Karli Edholm, MD

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

METHODS

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

 

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

The period between January 1, 2014, and December 31, 2014, was considered preintervention, and August 1, 2015, to July 31, 2016, was considered postintervention. January 1, 2015, to July 31, 2015, was considered a “run-in” period because it was the interval during which the interventions on the hospitalist service were being rolled out. Data from this period were not included in the pre- or postintervention analyses but are shown in Figure 1.

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

 

RESULTS

There were 46,215 visits originally included in the study. Ninety-two visits (0.2%) were excluded due to missing or invalid data. A total of 10,344 visits occurred during the “run-in” period between January 1, 2015, and July 31, 2015, leaving 35,871 patient visits during the pre- and postintervention periods. In the hospitalist group, there were 3442 visits before the intervention and 3700 after. There were 13,470 visits in the nonhospitalist group before the intervention and 15,259 after.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

Estimates from adjusted and unadjusted linear models are shown in Table 2. In adjusted models, telemetry utilization in the postintervention period was reduced by 69% (95% confidence interval [CI], −72% to −64%; P < .001) in the hospitalist group and by 22% (95% CI, −27% to −16%; P <.001) in the nonhospitalist group. Compared with nonhospitalists, hospitalists had a 60% greater reduction in telemetry rates (95% CI, −65% to −54%; P < .001).

In the randomly selected sample of patients pre- and postintervention who received telemetry monitoring, there was an increase in telemetry appropriateness on the hospitalist service (46% to 72%, P = .025; Table 1). In the nonhospitalist group, appropriate telemetry utilization did not change significantly. Of the 100 randomly selected patients in the hospitalist group after the intervention who did not receive telemetry, no patient had an AHA Class I indication, and only 4 patients had a Class II indication.^3,17

DISCUSSION

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

 

CONCLUSION

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

METHODS

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

 

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

The period between January 1, 2014, and December 31, 2014, was considered preintervention, and August 1, 2015, to July 31, 2016, was considered postintervention. January 1, 2015, to July 31, 2015, was considered a “run-in” period because it was the interval during which the interventions on the hospitalist service were being rolled out. Data from this period were not included in the pre- or postintervention analyses but are shown in Figure 1.

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

 

RESULTS

There were 46,215 visits originally included in the study. Ninety-two visits (0.2%) were excluded due to missing or invalid data. A total of 10,344 visits occurred during the “run-in” period between January 1, 2015, and July 31, 2015, leaving 35,871 patient visits during the pre- and postintervention periods. In the hospitalist group, there were 3442 visits before the intervention and 3700 after. There were 13,470 visits in the nonhospitalist group before the intervention and 15,259 after.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

Estimates from adjusted and unadjusted linear models are shown in Table 2. In adjusted models, telemetry utilization in the postintervention period was reduced by 69% (95% confidence interval [CI], −72% to −64%; P < .001) in the hospitalist group and by 22% (95% CI, −27% to −16%; P <.001) in the nonhospitalist group. Compared with nonhospitalists, hospitalists had a 60% greater reduction in telemetry rates (95% CI, −65% to −54%; P < .001).

In the randomly selected sample of patients pre- and postintervention who received telemetry monitoring, there was an increase in telemetry appropriateness on the hospitalist service (46% to 72%, P = .025; Table 1). In the nonhospitalist group, appropriate telemetry utilization did not change significantly. Of the 100 randomly selected patients in the hospitalist group after the intervention who did not receive telemetry, no patient had an AHA Class I indication, and only 4 patients had a Class II indication.^3,17

DISCUSSION

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

 

CONCLUSION

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

METHODS

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

 

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

The period between January 1, 2014, and December 31, 2014, was considered preintervention, and August 1, 2015, to July 31, 2016, was considered postintervention. January 1, 2015, to July 31, 2015, was considered a “run-in” period because it was the interval during which the interventions on the hospitalist service were being rolled out. Data from this period were not included in the pre- or postintervention analyses but are shown in Figure 1.

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

 

RESULTS

There were 46,215 visits originally included in the study. Ninety-two visits (0.2%) were excluded due to missing or invalid data. A total of 10,344 visits occurred during the “run-in” period between January 1, 2015, and July 31, 2015, leaving 35,871 patient visits during the pre- and postintervention periods. In the hospitalist group, there were 3442 visits before the intervention and 3700 after. There were 13,470 visits in the nonhospitalist group before the intervention and 15,259 after.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

Estimates from adjusted and unadjusted linear models are shown in Table 2. In adjusted models, telemetry utilization in the postintervention period was reduced by 69% (95% confidence interval [CI], −72% to −64%; P < .001) in the hospitalist group and by 22% (95% CI, −27% to −16%; P <.001) in the nonhospitalist group. Compared with nonhospitalists, hospitalists had a 60% greater reduction in telemetry rates (95% CI, −65% to −54%; P < .001).

