered out of range using proposed parameters. The data‐driven parameters identified 2 additional patients with low HR who did not have out‐of‐range HR or RR values using the current NIH reference ranges. Manual chart review of the RRT/CRA events in the 8 patients who had normal HR or RR using the data‐driven parameters revealed that RRT or CRA team interventions occurred for clinical indications that did not rely upon HR or RR measurement (eg, laboratory testing abnormalities, desaturation events) (Table 2).
Indication for event | Patient Age |
---|---|
| |
1. Desaturation and apnea | 10 months |
2. Hyperammonemia (abnormal lab result) | 5 years |
3. Acute hematemesis | 16 years |
4. Lightheadedness, feeling faint | 17 years |
5. Desaturation with significant oxygen requirement | 17 years |
6. Desaturation with significant oxygen requirement | 17 years |
7. Patient stated difficulty breathing | 18 years |
8. Difficulty breathing (anaphylactic shock)* | 18 years |
DISCUSSION
This is the first published study to analyze the safety of implementing data‐driven HR and RR parameters in hospitalized children. Based on retrospective analysis of a 12‐month cohort of patients requiring RRT or CRA team activation, our data‐driven HR and RR parameters were at least as safe as the NIH‐published reference ranges employed at our children's hospital. In addition to maintaining sensitivity to RRT and CRA events, the data‐driven parameters resulted in an estimated 55.6% fewer out‐of‐range measurements among medical/surgical pediatric inpatients.
Improper alarm settings are 1 of 4 major contributing factors to reported alarm‐related events,[1] and data‐driven HR and RR parameters provide a means by which to address the Joint Commission Sentinel Event Alert[1] and National Patient Safety Goal[3] regarding alarm management safety for hospitalized pediatric patients. Our results suggest that this evidence‐based approach may reduce the frequency of false alarms (thereby mitigating alarm fatigue), and should be studied prospectively for implementation in the clinical setting.
The selection of percentile values to define the new data‐driven parameter ranges involved various considerations. In an effort to minimize alarm fatigue, we considered using the 1st and 99th percentile values. However, our Medical Executive and Patient Safety Committees determined that the 99th percentile values for HR and RR for many of the age groups exceeded those that would raise clinical concern. A more conservative approach, applying the 5th and 95th percentile values, was deemed clinically appropriate and consistent with recommendations from the only other study to calculate data‐driven HR and RR parameters for hospitalized children.[24]
When taken in total, Bonafide et al.'s 2013 study demonstrated that up to 54% of vital sign values were abnormal according to textbook reference ranges.[24] Similarly, we estimated 55.6% fewer out‐of‐range HR and RR measurements with our data‐driven parameters. Although our 5th and 95th HR percentile and 5th percentile RR values are strikingly similar to those developed in the 2013 study,[24] the difference in 95th percentile RR values between the studies was potentially clinically significant, with our data‐driven upper RR values being 4.8 breaths per minute lower (more conservative) on average. Bonafide et al. transformed the RR values to fit a normal distribution, which might account for this difference. Ultimately, our safety analysis demonstrated that 24% fewer patients were considered out of range for high RR prior to RRT/CRA events with the data‐driven parameters compared to NIH norms. Even fewer RRT/CRA patients would have been considered out of range per Bonafide's less conservative 95% RR limits.
Importantly, all 8 patients in our safety analysis without abnormal vital sign measurements in the 12 hours preceding their clinical events according to the proposed data‐driven parameters (but identified as having high RR per current reference ranges) had RRT or CRA events triggered due to other significant clinical manifestations or vital sign abnormalities (eg, hypoxia). This finding is supported by the literature, which suggests that RRTs are rarely activated due to single vital sign abnormality alone. Prior analysis of RRT activations in our pediatric hospital demonstrated that only approximately 10% of RRTs were activated primarily on the basis of HR or RR vital sign abnormalities (5.6% tachycardia, 2.8% tachypnea, 1.4% bradycardia), whereas 36% were activated due to respiratory distress.[30] The clinical relevance of high RR in isolation is questionable given a recent pediatric study that raised all RR limits and decreased alarm frequency without adverse patient safety consequences.[31] Our results suggest that modifying HR and RR alarm parameters using data‐driven 5th and 95th percentile limits to decrease alarm frequency does not pose additional safety risk related to identification of RRT and CRA events. We encourage continued work toward development of multivariate or smart alarms that analyze multiple simultaneous vital sign measurements and trends to determine whether an alarm should be triggered.[32, 33]
The ability to demonstrate the safety of data‐driven HR and RR parameters is a precursor to hospital‐wide implementation. We believe it is crucial to perform a safety analysis prior to implementation due to the role vital signs play in clinical assessment and detection of patient deterioration.[30, 34, 35, 36, 37] Though a few studies have shown that modification of alarm parameters decreases alarm frequency,[5, 6, 10, 16, 17] to our knowledge no formal safety evaluations have ever been published. This study provides the first published safety evaluation of data‐driven HR and RR parameters.
By decreasing the quantity of out‐of‐range vital sign values while preserving the ability to detect patient deterioration, data‐driven vital sign alarm limits have the potential to decrease false monitor alarms, alarm‐generated noise, and alarm fatigue. Future work includes prospectively studying the impact of adoption of data‐driven vital sign parameters on monitor alarm burden and monitoring the safety of the changes. Additional safety analysis could include comparing the sensitivity and specificity of early warning score systems when data‐driven vital sign ranges are substituted for traditional physiologic parameters. Further personalization of vital sign parameters will involve incorporating patient‐specific characteristics (eg, demographics, diagnoses) into the data‐driven analysis to further decrease alarm burden while enhancing patient safety. Ultimately, using a patient's own physiologic data to define highly personalized vital sign parameter limits represents a truly precision approach, and could revolutionize the way hospitalized patients are monitored.
Numerous relevant issues are not yet addressed in this initial, single‐institution study. First, although the biomedical device integration facilitated the direct import of monitor data into the EHR (decreasing transcription errors), our analysis was performed using EHR‐charted data. As such, the effect on bedside monitor alarms was not directly evaluated in our study, including those due to technical alarms or patient artifact. Second, our overall sample size for the training set was quite large; however, in some cases the number of patients per age category was limited. Third, although we evaluated the identification of severe deterioration leading to RRT or CRA events, the sensitivity of the new limits to the need for other interventions (eg, fluid bolus for dehydration or escalation of respiratory support for asthma exacerbation) or unplanned transfers to the ICU was not assessed. Fourth, the analysis was retrospective, and so the impact of data‐driven alarm limits on length of stay and readmission could not be defined. Fifth, excluding all vital sign measurements from patients who spent any time in the ICU setting decreased the amount of data available for analysis. However, excluding sicker patients probably resulted in narrower data‐driven HR and RR ranges, leading to more conservative proposed parameters that are more likely to identify patient decompensation in our safety analysis. Finally, this was a single‐site study. We believe our data‐driven limits are applicable to other tertiary or quaternary care facilities given the similarity to those generated in a study performed in a comparable setting,[24] but generalizability to other settings may be limited if the local population is sufficiently different. Furthermore, because institutional policies (eg, indications for care escalation) differ, individual institutions should determine whether our analysis is applicable to their setting or if local safety evaluation is necessary.
CONCLUSION
A large proportion of HR and RR values for hospitalized children at our institution are out of range according to current vital sign reference ranges. Our new data‐driven alarm parameters for hospitalized children provide a potentially safe means by which to modify physiologic bedside monitor alarm limits, a first step toward customization of alarm limit settings in an effort to mitigate alarm fatigue.
Acknowledgements
The authors thank Debby Huang and Joshua Glandorf in the Information Services Department at Stanford Children's Health for assistance with data acquisition. No compensation was received for their contributions.
Disclosures: All authors gave approval of the final manuscript version submitted for publication and agreed to be accountable for all aspects of the work. Dr. Veena V. Goel conceptualized and designed the study; collected, managed, analyzed and interpreted the data; prepared and reviewed the initial manuscript; and approved the final manuscript as submitted. Ms. Sarah F. Poole contributed to the design of the study and performed the primary data analysis for the study. Ms. Poole critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Goel and Ms. Poole had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Paul J. Sharek and Dr. Jonathan P. Palma contributed to the study design and data interpretation. Drs. Sharek and Palma critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Dr. Terry S. Platchek, Dr. Natalie M. Pageler, and Dr. Christopher A. Longhurst contributed to the study design. Drs. Platchek, Pageler, and Longhurst critically revised the manuscript for important intellectual content and approved the final manuscript as submitted. Ms. Poole is supported by the Stanford Biosciences Graduate Program through a Fulbright New Zealand Science and Innovation Graduate Award and through the J.R. Templin Trust Scholarship. The authors report no conflicts of interest.
- The Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3. Available at: https://www.jointcommission.org/sea_issue_50/. Accessed October 12, 2013.
- Alarm fatigue” a factor in 2d death: UMass hospital cited for violations. The Boston Globe. September 21, 2011. Available at: https://www.bostonglobe.com/2011/09/20/umass/qSOhm8dYmmaq4uTHZb7FNM/story.html. Accessed December 19, 2014 . “
- The Joint Commission. Alarm system safety. Available at: https://www.jointcommission.org/assets/1/18/R3_Report_Issue_5_12_2_13_Final.pdf. Published December 11, 2013. Accessed October 12, 2013.
- ALARMED: adverse events in low‐risk patients with chest pain receiving continuous electrocardiographic monitoring in the emergency department. A pilot study. Am J Emerg Med. 2006;24(1):62–67. , , , , .
- Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19(1):28–34; quiz 35. , .
- Physiologic monitoring alarm load on medical/surgical floors of a community hospital. Biomed Instrum Technol. 2011;(suppl):29–36. , , .
- Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25(12):1360–1366. , , , , , .
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144. , , , et al.
- Alarm fatigue. Nurs Clin North Am. 2012;47(3):375–382. .
- Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277. .
- Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual. 2014;29(1):9–18. , , , .
- An evidence‐based approach to reduce nuisance alarms and alarm fatigue. Biomed Instrum Technol. 2011;(suppl):46–52. .
- Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PLoS One. 2014;9(10):e110274. , , , et al.
- Effect of altering alarm settings: a randomized controlled study. Biomed Instrum Technol. 2015;49(3):214–222. , , , , , .
- Alarm limit settings for early warning systems to identify at‐risk patients. J Adv Nurs. 2009;65(9):1844–1852. , , , , .
- A framework for reducing alarm fatigue on pediatric inpatient units. Hosp Pediatr. 2015;5(3):160–163. , .
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- The Johns Hopkins Hospital, , . The Harriet Lane Handbook. 20th ed. Philadelphia, PA: Elsevier Saunders; 2014.
- Nelson Textbook of Pediatrics. 19th ed. Philadelphia, PA.: Elsevier Saunders; 2011. , .
- Pediatric assessment. In: Pediatric Advanced Life Support: Provider Manual. Dallas, TX: American Heart Association; 2006:9–16. , , , .
- Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. Lancet. 2011;377(9770):1011–1018. , , , et al.
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131(4):e1150–e1157. , , , , , .
- National Institutes of Health. Age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc.nih.gov/ccc/pedweb/pedsstaff/age.html. Accessed July 26, 2015.
- Guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Part 9: pediatric basic life support. The American Heart Association in collaboration with the International Liaison Committee on Resuscitation. Circulation. 2000;102(8 suppl):I253–I290.
- Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary‐care hospital. Med J Aust. 1999;171(1):22–25. , , , , , .
- Duration of life‐threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–1634. , , , et al.
- Pediatric cardiopulmonary resuscitation: a collective review. Ann Emerg Med. 1999;33(2):195–205. , .
- Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):2267–2274. , , , et al.
- A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694. , , , et al.
- Collection of annotated data in a clinical validation study for alarm algorithms in intensive care—a methodologic framework. J Crit Care. 2010;25(1):128–135. , , , et al.
- Making ICU alarms meaningful: a comparison of traditional vs. trend‐based algorithms. Proc AMIA Symp. 1999:379–383. , , .
- Implementation of a medical emergency team in a large pediatric teaching hospital prevents respiratory and cardiopulmonary arrests outside the intensive care unit. Pediatr Crit Care Med. 2007;8(3):236–246; quiz 247. , , , et al.
- Centile‐based Early Warning Scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):969–970. .
- Centile‐based early warning scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):1013–1018. , , , , , .
- Reduction of paediatric in‐patient cardiac arrest and death with a medical emergency team: preliminary results. Arch Dis Child. 2005;90(11):1148–1152. , , , , .
- The Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3. Available at: https://www.jointcommission.org/sea_issue_50/. Accessed October 12, 2013.
- Alarm fatigue” a factor in 2d death: UMass hospital cited for violations. The Boston Globe. September 21, 2011. Available at: https://www.bostonglobe.com/2011/09/20/umass/qSOhm8dYmmaq4uTHZb7FNM/story.html. Accessed December 19, 2014 . “
- The Joint Commission. Alarm system safety. Available at: https://www.jointcommission.org/assets/1/18/R3_Report_Issue_5_12_2_13_Final.pdf. Published December 11, 2013. Accessed October 12, 2013.
