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Nanocapsules prevent release of nontargeted radiation
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.” 
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.”
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.”
Matching Workforce to Workload
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
Launch of rare-cancer trial spurs many more
MILAN – An ongoing phase III trial launched in 2012 that is testing whether adjuvant therapy aids women following removal of high-risk uterine leiomyosarcomas has also blazed a path for the International Rare Cancer Initiative, which has launched two other trials in rare cancers and is planning to start several more.
The advanced uterine leiomyosarcoma trial, which is testing an adjuvant regimen of up to four courses of gemcitabine (Gemzar) plus docetaxel (Taxotere) followed by doxorubicin (Adriamycin) for four courses should "answer the adjuvant chemotherapy question" for these patients, Dr. Martee L. Hensley said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology.
The trial, known as Gynecologic Oncology Group (GOG) 0277, was the first study organized by the International Rare Cancer Initiative (IRCI) to activate. The study involves more than 200 U.S. centers and will open in many more European centers once regulatory approvals occur, said Dr. Hensley, professor of medicine at Weill Cornell Medical College and a gynecologic medical oncologist at Memorial Sloan- Kettering Cancer Center, New York.
Oncologists diagnose about 1,200 uterine sarcomas annually in the United States, most of which are uterine-limited and histologically high grade. "Successful conduct of this study in this rare but high-risk disease will establish the standard of care for managing women who have undergone complete resection," she said in an interview.
"Conducting prospective randomized trials in rare cancers is a significant challenge. International collaboration is considered a key factor in success" by speeding patient accrual, identifying research questions of international importance, and designing a trial that is internationally accepted and generalizable, Dr. Hensley said. In 2011, five cancer organizations formed the IRCI: the U.S. National Cancer Institute, the European Organization for the Research and Treatment of Cancer, Cancer Research UK, the U.K. National Cancer Research Network, and the French Institut National du Cancer.
The IRCI defines rare cancers as generally having an incidence below 2 cases per 100,000 population, and it is charged to develop intervention trials for these cancers, especially randomized trials.
"Creation of the IRCI has provided some needed infrastructure and has been critical to the success of GOG 0277," Dr. Hensley said. "But one could also say that GOG 0277 is also key to the IRCI’s success. The work we have done for GOG 0277 will inform the design and conduct of future international studies" in rare cancers.
The IRCI includes nine committees, each of which develops trials for different rare-cancer types. These include the gynecologic sarcoma committee that Dr. Hensley serves on and which helped organize GOG 0277, and other committees for small bowel adenocarcinoma, salivary gland cancer, thymoma, ocular melanoma, relapsed or metastatic anal cancer, rare brain cancer, desmoplastic small-round-cell tumor, and penile cancer. The committees include representatives appointed by the founding organizations, which also appoint the members of the IRCI board, the body that determines which committees to form, explained Nicola Keat, a staffer at Cancer Research UK in London who serves as the IRCI coordinator.
The gynecologic sarcoma committee decided that the question of whether adjuvant chemotherapy following complete resection helps patients with uterus-limited, high-grade leiomyosarcoma had "primary importance," said Dr. Hensley. "We recognized that the IRCI provided an ideal opportunity."
The gynecologic sarcoma group also plans to open a prospective study of doxorubicin for chemotherapy-naive patients with advanced, high-grade undifferentiated sarcoma of the uterus, a rare and aggressive cancer with no standard treatment. The study would also assess whether cabozantinib (Cometriq) can further prolong progression-free survival, compared with placebo, in patients with stable disease or an objective response to doxorubicin. In addition, the committee would like to launch a trial of aromatase inhibition for patients with low-grade endometrial stromal sarcoma through the IRCI, she said.
Following the launch of GOG 0277, the IRCI opened enrollment of patients into a trial focused on advanced uveal melanoma at U.S. centers, with U.K. recruitment anticipated to start later this year, Ms. Keat said in an interview. A third IRCI-organized trial, for patients with advanced anal cancer, recently opened for enrollment at participating U.K. centers, she added.
GOG 0277 began in June 2012 and aims to enroll 216 patients. As of February 2014, it had accrued seven patients, but Dr. Hensley said she believed the study was on track to its targeted finish date in 2018, as patients will soon start to enroll in Europe. "We expect the study to open in the U.K. in the next couple of months," Ms. Keat said in late March.
Dr. Hensley said that her spouse is a Sanofi employee. Ms. Keat had no disclosures.
On Twitter @mitchelzoler
MILAN – An ongoing phase III trial launched in 2012 that is testing whether adjuvant therapy aids women following removal of high-risk uterine leiomyosarcomas has also blazed a path for the International Rare Cancer Initiative, which has launched two other trials in rare cancers and is planning to start several more.
The advanced uterine leiomyosarcoma trial, which is testing an adjuvant regimen of up to four courses of gemcitabine (Gemzar) plus docetaxel (Taxotere) followed by doxorubicin (Adriamycin) for four courses should "answer the adjuvant chemotherapy question" for these patients, Dr. Martee L. Hensley said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology.
The trial, known as Gynecologic Oncology Group (GOG) 0277, was the first study organized by the International Rare Cancer Initiative (IRCI) to activate. The study involves more than 200 U.S. centers and will open in many more European centers once regulatory approvals occur, said Dr. Hensley, professor of medicine at Weill Cornell Medical College and a gynecologic medical oncologist at Memorial Sloan- Kettering Cancer Center, New York.