In the randomly selected sample of patients pre- and postintervention who received telemetry monitoring, there was an increase in telemetry appropriateness on the hospitalist service (46% to 72%, P = .025; Table 1). In the nonhospitalist group, appropriate telemetry utilization did not change significantly. Of the 100 randomly selected patients in the hospitalist group after the intervention who did not receive telemetry, no patient had an AHA Class I indication, and only 4 patients had a Class II indication.^3,17

DISCUSSION

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

 

CONCLUSION

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin scale.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups. The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. New Engl J Med. 2015;372(24):2296–2306.

Visit our website for more hospitalist reviews of HM-focused research.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin scale.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups. The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. New Engl J Med. 2015;372(24):2296–2306.

Visit our website for more hospitalist reviews of HM-focused research.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin scale.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups. The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. New Engl J Med. 2015;372(24):2296–2306.

Visit our website for more hospitalist reviews of HM-focused research.

Clinical question: In post-operative cardiothoracic surgery patients, is high-flow nasal oxygen therapy inferior to BiPAP for resolution of acute respiratory failure?

Background: Acute respiratory failure is common following cardiothoracic surgery, and noninvasive ventilation often is used to avoid intubation. Noninvasive ventilation is resource-intensive and might be uncomfortable to patients. High-flow nasal oxygen therapy is an alternative modality, which provides large amounts of oxygen with more ease and patient comfort.

Study design: Multi-center, randomized, noninferiority trial.

Setting: Six ICUs in France.

Synopsis: Investigators randomized 830 patients who met criteria (obesity, heart failure, or failure of spontaneous breathing trial) after cardiothoracic surgery. These patients were prophylactically treated with high-flow nasal oxygen or BiPAP. Patients with sleep apnea, nausea/vomiting, agitation/confusion, or hemodynamic instability were excluded. Data collected included arterial blood gas, respiratory rate, and patient-rated dyspnea. The primary outcome was treatment failure as defined by reintubation and mechanical ventilation, a switch to the other study treatment, or study treatment discontinuation.

Complications included pneumothorax, colonic pseudoobstruction, and nosocomial pneumonia. The expected rate of failure for BiPAP was 20%. High-flow nasal oxygen therapy was not inferior to BiPAP, with similar treatment failure rates occurring in both groups (21.9% in BiPAP patients vs. 21% of high-flow nasal oxygen patients); 20% of patients experienced persistent discomfort with either treatment method.

There were no significant differences in complications between the two study groups. Limitations included lack of blinding and potential for bias, leading to treatment failure and crossover.

Bottom line: High-flow nasal oxygen was noninferior to BiPAP in patients with respiratory failure after cardiothoracic surgery.

Citation: Stéphan F, Barrucand B, Petit P, et al. High-flow nasal oxygen vs noninvasive positive airway pressure in hypoxemic patients after cardiothoracic surgery: a randomized clinical trial. JAMA. 2015;313(23):2331-2339.

Clinical question: In post-operative cardiothoracic surgery patients, is high-flow nasal oxygen therapy inferior to BiPAP for resolution of acute respiratory failure?

Background: Acute respiratory failure is common following cardiothoracic surgery, and noninvasive ventilation often is used to avoid intubation. Noninvasive ventilation is resource-intensive and might be uncomfortable to patients. High-flow nasal oxygen therapy is an alternative modality, which provides large amounts of oxygen with more ease and patient comfort.

Study design: Multi-center, randomized, noninferiority trial.

Setting: Six ICUs in France.

Synopsis: Investigators randomized 830 patients who met criteria (obesity, heart failure, or failure of spontaneous breathing trial) after cardiothoracic surgery. These patients were prophylactically treated with high-flow nasal oxygen or BiPAP. Patients with sleep apnea, nausea/vomiting, agitation/confusion, or hemodynamic instability were excluded. Data collected included arterial blood gas, respiratory rate, and patient-rated dyspnea. The primary outcome was treatment failure as defined by reintubation and mechanical ventilation, a switch to the other study treatment, or study treatment discontinuation.

Complications included pneumothorax, colonic pseudoobstruction, and nosocomial pneumonia. The expected rate of failure for BiPAP was 20%. High-flow nasal oxygen therapy was not inferior to BiPAP, with similar treatment failure rates occurring in both groups (21.9% in BiPAP patients vs. 21% of high-flow nasal oxygen patients); 20% of patients experienced persistent discomfort with either treatment method.

There were no significant differences in complications between the two study groups. Limitations included lack of blinding and potential for bias, leading to treatment failure and crossover.

Bottom line: High-flow nasal oxygen was noninferior to BiPAP in patients with respiratory failure after cardiothoracic surgery.