- ALARMED: adverse events in low‐risk patients with chest pain receiving continuous electrocardiographic monitoring in the emergency department. A pilot study. Am J Emerg Med. 2006;24(1):62–67. , , , , .
- Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19(1):28–34; quiz 35. , .
- Physiologic monitoring alarm load on medical/surgical floors of a community hospital. Biomed Instrum Technol. 2011;(suppl):29–36. , , .
- Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25(12):1360–1366. , , , , , .
- Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985. .
- Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;(suppl):38–45. , , , et al.
- Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144. , , , et al.
- Alarm fatigue. Nurs Clin North Am. 2012;47(3):375–382. .
- Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277. .
- Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual. 2014;29(1):9–18. , , , .
- An evidence‐based approach to reduce nuisance alarms and alarm fatigue. Biomed Instrum Technol. 2011;(suppl):46–52. .
- Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PLoS One. 2014;9(10):e110274. , , , et al.
- Effect of altering alarm settings: a randomized controlled study. Biomed Instrum Technol. 2015;49(3):214–222. , , , , , .
- Alarm limit settings for early warning systems to identify at‐risk patients. J Adv Nurs. 2009;65(9):1844–1852. , , , , .
- A framework for reducing alarm fatigue on pediatric inpatient units. Hosp Pediatr. 2015;5(3):160–163. , .
- Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351. , , , et al.
- The Johns Hopkins Hospital, , . The Harriet Lane Handbook. 20th ed. Philadelphia, PA: Elsevier Saunders; 2014.
- Nelson Textbook of Pediatrics. 19th ed. Philadelphia, PA.: Elsevier Saunders; 2011. , .
- Pediatric assessment. In: Pediatric Advanced Life Support: Provider Manual. Dallas, TX: American Heart Association; 2006:9–16. , , , .
- Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. Lancet. 2011;377(9770):1011–1018. , , , et al.
- Development of heart and respiratory rate percentile curves for hospitalized children. Pediatrics. 2013;131(4):e1150–e1157. , , , , , .
- National Institutes of Health. Age‐appropriate vital signs. Available at: https://web.archive.org/web/20041101222327/http://www.cc.nih.gov/ccc/pedweb/pedsstaff/age.html. Accessed July 26, 2015.
- Guidelines 2000 for cardiopulmonary resuscitation and emergency cardiovascular care. Part 9: pediatric basic life support. The American Heart Association in collaboration with the International Liaison Committee on Resuscitation. Circulation. 2000;102(8 suppl):I253–I290.
- Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary‐care hospital. Med J Aust. 1999;171(1):22–25. , , , , , .
- Duration of life‐threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–1634. , , , et al.
- Pediatric cardiopulmonary resuscitation: a collective review. Ann Emerg Med. 1999;33(2):195–205. , .
- Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a Children's Hospital. JAMA. 2007;298(19):2267–2274. , , , et al.
- A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694. , , , et al.
- Collection of annotated data in a clinical validation study for alarm algorithms in intensive care—a methodologic framework. J Crit Care. 2010;25(1):128–135. , , , et al.
- Making ICU alarms meaningful: a comparison of traditional vs. trend‐based algorithms. Proc AMIA Symp. 1999:379–383. , , .
- Implementation of a medical emergency team in a large pediatric teaching hospital prevents respiratory and cardiopulmonary arrests outside the intensive care unit. Pediatr Crit Care Med. 2007;8(3):236–246; quiz 247. , , , et al.
- Centile‐based Early Warning Scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):969–970. .
- Centile‐based early warning scores derived from statistical distributions of vital signs. Resuscitation. 2011;82(8):1013–1018. , , , , , .
- Reduction of paediatric in‐patient cardiac arrest and death with a medical emergency team: preliminary results. Arch Dis Child. 2005;90(11):1148–1152. , , , , .
FDA Advisory Panel Unanimously Backs Biosimilars for Humira, Enbrel
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
FDA advisory panel unanimously backs biosimilars for Humira, Enbrel
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
On Twitter @mitchelzoler
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
On Twitter @mitchelzoler
The Food and Drug Administration’s Arthritis Advisory Committee, together with an added complement of dermatologists and gastroenterologists, unanimously recommended during meetings on July 12 and 13 that the agency license a biosimilar Humira (adalimumab) that is made by Amgen and a biosimilar Enbrel (etanercept) that is made by Sandoz for many of the same indications held by the reference drugs.
The FDA advisory panel that endorsed biosimilar Humira recommended the agent’s approval in a 26-0 vote for many, but not all of the indications currently assigned to Humira itself: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis in patients at least 4 years old, plaque psoriasis, adult Crohn’s disease, and adult ulcerative colitis.
A slightly different group of 20 advisory panel members (without any gastroenterologists) voted 20-0 in favor of the FDA granting biosimilar Enbrel all five of the indications now held by Enbrel: rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, juvenile idiopathic arthritis, and plaque psoriasis.
The biosimilar Humira and the biosimilar Enbrel are, respectively, the third and fourth candidate biosimilars to emerge from the FDA’s development program and receive advisory committee scrutiny and support. The first agent through the process, biosimilar filgrastim (Zarxio) received FDA approval in 2015 and is available in the United States. Although the second biosimilar through the process, the tumor necrosis factor inhibitor Inflectra that is biosimilar Remicade (infliximab), received FDA approval in April of this year, it has not yet become available for sale, although a spokeswoman for the company that will market it, Pfizer, said that the company expects to start U.S. sales of Inflectra before the end of 2016.
While the Arthritis Advisory Committee ended each of its daylong deliberations for each of the two candidate biosimilars with unanimous support, the panelists’ discussions among themselves and with FDA staffers reflected some uncertainty with the biosimilar concept, especially during the first day when they focused on biosimilar Humira. The major sticking point revolved around the regulatory pathway to approval first established by the Biologics Price Competition and Innovation Act of 2009 and subsequently refined by the FDA that allows a candidate biosimilar to establish its biosimilarity primarily though the results of analytical studies that establish that the candidate molecule is highly similar to the reference molecule. This approval scheme uses clinical trials in a confirmatory role to establish biosimilarity rather than as the linchpin of approval.
It also means that the FDA can grant clinical indications to the biosimilar drug based not on the results from clinical trials, but based entirely on what have already been demonstrated as safe and effective clinical applications for the reference drug. For example, the biosimilar Humira underwent testing in two clinical studies showing similar efficacy and safety as Humira in patients with rheumatoid arthritis and in patients with plaque psoriasis, but received endorsements based on extrapolations for an additional five indications. Biosimilar Enbrel was compared with Enbrel in patients with plaque psoriasis only and still received extrapolated indications for the additional four rheumatologic conditions.
“This is a new level of extrapolation, across indications,” noted Sarah E. Streett, MD, a gastroenterologist at Stanford (Calif.) University, one of several panelists who initially voiced uncertainty about the concept.
But FDA staffer Nikolay P. Nikolov, MD, who led the agency’s presentation, assured the panelists that the concept of extrapolation was at the heart of biosimilar development and regulatory assessment.
“We have confidence from the data that the two molecules [the reference drug and biosimilar drug] are so similar that we can rely on the safety and efficacy of the reference product. The premise of our approach to biosimilars is that this is not a new molecule that we know nothing about.”
The other uncertainty about biosimilar Humira and biosimilar Enbrel that raised concerns of many panelists were the prospects for nonmedical switching once these drugs reach the market. Nonmedical switching refers to when an insurance company or pharmacy benefit manager substitutes a biosimilar for a reference drug without approval from or even the knowledge of the prescribing physician or the patient. Many of the people who spoke during the public forum period on both days of hearings voiced their concerns about this prospect.
“Nonmedical switching is a major concern of clinicians and policy makers, and we need greater clarification from the FDA,” said committee chair Daniel H. Solomon, MD, a rheumatologist and professor of medicine at Harvard Medical School in Boston.
“I see a remarkable disconnect between the public’s concerns [about nonmedical switching] and the charge to the committee. These are essential issues that need a forum to be aired out,” said panelist Steven F. Solga, MD, chief of gastroenterology at St. Luke’s Hospital in Bethlehem, Pa.
Dr. Nikolov assured committee members that the FDA recognized this concern and was working on it. “We appreciate the disconnect between the charge and the concerns of the community. I assure you that the issues brought up will be part of our discussions so we can get this [biosimilar pathway] implemented the right way.”
According to the FDA’s regulations, a biosimilar designation does not allow for nonmedical switching, something that could only happen under a related but distinct designation known as interchangeability. During the committee meeting on July 13, a FDA staffer said that the agency is currently developing guidance for an “interchangeable” designation and plans to have it available before then end of 2016.
On Twitter @mitchelzoler
Study identifies important predictors for PC/PGL
BALTIMORE – Tumor size and the presence of mutations of the succinate dehydrogenase complex subunit B (SDHB) gene may be reliable indicators of prognosis after surgery for pheochromocytoma and abdominal paraganglioma, investigators in a National Cancer Institute–funded study have reported.
“The staging of pheochromocytoma and abdominal paraganglioma can be difficult, but it is critical for optimal patient care,” Yasmine Assadipour, MD, of the National Cancer Institute, Bethesda, Md., and the George Washington University Hospital, Washington, reported at the annual meeting of the American Association of Endocrine Surgeons.
“Any clinically relevant grading or prognostic system should include SDHB mutation status and primary tumor size as prime features of scoring,” Dr. Assadipour said. “Histologic features such as Ki-67 or mitotic index may not be as useful for prognostic information in patients with pheochromocytoma and abdominal paraganglioma, particularly in the setting of SDHB mutation.”
Dr. Assadipour and her coinvestigators focused their investigation on mutations of the SDHB (succinate dehydrogenase complex subunit B) gene, which codes for one of four subunits comprising a mitochondrial protein.
They also considered primary tumor size, functionality, pathology, surgical approach, and histologic features including Ki-67 index and mitotic index. The study was a retrospective analysis of 84 patients who had surgery for PC [pheochromocytoma] or PGL [paraganglioma] and had germ line genetic testing. Of the 84 patients, 35 patients had sporadic disease and 49 had germ line SDHB mutation. The study analyzed tumor samples for Ki-67/MIB-1 staining and mitotic index.
“In a univariate analysis, SDHB mutation, tumor size and surgical approach were associated with local regional recurrence,” Dr. Assadipour said. “In a multivariate analysis, the only independent risk factors were SDHB mutation status and tumor size; Ki-67 and mitotic index did not have any association with recurrence.”
The researchers found similar results when they looked at distant metastasis. “SDHB mutation, tumor size, abdominal paraganglioma and surgical approach were associated with distant metastasis,” Dr. Assadipour said. “Again, Ki-67 and mitotic index were not.”
In the multivariate analysis, again, only patient SDHB status and tumor size were independently associated with metastasis.”
The incidence of local recurrence in patients with the SDHB mutation was 47.6% vs. 9.1% in those without the gene mutation, Dr. Assadipour said. The rates of distant metastasis showed a similar disparity: 56.5% and 9.1%, respectively.
Patients with the SDHB mutation presented at a younger age than those without the mutation, 33 vs. 49.6 years old. Among the 65 patients who underwent R0 primary tumor resection, those with the SDHB mutation, paraganglioma, and larger tumor size had a shorter disease-free survival, Dr. Assadipour said.
In analyzing tumor size, Dr. Assadipour said two stratifications were studied: evaluating tumors sized 0-3 cm, 3-6 cm and 6 cm and larger; and 0-5 cm and 5 cm and larger. “Tumors over 6 cm had the shortest disease-free survival, and even when we applied the under-5 cm and over-5 cm scale, we clearly saw a difference in disease-free survival,” she said. Ki-67 and mitotic index were not related to disease-free survival.
The presence of a SDHB mutation had a hazard ratio of 16.2, while tumor diameter greater than 6 cm had a HR of 15.4, Dr. Assadipour said. These were the only independent risk factors for local recurrence, distant metastases and shorter disease-free interval found in the study.
During the discussion, Lawrence T. Kim, MD, of the University of North Carolina asked if the researchers found any differences in outcomes related to the surgical approach. “We were unable to identify whether any surgical approach improved or worsened outcomes on multivariate analysis” Dr. Assadipour said.
Thomas J. Fahey, MD, of New York asked what she would recommend for surgical approaches for patients with PC and PGL.
“Our general recommendation is that an adrenal pheochromocytoma that is over 6 cm carries a higher risk of recurrence and distant metastasis so an open approach with lymph node dissection ensuring negative surgical margins should be considered,” Dr. Assadipour said. “For abdominal paragangliomas, unless they are quite small and in a favorable location, we would generally recommend an open approach.”
The study was supported by the intramural program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health. Dr. Assadipour and her coauthors had no financial relationships to disclose.
BALTIMORE – Tumor size and the presence of mutations of the succinate dehydrogenase complex subunit B (SDHB) gene may be reliable indicators of prognosis after surgery for pheochromocytoma and abdominal paraganglioma, investigators in a National Cancer Institute–funded study have reported.