Oncologists diagnose about 1,200 uterine sarcomas annually in the United States, most of which are uterine-limited and histologically high grade. "Successful conduct of this study in this rare but high-risk disease will establish the standard of care for managing women who have undergone complete resection," she said in an interview.
"Conducting prospective randomized trials in rare cancers is a significant challenge. International collaboration is considered a key factor in success" by speeding patient accrual, identifying research questions of international importance, and designing a trial that is internationally accepted and generalizable, Dr. Hensley said. In 2011, five cancer organizations formed the IRCI: the U.S. National Cancer Institute, the European Organization for the Research and Treatment of Cancer, Cancer Research UK, the U.K. National Cancer Research Network, and the French Institut National du Cancer.
The IRCI defines rare cancers as generally having an incidence below 2 cases per 100,000 population, and it is charged to develop intervention trials for these cancers, especially randomized trials.
"Creation of the IRCI has provided some needed infrastructure and has been critical to the success of GOG 0277," Dr. Hensley said. "But one could also say that GOG 0277 is also key to the IRCI’s success. The work we have done for GOG 0277 will inform the design and conduct of future international studies" in rare cancers.
The IRCI includes nine committees, each of which develops trials for different rare-cancer types. These include the gynecologic sarcoma committee that Dr. Hensley serves on and which helped organize GOG 0277, and other committees for small bowel adenocarcinoma, salivary gland cancer, thymoma, ocular melanoma, relapsed or metastatic anal cancer, rare brain cancer, desmoplastic small-round-cell tumor, and penile cancer. The committees include representatives appointed by the founding organizations, which also appoint the members of the IRCI board, the body that determines which committees to form, explained Nicola Keat, a staffer at Cancer Research UK in London who serves as the IRCI coordinator.
The gynecologic sarcoma committee decided that the question of whether adjuvant chemotherapy following complete resection helps patients with uterus-limited, high-grade leiomyosarcoma had "primary importance," said Dr. Hensley. "We recognized that the IRCI provided an ideal opportunity."
The gynecologic sarcoma group also plans to open a prospective study of doxorubicin for chemotherapy-naive patients with advanced, high-grade undifferentiated sarcoma of the uterus, a rare and aggressive cancer with no standard treatment. The study would also assess whether cabozantinib (Cometriq) can further prolong progression-free survival, compared with placebo, in patients with stable disease or an objective response to doxorubicin. In addition, the committee would like to launch a trial of aromatase inhibition for patients with low-grade endometrial stromal sarcoma through the IRCI, she said.
Following the launch of GOG 0277, the IRCI opened enrollment of patients into a trial focused on advanced uveal melanoma at U.S. centers, with U.K. recruitment anticipated to start later this year, Ms. Keat said in an interview. A third IRCI-organized trial, for patients with advanced anal cancer, recently opened for enrollment at participating U.K. centers, she added.
GOG 0277 began in June 2012 and aims to enroll 216 patients. As of February 2014, it had accrued seven patients, but Dr. Hensley said she believed the study was on track to its targeted finish date in 2018, as patients will soon start to enroll in Europe. "We expect the study to open in the U.K. in the next couple of months," Ms. Keat said in late March.
Dr. Hensley said that her spouse is a Sanofi employee. Ms. Keat had no disclosures.
On Twitter @mitchelzoler
MILAN – An ongoing phase III trial launched in 2012 that is testing whether adjuvant therapy aids women following removal of high-risk uterine leiomyosarcomas has also blazed a path for the International Rare Cancer Initiative, which has launched two other trials in rare cancers and is planning to start several more.
The advanced uterine leiomyosarcoma trial, which is testing an adjuvant regimen of up to four courses of gemcitabine (Gemzar) plus docetaxel (Taxotere) followed by doxorubicin (Adriamycin) for four courses should "answer the adjuvant chemotherapy question" for these patients, Dr. Martee L. Hensley said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology.
The trial, known as Gynecologic Oncology Group (GOG) 0277, was the first study organized by the International Rare Cancer Initiative (IRCI) to activate. The study involves more than 200 U.S. centers and will open in many more European centers once regulatory approvals occur, said Dr. Hensley, professor of medicine at Weill Cornell Medical College and a gynecologic medical oncologist at Memorial Sloan- Kettering Cancer Center, New York.
Oncologists diagnose about 1,200 uterine sarcomas annually in the United States, most of which are uterine-limited and histologically high grade. "Successful conduct of this study in this rare but high-risk disease will establish the standard of care for managing women who have undergone complete resection," she said in an interview.
"Conducting prospective randomized trials in rare cancers is a significant challenge. International collaboration is considered a key factor in success" by speeding patient accrual, identifying research questions of international importance, and designing a trial that is internationally accepted and generalizable, Dr. Hensley said. In 2011, five cancer organizations formed the IRCI: the U.S. National Cancer Institute, the European Organization for the Research and Treatment of Cancer, Cancer Research UK, the U.K. National Cancer Research Network, and the French Institut National du Cancer.
The IRCI defines rare cancers as generally having an incidence below 2 cases per 100,000 population, and it is charged to develop intervention trials for these cancers, especially randomized trials.
"Creation of the IRCI has provided some needed infrastructure and has been critical to the success of GOG 0277," Dr. Hensley said. "But one could also say that GOG 0277 is also key to the IRCI’s success. The work we have done for GOG 0277 will inform the design and conduct of future international studies" in rare cancers.