Citation: Stéphan F, Barrucand B, Petit P, et al. High-flow nasal oxygen vs noninvasive positive airway pressure in hypoxemic patients after cardiothoracic surgery: a randomized clinical trial. JAMA. 2015;313(23):2331-2339.

Clinical question: In post-operative cardiothoracic surgery patients, is high-flow nasal oxygen therapy inferior to BiPAP for resolution of acute respiratory failure?

Background: Acute respiratory failure is common following cardiothoracic surgery, and noninvasive ventilation often is used to avoid intubation. Noninvasive ventilation is resource-intensive and might be uncomfortable to patients. High-flow nasal oxygen therapy is an alternative modality, which provides large amounts of oxygen with more ease and patient comfort.

Study design: Multi-center, randomized, noninferiority trial.

Setting: Six ICUs in France.

Synopsis: Investigators randomized 830 patients who met criteria (obesity, heart failure, or failure of spontaneous breathing trial) after cardiothoracic surgery. These patients were prophylactically treated with high-flow nasal oxygen or BiPAP. Patients with sleep apnea, nausea/vomiting, agitation/confusion, or hemodynamic instability were excluded. Data collected included arterial blood gas, respiratory rate, and patient-rated dyspnea. The primary outcome was treatment failure as defined by reintubation and mechanical ventilation, a switch to the other study treatment, or study treatment discontinuation.

Complications included pneumothorax, colonic pseudoobstruction, and nosocomial pneumonia. The expected rate of failure for BiPAP was 20%. High-flow nasal oxygen therapy was not inferior to BiPAP, with similar treatment failure rates occurring in both groups (21.9% in BiPAP patients vs. 21% of high-flow nasal oxygen patients); 20% of patients experienced persistent discomfort with either treatment method.

There were no significant differences in complications between the two study groups. Limitations included lack of blinding and potential for bias, leading to treatment failure and crossover.

Bottom line: High-flow nasal oxygen was noninferior to BiPAP in patients with respiratory failure after cardiothoracic surgery.

Citation: Stéphan F, Barrucand B, Petit P, et al. High-flow nasal oxygen vs noninvasive positive airway pressure in hypoxemic patients after cardiothoracic surgery: a randomized clinical trial. JAMA. 2015;313(23):2331-2339.

Clinical question: Does routine oxygen supplementation in patients with STEMI increase myocardial injury?

Background: Because of physiologic and clinical studies conducted before the era of acute coronary intervention, supplemental oxygen routinely is administered to patients with STEMI, regardless of oxygen saturation; however, recent studies have shown possible adverse effects of oxygen, including increased reperfusion injury and increased adverse outcomes in small clinical trials.

Study design: Multicenter, prospective, randomized, controlled trial (RCT).

Setting: Nine metropolitan hospitals.

Synopsis: This multicenter study included 441 patients with STEMI who were 18 years or older and were randomized by paramedics to receive either 8 L/min of oxygen or no supplemental oxygen. All patients then received protocolized care. The primary endpoint of myocardial infarct size, determined by mean peak of creatine kinase, was significantly increased in the oxygen group compared to the no oxygen group (1948 vs. 1543 U/L; means ratio, 1.27; 95% confidence interval, 1.04-1.52; P=0.01). There were nonsignificant increases of secondary endpoints in the oxygen group, including rate of recurrent myocardial infarction (5.5% vs. 0.9%; P=0.006), frequency of arrhythmia (40.4% vs. 31.4%; P=0.05), and size of infarct on six-month cardiac MRI (n=139; 20.3 vs. 13.1 g; P=0.04).

This study has several limitations: It was powered to detect differences in biomarkers (not clinical endpoints) and the treatment was not blinded to paramedics, patients, or cardiology teams.

Bottom line: Supplemental oxygen administration in patients with STEMI might increase infarct size and lead to poorer clinical outcomes; however, larger clinical trials are warranted.

Citation: Stub D, Smith K, Bernard S, et al. Air versus oxygen in ST-segment-elevation myocardial infarction. Circulation. 2015;131(24):2143-2150.

Clinical question: Does routine oxygen supplementation in patients with STEMI increase myocardial injury?

Background: Because of physiologic and clinical studies conducted before the era of acute coronary intervention, supplemental oxygen routinely is administered to patients with STEMI, regardless of oxygen saturation; however, recent studies have shown possible adverse effects of oxygen, including increased reperfusion injury and increased adverse outcomes in small clinical trials.

Study design: Multicenter, prospective, randomized, controlled trial (RCT).

Setting: Nine metropolitan hospitals.