“The staging of pheochromocytoma and abdominal paraganglioma can be difficult, but it is critical for optimal patient care,” Yasmine Assadipour, MD, of the National Cancer Institute, Bethesda, Md., and the George Washington University Hospital, Washington, reported at the annual meeting of the American Association of Endocrine Surgeons.
“Any clinically relevant grading or prognostic system should include SDHB mutation status and primary tumor size as prime features of scoring,” Dr. Assadipour said. “Histologic features such as Ki-67 or mitotic index may not be as useful for prognostic information in patients with pheochromocytoma and abdominal paraganglioma, particularly in the setting of SDHB mutation.”
Dr. Assadipour and her coinvestigators focused their investigation on mutations of the SDHB (succinate dehydrogenase complex subunit B) gene, which codes for one of four subunits comprising a mitochondrial protein.
They also considered primary tumor size, functionality, pathology, surgical approach, and histologic features including Ki-67 index and mitotic index. The study was a retrospective analysis of 84 patients who had surgery for PC [pheochromocytoma] or PGL [paraganglioma] and had germ line genetic testing. Of the 84 patients, 35 patients had sporadic disease and 49 had germ line SDHB mutation. The study analyzed tumor samples for Ki-67/MIB-1 staining and mitotic index.
“In a univariate analysis, SDHB mutation, tumor size and surgical approach were associated with local regional recurrence,” Dr. Assadipour said. “In a multivariate analysis, the only independent risk factors were SDHB mutation status and tumor size; Ki-67 and mitotic index did not have any association with recurrence.”
The researchers found similar results when they looked at distant metastasis. “SDHB mutation, tumor size, abdominal paraganglioma and surgical approach were associated with distant metastasis,” Dr. Assadipour said. “Again, Ki-67 and mitotic index were not.”
In the multivariate analysis, again, only patient SDHB status and tumor size were independently associated with metastasis.”
The incidence of local recurrence in patients with the SDHB mutation was 47.6% vs. 9.1% in those without the gene mutation, Dr. Assadipour said. The rates of distant metastasis showed a similar disparity: 56.5% and 9.1%, respectively.
Patients with the SDHB mutation presented at a younger age than those without the mutation, 33 vs. 49.6 years old. Among the 65 patients who underwent R0 primary tumor resection, those with the SDHB mutation, paraganglioma, and larger tumor size had a shorter disease-free survival, Dr. Assadipour said.
In analyzing tumor size, Dr. Assadipour said two stratifications were studied: evaluating tumors sized 0-3 cm, 3-6 cm and 6 cm and larger; and 0-5 cm and 5 cm and larger. “Tumors over 6 cm had the shortest disease-free survival, and even when we applied the under-5 cm and over-5 cm scale, we clearly saw a difference in disease-free survival,” she said. Ki-67 and mitotic index were not related to disease-free survival.
The presence of a SDHB mutation had a hazard ratio of 16.2, while tumor diameter greater than 6 cm had a HR of 15.4, Dr. Assadipour said. These were the only independent risk factors for local recurrence, distant metastases and shorter disease-free interval found in the study.
During the discussion, Lawrence T. Kim, MD, of the University of North Carolina asked if the researchers found any differences in outcomes related to the surgical approach. “We were unable to identify whether any surgical approach improved or worsened outcomes on multivariate analysis” Dr. Assadipour said.
Thomas J. Fahey, MD, of New York asked what she would recommend for surgical approaches for patients with PC and PGL.
“Our general recommendation is that an adrenal pheochromocytoma that is over 6 cm carries a higher risk of recurrence and distant metastasis so an open approach with lymph node dissection ensuring negative surgical margins should be considered,” Dr. Assadipour said. “For abdominal paragangliomas, unless they are quite small and in a favorable location, we would generally recommend an open approach.”
The study was supported by the intramural program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health. Dr. Assadipour and her coauthors had no financial relationships to disclose.
BALTIMORE – Tumor size and the presence of mutations of the succinate dehydrogenase complex subunit B (SDHB) gene may be reliable indicators of prognosis after surgery for pheochromocytoma and abdominal paraganglioma, investigators in a National Cancer Institute–funded study have reported.
“The staging of pheochromocytoma and abdominal paraganglioma can be difficult, but it is critical for optimal patient care,” Yasmine Assadipour, MD, of the National Cancer Institute, Bethesda, Md., and the George Washington University Hospital, Washington, reported at the annual meeting of the American Association of Endocrine Surgeons.
“Any clinically relevant grading or prognostic system should include SDHB mutation status and primary tumor size as prime features of scoring,” Dr. Assadipour said. “Histologic features such as Ki-67 or mitotic index may not be as useful for prognostic information in patients with pheochromocytoma and abdominal paraganglioma, particularly in the setting of SDHB mutation.”
Dr. Assadipour and her coinvestigators focused their investigation on mutations of the SDHB (succinate dehydrogenase complex subunit B) gene, which codes for one of four subunits comprising a mitochondrial protein.
They also considered primary tumor size, functionality, pathology, surgical approach, and histologic features including Ki-67 index and mitotic index. The study was a retrospective analysis of 84 patients who had surgery for PC [pheochromocytoma] or PGL [paraganglioma] and had germ line genetic testing. Of the 84 patients, 35 patients had sporadic disease and 49 had germ line SDHB mutation. The study analyzed tumor samples for Ki-67/MIB-1 staining and mitotic index.
“In a univariate analysis, SDHB mutation, tumor size and surgical approach were associated with local regional recurrence,” Dr. Assadipour said. “In a multivariate analysis, the only independent risk factors were SDHB mutation status and tumor size; Ki-67 and mitotic index did not have any association with recurrence.”
The researchers found similar results when they looked at distant metastasis. “SDHB mutation, tumor size, abdominal paraganglioma and surgical approach were associated with distant metastasis,” Dr. Assadipour said. “Again, Ki-67 and mitotic index were not.”
In the multivariate analysis, again, only patient SDHB status and tumor size were independently associated with metastasis.”
The incidence of local recurrence in patients with the SDHB mutation was 47.6% vs. 9.1% in those without the gene mutation, Dr. Assadipour said. The rates of distant metastasis showed a similar disparity: 56.5% and 9.1%, respectively.
Patients with the SDHB mutation presented at a younger age than those without the mutation, 33 vs. 49.6 years old. Among the 65 patients who underwent R0 primary tumor resection, those with the SDHB mutation, paraganglioma, and larger tumor size had a shorter disease-free survival, Dr. Assadipour said.
In analyzing tumor size, Dr. Assadipour said two stratifications were studied: evaluating tumors sized 0-3 cm, 3-6 cm and 6 cm and larger; and 0-5 cm and 5 cm and larger. “Tumors over 6 cm had the shortest disease-free survival, and even when we applied the under-5 cm and over-5 cm scale, we clearly saw a difference in disease-free survival,” she said. Ki-67 and mitotic index were not related to disease-free survival.
The presence of a SDHB mutation had a hazard ratio of 16.2, while tumor diameter greater than 6 cm had a HR of 15.4, Dr. Assadipour said. These were the only independent risk factors for local recurrence, distant metastases and shorter disease-free interval found in the study.
During the discussion, Lawrence T. Kim, MD, of the University of North Carolina asked if the researchers found any differences in outcomes related to the surgical approach. “We were unable to identify whether any surgical approach improved or worsened outcomes on multivariate analysis” Dr. Assadipour said.
Thomas J. Fahey, MD, of New York asked what she would recommend for surgical approaches for patients with PC and PGL.
“Our general recommendation is that an adrenal pheochromocytoma that is over 6 cm carries a higher risk of recurrence and distant metastasis so an open approach with lymph node dissection ensuring negative surgical margins should be considered,” Dr. Assadipour said. “For abdominal paragangliomas, unless they are quite small and in a favorable location, we would generally recommend an open approach.”
The study was supported by the intramural program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health. Dr. Assadipour and her coauthors had no financial relationships to disclose.
AT AAES 2016
Key clinical point: SDHB mutation and tumor size may be better predictors of outcomes in patients with pheochromocytoma and abdominal paraganglioma than are other previously identified predictors.
Major finding: The incidence of local recurrence in patients with the SDHB mutation was 47.6% vs. 9.1% in those without the gene mutation.
Data source: Retrospective analysis of 84 patients with PC/PGL evaluated by the Surgical Endocrine Oncology branch at George Washington University Hospital from 1998-2015.
Disclosures: The study was supported by the intramural program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health. Dr. Assadipour and her coauthors reported having no financial disclosures.
CMS: Projected overall growth rate in health spending holding firm
Health spending is projected to grow on average 5.8% from 2015-2025, the same projected rate of grown as announced last year covering 2014-2024, according the CMS Office of the Actuary.
However, health care is projected to make up 20.1% of the economy at the end of the 10-year period, up from 17.5% in 2014, as health spending is projected to grow 1.3 percentage points faster than gross domestic product from 2015-2025. The analysis was published online July 13 in the journal Health Affairs.
Health spending for 2015 is projected to have grown 5.5%, up from 5.3% in 2014, and to have reached $3.2 trillion, driven in part by increased use of health services by newly insured patients. More than 9 in 10 (92%) of U.S. residents are projected to be insured by 2025, according to the actuary’s office.
While national spending per capita is projected to exceed $10,000 for the first time in 2016, spending growth is projected to slow to 4.8%, driven by slowdowns in Medicaid spending after 2 years of rapid growth.
Private health insurance expenditures are expected to grow at a similar rate (5.4%) through 2025, CMS actuaries said.
Medical price inflation slowed to 0.8% in 2015 from 1.4% in 2014. Hospital prices increased by 0.9% while prices for physician services dropped 1.1%, they noted.
Prescription drug spending is projected to grow an average of 6.7% from 2016 to 2025, slowing down from 12.2% in 2014 and 8.1% in 2015 when a number of high-priced specialty drugs, including those treating hepatitis C, were driving higher spending.
Health spending is projected to grow on average 5.8% from 2015-2025, the same projected rate of grown as announced last year covering 2014-2024, according the CMS Office of the Actuary.
However, health care is projected to make up 20.1% of the economy at the end of the 10-year period, up from 17.5% in 2014, as health spending is projected to grow 1.3 percentage points faster than gross domestic product from 2015-2025. The analysis was published online July 13 in the journal Health Affairs.
Health spending for 2015 is projected to have grown 5.5%, up from 5.3% in 2014, and to have reached $3.2 trillion, driven in part by increased use of health services by newly insured patients. More than 9 in 10 (92%) of U.S. residents are projected to be insured by 2025, according to the actuary’s office.
While national spending per capita is projected to exceed $10,000 for the first time in 2016, spending growth is projected to slow to 4.8%, driven by slowdowns in Medicaid spending after 2 years of rapid growth.
Private health insurance expenditures are expected to grow at a similar rate (5.4%) through 2025, CMS actuaries said.
Medical price inflation slowed to 0.8% in 2015 from 1.4% in 2014. Hospital prices increased by 0.9% while prices for physician services dropped 1.1%, they noted.
Prescription drug spending is projected to grow an average of 6.7% from 2016 to 2025, slowing down from 12.2% in 2014 and 8.1% in 2015 when a number of high-priced specialty drugs, including those treating hepatitis C, were driving higher spending.
Health spending is projected to grow on average 5.8% from 2015-2025, the same projected rate of grown as announced last year covering 2014-2024, according the CMS Office of the Actuary.
However, health care is projected to make up 20.1% of the economy at the end of the 10-year period, up from 17.5% in 2014, as health spending is projected to grow 1.3 percentage points faster than gross domestic product from 2015-2025. The analysis was published online July 13 in the journal Health Affairs.
Health spending for 2015 is projected to have grown 5.5%, up from 5.3% in 2014, and to have reached $3.2 trillion, driven in part by increased use of health services by newly insured patients. More than 9 in 10 (92%) of U.S. residents are projected to be insured by 2025, according to the actuary’s office.
While national spending per capita is projected to exceed $10,000 for the first time in 2016, spending growth is projected to slow to 4.8%, driven by slowdowns in Medicaid spending after 2 years of rapid growth.
Private health insurance expenditures are expected to grow at a similar rate (5.4%) through 2025, CMS actuaries said.
Medical price inflation slowed to 0.8% in 2015 from 1.4% in 2014. Hospital prices increased by 0.9% while prices for physician services dropped 1.1%, they noted.
Prescription drug spending is projected to grow an average of 6.7% from 2016 to 2025, slowing down from 12.2% in 2014 and 8.1% in 2015 when a number of high-priced specialty drugs, including those treating hepatitis C, were driving higher spending.