The IRCI includes nine committees, each of which develops trials for different rare-cancer types. These include the gynecologic sarcoma committee that Dr. Hensley serves on and which helped organize GOG 0277, and other committees for small bowel adenocarcinoma, salivary gland cancer, thymoma, ocular melanoma, relapsed or metastatic anal cancer, rare brain cancer, desmoplastic small-round-cell tumor, and penile cancer. The committees include representatives appointed by the founding organizations, which also appoint the members of the IRCI board, the body that determines which committees to form, explained Nicola Keat, a staffer at Cancer Research UK in London who serves as the IRCI coordinator.
The gynecologic sarcoma committee decided that the question of whether adjuvant chemotherapy following complete resection helps patients with uterus-limited, high-grade leiomyosarcoma had "primary importance," said Dr. Hensley. "We recognized that the IRCI provided an ideal opportunity."
The gynecologic sarcoma group also plans to open a prospective study of doxorubicin for chemotherapy-naive patients with advanced, high-grade undifferentiated sarcoma of the uterus, a rare and aggressive cancer with no standard treatment. The study would also assess whether cabozantinib (Cometriq) can further prolong progression-free survival, compared with placebo, in patients with stable disease or an objective response to doxorubicin. In addition, the committee would like to launch a trial of aromatase inhibition for patients with low-grade endometrial stromal sarcoma through the IRCI, she said.
Following the launch of GOG 0277, the IRCI opened enrollment of patients into a trial focused on advanced uveal melanoma at U.S. centers, with U.K. recruitment anticipated to start later this year, Ms. Keat said in an interview. A third IRCI-organized trial, for patients with advanced anal cancer, recently opened for enrollment at participating U.K. centers, she added.
GOG 0277 began in June 2012 and aims to enroll 216 patients. As of February 2014, it had accrued seven patients, but Dr. Hensley said she believed the study was on track to its targeted finish date in 2018, as patients will soon start to enroll in Europe. "We expect the study to open in the U.K. in the next couple of months," Ms. Keat said in late March.
Dr. Hensley said that her spouse is a Sanofi employee. Ms. Keat had no disclosures.
On Twitter @mitchelzoler
EXPERT ANALYSIS FROM SARCOMA AND GIST 2014
Pulsed Dye Laser for the Treatment of Macular Amyloidosis: A Case Report
Cutaneous Manifestations of Injectable Drug Use: Hidden Secrets
Giant Cell Tumor of the Tendon Sheath
Product gets orphan designation for AML
The US Food and Drug Administration (FDA) has granted orphan designation for eryaspase to treat acute myeloid leukemia (AML).
Eryaspase, also known as ERY-ASP or GRASPA, is L-asparaginase encapsulated in red blood cells.
These donor-derived, enzyme-loaded red blood cells function as bioreactors to eliminate circulating asparagine and “starve”
leukemic cells, thereby inducing their death.
Research has suggested this delivery system provides improved pharmacodynamics. It protects L-aspariginase from circulating proteolytic enzymes and prevents early liver or renal clearance.
The system also appears to reduce the risk of adverse events.
Eryaspase is currently under investigation in a phase 3 trial for acute lymphoblastic leukemia (ALL) and a phase 2b trial for AML in Europe. A phase 1 study in adult ALL is being launched in the US.
Eryaspase now has orphan designation for ALL, AML and pancreatic cancer, both in Europe and the US.
In the US, orphan designation is generally granted for drugs or biologics intended to treat disorders of high unmet medical need that affect fewer than 200,000 people.
This designation conveys special incentives to the product’s sponsor, including 7 years of US market exclusivity for the drug or biologic upon FDA approval, a prescription drug user fee waiver, and certain tax credits. 
The US Food and Drug Administration (FDA) has granted orphan designation for eryaspase to treat acute myeloid leukemia (AML).
Eryaspase, also known as ERY-ASP or GRASPA, is L-asparaginase encapsulated in red blood cells.
These donor-derived, enzyme-loaded red blood cells function as bioreactors to eliminate circulating asparagine and “starve”
leukemic cells, thereby inducing their death.
Research has suggested this delivery system provides improved pharmacodynamics. It protects L-aspariginase from circulating proteolytic enzymes and prevents early liver or renal clearance.
The system also appears to reduce the risk of adverse events.
Eryaspase is currently under investigation in a phase 3 trial for acute lymphoblastic leukemia (ALL) and a phase 2b trial for AML in Europe. A phase 1 study in adult ALL is being launched in the US.
Eryaspase now has orphan designation for ALL, AML and pancreatic cancer, both in Europe and the US.
In the US, orphan designation is generally granted for drugs or biologics intended to treat disorders of high unmet medical need that affect fewer than 200,000 people.
This designation conveys special incentives to the product’s sponsor, including 7 years of US market exclusivity for the drug or biologic upon FDA approval, a prescription drug user fee waiver, and certain tax credits.
The US Food and Drug Administration (FDA) has granted orphan designation for eryaspase to treat acute myeloid leukemia (AML).
Eryaspase, also known as ERY-ASP or GRASPA, is L-asparaginase encapsulated in red blood cells.
These donor-derived, enzyme-loaded red blood cells function as bioreactors to eliminate circulating asparagine and “starve”
leukemic cells, thereby inducing their death.
Research has suggested this delivery system provides improved pharmacodynamics. It protects L-aspariginase from circulating proteolytic enzymes and prevents early liver or renal clearance.
The system also appears to reduce the risk of adverse events.
Eryaspase is currently under investigation in a phase 3 trial for acute lymphoblastic leukemia (ALL) and a phase 2b trial for AML in Europe. A phase 1 study in adult ALL is being launched in the US.