Synopsis: This multicenter study included 441 patients with STEMI who were 18 years or older and were randomized by paramedics to receive either 8 L/min of oxygen or no supplemental oxygen. All patients then received protocolized care. The primary endpoint of myocardial infarct size, determined by mean peak of creatine kinase, was significantly increased in the oxygen group compared to the no oxygen group (1948 vs. 1543 U/L; means ratio, 1.27; 95% confidence interval, 1.04-1.52; P=0.01). There were nonsignificant increases of secondary endpoints in the oxygen group, including rate of recurrent myocardial infarction (5.5% vs. 0.9%; P=0.006), frequency of arrhythmia (40.4% vs. 31.4%; P=0.05), and size of infarct on six-month cardiac MRI (n=139; 20.3 vs. 13.1 g; P=0.04).

This study has several limitations: It was powered to detect differences in biomarkers (not clinical endpoints) and the treatment was not blinded to paramedics, patients, or cardiology teams.

Bottom line: Supplemental oxygen administration in patients with STEMI might increase infarct size and lead to poorer clinical outcomes; however, larger clinical trials are warranted.

Citation: Stub D, Smith K, Bernard S, et al. Air versus oxygen in ST-segment-elevation myocardial infarction. Circulation. 2015;131(24):2143-2150.

Clinical question: Does routine oxygen supplementation in patients with STEMI increase myocardial injury?

Background: Because of physiologic and clinical studies conducted before the era of acute coronary intervention, supplemental oxygen routinely is administered to patients with STEMI, regardless of oxygen saturation; however, recent studies have shown possible adverse effects of oxygen, including increased reperfusion injury and increased adverse outcomes in small clinical trials.

Study design: Multicenter, prospective, randomized, controlled trial (RCT).

Setting: Nine metropolitan hospitals.

Synopsis: This multicenter study included 441 patients with STEMI who were 18 years or older and were randomized by paramedics to receive either 8 L/min of oxygen or no supplemental oxygen. All patients then received protocolized care. The primary endpoint of myocardial infarct size, determined by mean peak of creatine kinase, was significantly increased in the oxygen group compared to the no oxygen group (1948 vs. 1543 U/L; means ratio, 1.27; 95% confidence interval, 1.04-1.52; P=0.01). There were nonsignificant increases of secondary endpoints in the oxygen group, including rate of recurrent myocardial infarction (5.5% vs. 0.9%; P=0.006), frequency of arrhythmia (40.4% vs. 31.4%; P=0.05), and size of infarct on six-month cardiac MRI (n=139; 20.3 vs. 13.1 g; P=0.04).

This study has several limitations: It was powered to detect differences in biomarkers (not clinical endpoints) and the treatment was not blinded to paramedics, patients, or cardiology teams.

Bottom line: Supplemental oxygen administration in patients with STEMI might increase infarct size and lead to poorer clinical outcomes; however, larger clinical trials are warranted.

Citation: Stub D, Smith K, Bernard S, et al. Air versus oxygen in ST-segment-elevation myocardial infarction. Circulation. 2015;131(24):2143-2150.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin score.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups.

The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptoms onset in ischemic stroke. New Engl J Med. 2015;372(24):2296-2306.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin score.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups.

The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptoms onset in ischemic stroke. New Engl J Med. 2015;372(24):2296-2306.

Clinical question: Does thrombectomy, in conjunction with medical therapy, improve functional independence in patients with an acute proximal anterior stroke?

Background: Revascularization of proximal anterior strokes with alteplase alone occurs less than 50% of the time. First-generation thrombectomy devices (i.e., Merci and Penumbra) have not shown improvement in revascularization or functional outcomes; however, the development of thrombectomy stent retriever devices has led to more promising results, with several recent studies demonstrating functional improvement using endovascular retrieval in addition to medical therapy in proximal anterior circulation strokes.

Study design: Prospective, multicenter, randomized, sequential, open-label, phase 3 study with blinded evaluation.

Setting: Four hospitals in Spain.

Synopsis: Approximately 200 patients who were diagnosed within eight hours of onset of a large vessel anterior stroke were randomly assigned to medical therapy (alteplase) plus endovascular treatment versus medical therapy alone. In order to reduce selection bias, the study was conducted within a population-based registry of acute stroke patients from the same area. The major exclusion criterion was evidence of a large infarct on imaging. The primary outcome was severity of disability at 90 days based on the modified Rankin score.

Study results showed a significant improvement in functional status in the thrombectomy group, with 66% of patients demonstrating revascularization. The rate of death and intracranial hemorrhage was similar between both groups.

The trial stopped recruitment after the first interim analysis given lack of equipoise, with emerging literature supporting endovascular therapy.

Bottom line: Thrombectomy performed in proximal, large vessel anterior circulation strokes within eight hours of onset of symptoms improves functional status at 90 days.

Citation: Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptoms onset in ischemic stroke. New Engl J Med. 2015;372(24):2296-2306.