FROM HEALTH AFFAIRS
Primary Cutaneous Dermal Mucinosis on Herpes Zoster Scars
Mucin is an amorphous gelatinous substance that is found in a large variety of tissues. There are 2 types of cutaneous mucin: dermal and epithelial. Both types appear as basophilic shreds and granules with hematoxylin and eosin stain.1 Epithelial mucin (sialomucin) is found mainly in the gastrointestinal tract and lungs. In the skin, it is present in the cytoplasm of the dark cells of the eccrine glands and in the apocrine secretory cells. Epithelial mucin contains both neutral and acid glycosaminoglycans, stains positive with Alcian blue (pH 2.5) and periodic acid–Schiff, is resistant to hyaluronidase, and does not stain metachromatically with toluidine blue. Dermal mucin is composed of acid glycosaminoglycans (eg, dermatan sulfate, chondroitin 6-sulfate, chondroitin 4-sulfate, hyaluronic acid) and normally is produced by dermal fibroblasts. Dermal mucin stains positive with Alcian blue (pH 2.5); is periodic acid–Schiff negative and sensitive to hyaluronidase; and shows metachromasia with toluidine blue, methylene blue, and thionine.
Cutaneous mucinosis comprises a heterogeneous group of skin disorders characterized by the deposition of mucin in the interstices of the dermis. These diseases may be classified as primary mucinosis with the mucin deposition as the main histologic feature resulting in clinically distinctive lesions and secondary mucinosis with the mucin deposition as an additional histologic finding within the context of an independent skin disease or lesion (eg, basal cell carcinoma) with deposits of mucin in the stroma. Primary cutaneous mucinosis may be subclassified into 2 groups: degenerative-inflammatory mucinoses and neoplastic-hamartomatous mucinoses. According to the histologic features, the degenerative-inflammatory mucinoses are better divided into dermal and follicular mucinoses.2 We describe a case of primary cutaneous dermal mucinosis on herpes zoster (HZ) scars as an isotopic response.
Case Report
A 33-year-old man presented to the dermatology department with slightly pruritic lesions on the left side of the chest and back that had appeared progressively at the site of HZ scars that had healed without treatment 9 months prior. Dermatologic examination revealed sharply defined whitish papules (Figure 1) measuring 2 to 4 mm in diameter with a smooth surface and linear distribution over the area of the left T8 and T9 dermatomes. The patient reported no postherpetic neuralgia and was otherwise healthy. Laboratory tests including a complete blood cell count, biochemistry, urinalysis, and determination of free thyroid hormones were within reference range. Serologic tests for human immunodeficiency virus, hepatitis B and C viruses, and syphilis were negative. Antinuclear antibodies also were negative.
Histopathology demonstrated abundant bluish granular material between collagen bundles of the papillary dermis (Figure 2). No cytopathologic signs of active herpetic infection were seen. The Alcian blue stain at pH 2.5 was strongly positive for mucin, which confirmed the diagnosis of primary cutaneous dermal mucinosis.
Topical corticosteroids were applied for 2 months with no notable improvement. The lesions gradually improved without any other therapy during the subsequent 6 months.
Comment
The occurrence of a new skin disease at the exact site of a prior unrelated cutaneous disorder that had already resolved was first reported by Wyburn-Mason3 in 1955. Forty years later, the term isotopic response was coined by Wolf et al4 to describe this phenomenon. Diverse types of skin diseases such as herpes simplex virus,5 varicella-zoster infections,4 and thrombophlebitis4 have been implicated in cases of isotopic response, but the most frequently associated primary disorder by far is cutaneous HZ.
Several benign and malignant disorders may occur at sites of resolved HZ lesions, including granulomatous dermatitis,6 granuloma annulare,7 fungal granuloma,8 fungal folliculitis,9 psoriasis,10 morphea,11 lichen sclerosus,12 Kaposi sarcoma,13 the lichenoid variant of chronic graft-versus-host disease,14 cutaneous sarcoidosis,15 granulomatous folliculitis,16 comedones,17 furuncles,18 erythema annulare centrifugum,19 eosinophilic dermatosis,20 cutaneous pseudolymphoma,21 granulomatous vasculitis,22 Rosai-Dorfman disease,12 xanthomatous changes,23 tuberculoid granulomas,24 acneform eruption,25 lichen planus,26 acquired reactive perforating collagenosis,27 lymphoma,28 leukemia,29 angiosarcoma,30 basal cell carcinoma,31 squamous cell carcinoma, and cutaneous metastasis from internal carcinoma.32 The interval between the acute HZ episode and presentation of the second disease is quite variable, ranging from days to several months. Postzoster isotopic response has been described in individuals with varying degrees of immune response, affecting both immunocompetent12 and immunocompromised patients.14 There is no predilection for age, sex, or race. It also seems that antiviral treatment during the active episode does not prevent the development of secondary reactions.Kim et al33 reported a 59-year-old woman who developed flesh-colored or erythematous papules on HZ scars over the area of the left T1 and T2 dermatomes 1 week after the active viral process. Histopathologic study demonstrated deposition of mucin between collagen bundles in the dermis. The authors established the diagnosis of secondary cutaneous mucinosis as an isotopic response.33 Nevertheless, we believe that based on the aforementioned classification of cutaneous mucinosis,2 both this case and our case are better considered as primary cutaneous dermal mucinosis, as the mucin deposition in the dermis was the main histologic finding resulting in a distinctive cutaneous disorder. In the case reported by Kim et al,33 a possible relationship between cutaneous mucinosis and postherpetic neuralgia was suggested based on the slow regression of skin lesions in accordance with the improvement of the neuralgic pain; however, our patient did not have postherpetic neuralgia and the lesions persisted unchanged several months after the acute HZ episode. In the literature, there are reports of primary cutaneous dermal mucinosis associated with altered thyroid function34; autoimmune connective tissue diseases, mostly lupus erythematosus35; monoclonal gammopathy36; and human immunodeficiency virus infection,37 but these possibilities were ruled out in our patient by pertinent laboratory studies.
The pathogenesis of the postherpetic isotopic response remains unknown, but several mechanisms have been proposed. Some authors have suggested that postzoster dermatoses may represent isomorphic response of Köbner phenomenon.13,15 Although isomorphic and isotopic responses share some similarities, these terms describe 2 different phenomena: the first refers to the appearance of the same cutaneous disorder at a different site favored by trauma, while the second manifests a new and unrelated disease at the same location.38 Local anatomic changes such as altered microcirculation, collagen rearrangement, and an imperfect skin barrier may promote a prolonged local inflammatory response. Moreover, the destruction of nerve fibers by the varicella-zoster virus may indirectly influence the local immune system through the release of specific neuropeptides in the skin.39 It has been speculated that some secondary reactions may be the result of type III and type IV hypersensitivity reactions40 to viral antigens or to tissue antigens modified by the virus, inducing either immune hypersensitivity or local immune suppression.41 Some authors have documented the presence of varicella-zoster DNA within early postzoster lesions6,7 by using polymerase chain reaction in early lesions but not in late-stage and residual lesions.12,22 Nikkels et al42 studied early granulomatous lesions by immunohistochemistry and in situ hybridization techniques and concluded that major viral envelope glycoproteins (glycoproteins I and II) rather than complete viral particles could be responsible for delayed-type hypersensitivity reactions. All these findings suggest that secondary reactions presenting on HZ scars are mainly the result of atypical immune reactions to local antigenic stimuli.
The pathogenesis of our case is unknown. From a theoretical point of view, it is possible that varicella-zoster virus may induce fibroblastic proliferation and mucin production on HZ scars; however, if HZ is a frequent process and the virus may induce mucin production, then focal dermal mucinosis in an HZ scar should be a common finding. In our patient, there was no associated disease favoring the development of the cutaneous mucinosis. These localized variants of primary cutaneous mucinosis usually do not require therapy, and a wait-and-see approach is recommended. Topical applications of corticosteroids, pimecrolimus, or tacrolimus, as well as oral isotretinoin, may have some benefit,43 but spontaneous resolution may occur.44 In our patient, topical corticosteroids were applied 2 months following initial presentation without any benefit and the cutaneous lesions gradually improved without any therapy during the subsequent 6 months. Focal dermal mucinosis should be added to the list of cutaneous reactions that may develop in HZ scars.
- Truhan AP, Roenigk HH Jr. The cutaneous mucinoses. J Am Acad Dermatol. 1986;14:1-18.
- Rongioletti F, Rebora A. Cutaneous mucinoses: microscopic criteria for diagnosis. Am J Dermatopathol. 2001;23:257-267.
- Wyburn-Mason R. Malignant change arising in tissues affected by herpes. BMJ. 1955;2:1106-1109.
- Wolf R, Brenner S, Ruocco V, et al. Isotopic response. Int J Dermatol. 1995;34:341-348.
- Ruocco E. Genital warts at the site of healed herpes progenitalis: the isotopic response. Int J Dermatol. 2000;39:705-706.
- Serfling U, Penneys NS, Zhu WY, et al. Varicella-zoster virus DNA in granulomatous skin lesions following herpes zoster. a study by the polymerase chain reaction. J Cutan Pathol. 1993;20:28-33.
- Gibney MD, Nahass GT, Leonardi CL. Cutaneous reactions following herpes zoster infections: report of three cases and a review of the literature. Br J Dermatol. 1996;134:504-509.
- Huang CW, Tu ME, Wu YH, et al. Isotopic response of fungal granuloma following facial herpes zoster infections-report of three cases. Int J Dermatol. 2007;46:1141-1145.
- Tüzün Y, Işçimen A, Göksügür N, et al. Wolf’s isotopic response: Trichophyton rubrum folliculitis appearing on a herpes zoster scar. Int J Dermatol. 2000;39:766-768.
- Allegue F, Fachal C, Romo M, et al. Psoriasis at the site of healed herpes zoster: Wolf’s isotopic response. Actas Dermosifiliogr. 2007;98:576-578.
- Forschner A, Metzler G, Rassner G, et al. Morphea with features of lichen sclerosus et atrophicus at the site of a herpes zoster scar: another case of an isotopic response. Int J Dermatol. 2005;44:524-525.
- Requena L, Kutzner H, Escalonilla P, et al. Cutaneous reactions at sites of herpes zoster scars: an expanded spectrum. Br J Dermatol. 1998;138:161-168.
- Niedt GW, Prioleau PG. Kaposi’s sarcoma occurring in a dermatome previously involved by herpes zoster. J Am Acad Dermatol. 1988;18:448-451.
- Sanli H, Anadolu R, Arat M, et al. Dermatomal lichenoid graft-versus-host disease within herpes zoster scars. Int J Dermatol. 2003;42:562-564.
- Cecchi R, Giomi A. Scar sarcoidosis following herpes zoster. J Eur Acad Dermatol Venereol. 1999;12:280-282.
- Fernández-Redondo V, Amrouni B, Varela E, et al. Granulomatous folliculitis at sites of herpes zoster scars: Wolf’s isotopic response. J Eur Acad Dermatol Venereol. 2002;16:628-630.
- Sanchez-Salas MP. Appearance of comedones at the site of healed herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2011;50:633-634.
- Ghorpade A. Wolf’s isotopic response—furuncles at the site of healed herpes zoster in an Indian male. Int J Dermatol. 2010;49:105-107.
- Lee HW, Lee DK, Rhee DY, et al. Erythema annulare centrifugum following herpes zoster infection: Wolf’s isotopic response? Br J Dermatol. 2005;153:1241-1243.
- Mitsuhashi Y, Kondo S. Post-zoster eosinophilic dermatosis. Br J Dermatol. 1997;136:465-466.
- Roo E, Villegas C, Lopez-Bran E, et al. Postzoster cutaneous pseudolymphoma. Arch Dermatol. 1994;130:661-663.
- Langenberg A, Yen TS, LeBoit PE. Granulomatous vasculitis occurring after cutaneous herpes zoster despite absence of viral genome. J Am Acad Dermatol. 1991;24:429-433.
- Weidman F, Boston LN. Generalized xanthoma tuberosum with xantomathous changes in fresh scars of intercurrent zoster. Arch Intern Med. 1937;59:793-822.
- Olalquiaga J, Minaño R, Barrio J. Granuloma tuberculoide post-herpético en un paciente con leucemia linfocítica crónica. Med Cutan ILA. 1995;23:113-115.
- Stubbings JM, Goodfield MJ. An unusual distribution of an acneiform rash due to herpes zoster infection. Clin Exp Dermatol. 1993;18:92-93.
- Shemer A, Weiss G, Trau H. Wolf’s isotopic response: a case of zosteriform lichen planus on the site of healed herpes zoster. J Eur Acad Dermatol Venereol. 2001;15:445-447.
- Bang SW, Kim YK, Whang KU. Acquired reactive perforating collagenosis: unilateral umbilicated papules along the lesions of herpes zoster. J Am Acad Dermatol. 1997;36:778-779.
- Paydaş S, Sahin B, Yavuz S, et al. Lymphomatous skin infiltration at the site of previous varicella zoster virus infection in a patient with T cell lymphoma. Leuk Lymphoma. 2000;37:229-232.
- Cerroni L, Kerl H. Cutaneous localization of B-cell chronic lymphocytic leukemia at the site of varicella/herpes virus eruptions. J Am Acad Dermatol. 1997;37:1022.
- Hudson CP, Hanno R, Callen JP. Cutaneous angiosarcoma in a site of healed herpes zoster. Int J Dermatol. 1984;23:404-407.
- Wyburn-Mason R. Visceral lesions in herpes zoster. Br Med J. 1957;1:678-681.