Eryaspase now has orphan designation for ALL, AML and pancreatic cancer, both in Europe and the US.
In the US, orphan designation is generally granted for drugs or biologics intended to treat disorders of high unmet medical need that affect fewer than 200,000 people.
This designation conveys special incentives to the product’s sponsor, including 7 years of US market exclusivity for the drug or biologic upon FDA approval, a prescription drug user fee waiver, and certain tax credits.
Group uncovers inconsistent reporting of AEs, deaths
OHSU School of Medicine
A new analysis indicates that researchers sometimes exclude unfavorable trial data, whether reporting results in medical journals or on ClinicalTrials.gov.
A group of investigators analyzed trials in which data was reported both on the government website and in journals. And they found that discrepancies between the 2 sources were common.
Adverse events (AEs) were more likely to be reported on ClinicalTrials.gov and excluded from reports in journals.
But deaths seemed to be underreported or inconsistently reported on ClinicalTrials.gov when compared to journals.
“This is the most comprehensive study of ClinicalTrials.gov to date,” said study author Mark Helfand, MD, of Oregon Health & Science University.
“It shows that patients and clinicians could use [the site] to find information that is not available in the published literature, particularly to get more complete information about the harms of various treatment options. It also shows that, to best serve the public, death rates and some other items in ClinicalTrials.gov should be audited to keep them up to date.”
Dr Helfand and his colleagues reported these findings in Annals of Internal Medicine.
The researchers evaluated 110 trials that were completed by January 1, 2009, and reported on ClinicalTrials.gov. The team looked only at trials completed by 2009 to allow for the results to be later published in medical journals. Most of the trials were industry-sponsored.
Analyses revealed a number of discrepancies between data on ClinicalTrials.gov and in medical journals. For instance, 80% (n=88) of the trials had inconsistencies in secondary outcome measures.
In 15% (n=16) of trials, there were inconsistencies in the description of the primary outcome. And in 20% (n=22) of trials, there were inconsistencies in the primary outcome value. Still, in most cases, these discrepancies were small and did not affect the statistical significance of the results.
There were inconsistencies in AE reporting as well. Of the 84 trials in which a serious AE was reported on ClinicalTrials.gov, 11 published papers did not mention serious AEs, 5 reported that there were no serious AEs, and 21 reported a different
number of serious AEs.
So of the trials that had inconsistent AE reporting, 87% had more serious AEs listed on ClinicalTrials.gov than in the journal.
On the other hand, deaths seemed to be underreported on ClinicalTrials.gov compared to journals. For instance, in 17% of trials that did not report deaths on ClinicalTrials.gov, deaths were reported in the journal article.
Prior studies have indicated ClinicalTrials.gov does not have a uniform way of reporting deaths, and that may lead to inconsistencies.
OHSU School of Medicine
A new analysis indicates that researchers sometimes exclude unfavorable trial data, whether reporting results in medical journals or on ClinicalTrials.gov.
A group of investigators analyzed trials in which data was reported both on the government website and in journals. And they found that discrepancies between the 2 sources were common.
Adverse events (AEs) were more likely to be reported on ClinicalTrials.gov and excluded from reports in journals.
But deaths seemed to be underreported or inconsistently reported on ClinicalTrials.gov when compared to journals.
“This is the most comprehensive study of ClinicalTrials.gov to date,” said study author Mark Helfand, MD, of Oregon Health & Science University.
“It shows that patients and clinicians could use [the site] to find information that is not available in the published literature, particularly to get more complete information about the harms of various treatment options. It also shows that, to best serve the public, death rates and some other items in ClinicalTrials.gov should be audited to keep them up to date.”
Dr Helfand and his colleagues reported these findings in Annals of Internal Medicine.
The researchers evaluated 110 trials that were completed by January 1, 2009, and reported on ClinicalTrials.gov. The team looked only at trials completed by 2009 to allow for the results to be later published in medical journals. Most of the trials were industry-sponsored.
Analyses revealed a number of discrepancies between data on ClinicalTrials.gov and in medical journals. For instance, 80% (n=88) of the trials had inconsistencies in secondary outcome measures.
In 15% (n=16) of trials, there were inconsistencies in the description of the primary outcome. And in 20% (n=22) of trials, there were inconsistencies in the primary outcome value. Still, in most cases, these discrepancies were small and did not affect the statistical significance of the results.
There were inconsistencies in AE reporting as well. Of the 84 trials in which a serious AE was reported on ClinicalTrials.gov, 11 published papers did not mention serious AEs, 5 reported that there were no serious AEs, and 21 reported a different
number of serious AEs.
So of the trials that had inconsistent AE reporting, 87% had more serious AEs listed on ClinicalTrials.gov than in the journal.
On the other hand, deaths seemed to be underreported on ClinicalTrials.gov compared to journals. For instance, in 17% of trials that did not report deaths on ClinicalTrials.gov, deaths were reported in the journal article.
Prior studies have indicated ClinicalTrials.gov does not have a uniform way of reporting deaths, and that may lead to inconsistencies.
OHSU School of Medicine
A new analysis indicates that researchers sometimes exclude unfavorable trial data, whether reporting results in medical journals or on ClinicalTrials.gov.
A group of investigators analyzed trials in which data was reported both on the government website and in journals. And they found that discrepancies between the 2 sources were common.
Adverse events (AEs) were more likely to be reported on ClinicalTrials.gov and excluded from reports in journals.
But deaths seemed to be underreported or inconsistently reported on ClinicalTrials.gov when compared to journals.