Clinical question: Does interdisciplinary care on general medical wards improve patient outcomes?

Background: Patients on general medical wards might experience errors and/or preventable deaths. One mechanism that could reduce the risk of errors or preventable death is the utilization of interdisciplinary team care.

Study design: Systematic review.

Setting: Studies published in English from 1998 through 2013 in Embase, MEDLINE, and PsycINFO.

Synopsis: Reports of interdisciplinary team care interventions on general medical wards in which the care was evaluated against an objective patient outcome were reviewed. Outcomes were grouped into early (less than 30 days) or late (30 days to 12 months). Thirty studies of more than 66,000 total patients were included; these were composed of RCTs, nonrandomized cluster trials, and controlled before-after studies.

Interventions either altered the composition of the care team or addressed the logistics of team practice. Studies evaluated complications of care, length of stay, readmissions, and mortality. Although some evidence showed interdisciplinary care reduces complications, the majority of studies did not show significant improvements in any other outcomes.

Bottom line: In a systematic review, interdisciplinary team care in general medical wards is not associated with reduced complications, length of stay, readmissions, or mortality.

Citation: Pannick S, Davis R, Ashrafian H, et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288-1298.

Clinical question: Does interdisciplinary care on general medical wards improve patient outcomes?

Background: Patients on general medical wards might experience errors and/or preventable deaths. One mechanism that could reduce the risk of errors or preventable death is the utilization of interdisciplinary team care.

Study design: Systematic review.

Setting: Studies published in English from 1998 through 2013 in Embase, MEDLINE, and PsycINFO.

Synopsis: Reports of interdisciplinary team care interventions on general medical wards in which the care was evaluated against an objective patient outcome were reviewed. Outcomes were grouped into early (less than 30 days) or late (30 days to 12 months). Thirty studies of more than 66,000 total patients were included; these were composed of RCTs, nonrandomized cluster trials, and controlled before-after studies.

Interventions either altered the composition of the care team or addressed the logistics of team practice. Studies evaluated complications of care, length of stay, readmissions, and mortality. Although some evidence showed interdisciplinary care reduces complications, the majority of studies did not show significant improvements in any other outcomes.

Bottom line: In a systematic review, interdisciplinary team care in general medical wards is not associated with reduced complications, length of stay, readmissions, or mortality.

Citation: Pannick S, Davis R, Ashrafian H, et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288-1298.

Clinical question: Does interdisciplinary care on general medical wards improve patient outcomes?

Background: Patients on general medical wards might experience errors and/or preventable deaths. One mechanism that could reduce the risk of errors or preventable death is the utilization of interdisciplinary team care.

Study design: Systematic review.

Setting: Studies published in English from 1998 through 2013 in Embase, MEDLINE, and PsycINFO.

Synopsis: Reports of interdisciplinary team care interventions on general medical wards in which the care was evaluated against an objective patient outcome were reviewed. Outcomes were grouped into early (less than 30 days) or late (30 days to 12 months). Thirty studies of more than 66,000 total patients were included; these were composed of RCTs, nonrandomized cluster trials, and controlled before-after studies.

Interventions either altered the composition of the care team or addressed the logistics of team practice. Studies evaluated complications of care, length of stay, readmissions, and mortality. Although some evidence showed interdisciplinary care reduces complications, the majority of studies did not show significant improvements in any other outcomes.

Bottom line: In a systematic review, interdisciplinary team care in general medical wards is not associated with reduced complications, length of stay, readmissions, or mortality.

Citation: Pannick S, Davis R, Ashrafian H, et al. Effects of interdisciplinary team care interventions on general medical wards: a systematic review. JAMA Intern Med. 2015;175(8):1288-1298.

Clinical question: Is there an association between objective measures of surgical quality and patient satisfaction as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey?

Background: The Centers for Medicare and Medicaid Services (CMS) has tied financial reimbursement to patient satisfaction scores. It is unknown whether high-quality surgery correlates with higher patient satisfaction scores.

Study design: Retrospective, observational study.

Setting: One hundred eighty hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP).

Synopsis: The study included 103,866 Medicare patients 65 and older who had surgery at a participating ACS NSQIP hospital between 2004-2008. Data regarding these patients were collected from a linked database (including Medicare inpatient claims, ACS NSQIP, the American Hospital Association annual survey, and Hospital Compare). Analysis of five 30-day outcomes was performed in order to assess surgical quality:

• Post-operative mortality;
• Major complication;
• Minor complication;
• Death following a complication; and
• Readmission.

Participating hospitals were grouped into quartiles based upon their performance on the HCAHPS survey.

Hospitals that performed in the highest quartile on the HCAHPS survey had significantly lower 30-day mortality, death following complications, and minor complications. No differences were detected in hospital readmissions or major complications based on patient satisfaction.