- Caroti A. Metastasi cutanee di a adenocarcinoma papillifero ovarico in sede di herpes zoster. Chron Dermatol. 1987;18:769-773.
- Kim MB, Jwa SW, Ko HC, et al. A case of secondary cutaneous mucinosis following herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2009;48:212-214.
- Burman KD, McKinley-Grant L. Dermatologic aspects of thyroid disease. Clin Dermatol. 2006;24:247-255.
- Shekari AM, Ghiasi M, Ghasemi E, et al. Papulonodular mucinosis indicating systemic lupus erythematosus. Clin Exp Dermatol. 2009;34:558-560.
- Dinneen AM, Dicken CH. Scleromyxedema. J Am Acad Dermatol. 1995;33:37-43.
- Rongioletti F, Ghigliotti G, De Marchi R, et al. Cutaneous mucinoses and HIV infection. Br J Dermatol. 1998;139:1077-1080.
- Krahl D, Hartschuh W, Tilgen W. Granuloma annulare perforans in herpes zoster scars. J Am Acad Dermatol. 1993;29:859-862.
- Wolf R, Lotti T, Ruocco V. Isomorphic versus isotopic response: data and hypotheses. J Eur Acad Dermatol Venereol. 2003;17:123-125.
- Fisher G, Jaworski R. Granuloma formation in herpes zoster scars. J Am Acad Dermatol. 1987;16:1261-1263.
- Ruocco V, Grimaldi Filioli F. La risposta isotopica post-erpetica: possibile sequela di un locus minoris resistentiae acquisito. G Ital Dermatol Venereol. 1999;134:547-552.
- Nikkels AF, Debrus S, Delvenne P, et al. Viral glycoproteins in herpesviridae granulomas. Am J Dermatopathol. 1994;16:588-592.
- Rongioletti F, Zaccaria E, Cozzani E, et al. Treatment of localized lichen myxedematosus of discrete type with tacrolimus ointment. J Am Acad Dermatol. 2008;5:530-532.
- Kwon OS, Moon SE, Kim JA, et al. Lichen myxodematosus with rapid spontaneous regression. Br J Dermatol. 1997;136:295-296.
Mucin is an amorphous gelatinous substance that is found in a large variety of tissues. There are 2 types of cutaneous mucin: dermal and epithelial. Both types appear as basophilic shreds and granules with hematoxylin and eosin stain.1 Epithelial mucin (sialomucin) is found mainly in the gastrointestinal tract and lungs. In the skin, it is present in the cytoplasm of the dark cells of the eccrine glands and in the apocrine secretory cells. Epithelial mucin contains both neutral and acid glycosaminoglycans, stains positive with Alcian blue (pH 2.5) and periodic acid–Schiff, is resistant to hyaluronidase, and does not stain metachromatically with toluidine blue. Dermal mucin is composed of acid glycosaminoglycans (eg, dermatan sulfate, chondroitin 6-sulfate, chondroitin 4-sulfate, hyaluronic acid) and normally is produced by dermal fibroblasts. Dermal mucin stains positive with Alcian blue (pH 2.5); is periodic acid–Schiff negative and sensitive to hyaluronidase; and shows metachromasia with toluidine blue, methylene blue, and thionine.
Cutaneous mucinosis comprises a heterogeneous group of skin disorders characterized by the deposition of mucin in the interstices of the dermis. These diseases may be classified as primary mucinosis with the mucin deposition as the main histologic feature resulting in clinically distinctive lesions and secondary mucinosis with the mucin deposition as an additional histologic finding within the context of an independent skin disease or lesion (eg, basal cell carcinoma) with deposits of mucin in the stroma. Primary cutaneous mucinosis may be subclassified into 2 groups: degenerative-inflammatory mucinoses and neoplastic-hamartomatous mucinoses. According to the histologic features, the degenerative-inflammatory mucinoses are better divided into dermal and follicular mucinoses.2 We describe a case of primary cutaneous dermal mucinosis on herpes zoster (HZ) scars as an isotopic response.
Case Report
A 33-year-old man presented to the dermatology department with slightly pruritic lesions on the left side of the chest and back that had appeared progressively at the site of HZ scars that had healed without treatment 9 months prior. Dermatologic examination revealed sharply defined whitish papules (Figure 1) measuring 2 to 4 mm in diameter with a smooth surface and linear distribution over the area of the left T8 and T9 dermatomes. The patient reported no postherpetic neuralgia and was otherwise healthy. Laboratory tests including a complete blood cell count, biochemistry, urinalysis, and determination of free thyroid hormones were within reference range. Serologic tests for human immunodeficiency virus, hepatitis B and C viruses, and syphilis were negative. Antinuclear antibodies also were negative.
Histopathology demonstrated abundant bluish granular material between collagen bundles of the papillary dermis (Figure 2). No cytopathologic signs of active herpetic infection were seen. The Alcian blue stain at pH 2.5 was strongly positive for mucin, which confirmed the diagnosis of primary cutaneous dermal mucinosis.
Topical corticosteroids were applied for 2 months with no notable improvement. The lesions gradually improved without any other therapy during the subsequent 6 months.
Comment
The occurrence of a new skin disease at the exact site of a prior unrelated cutaneous disorder that had already resolved was first reported by Wyburn-Mason3 in 1955. Forty years later, the term isotopic response was coined by Wolf et al4 to describe this phenomenon. Diverse types of skin diseases such as herpes simplex virus,5 varicella-zoster infections,4 and thrombophlebitis4 have been implicated in cases of isotopic response, but the most frequently associated primary disorder by far is cutaneous HZ.
Several benign and malignant disorders may occur at sites of resolved HZ lesions, including granulomatous dermatitis,6 granuloma annulare,7 fungal granuloma,8 fungal folliculitis,9 psoriasis,10 morphea,11 lichen sclerosus,12 Kaposi sarcoma,13 the lichenoid variant of chronic graft-versus-host disease,14 cutaneous sarcoidosis,15 granulomatous folliculitis,16 comedones,17 furuncles,18 erythema annulare centrifugum,19 eosinophilic dermatosis,20 cutaneous pseudolymphoma,21 granulomatous vasculitis,22 Rosai-Dorfman disease,12 xanthomatous changes,23 tuberculoid granulomas,24 acneform eruption,25 lichen planus,26 acquired reactive perforating collagenosis,27 lymphoma,28 leukemia,29 angiosarcoma,30 basal cell carcinoma,31 squamous cell carcinoma, and cutaneous metastasis from internal carcinoma.32 The interval between the acute HZ episode and presentation of the second disease is quite variable, ranging from days to several months. Postzoster isotopic response has been described in individuals with varying degrees of immune response, affecting both immunocompetent12 and immunocompromised patients.14 There is no predilection for age, sex, or race. It also seems that antiviral treatment during the active episode does not prevent the development of secondary reactions.Kim et al33 reported a 59-year-old woman who developed flesh-colored or erythematous papules on HZ scars over the area of the left T1 and T2 dermatomes 1 week after the active viral process. Histopathologic study demonstrated deposition of mucin between collagen bundles in the dermis. The authors established the diagnosis of secondary cutaneous mucinosis as an isotopic response.33 Nevertheless, we believe that based on the aforementioned classification of cutaneous mucinosis,2 both this case and our case are better considered as primary cutaneous dermal mucinosis, as the mucin deposition in the dermis was the main histologic finding resulting in a distinctive cutaneous disorder. In the case reported by Kim et al,33 a possible relationship between cutaneous mucinosis and postherpetic neuralgia was suggested based on the slow regression of skin lesions in accordance with the improvement of the neuralgic pain; however, our patient did not have postherpetic neuralgia and the lesions persisted unchanged several months after the acute HZ episode. In the literature, there are reports of primary cutaneous dermal mucinosis associated with altered thyroid function34; autoimmune connective tissue diseases, mostly lupus erythematosus35; monoclonal gammopathy36; and human immunodeficiency virus infection,37 but these possibilities were ruled out in our patient by pertinent laboratory studies.
The pathogenesis of the postherpetic isotopic response remains unknown, but several mechanisms have been proposed. Some authors have suggested that postzoster dermatoses may represent isomorphic response of Köbner phenomenon.13,15 Although isomorphic and isotopic responses share some similarities, these terms describe 2 different phenomena: the first refers to the appearance of the same cutaneous disorder at a different site favored by trauma, while the second manifests a new and unrelated disease at the same location.38 Local anatomic changes such as altered microcirculation, collagen rearrangement, and an imperfect skin barrier may promote a prolonged local inflammatory response. Moreover, the destruction of nerve fibers by the varicella-zoster virus may indirectly influence the local immune system through the release of specific neuropeptides in the skin.39 It has been speculated that some secondary reactions may be the result of type III and type IV hypersensitivity reactions40 to viral antigens or to tissue antigens modified by the virus, inducing either immune hypersensitivity or local immune suppression.41 Some authors have documented the presence of varicella-zoster DNA within early postzoster lesions6,7 by using polymerase chain reaction in early lesions but not in late-stage and residual lesions.12,22 Nikkels et al42 studied early granulomatous lesions by immunohistochemistry and in situ hybridization techniques and concluded that major viral envelope glycoproteins (glycoproteins I and II) rather than complete viral particles could be responsible for delayed-type hypersensitivity reactions. All these findings suggest that secondary reactions presenting on HZ scars are mainly the result of atypical immune reactions to local antigenic stimuli.
The pathogenesis of our case is unknown. From a theoretical point of view, it is possible that varicella-zoster virus may induce fibroblastic proliferation and mucin production on HZ scars; however, if HZ is a frequent process and the virus may induce mucin production, then focal dermal mucinosis in an HZ scar should be a common finding. In our patient, there was no associated disease favoring the development of the cutaneous mucinosis. These localized variants of primary cutaneous mucinosis usually do not require therapy, and a wait-and-see approach is recommended. Topical applications of corticosteroids, pimecrolimus, or tacrolimus, as well as oral isotretinoin, may have some benefit,43 but spontaneous resolution may occur.44 In our patient, topical corticosteroids were applied 2 months following initial presentation without any benefit and the cutaneous lesions gradually improved without any therapy during the subsequent 6 months. Focal dermal mucinosis should be added to the list of cutaneous reactions that may develop in HZ scars.
Mucin is an amorphous gelatinous substance that is found in a large variety of tissues. There are 2 types of cutaneous mucin: dermal and epithelial. Both types appear as basophilic shreds and granules with hematoxylin and eosin stain.1 Epithelial mucin (sialomucin) is found mainly in the gastrointestinal tract and lungs. In the skin, it is present in the cytoplasm of the dark cells of the eccrine glands and in the apocrine secretory cells. Epithelial mucin contains both neutral and acid glycosaminoglycans, stains positive with Alcian blue (pH 2.5) and periodic acid–Schiff, is resistant to hyaluronidase, and does not stain metachromatically with toluidine blue. Dermal mucin is composed of acid glycosaminoglycans (eg, dermatan sulfate, chondroitin 6-sulfate, chondroitin 4-sulfate, hyaluronic acid) and normally is produced by dermal fibroblasts. Dermal mucin stains positive with Alcian blue (pH 2.5); is periodic acid–Schiff negative and sensitive to hyaluronidase; and shows metachromasia with toluidine blue, methylene blue, and thionine.
Cutaneous mucinosis comprises a heterogeneous group of skin disorders characterized by the deposition of mucin in the interstices of the dermis. These diseases may be classified as primary mucinosis with the mucin deposition as the main histologic feature resulting in clinically distinctive lesions and secondary mucinosis with the mucin deposition as an additional histologic finding within the context of an independent skin disease or lesion (eg, basal cell carcinoma) with deposits of mucin in the stroma. Primary cutaneous mucinosis may be subclassified into 2 groups: degenerative-inflammatory mucinoses and neoplastic-hamartomatous mucinoses. According to the histologic features, the degenerative-inflammatory mucinoses are better divided into dermal and follicular mucinoses.2 We describe a case of primary cutaneous dermal mucinosis on herpes zoster (HZ) scars as an isotopic response.
Case Report
A 33-year-old man presented to the dermatology department with slightly pruritic lesions on the left side of the chest and back that had appeared progressively at the site of HZ scars that had healed without treatment 9 months prior. Dermatologic examination revealed sharply defined whitish papules (Figure 1) measuring 2 to 4 mm in diameter with a smooth surface and linear distribution over the area of the left T8 and T9 dermatomes. The patient reported no postherpetic neuralgia and was otherwise healthy. Laboratory tests including a complete blood cell count, biochemistry, urinalysis, and determination of free thyroid hormones were within reference range. Serologic tests for human immunodeficiency virus, hepatitis B and C viruses, and syphilis were negative. Antinuclear antibodies also were negative.
Histopathology demonstrated abundant bluish granular material between collagen bundles of the papillary dermis (Figure 2). No cytopathologic signs of active herpetic infection were seen. The Alcian blue stain at pH 2.5 was strongly positive for mucin, which confirmed the diagnosis of primary cutaneous dermal mucinosis.
Topical corticosteroids were applied for 2 months with no notable improvement. The lesions gradually improved without any other therapy during the subsequent 6 months.