“This is the most comprehensive study of ClinicalTrials.gov to date,” said study author Mark Helfand, MD, of Oregon Health & Science University.
“It shows that patients and clinicians could use [the site] to find information that is not available in the published literature, particularly to get more complete information about the harms of various treatment options. It also shows that, to best serve the public, death rates and some other items in ClinicalTrials.gov should be audited to keep them up to date.”
Dr Helfand and his colleagues reported these findings in Annals of Internal Medicine.
The researchers evaluated 110 trials that were completed by January 1, 2009, and reported on ClinicalTrials.gov. The team looked only at trials completed by 2009 to allow for the results to be later published in medical journals. Most of the trials were industry-sponsored.
Analyses revealed a number of discrepancies between data on ClinicalTrials.gov and in medical journals. For instance, 80% (n=88) of the trials had inconsistencies in secondary outcome measures.
In 15% (n=16) of trials, there were inconsistencies in the description of the primary outcome. And in 20% (n=22) of trials, there were inconsistencies in the primary outcome value. Still, in most cases, these discrepancies were small and did not affect the statistical significance of the results.
There were inconsistencies in AE reporting as well. Of the 84 trials in which a serious AE was reported on ClinicalTrials.gov, 11 published papers did not mention serious AEs, 5 reported that there were no serious AEs, and 21 reported a different
number of serious AEs.
So of the trials that had inconsistent AE reporting, 87% had more serious AEs listed on ClinicalTrials.gov than in the journal.
On the other hand, deaths seemed to be underreported on ClinicalTrials.gov compared to journals. For instance, in 17% of trials that did not report deaths on ClinicalTrials.gov, deaths were reported in the journal article.
Prior studies have indicated ClinicalTrials.gov does not have a uniform way of reporting deaths, and that may lead to inconsistencies.
How MRP-14 triggers thrombosis
artery; platelets loaded with
MRP-14 shown in yellow.
Journal of Clinical Investigation
Investigators say they’ve discovered how myeloid related protein-14 (MRP-14) generates thrombi that can trigger myocardial infarction (MI) or stroke.
Previous research showed that MRP-14 is elevated in platelets from patients who present with acute MI.
In the current study, researchers found that platelet-derived MRP-14 directly regulates thrombosis, and CD36 is required for this process.
The team therefore believes we could target this platelet-dependent pathway to treat atherothrombotic disorders.
“This is exciting because we have now closed the loop of our original finding that MRP-14 is a heart attack gene,” said investigator Daniel I. Simon, MD, of the University Hospitals Harrington Heart & Vascular Institute in Cleveland, Ohio.
“We now describe a whole new pathway that shows clotting platelets have MRP-14 inside them, that platelets secrete MRP-14, and that MRP-14 binds to a platelet receptor called CD36 to activate platelets.”
Dr Simon and his colleagues recounted these findings in The Journal of Clinical Investigation.
The research alternated between the cardiac catheterization lab (where researchers were investigating MI patients) to the basic research lab (where the investigators were probing mechanisms of disease).
The clinical portion of this research yielded thrombi—extracted from an occluded heart artery—that were loaded with platelets containing MRP-14.
“It is remarkable that this abundant platelet protein promoting thrombosis could have gone undetected until now,” Dr Simon said.
In experiments on MRP-14-deficient mice, he and his colleagues observed MRP-14 in action. One key finding was that, while MRP-14 is required for pathologic thrombosis, it does not appear to be involved in the natural, primary hemostasis response to prevent bleeding.
“The practical significance of this research is that it may provide a new target to develop more effective and safer antithrombotic agents,” Dr Simon said.
“If we could develop an agent that affects pathologic clotting and not hemostasis, that would be a home run. You would have a safer medication to treat pathologic clotting in heart attack and stroke.”
artery; platelets loaded with
MRP-14 shown in yellow.
Journal of Clinical Investigation
Investigators say they’ve discovered how myeloid related protein-14 (MRP-14) generates thrombi that can trigger myocardial infarction (MI) or stroke.
Previous research showed that MRP-14 is elevated in platelets from patients who present with acute MI.
In the current study, researchers found that platelet-derived MRP-14 directly regulates thrombosis, and CD36 is required for this process.
The team therefore believes we could target this platelet-dependent pathway to treat atherothrombotic disorders.
“This is exciting because we have now closed the loop of our original finding that MRP-14 is a heart attack gene,” said investigator Daniel I. Simon, MD, of the University Hospitals Harrington Heart & Vascular Institute in Cleveland, Ohio.
“We now describe a whole new pathway that shows clotting platelets have MRP-14 inside them, that platelets secrete MRP-14, and that MRP-14 binds to a platelet receptor called CD36 to activate platelets.”
Dr Simon and his colleagues recounted these findings in The Journal of Clinical Investigation.
The research alternated between the cardiac catheterization lab (where researchers were investigating MI patients) to the basic research lab (where the investigators were probing mechanisms of disease).
The clinical portion of this research yielded thrombi—extracted from an occluded heart artery—that were loaded with platelets containing MRP-14.
“It is remarkable that this abundant platelet protein promoting thrombosis could have gone undetected until now,” Dr Simon said.
In experiments on MRP-14-deficient mice, he and his colleagues observed MRP-14 in action. One key finding was that, while MRP-14 is required for pathologic thrombosis, it does not appear to be involved in the natural, primary hemostasis response to prevent bleeding.
“The practical significance of this research is that it may provide a new target to develop more effective and safer antithrombotic agents,” Dr Simon said.