The results of this study may not be generalizable to all hospitals given the fact that the dataset is from Medicare patients only and participation in the ACS NSQIP is voluntary.

Bottom line: Using data from a national database, researchers found a positive association between patient satisfaction scores and objective measures of surgical quality.

Citation: Sacks GD, Lawson EH, Dawes AJ, et al. Relationship between hospital performance on a patient satisfaction survey and surgical quality [published online ahead of print June 24, 2015]. JAMA Surg. doi:10.1001/jamasurg.2015.1108.

Clinical question: Is there an association between objective measures of surgical quality and patient satisfaction as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey?

Background: The Centers for Medicare and Medicaid Services (CMS) has tied financial reimbursement to patient satisfaction scores. It is unknown whether high-quality surgery correlates with higher patient satisfaction scores.

Study design: Retrospective, observational study.

Setting: One hundred eighty hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP).

Synopsis: The study included 103,866 Medicare patients 65 and older who had surgery at a participating ACS NSQIP hospital between 2004-2008. Data regarding these patients were collected from a linked database (including Medicare inpatient claims, ACS NSQIP, the American Hospital Association annual survey, and Hospital Compare). Analysis of five 30-day outcomes was performed in order to assess surgical quality:

• Post-operative mortality;
• Major complication;
• Minor complication;
• Death following a complication; and
• Readmission.

Participating hospitals were grouped into quartiles based upon their performance on the HCAHPS survey.

Hospitals that performed in the highest quartile on the HCAHPS survey had significantly lower 30-day mortality, death following complications, and minor complications. No differences were detected in hospital readmissions or major complications based on patient satisfaction.

The results of this study may not be generalizable to all hospitals given the fact that the dataset is from Medicare patients only and participation in the ACS NSQIP is voluntary.

Bottom line: Using data from a national database, researchers found a positive association between patient satisfaction scores and objective measures of surgical quality.

Citation: Sacks GD, Lawson EH, Dawes AJ, et al. Relationship between hospital performance on a patient satisfaction survey and surgical quality [published online ahead of print June 24, 2015]. JAMA Surg. doi:10.1001/jamasurg.2015.1108.

Clinical question: Is there an association between objective measures of surgical quality and patient satisfaction as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey?

Background: The Centers for Medicare and Medicaid Services (CMS) has tied financial reimbursement to patient satisfaction scores. It is unknown whether high-quality surgery correlates with higher patient satisfaction scores.

Study design: Retrospective, observational study.

Setting: One hundred eighty hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP).

Synopsis: The study included 103,866 Medicare patients 65 and older who had surgery at a participating ACS NSQIP hospital between 2004-2008. Data regarding these patients were collected from a linked database (including Medicare inpatient claims, ACS NSQIP, the American Hospital Association annual survey, and Hospital Compare). Analysis of five 30-day outcomes was performed in order to assess surgical quality:

• Post-operative mortality;
• Major complication;
• Minor complication;
• Death following a complication; and
• Readmission.

Participating hospitals were grouped into quartiles based upon their performance on the HCAHPS survey.

Hospitals that performed in the highest quartile on the HCAHPS survey had significantly lower 30-day mortality, death following complications, and minor complications. No differences were detected in hospital readmissions or major complications based on patient satisfaction.

The results of this study may not be generalizable to all hospitals given the fact that the dataset is from Medicare patients only and participation in the ACS NSQIP is voluntary.

Bottom line: Using data from a national database, researchers found a positive association between patient satisfaction scores and objective measures of surgical quality.

Citation: Sacks GD, Lawson EH, Dawes AJ, et al. Relationship between hospital performance on a patient satisfaction survey and surgical quality [published online ahead of print June 24, 2015]. JAMA Surg. doi:10.1001/jamasurg.2015.1108.

Clinical question: Among patients with a first episode of unprovoked pulmonary embolism (PE), what are the benefits and harms of extending the duration of anticoagulation for secondary prophylaxis against recurrent VTE?

Background: Optimal duration of anticoagulation after initial unprovoked PE is not known. Prior studies demonstrated risk reduction of recurrent VTE while on therapy but have had inadequate long-term monitoring of patients or enrollment of patients with PE, who are known to have a higher case-fatality rate of recurrent VTE than patients with DVT.

Study design: Multicenter, randomized, double-blinded, parallel-grouped, placebo-controlled trial.

Setting: Fourteen French hospitals from 2007 to 2012.

Synopsis: Investigators randomized 371 patients with a first episode of symptomatic unprovoked PE who had completed six months of warfarin to 18 additional months of warfarin or placebo. Patients were followed for 24 months after discontinuation of therapy.