Comment
The occurrence of a new skin disease at the exact site of a prior unrelated cutaneous disorder that had already resolved was first reported by Wyburn-Mason3 in 1955. Forty years later, the term isotopic response was coined by Wolf et al4 to describe this phenomenon. Diverse types of skin diseases such as herpes simplex virus,5 varicella-zoster infections,4 and thrombophlebitis4 have been implicated in cases of isotopic response, but the most frequently associated primary disorder by far is cutaneous HZ.
Several benign and malignant disorders may occur at sites of resolved HZ lesions, including granulomatous dermatitis,6 granuloma annulare,7 fungal granuloma,8 fungal folliculitis,9 psoriasis,10 morphea,11 lichen sclerosus,12 Kaposi sarcoma,13 the lichenoid variant of chronic graft-versus-host disease,14 cutaneous sarcoidosis,15 granulomatous folliculitis,16 comedones,17 furuncles,18 erythema annulare centrifugum,19 eosinophilic dermatosis,20 cutaneous pseudolymphoma,21 granulomatous vasculitis,22 Rosai-Dorfman disease,12 xanthomatous changes,23 tuberculoid granulomas,24 acneform eruption,25 lichen planus,26 acquired reactive perforating collagenosis,27 lymphoma,28 leukemia,29 angiosarcoma,30 basal cell carcinoma,31 squamous cell carcinoma, and cutaneous metastasis from internal carcinoma.32 The interval between the acute HZ episode and presentation of the second disease is quite variable, ranging from days to several months. Postzoster isotopic response has been described in individuals with varying degrees of immune response, affecting both immunocompetent12 and immunocompromised patients.14 There is no predilection for age, sex, or race. It also seems that antiviral treatment during the active episode does not prevent the development of secondary reactions.Kim et al33 reported a 59-year-old woman who developed flesh-colored or erythematous papules on HZ scars over the area of the left T1 and T2 dermatomes 1 week after the active viral process. Histopathologic study demonstrated deposition of mucin between collagen bundles in the dermis. The authors established the diagnosis of secondary cutaneous mucinosis as an isotopic response.33 Nevertheless, we believe that based on the aforementioned classification of cutaneous mucinosis,2 both this case and our case are better considered as primary cutaneous dermal mucinosis, as the mucin deposition in the dermis was the main histologic finding resulting in a distinctive cutaneous disorder. In the case reported by Kim et al,33 a possible relationship between cutaneous mucinosis and postherpetic neuralgia was suggested based on the slow regression of skin lesions in accordance with the improvement of the neuralgic pain; however, our patient did not have postherpetic neuralgia and the lesions persisted unchanged several months after the acute HZ episode. In the literature, there are reports of primary cutaneous dermal mucinosis associated with altered thyroid function34; autoimmune connective tissue diseases, mostly lupus erythematosus35; monoclonal gammopathy36; and human immunodeficiency virus infection,37 but these possibilities were ruled out in our patient by pertinent laboratory studies.
The pathogenesis of the postherpetic isotopic response remains unknown, but several mechanisms have been proposed. Some authors have suggested that postzoster dermatoses may represent isomorphic response of Köbner phenomenon.13,15 Although isomorphic and isotopic responses share some similarities, these terms describe 2 different phenomena: the first refers to the appearance of the same cutaneous disorder at a different site favored by trauma, while the second manifests a new and unrelated disease at the same location.38 Local anatomic changes such as altered microcirculation, collagen rearrangement, and an imperfect skin barrier may promote a prolonged local inflammatory response. Moreover, the destruction of nerve fibers by the varicella-zoster virus may indirectly influence the local immune system through the release of specific neuropeptides in the skin.39 It has been speculated that some secondary reactions may be the result of type III and type IV hypersensitivity reactions40 to viral antigens or to tissue antigens modified by the virus, inducing either immune hypersensitivity or local immune suppression.41 Some authors have documented the presence of varicella-zoster DNA within early postzoster lesions6,7 by using polymerase chain reaction in early lesions but not in late-stage and residual lesions.12,22 Nikkels et al42 studied early granulomatous lesions by immunohistochemistry and in situ hybridization techniques and concluded that major viral envelope glycoproteins (glycoproteins I and II) rather than complete viral particles could be responsible for delayed-type hypersensitivity reactions. All these findings suggest that secondary reactions presenting on HZ scars are mainly the result of atypical immune reactions to local antigenic stimuli.
The pathogenesis of our case is unknown. From a theoretical point of view, it is possible that varicella-zoster virus may induce fibroblastic proliferation and mucin production on HZ scars; however, if HZ is a frequent process and the virus may induce mucin production, then focal dermal mucinosis in an HZ scar should be a common finding. In our patient, there was no associated disease favoring the development of the cutaneous mucinosis. These localized variants of primary cutaneous mucinosis usually do not require therapy, and a wait-and-see approach is recommended. Topical applications of corticosteroids, pimecrolimus, or tacrolimus, as well as oral isotretinoin, may have some benefit,43 but spontaneous resolution may occur.44 In our patient, topical corticosteroids were applied 2 months following initial presentation without any benefit and the cutaneous lesions gradually improved without any therapy during the subsequent 6 months. Focal dermal mucinosis should be added to the list of cutaneous reactions that may develop in HZ scars.
- Truhan AP, Roenigk HH Jr. The cutaneous mucinoses. J Am Acad Dermatol. 1986;14:1-18.
- Rongioletti F, Rebora A. Cutaneous mucinoses: microscopic criteria for diagnosis. Am J Dermatopathol. 2001;23:257-267.
- Wyburn-Mason R. Malignant change arising in tissues affected by herpes. BMJ. 1955;2:1106-1109.
- Wolf R, Brenner S, Ruocco V, et al. Isotopic response. Int J Dermatol. 1995;34:341-348.
- Ruocco E. Genital warts at the site of healed herpes progenitalis: the isotopic response. Int J Dermatol. 2000;39:705-706.
- Serfling U, Penneys NS, Zhu WY, et al. Varicella-zoster virus DNA in granulomatous skin lesions following herpes zoster. a study by the polymerase chain reaction. J Cutan Pathol. 1993;20:28-33.
- Gibney MD, Nahass GT, Leonardi CL. Cutaneous reactions following herpes zoster infections: report of three cases and a review of the literature. Br J Dermatol. 1996;134:504-509.
- Huang CW, Tu ME, Wu YH, et al. Isotopic response of fungal granuloma following facial herpes zoster infections-report of three cases. Int J Dermatol. 2007;46:1141-1145.
- Tüzün Y, Işçimen A, Göksügür N, et al. Wolf’s isotopic response: Trichophyton rubrum folliculitis appearing on a herpes zoster scar. Int J Dermatol. 2000;39:766-768.
- Allegue F, Fachal C, Romo M, et al. Psoriasis at the site of healed herpes zoster: Wolf’s isotopic response. Actas Dermosifiliogr. 2007;98:576-578.
- Forschner A, Metzler G, Rassner G, et al. Morphea with features of lichen sclerosus et atrophicus at the site of a herpes zoster scar: another case of an isotopic response. Int J Dermatol. 2005;44:524-525.
- Requena L, Kutzner H, Escalonilla P, et al. Cutaneous reactions at sites of herpes zoster scars: an expanded spectrum. Br J Dermatol. 1998;138:161-168.
- Niedt GW, Prioleau PG. Kaposi’s sarcoma occurring in a dermatome previously involved by herpes zoster. J Am Acad Dermatol. 1988;18:448-451.
- Sanli H, Anadolu R, Arat M, et al. Dermatomal lichenoid graft-versus-host disease within herpes zoster scars. Int J Dermatol. 2003;42:562-564.
- Cecchi R, Giomi A. Scar sarcoidosis following herpes zoster. J Eur Acad Dermatol Venereol. 1999;12:280-282.
- Fernández-Redondo V, Amrouni B, Varela E, et al. Granulomatous folliculitis at sites of herpes zoster scars: Wolf’s isotopic response. J Eur Acad Dermatol Venereol. 2002;16:628-630.
- Sanchez-Salas MP. Appearance of comedones at the site of healed herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2011;50:633-634.
- Ghorpade A. Wolf’s isotopic response—furuncles at the site of healed herpes zoster in an Indian male. Int J Dermatol. 2010;49:105-107.
- Lee HW, Lee DK, Rhee DY, et al. Erythema annulare centrifugum following herpes zoster infection: Wolf’s isotopic response? Br J Dermatol. 2005;153:1241-1243.
- Mitsuhashi Y, Kondo S. Post-zoster eosinophilic dermatosis. Br J Dermatol. 1997;136:465-466.
- Roo E, Villegas C, Lopez-Bran E, et al. Postzoster cutaneous pseudolymphoma. Arch Dermatol. 1994;130:661-663.
- Langenberg A, Yen TS, LeBoit PE. Granulomatous vasculitis occurring after cutaneous herpes zoster despite absence of viral genome. J Am Acad Dermatol. 1991;24:429-433.
- Weidman F, Boston LN. Generalized xanthoma tuberosum with xantomathous changes in fresh scars of intercurrent zoster. Arch Intern Med. 1937;59:793-822.
- Olalquiaga J, Minaño R, Barrio J. Granuloma tuberculoide post-herpético en un paciente con leucemia linfocítica crónica. Med Cutan ILA. 1995;23:113-115.
- Stubbings JM, Goodfield MJ. An unusual distribution of an acneiform rash due to herpes zoster infection. Clin Exp Dermatol. 1993;18:92-93.
- Shemer A, Weiss G, Trau H. Wolf’s isotopic response: a case of zosteriform lichen planus on the site of healed herpes zoster. J Eur Acad Dermatol Venereol. 2001;15:445-447.
- Bang SW, Kim YK, Whang KU. Acquired reactive perforating collagenosis: unilateral umbilicated papules along the lesions of herpes zoster. J Am Acad Dermatol. 1997;36:778-779.
- Paydaş S, Sahin B, Yavuz S, et al. Lymphomatous skin infiltration at the site of previous varicella zoster virus infection in a patient with T cell lymphoma. Leuk Lymphoma. 2000;37:229-232.
- Cerroni L, Kerl H. Cutaneous localization of B-cell chronic lymphocytic leukemia at the site of varicella/herpes virus eruptions. J Am Acad Dermatol. 1997;37:1022.
- Hudson CP, Hanno R, Callen JP. Cutaneous angiosarcoma in a site of healed herpes zoster. Int J Dermatol. 1984;23:404-407.
- Wyburn-Mason R. Visceral lesions in herpes zoster. Br Med J. 1957;1:678-681.
- Caroti A. Metastasi cutanee di a adenocarcinoma papillifero ovarico in sede di herpes zoster. Chron Dermatol. 1987;18:769-773.
- Kim MB, Jwa SW, Ko HC, et al. A case of secondary cutaneous mucinosis following herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2009;48:212-214.
- Burman KD, McKinley-Grant L. Dermatologic aspects of thyroid disease. Clin Dermatol. 2006;24:247-255.
- Shekari AM, Ghiasi M, Ghasemi E, et al. Papulonodular mucinosis indicating systemic lupus erythematosus. Clin Exp Dermatol. 2009;34:558-560.
- Dinneen AM, Dicken CH. Scleromyxedema. J Am Acad Dermatol. 1995;33:37-43.
- Rongioletti F, Ghigliotti G, De Marchi R, et al. Cutaneous mucinoses and HIV infection. Br J Dermatol. 1998;139:1077-1080.
- Krahl D, Hartschuh W, Tilgen W. Granuloma annulare perforans in herpes zoster scars. J Am Acad Dermatol. 1993;29:859-862.
- Wolf R, Lotti T, Ruocco V. Isomorphic versus isotopic response: data and hypotheses. J Eur Acad Dermatol Venereol. 2003;17:123-125.
- Fisher G, Jaworski R. Granuloma formation in herpes zoster scars. J Am Acad Dermatol. 1987;16:1261-1263.
- Ruocco V, Grimaldi Filioli F. La risposta isotopica post-erpetica: possibile sequela di un locus minoris resistentiae acquisito. G Ital Dermatol Venereol. 1999;134:547-552.
- Nikkels AF, Debrus S, Delvenne P, et al. Viral glycoproteins in herpesviridae granulomas. Am J Dermatopathol. 1994;16:588-592.
- Rongioletti F, Zaccaria E, Cozzani E, et al. Treatment of localized lichen myxedematosus of discrete type with tacrolimus ointment. J Am Acad Dermatol. 2008;5:530-532.
- Kwon OS, Moon SE, Kim JA, et al. Lichen myxodematosus with rapid spontaneous regression. Br J Dermatol. 1997;136:295-296.
- Truhan AP, Roenigk HH Jr. The cutaneous mucinoses. J Am Acad Dermatol. 1986;14:1-18.
- Rongioletti F, Rebora A. Cutaneous mucinoses: microscopic criteria for diagnosis. Am J Dermatopathol. 2001;23:257-267.
- Wyburn-Mason R. Malignant change arising in tissues affected by herpes. BMJ. 1955;2:1106-1109.