“If we could develop an agent that affects pathologic clotting and not hemostasis, that would be a home run. You would have a safer medication to treat pathologic clotting in heart attack and stroke.”
artery; platelets loaded with
MRP-14 shown in yellow.
Journal of Clinical Investigation
Investigators say they’ve discovered how myeloid related protein-14 (MRP-14) generates thrombi that can trigger myocardial infarction (MI) or stroke.
Previous research showed that MRP-14 is elevated in platelets from patients who present with acute MI.
In the current study, researchers found that platelet-derived MRP-14 directly regulates thrombosis, and CD36 is required for this process.
The team therefore believes we could target this platelet-dependent pathway to treat atherothrombotic disorders.
“This is exciting because we have now closed the loop of our original finding that MRP-14 is a heart attack gene,” said investigator Daniel I. Simon, MD, of the University Hospitals Harrington Heart & Vascular Institute in Cleveland, Ohio.
“We now describe a whole new pathway that shows clotting platelets have MRP-14 inside them, that platelets secrete MRP-14, and that MRP-14 binds to a platelet receptor called CD36 to activate platelets.”
Dr Simon and his colleagues recounted these findings in The Journal of Clinical Investigation.
The research alternated between the cardiac catheterization lab (where researchers were investigating MI patients) to the basic research lab (where the investigators were probing mechanisms of disease).
The clinical portion of this research yielded thrombi—extracted from an occluded heart artery—that were loaded with platelets containing MRP-14.
“It is remarkable that this abundant platelet protein promoting thrombosis could have gone undetected until now,” Dr Simon said.
In experiments on MRP-14-deficient mice, he and his colleagues observed MRP-14 in action. One key finding was that, while MRP-14 is required for pathologic thrombosis, it does not appear to be involved in the natural, primary hemostasis response to prevent bleeding.
“The practical significance of this research is that it may provide a new target to develop more effective and safer antithrombotic agents,” Dr Simon said.
“If we could develop an agent that affects pathologic clotting and not hemostasis, that would be a home run. You would have a safer medication to treat pathologic clotting in heart attack and stroke.”
Healthcare Utilization after Sepsis
Sepsis, the systemic inflammatory response to infection, is a major public health concern.[1] Worldwide, sepsis affects millions of hospitalized patients each year.[2] In the United States, it is the single most expensive cause of hospitalization.[3, 4, 5, 6] Multiple studies suggest that sepsis hospitalizations are also increasing in frequency.[3, 6, 7, 8, 9, 10]
Improved sepsis care has dramatically reduced in‐hospital mortality.[11, 12, 13] However, the result is a growing number of sepsis survivors discharged with new disability.[1, 9, 14, 15, 16] Despite being a common cause of hospitalization, little is known about how to improve postsepsis care.[15, 17, 18, 19] This contrasts with other, often less common, hospital conditions for which many studies evaluating readmission and postdischarge care are available.[20, 21, 22, 23] Identifying the factors contributing to high utilization could lend critical insight to designing interventions that improve long‐term sepsis outcomes.[24]
We conducted a retrospective study of sepsis patients discharged in 2010 at Kaiser Permanente Northern California (KPNC) to describe their posthospital trajectories. In this diverse community‐hospitalbased population, we sought to identify the patient‐level factors that impact the posthospital healthcare utilization of sepsis survivors.
METHODS
This study was approved by the KPNC institutional review board.
Setting
We conducted a retrospective study of sepsis patients aged 18 years admitted to KPNC hospitals in 2010 whose hospitalizations included an overnight stay, began in a KPNC hospital, and was not for peripartum care. We identified sepsis based on International Classification of Disease, 9th Edition principal diagnosis codes used at KPNC, which capture a similar population to that from the Angus definition (see Supporting Appendix, Table 1, in the online version of this article).[7, 25, 26] We denoted each patient's first sepsis hospitalization as the index event.
| Predicted Hospital Mortality Quartiles (n=1,586 for Each Group) | |||||
|---|---|---|---|---|---|
| Overall | 1 | 2 | 3 | 4 | |
| |||||
| Baseline | |||||
| Age, y, mean | 71.915.7 | 62.317.8 | 71.214.2 | 75.612.7 | 78.612.2 |
| By age category | |||||
| <45 years | 410 (6.5) | 290 (18.3) | 71 (4.5) | 25 (1.6) | 24 (1.5) |
| 4564 years | 1,425 (22.5) | 539 (34.0) | 407 (25.7) | 292 (18.4) | 187 (11.8) |
| 6584 years | 3,036 (47.9) | 601 (37.9) | 814 (51.3) | 832 (52.5) | 789 (49.8) |
| 85 years | 1,473 (23.2) | 156 (9.8) | 294 (18.5) | 437 (27.6) | 586 (37.0) |
| Male | 2,973 (46.9) | 686 (43.3) | 792 (49.9) | 750 (47.3) | 745 (47.0) |
| Comorbidity | |||||
| COPS2 score | 5143 | 2627 | 5441 | 6445 | 6245 |
| Charlson score | 2.01.5 | 1.31.2 | 2.11.4 | 2.41.5 | 2.41.5 |
| Hospitalization | |||||
| LAPS2 severity score | 10742 | 6621 | 9020 | 11423 | 15928 |
| Admitted via emergency department | 6,176 (97.4) | 1,522 (96.0) | 1,537 (96.9) | 1,539 (97.0) | 1,578 (99.5) |
| Direct ICU admission | 1,730 (27.3) | 169 (10.7) | 309 (19.5) | 482 (30.4) | 770 (48.6) |
| ICU transfer, at any time | 2,206 (34.8) | 279 (17.6) | 474 (29.9) | 603 (38.0) | 850 (53.6) |
| Hospital mortality | |||||
| Predicted, % | 10.513.8 | 1.00.1 | 3.40.1 | 8.32.3 | 29.415.8 |
| Observed | 865 (13.6) | 26 (1.6) | 86 (5.4) | 197 (12.4) | 556 (35.1) |
| Hospital length of stay, d | 5.86.4 | 4.43.8 | 5.45.7 | 6.68.0 | 6.66.9 |