During the treatment period, the primary outcome (composite of recurrent VTE and major bleeding) occurred in 3.3% of the warfarin group vs. 13.5% of the placebo group (HR 0.22). This difference was primarily due to reduction in risk of recurrent VTE (1.7% vs. 13.5%, HR 0.15), with only minimal increased bleeding risk (2.2% vs. 0.5%, NS).

There was no significant difference in the composite outcome (20.8% in warfarin group vs. 24% in placebo) on analysis of the entire study period (treatment and follow-up), however, suggesting the benefit of extended warfarin therapy upon recurrent VTE risk diminished upon cessation.

Bottom line: Patients treated with extended-therapy warfarin after a first unprovoked PE have a decreased risk of recurrent VTE compared to standard therapy only while on treatment; the risk of recurrent VTE returns upon cessation of therapy.

Citation: Couturand F, Sanchez O, Pernod G, et al. Six months vs extended oral anticoagulation after a first episode of pulmonary embolism: The PADIS-PE randomized clinical trial. JAMA. 2015;314(1):31-40.

Clinical question: Among patients with a first episode of unprovoked pulmonary embolism (PE), what are the benefits and harms of extending the duration of anticoagulation for secondary prophylaxis against recurrent VTE?

Background: Optimal duration of anticoagulation after initial unprovoked PE is not known. Prior studies demonstrated risk reduction of recurrent VTE while on therapy but have had inadequate long-term monitoring of patients or enrollment of patients with PE, who are known to have a higher case-fatality rate of recurrent VTE than patients with DVT.

Study design: Multicenter, randomized, double-blinded, parallel-grouped, placebo-controlled trial.

Setting: Fourteen French hospitals from 2007 to 2012.

Synopsis: Investigators randomized 371 patients with a first episode of symptomatic unprovoked PE who had completed six months of warfarin to 18 additional months of warfarin or placebo. Patients were followed for 24 months after discontinuation of therapy.

During the treatment period, the primary outcome (composite of recurrent VTE and major bleeding) occurred in 3.3% of the warfarin group vs. 13.5% of the placebo group (HR 0.22). This difference was primarily due to reduction in risk of recurrent VTE (1.7% vs. 13.5%, HR 0.15), with only minimal increased bleeding risk (2.2% vs. 0.5%, NS).

There was no significant difference in the composite outcome (20.8% in warfarin group vs. 24% in placebo) on analysis of the entire study period (treatment and follow-up), however, suggesting the benefit of extended warfarin therapy upon recurrent VTE risk diminished upon cessation.

Bottom line: Patients treated with extended-therapy warfarin after a first unprovoked PE have a decreased risk of recurrent VTE compared to standard therapy only while on treatment; the risk of recurrent VTE returns upon cessation of therapy.

Citation: Couturand F, Sanchez O, Pernod G, et al. Six months vs extended oral anticoagulation after a first episode of pulmonary embolism: The PADIS-PE randomized clinical trial. JAMA. 2015;314(1):31-40.

Clinical question: Among patients with a first episode of unprovoked pulmonary embolism (PE), what are the benefits and harms of extending the duration of anticoagulation for secondary prophylaxis against recurrent VTE?

Background: Optimal duration of anticoagulation after initial unprovoked PE is not known. Prior studies demonstrated risk reduction of recurrent VTE while on therapy but have had inadequate long-term monitoring of patients or enrollment of patients with PE, who are known to have a higher case-fatality rate of recurrent VTE than patients with DVT.

Study design: Multicenter, randomized, double-blinded, parallel-grouped, placebo-controlled trial.

Setting: Fourteen French hospitals from 2007 to 2012.

Synopsis: Investigators randomized 371 patients with a first episode of symptomatic unprovoked PE who had completed six months of warfarin to 18 additional months of warfarin or placebo. Patients were followed for 24 months after discontinuation of therapy.

During the treatment period, the primary outcome (composite of recurrent VTE and major bleeding) occurred in 3.3% of the warfarin group vs. 13.5% of the placebo group (HR 0.22). This difference was primarily due to reduction in risk of recurrent VTE (1.7% vs. 13.5%, HR 0.15), with only minimal increased bleeding risk (2.2% vs. 0.5%, NS).

There was no significant difference in the composite outcome (20.8% in warfarin group vs. 24% in placebo) on analysis of the entire study period (treatment and follow-up), however, suggesting the benefit of extended warfarin therapy upon recurrent VTE risk diminished upon cessation.

Bottom line: Patients treated with extended-therapy warfarin after a first unprovoked PE have a decreased risk of recurrent VTE compared to standard therapy only while on treatment; the risk of recurrent VTE returns upon cessation of therapy.

Citation: Couturand F, Sanchez O, Pernod G, et al. Six months vs extended oral anticoagulation after a first episode of pulmonary embolism: The PADIS-PE randomized clinical trial. JAMA. 2015;314(1):31-40.