- Wolf R, Brenner S, Ruocco V, et al. Isotopic response. Int J Dermatol. 1995;34:341-348.
- Ruocco E. Genital warts at the site of healed herpes progenitalis: the isotopic response. Int J Dermatol. 2000;39:705-706.
- Serfling U, Penneys NS, Zhu WY, et al. Varicella-zoster virus DNA in granulomatous skin lesions following herpes zoster. a study by the polymerase chain reaction. J Cutan Pathol. 1993;20:28-33.
- Gibney MD, Nahass GT, Leonardi CL. Cutaneous reactions following herpes zoster infections: report of three cases and a review of the literature. Br J Dermatol. 1996;134:504-509.
- Huang CW, Tu ME, Wu YH, et al. Isotopic response of fungal granuloma following facial herpes zoster infections-report of three cases. Int J Dermatol. 2007;46:1141-1145.
- Tüzün Y, Işçimen A, Göksügür N, et al. Wolf’s isotopic response: Trichophyton rubrum folliculitis appearing on a herpes zoster scar. Int J Dermatol. 2000;39:766-768.
- Allegue F, Fachal C, Romo M, et al. Psoriasis at the site of healed herpes zoster: Wolf’s isotopic response. Actas Dermosifiliogr. 2007;98:576-578.
- Forschner A, Metzler G, Rassner G, et al. Morphea with features of lichen sclerosus et atrophicus at the site of a herpes zoster scar: another case of an isotopic response. Int J Dermatol. 2005;44:524-525.
- Requena L, Kutzner H, Escalonilla P, et al. Cutaneous reactions at sites of herpes zoster scars: an expanded spectrum. Br J Dermatol. 1998;138:161-168.
- Niedt GW, Prioleau PG. Kaposi’s sarcoma occurring in a dermatome previously involved by herpes zoster. J Am Acad Dermatol. 1988;18:448-451.
- Sanli H, Anadolu R, Arat M, et al. Dermatomal lichenoid graft-versus-host disease within herpes zoster scars. Int J Dermatol. 2003;42:562-564.
- Cecchi R, Giomi A. Scar sarcoidosis following herpes zoster. J Eur Acad Dermatol Venereol. 1999;12:280-282.
- Fernández-Redondo V, Amrouni B, Varela E, et al. Granulomatous folliculitis at sites of herpes zoster scars: Wolf’s isotopic response. J Eur Acad Dermatol Venereol. 2002;16:628-630.
- Sanchez-Salas MP. Appearance of comedones at the site of healed herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2011;50:633-634.
- Ghorpade A. Wolf’s isotopic response—furuncles at the site of healed herpes zoster in an Indian male. Int J Dermatol. 2010;49:105-107.
- Lee HW, Lee DK, Rhee DY, et al. Erythema annulare centrifugum following herpes zoster infection: Wolf’s isotopic response? Br J Dermatol. 2005;153:1241-1243.
- Mitsuhashi Y, Kondo S. Post-zoster eosinophilic dermatosis. Br J Dermatol. 1997;136:465-466.
- Roo E, Villegas C, Lopez-Bran E, et al. Postzoster cutaneous pseudolymphoma. Arch Dermatol. 1994;130:661-663.
- Langenberg A, Yen TS, LeBoit PE. Granulomatous vasculitis occurring after cutaneous herpes zoster despite absence of viral genome. J Am Acad Dermatol. 1991;24:429-433.
- Weidman F, Boston LN. Generalized xanthoma tuberosum with xantomathous changes in fresh scars of intercurrent zoster. Arch Intern Med. 1937;59:793-822.
- Olalquiaga J, Minaño R, Barrio J. Granuloma tuberculoide post-herpético en un paciente con leucemia linfocítica crónica. Med Cutan ILA. 1995;23:113-115.
- Stubbings JM, Goodfield MJ. An unusual distribution of an acneiform rash due to herpes zoster infection. Clin Exp Dermatol. 1993;18:92-93.
- Shemer A, Weiss G, Trau H. Wolf’s isotopic response: a case of zosteriform lichen planus on the site of healed herpes zoster. J Eur Acad Dermatol Venereol. 2001;15:445-447.
- Bang SW, Kim YK, Whang KU. Acquired reactive perforating collagenosis: unilateral umbilicated papules along the lesions of herpes zoster. J Am Acad Dermatol. 1997;36:778-779.
- Paydaş S, Sahin B, Yavuz S, et al. Lymphomatous skin infiltration at the site of previous varicella zoster virus infection in a patient with T cell lymphoma. Leuk Lymphoma. 2000;37:229-232.
- Cerroni L, Kerl H. Cutaneous localization of B-cell chronic lymphocytic leukemia at the site of varicella/herpes virus eruptions. J Am Acad Dermatol. 1997;37:1022.
- Hudson CP, Hanno R, Callen JP. Cutaneous angiosarcoma in a site of healed herpes zoster. Int J Dermatol. 1984;23:404-407.
- Wyburn-Mason R. Visceral lesions in herpes zoster. Br Med J. 1957;1:678-681.
- Caroti A. Metastasi cutanee di a adenocarcinoma papillifero ovarico in sede di herpes zoster. Chron Dermatol. 1987;18:769-773.
- Kim MB, Jwa SW, Ko HC, et al. A case of secondary cutaneous mucinosis following herpes zoster: Wolf’s isotopic response. Int J Dermatol. 2009;48:212-214.
- Burman KD, McKinley-Grant L. Dermatologic aspects of thyroid disease. Clin Dermatol. 2006;24:247-255.
- Shekari AM, Ghiasi M, Ghasemi E, et al. Papulonodular mucinosis indicating systemic lupus erythematosus. Clin Exp Dermatol. 2009;34:558-560.
- Dinneen AM, Dicken CH. Scleromyxedema. J Am Acad Dermatol. 1995;33:37-43.
- Rongioletti F, Ghigliotti G, De Marchi R, et al. Cutaneous mucinoses and HIV infection. Br J Dermatol. 1998;139:1077-1080.
- Krahl D, Hartschuh W, Tilgen W. Granuloma annulare perforans in herpes zoster scars. J Am Acad Dermatol. 1993;29:859-862.
- Wolf R, Lotti T, Ruocco V. Isomorphic versus isotopic response: data and hypotheses. J Eur Acad Dermatol Venereol. 2003;17:123-125.
- Fisher G, Jaworski R. Granuloma formation in herpes zoster scars. J Am Acad Dermatol. 1987;16:1261-1263.
- Ruocco V, Grimaldi Filioli F. La risposta isotopica post-erpetica: possibile sequela di un locus minoris resistentiae acquisito. G Ital Dermatol Venereol. 1999;134:547-552.
- Nikkels AF, Debrus S, Delvenne P, et al. Viral glycoproteins in herpesviridae granulomas. Am J Dermatopathol. 1994;16:588-592.
- Rongioletti F, Zaccaria E, Cozzani E, et al. Treatment of localized lichen myxedematosus of discrete type with tacrolimus ointment. J Am Acad Dermatol. 2008;5:530-532.
- Kwon OS, Moon SE, Kim JA, et al. Lichen myxodematosus with rapid spontaneous regression. Br J Dermatol. 1997;136:295-296.
Practice Points
- Focal mucinosis is a histopathologic finding that may be seen in different cutaneous disorders. It is an exceptional histopathologic finding that has rarely been described in herpes zoster scars.
- In most cases, focal mucinosis is just a histopathologic finding with no therapeutic consequences.
Olanzapine helps prevent nausea in patients on chemo
Olanzapine is more effective than placebo, in combination with a 5-HT3-receptor antagonist and an NK1-receptor antagonist, in preventing nausea in patients undergoing chemotherapy, according to investigators.
“This large, randomized, double-blind, placebo-controlled, phase III trial showed that it is more effective to combine olanzapine than placebo with an NK1-receptor antagonist, a 5-HT3–receptor antagonist, and dexamethasone for the prevention of nausea and vomiting in patients who have not received previous chemotherapy but are currently receiving highly emetogenic chemotherapy,” reported Rudolph Navari, MD, PhD, of the World Health Organization, Geneva, and his associates (N Engl J Med. 2016;375:134-42).
Patients were randomized to receive olanzapine or the placebo, along with a 5-HT3-receptor antagonist (either palonosetron intravenously, granisetron intravenously or orally, or ondansetron intravenously or orally) and an NK1-receptor antagonist (fosaprepitant intravenously or aprepitant orally). The olanzapine (n = 192) and placebo (n = 188) groups were balanced with respect to age, race, sex, and chemotherapy administered.
Patients kept daily records of nausea and episodes of vomiting. The proportion of patients who reported no nausea or who experienced no clinically significant nausea was significantly greater in the olanzapine group than in the placebo group (37% vs. 22%, P = .002 and 67% vs. 49%, P = .001).
Patients receiving olanzapine had significantly increased sedation (severe in 5%) on day 2 compared with baseline, Dr. Navari and his associates reported. The sedation resolved on days 3, 4, and 5 even though patients continued to receive the drug on days 3 and 4. No patients discontinued the study because of sedation.
The National Cancer Institute funded the study. One investigator reported receiving financial support from Merck and Co. The other investigators reported having no disclosures.
On Twitter @jessnicolecraig
Olanzapine is more effective than placebo, in combination with a 5-HT3-receptor antagonist and an NK1-receptor antagonist, in preventing nausea in patients undergoing chemotherapy, according to investigators.
“This large, randomized, double-blind, placebo-controlled, phase III trial showed that it is more effective to combine olanzapine than placebo with an NK1-receptor antagonist, a 5-HT3–receptor antagonist, and dexamethasone for the prevention of nausea and vomiting in patients who have not received previous chemotherapy but are currently receiving highly emetogenic chemotherapy,” reported Rudolph Navari, MD, PhD, of the World Health Organization, Geneva, and his associates (N Engl J Med. 2016;375:134-42).
Patients were randomized to receive olanzapine or the placebo, along with a 5-HT3-receptor antagonist (either palonosetron intravenously, granisetron intravenously or orally, or ondansetron intravenously or orally) and an NK1-receptor antagonist (fosaprepitant intravenously or aprepitant orally). The olanzapine (n = 192) and placebo (n = 188) groups were balanced with respect to age, race, sex, and chemotherapy administered.
Patients kept daily records of nausea and episodes of vomiting. The proportion of patients who reported no nausea or who experienced no clinically significant nausea was significantly greater in the olanzapine group than in the placebo group (37% vs. 22%, P = .002 and 67% vs. 49%, P = .001).
Patients receiving olanzapine had significantly increased sedation (severe in 5%) on day 2 compared with baseline, Dr. Navari and his associates reported. The sedation resolved on days 3, 4, and 5 even though patients continued to receive the drug on days 3 and 4. No patients discontinued the study because of sedation.
The National Cancer Institute funded the study. One investigator reported receiving financial support from Merck and Co. The other investigators reported having no disclosures.
On Twitter @jessnicolecraig
Olanzapine is more effective than placebo, in combination with a 5-HT3-receptor antagonist and an NK1-receptor antagonist, in preventing nausea in patients undergoing chemotherapy, according to investigators.
“This large, randomized, double-blind, placebo-controlled, phase III trial showed that it is more effective to combine olanzapine than placebo with an NK1-receptor antagonist, a 5-HT3–receptor antagonist, and dexamethasone for the prevention of nausea and vomiting in patients who have not received previous chemotherapy but are currently receiving highly emetogenic chemotherapy,” reported Rudolph Navari, MD, PhD, of the World Health Organization, Geneva, and his associates (N Engl J Med. 2016;375:134-42).
Patients were randomized to receive olanzapine or the placebo, along with a 5-HT3-receptor antagonist (either palonosetron intravenously, granisetron intravenously or orally, or ondansetron intravenously or orally) and an NK1-receptor antagonist (fosaprepitant intravenously or aprepitant orally). The olanzapine (n = 192) and placebo (n = 188) groups were balanced with respect to age, race, sex, and chemotherapy administered.
Patients kept daily records of nausea and episodes of vomiting. The proportion of patients who reported no nausea or who experienced no clinically significant nausea was significantly greater in the olanzapine group than in the placebo group (37% vs. 22%, P = .002 and 67% vs. 49%, P = .001).
Patients receiving olanzapine had significantly increased sedation (severe in 5%) on day 2 compared with baseline, Dr. Navari and his associates reported. The sedation resolved on days 3, 4, and 5 even though patients continued to receive the drug on days 3 and 4. No patients discontinued the study because of sedation.
The National Cancer Institute funded the study. One investigator reported receiving financial support from Merck and Co. The other investigators reported having no disclosures.
On Twitter @jessnicolecraig
FROM THE NEW ENGLAND JOURNAL OF MEDICINE
Key clinical point: Olanzapine significantly reduced episodes of nausea, compared with placebo.
Major finding: The proportion of patients who experienced no clinically significant nausea was significantly greater in the olanzapine group than in the placebo group (67% vs. 49%, P = .001).
Data source: A randomized, double-blind phase III trial of 380 patients receiving chemotherapy for malignant cancer.