We linked hospital episodes with existing KPNC inpatient databases to describe patient characteristics.[27, 28, 29, 30] We categorized patients by age (45, 4564, 6584, and 85 years) and used Charlson comorbidity scores and Comorbidity Point Scores 2 (COPS2) to quantify comorbid illness burden.[28, 30, 31, 32] We quantified acute severity of illness using the Laboratory Acute Physiology Scores 2 (LAPS2), which incorporates 15 laboratory values, 5 vital signs, and mental status prior to hospital admission (including emergency department data).[30] Both the COPS2 and LAPS2 are independently associated with hospital mortality.[30, 31] We also generated a summary predicted risk of hospital mortality based on a validated risk model and stratified patients by quartiles.[30] We determined whether patients were admitted to the intensive care unit (ICU).[29]
Outcomes
We used patients' health insurance administrative data to quantify postsepsis utilization. Within the KPNC integrated healthcare delivery system, uniform information systems capture all healthcare utilization of insured members including services received at non‐KPNC facilities.[28, 30] We collected utilization data from the year preceding index hospitalization (presepsis) and for the year after discharge date or until death (postsepsis). We ascertained mortality after discharge from KPNC medical records as well as state and national death record files.
We grouped services into facility‐based or outpatient categories. Facility‐based services included inpatient admission, subacute nursing facility or long‐term acute care, and emergency department visits. We grouped outpatient services as hospice, home health, outpatient surgery, clinic, or other (eg, laboratory). We excluded patients whose utilization records were not available over the full presepsis interval. Among these 1211 patients (12.5% of total), the median length of records prior to index hospitalization was 67 days, with a mean value of 117 days.
Statistical Analysis
Our primary outcomes of interest were hospital readmission and utilization in the year after sepsis. We defined a hospital readmission as any inpatient stay after the index hospitalization grouped within 1‐, 3‐, 6‐, and 12‐month intervals. We designated those within 30 days as an early readmission. We grouped readmission principal diagnoses, where available, by the 17 Healthcare Cost and Utilization Project (HCUP) Clinical Classifications Software multilevel categories with sepsis in the infectious category.[33, 34] In secondary analysis, we also designated other infectious diagnoses not included in the standard HCUP infection category (eg, pneumonia, meningitis, cellulitis) as infection (see Supporting Appendix in the online version of this article).
We quantified outpatient utilization based on the number of episodes recorded. For facility‐based utilization, we calculated patient length of stay intervals. Because patients surviving their index hospitalization might not survive the entire year after discharge, we also calculated utilization adjusted for patients' living days by dividing the total facility length of stay by the number of living days after discharge.
Continuous data are represented as mean (standard deviation [SD]) and categorical data as number (%). We compared groups with analysis of variance or 2 testing. We estimated survival with Kaplan‐Meier analysis (95% confidence interval) and compared groups with log‐rank testing. We compared pre‐ and postsepsis healthcare utilization with paired t tests.
To identify factors associated with early readmission after sepsis, we used a competing risks regression model.[35] The dependent variable was time to readmission and the competing hazard was death within 30 days without early readmission; patients without early readmission or death were censored at 30 days. The independent variables included age, gender, comorbid disease burden (COPS2), acute severity of illness (LAPS2), any use of intensive care, total index length of stay, and percentage of living days prior to sepsis hospitalization spent utilizing facility‐based care. We also used logistic regression to quantify the association between these variables and high postsepsis utilization; we defined high utilization as 15% of living days postsepsis spent in facility‐based care. For each model, we quantified the relative contribution of each predictor variable to model performance based on differences in log likelihoods.[35, 36] We conducted analyses using STATA/SE version 11.2 (StataCorp, College Station, TX) and considered a P value of <0.05 to be significant.
RESULTS
Cohort Characteristics
Our study cohort included 6344 patients with index sepsis hospitalizations in 2010 (Table 1). Mean age was 72 (SD 16) years including 1835 (28.9%) patients aged <65 years. During index hospitalizations, higher predicted mortality was associated with increased age, comorbid disease burden, and severity of illness (P<0.01 for each). ICU utilization increased across predicted mortality strata; for example, 10.7% of patients in the lowest quartile were admitted directly to the ICU compared with 48.6% in the highest quartile. In the highest quartile, observed mortality was 35.1%.
One‐Year Survival
A total of 5479 (86.4%) patients survived their index sepsis hospitalization. Overall survival after living discharge was 90.5% (range, 89.6%91.2%) at 30 days and 71.3% (range, 70.1%72.5%) at 1 year. However, postsepsis survival was strongly modified by age (Figure 1). For example, 1‐year survival was 94.1% (range, 91.2%96.0%) for <45 year olds and 54.4% (range, 51.5%57.2%) for 85 year olds (P<0.01). Survival was also modified by predicted mortality, however, not by ICU admission during index hospitalization (P=0.18) (see Supporting Appendix, Figure 1, in the online version of this article).