Clinical question: Should the Wells score be used for DVT risk stratification in the hospital?

Background: The Wells score was derived to reduce lower extremity ultrasounds (LEUS) in the outpatient evaluation of DVTs. There has never been a large prospective trial to validate its use in hospitalized patients.

Study design: Single-center, prospective cohort study.

Setting: Quaternary care, academic hospital.

Synopsis: Between November 2012 and December 2013, all inpatients at a single medical center who underwent a LEUS for suspected DVT, including 1,135 inpatients 16 years or older, had Wells risk factors recorded. The incidence of proximal DVTs noted for low, moderate, and high pretest probability groups were 5.9%, 9.5%, and 16.4%, respectively. Compared to the outpatient incidence of 3.0%, 16.6%, and 74.6% reported by Wells and colleagues, there were nonsignificant differences among inpatient groups. The difference between low and moderate pretest probability groups was not significant.

Discrimination of risk for DVT in hospitalized patients performed only slightly better than chance (AUC, 0.60) and the failure rate was double that of the original outpatient study (5.9% vs. 3.0%).

A possible explanation for these findings is the increased prevalence of immobilization (6x), cancer (3x), and risk factors not included in the Wells score (COPD, heart failure, and infection) in hospitalized patients.

Bottom line: The Wells score may not be sufficient to rule out DVT or influence management in the inpatient setting.

Citation: Silveira PC, Ip IK, Goldhaber SZ, Piazza G, Benson CB, Khorasani R. Performance of Wells score for deep vein thrombosis in the inpatient setting. JAMA Intern Med. 2015;175(7):1112-1117.

Clinical question: Should the Wells score be used for DVT risk stratification in the hospital?

Background: The Wells score was derived to reduce lower extremity ultrasounds (LEUS) in the outpatient evaluation of DVTs. There has never been a large prospective trial to validate its use in hospitalized patients.

Study design: Single-center, prospective cohort study.

Setting: Quaternary care, academic hospital.

Synopsis: Between November 2012 and December 2013, all inpatients at a single medical center who underwent a LEUS for suspected DVT, including 1,135 inpatients 16 years or older, had Wells risk factors recorded. The incidence of proximal DVTs noted for low, moderate, and high pretest probability groups were 5.9%, 9.5%, and 16.4%, respectively. Compared to the outpatient incidence of 3.0%, 16.6%, and 74.6% reported by Wells and colleagues, there were nonsignificant differences among inpatient groups. The difference between low and moderate pretest probability groups was not significant.

Discrimination of risk for DVT in hospitalized patients performed only slightly better than chance (AUC, 0.60) and the failure rate was double that of the original outpatient study (5.9% vs. 3.0%).

A possible explanation for these findings is the increased prevalence of immobilization (6x), cancer (3x), and risk factors not included in the Wells score (COPD, heart failure, and infection) in hospitalized patients.

Bottom line: The Wells score may not be sufficient to rule out DVT or influence management in the inpatient setting.

Citation: Silveira PC, Ip IK, Goldhaber SZ, Piazza G, Benson CB, Khorasani R. Performance of Wells score for deep vein thrombosis in the inpatient setting. JAMA Intern Med. 2015;175(7):1112-1117.

Clinical question: Should the Wells score be used for DVT risk stratification in the hospital?

Background: The Wells score was derived to reduce lower extremity ultrasounds (LEUS) in the outpatient evaluation of DVTs. There has never been a large prospective trial to validate its use in hospitalized patients.

Study design: Single-center, prospective cohort study.

Setting: Quaternary care, academic hospital.

Synopsis: Between November 2012 and December 2013, all inpatients at a single medical center who underwent a LEUS for suspected DVT, including 1,135 inpatients 16 years or older, had Wells risk factors recorded. The incidence of proximal DVTs noted for low, moderate, and high pretest probability groups were 5.9%, 9.5%, and 16.4%, respectively. Compared to the outpatient incidence of 3.0%, 16.6%, and 74.6% reported by Wells and colleagues, there were nonsignificant differences among inpatient groups. The difference between low and moderate pretest probability groups was not significant.

Discrimination of risk for DVT in hospitalized patients performed only slightly better than chance (AUC, 0.60) and the failure rate was double that of the original outpatient study (5.9% vs. 3.0%).

A possible explanation for these findings is the increased prevalence of immobilization (6x), cancer (3x), and risk factors not included in the Wells score (COPD, heart failure, and infection) in hospitalized patients.

Bottom line: The Wells score may not be sufficient to rule out DVT or influence management in the inpatient setting.

Citation: Silveira PC, Ip IK, Goldhaber SZ, Piazza G, Benson CB, Khorasani R. Performance of Wells score for deep vein thrombosis in the inpatient setting. JAMA Intern Med. 2015;175(7):1112-1117.