Disclosures: The National Cancer Institute funded the study. One investigator reported receiving financial support from Merck and Co. The other investigators reported having no disclosures.
Ipilimumab may restore antitumor immunity after relapse from HSCT
Early data hint that immune checkpoint inhibitors may be able to restore antitumor activity in patients with hematologic malignancies that have relapsed after allogeneic transplant.
Among 22 patients with relapsed hematologic cancers following allogeneic hematopoietic stem cell transplantation (HSCT) in a phase I/Ib study, treatment with the anti-CTLA-4 antibody ipilimumab (Yervoy) at a dose of 10 mg/kg was associated with complete responses in five patients, partial responses in two, and decreased tumor burden in six, reported Matthew S. Davids, MD, of the Dana-Farber Cancer Institute in Boston, and his colleagues.
“CTLA-4 blockade was a feasible approach for the treatment of patients with relapsed hematologic cancer after transplantation. Complete remissions with some durability were observed, even in patients with refractory myeloid cancers,” they wrote (N Engl J Med. 2016 Jul 14. doi: 10.1056/NEJMoa1601202).
More than one-third of patients who undergo HSCT for hematologic malignancies such as lymphoma, multiple myeloma, or leukemia will experience a relapse, and most will die within a year of relapse despite salvage therapies or retransplantation, the authors noted.
“Immune escape (i.e., tumor evasion of the donor immune system) contributes to relapse after allogeneic HSCT, and immune checkpoint inhibitory pathways probably play an important role,” they wrote.
Selective CTLA-4 blockade has been shown in mouse models to treat late relapse after transplantation by augmenting graft-versus-tumor response without apparent exacerbation of graft-versus-host disease (GVHD). To see whether the use of a CTLA-4 inhibitor could have the same effect in humans, the investigators instituted a single-group, open-label, dose-finding, safety and efficacy study of ipilimumab in 28 patients from six treatment sites.
The patients had all undergone allogeneic HSCT more than 3 months before the start of the study. The diagnoses included acute myeloid leukemia (AML) in 12 patients (including 3 with leukemia cutis and 1 with a myeloid sarcoma), Hodgkin lymphoma in 7, non-Hodgkin lymphoma in 4, myelodysplastic syndrome (MDS) in 2, and multiple myeloma, myeloproliferative neoplasm, and acute lymphoblastic leukemia in 1 patient each. Eight of the patients had previously had either grade I or II acute GVHD; 16 had had chronic GVHD.
Patients received induction therapy with ipilimumab at a dose of either 3 mg/kg (6 patients), or 10 mg/kg (22 patients) every 3 weeks for a total of 4 doses. Patients who experienced a clinical benefit from the drug could receive additional doses every 12 weeks for up to 60 weeks.
There were no clinical responses meeting study criteria in any of the patients who received the 3-mg/kg dose. Among the 22 who received the 10-mg/kg dose, however, the rate of complete responses was 23% (5 of 22), partial responses 9% (2 of 22), and decreased tumor burden 27% (6 of 22). The remaining nine patients experienced disease progression.
Four of the complete responses occurred in patients with extramedullary AML, and one occurred in a patient with MDS transforming into AML.
The safety analysis, which included all 28 patients evaluable for adverse events, showed four discontinuations due to dose-limiting chronic GVHD of the liver in the 3 patients, and acute GVHD of the gut in 1, and to severe immune-related events in one additional patient, leading to the patient’s death.
Other grade 3 or greater adverse events possibly related to ipilimumab included acute kidney injury (one patient) , corneal ulcer (one), thrombocytopenia (nine), neutropenia (three), anemia and pleural effusion (two).
The investigators point out that therapy to stimulate a graft-versus-tumor effect has the potential to promote or exacerbate GVHD, as occurred in four patients in the study. The GVHD in these patients was effectively managed with glucocorticoids, however.
The National Institutes of Health, Leukemia and Lymphoma Society, Pasquarello Tissue Bank, and Dana-Farber Cancer Institute supported the study. Dr. Davids disclosed grants from ASCO, the Pasquarello Tissue Bank, NIH, NCI, and Leukemia and Lymphoma society, and personal fees from several companies outside the study. Several coauthors disclosed relationships with various pharmaceutical companies, including Bristol-Myers Squibb, maker of ipilimumab.
Early data hint that immune checkpoint inhibitors may be able to restore antitumor activity in patients with hematologic malignancies that have relapsed after allogeneic transplant.
Among 22 patients with relapsed hematologic cancers following allogeneic hematopoietic stem cell transplantation (HSCT) in a phase I/Ib study, treatment with the anti-CTLA-4 antibody ipilimumab (Yervoy) at a dose of 10 mg/kg was associated with complete responses in five patients, partial responses in two, and decreased tumor burden in six, reported Matthew S. Davids, MD, of the Dana-Farber Cancer Institute in Boston, and his colleagues.
“CTLA-4 blockade was a feasible approach for the treatment of patients with relapsed hematologic cancer after transplantation. Complete remissions with some durability were observed, even in patients with refractory myeloid cancers,” they wrote (N Engl J Med. 2016 Jul 14. doi: 10.1056/NEJMoa1601202).
More than one-third of patients who undergo HSCT for hematologic malignancies such as lymphoma, multiple myeloma, or leukemia will experience a relapse, and most will die within a year of relapse despite salvage therapies or retransplantation, the authors noted.
“Immune escape (i.e., tumor evasion of the donor immune system) contributes to relapse after allogeneic HSCT, and immune checkpoint inhibitory pathways probably play an important role,” they wrote.
Selective CTLA-4 blockade has been shown in mouse models to treat late relapse after transplantation by augmenting graft-versus-tumor response without apparent exacerbation of graft-versus-host disease (GVHD). To see whether the use of a CTLA-4 inhibitor could have the same effect in humans, the investigators instituted a single-group, open-label, dose-finding, safety and efficacy study of ipilimumab in 28 patients from six treatment sites.
The patients had all undergone allogeneic HSCT more than 3 months before the start of the study. The diagnoses included acute myeloid leukemia (AML) in 12 patients (including 3 with leukemia cutis and 1 with a myeloid sarcoma), Hodgkin lymphoma in 7, non-Hodgkin lymphoma in 4, myelodysplastic syndrome (MDS) in 2, and multiple myeloma, myeloproliferative neoplasm, and acute lymphoblastic leukemia in 1 patient each. Eight of the patients had previously had either grade I or II acute GVHD; 16 had had chronic GVHD.
Patients received induction therapy with ipilimumab at a dose of either 3 mg/kg (6 patients), or 10 mg/kg (22 patients) every 3 weeks for a total of 4 doses. Patients who experienced a clinical benefit from the drug could receive additional doses every 12 weeks for up to 60 weeks.
There were no clinical responses meeting study criteria in any of the patients who received the 3-mg/kg dose. Among the 22 who received the 10-mg/kg dose, however, the rate of complete responses was 23% (5 of 22), partial responses 9% (2 of 22), and decreased tumor burden 27% (6 of 22). The remaining nine patients experienced disease progression.
Four of the complete responses occurred in patients with extramedullary AML, and one occurred in a patient with MDS transforming into AML.
The safety analysis, which included all 28 patients evaluable for adverse events, showed four discontinuations due to dose-limiting chronic GVHD of the liver in the 3 patients, and acute GVHD of the gut in 1, and to severe immune-related events in one additional patient, leading to the patient’s death.
Other grade 3 or greater adverse events possibly related to ipilimumab included acute kidney injury (one patient) , corneal ulcer (one), thrombocytopenia (nine), neutropenia (three), anemia and pleural effusion (two).
The investigators point out that therapy to stimulate a graft-versus-tumor effect has the potential to promote or exacerbate GVHD, as occurred in four patients in the study. The GVHD in these patients was effectively managed with glucocorticoids, however.
The National Institutes of Health, Leukemia and Lymphoma Society, Pasquarello Tissue Bank, and Dana-Farber Cancer Institute supported the study. Dr. Davids disclosed grants from ASCO, the Pasquarello Tissue Bank, NIH, NCI, and Leukemia and Lymphoma society, and personal fees from several companies outside the study. Several coauthors disclosed relationships with various pharmaceutical companies, including Bristol-Myers Squibb, maker of ipilimumab.
Early data hint that immune checkpoint inhibitors may be able to restore antitumor activity in patients with hematologic malignancies that have relapsed after allogeneic transplant.
Among 22 patients with relapsed hematologic cancers following allogeneic hematopoietic stem cell transplantation (HSCT) in a phase I/Ib study, treatment with the anti-CTLA-4 antibody ipilimumab (Yervoy) at a dose of 10 mg/kg was associated with complete responses in five patients, partial responses in two, and decreased tumor burden in six, reported Matthew S. Davids, MD, of the Dana-Farber Cancer Institute in Boston, and his colleagues.
“CTLA-4 blockade was a feasible approach for the treatment of patients with relapsed hematologic cancer after transplantation. Complete remissions with some durability were observed, even in patients with refractory myeloid cancers,” they wrote (N Engl J Med. 2016 Jul 14. doi: 10.1056/NEJMoa1601202).
More than one-third of patients who undergo HSCT for hematologic malignancies such as lymphoma, multiple myeloma, or leukemia will experience a relapse, and most will die within a year of relapse despite salvage therapies or retransplantation, the authors noted.
“Immune escape (i.e., tumor evasion of the donor immune system) contributes to relapse after allogeneic HSCT, and immune checkpoint inhibitory pathways probably play an important role,” they wrote.
Selective CTLA-4 blockade has been shown in mouse models to treat late relapse after transplantation by augmenting graft-versus-tumor response without apparent exacerbation of graft-versus-host disease (GVHD). To see whether the use of a CTLA-4 inhibitor could have the same effect in humans, the investigators instituted a single-group, open-label, dose-finding, safety and efficacy study of ipilimumab in 28 patients from six treatment sites.
The patients had all undergone allogeneic HSCT more than 3 months before the start of the study. The diagnoses included acute myeloid leukemia (AML) in 12 patients (including 3 with leukemia cutis and 1 with a myeloid sarcoma), Hodgkin lymphoma in 7, non-Hodgkin lymphoma in 4, myelodysplastic syndrome (MDS) in 2, and multiple myeloma, myeloproliferative neoplasm, and acute lymphoblastic leukemia in 1 patient each. Eight of the patients had previously had either grade I or II acute GVHD; 16 had had chronic GVHD.
Patients received induction therapy with ipilimumab at a dose of either 3 mg/kg (6 patients), or 10 mg/kg (22 patients) every 3 weeks for a total of 4 doses. Patients who experienced a clinical benefit from the drug could receive additional doses every 12 weeks for up to 60 weeks.
There were no clinical responses meeting study criteria in any of the patients who received the 3-mg/kg dose. Among the 22 who received the 10-mg/kg dose, however, the rate of complete responses was 23% (5 of 22), partial responses 9% (2 of 22), and decreased tumor burden 27% (6 of 22). The remaining nine patients experienced disease progression.
Four of the complete responses occurred in patients with extramedullary AML, and one occurred in a patient with MDS transforming into AML.
The safety analysis, which included all 28 patients evaluable for adverse events, showed four discontinuations due to dose-limiting chronic GVHD of the liver in the 3 patients, and acute GVHD of the gut in 1, and to severe immune-related events in one additional patient, leading to the patient’s death.
Other grade 3 or greater adverse events possibly related to ipilimumab included acute kidney injury (one patient) , corneal ulcer (one), thrombocytopenia (nine), neutropenia (three), anemia and pleural effusion (two).
The investigators point out that therapy to stimulate a graft-versus-tumor effect has the potential to promote or exacerbate GVHD, as occurred in four patients in the study. The GVHD in these patients was effectively managed with glucocorticoids, however.
The National Institutes of Health, Leukemia and Lymphoma Society, Pasquarello Tissue Bank, and Dana-Farber Cancer Institute supported the study. Dr. Davids disclosed grants from ASCO, the Pasquarello Tissue Bank, NIH, NCI, and Leukemia and Lymphoma society, and personal fees from several companies outside the study. Several coauthors disclosed relationships with various pharmaceutical companies, including Bristol-Myers Squibb, maker of ipilimumab.
FROM THE NEW ENGLAND JOURNAL OF MEDICINE
Key clinical point: Anti-CTLA-4 therapy may restore graft-versus-tumor effect in patients with hematologic malignancies relapsed after allogeneic transplantation.
Major finding: Five of 22 patients on a 10-mg/kg dose of ipilimumab had a complete response.
Data source: Phase I/Ib investigator-initiated study of 28 patients with hematologic malignancies relapsed after allogeneic hematopoietic stem cell transplantation.
Disclosures: The National Institutes of Health, Leukemia and Lymphoma Society, Pasquarello Tissue Bank, and Dana-Farber Cancer Institute supported the study. Dr. Davids disclosed grants from ASCO, the Pasquarello Tissue Bank, NIH, NCI, and Leukemia and Lymphoma society, and personal fees from several companies outside the study. Several coauthors disclosed relationships with various pharmaceutical companies, including Bristol-Myers Squibb, maker of ipilimumab.