Hospital Readmission
Overall, 978 (17.9%) patients had early readmission after index discharge (Table 2); nearly half were readmitted at least once in the year following discharge. Rehospitalization frequency was slightly lower when including patients with incomplete presepsis data (see Supporting Appendix, Table 2, in the online version of this article). The frequency of hospital readmission varied based on patient age and severity of illness. For example, 22.3% of patients in the highest predicted mortality quartile had early readmission compared with 11.6% in the lowest. The median time from discharge to early readmission was 11 days. Principal diagnoses were available for 78.6% of all readmissions (see Supporting Appendix, Table 3, in the online version of this article). Between 28.3% and 42.7% of those readmissions were for infectious diagnoses (including sepsis).
| Predicted Mortality Quartile | |||||
|---|---|---|---|---|---|
| Readmission | Overall | 1 | 2 | 3 | 4 |
| Within 30 days | 978 (17.9) | 158 (11.6) | 242 (17.7) | 274 (20.0) | 304 (22.3) |
| Within 90 days | 1,643 (30.1) | 276 (20.2) | 421 (30.8) | 463 (33.9) | 483 (35.4) |
| Within 180 days | 2,061 (37.7) | 368 (26.9) | 540 (39.5) | 584 (42.7) | 569 (41.7) |
| Within 365 days | 2,618 (47.9) | 498 (36.4) | 712 (52.1) | 723 (52.9) | 685 (50.2) |
| Variable | Hazard Ratio for Early Readmission | Odds Ratio for High Utilization | ||
|---|---|---|---|---|
| HR (95% CI) | Relative Contribution | OR (95% CI) | Relative Contribution | |
| ||||
| Age category | 1.2% | 11.1% | ||
| <45 years | 1.00 [reference] | 1.00 [reference] | ||
| 4564 years | 0.86 (0.64‐1.16) | 2.22 (1.30‐3.83)a | ||
| 6584 years | 0.92 (0.69‐1.21) | 3.66 (2.17‐6.18)a | ||
| 85 years | 0.95 (0.70‐1.28) | 4.98 (2.92‐8.50)a | ||
| Male | 0.99 (0.88‐1.13) | 0.0% | 0.86 (0.74‐1.00) | 0.1% |
| Severity of illness (LAPS2) | 1.08 (1.04‐1.12)a | 12.4% | 1.22 (1.17‐1.27)a | 11.3% |
| Comorbid illness (COPS2) | 1.16 (1.12‐1.19)a | 73.9% | 1.13 (1.09‐1.17)a | 5.9% |
| Intensive care | 1.21 (1.05‐1.40)a | 5.2% | 1.02 (0.85‐1.21) | 0.0% |
| Hospital length of stay, day | 1.01 (1.001.02)b | 6.6% | 1.04 (1.03‐1.06)a | 6.9% |
| Prior utilization, per 10% | 0.98 (0.95‐1.02) | 0.7% | 1.74 (1.61‐1.88)a | 64.2% |
Healthcare Utilization
The unadjusted difference between pre‐ and postsepsis healthcare utilization among survivors was statistically significant for most categories but of modest clinical significance (see Supporting Appendix, Table 4, in the online version of this article). For example, the mean number of presepsis hospitalizations was 0.9 (1.4) compared to 1.0 (1.5) postsepsis (P<0.01). After adjusting for postsepsis living days, the difference in utilization was more pronounced (Figure 2). Overall, there was roughly a 3‐fold increase in the mean percentage of living days spent in facility‐based care between patients' pre‐ and postsepsis phases (5.3% vs 15.0%, P<0.01). Again, the difference was strongly modified by age. For patients aged <45 years, the difference was not statistically significant (2.4% vs 2.9%, P=0.32), whereas for those aged 65 years, it was highly significant (6.2% vs 18.5%, P<0.01).
Factors associated with early readmission included severity of illness, comorbid disease burden, index hospital length of stay, and intensive care (Table 3). However, the dominant factor explaining variation in the risk of early readmission was patients' prior comorbid disease burden (73.9%), followed by acute severity of illness (12.4%), total hospital length of stay (6.6%), and the need for intensive care (5.2%). Severity of illness and age were also significantly associated with higher odds of high postsepsis utilization; however, the dominant factor contributing to this risk was a history of high presepsis utilization (64.2%).
DISCUSSION
In this population‐based study in a community healthcare system, the impact of sepsis extended well beyond the initial hospitalization. One in 6 sepsis survivors was readmitted within 30 days, and roughly half were readmitted within 1 year. Fewer than half of rehospitalizations were for sepsis. Patients had a 3‐fold increase in the percentage of living days spent in hospitals or care facilities after sepsis hospitalization. Although age and acute severity of illness strongly modified healthcare utilization and mortality after sepsis, the dominant factors contributing to early readmission and high utilization ratescomorbid disease burden and presepsis healthcare utilizationwere present prior to hospitalization.
Sepsis is the single most expensive cause of US hospitalizations.[3, 4, 5] Despite its prevalence, there are little contemporary data identifying factors that impact healthcare utilization among sepsis survivors.[9, 16, 17, 19, 24, 36, 37] Recently, Prescott and others found that in Medicare beneficiaries, following severe sepsis, healthcare utilization was markedly increased.[17] More than one‐quarter of survivors were readmitted within 30 days, and 63.8% were readmitted within a year. Severe sepsis survivors also spent an average of 26% of their living days in a healthcare facility, a nearly 4‐fold increase compared to their presepsis phase. The current study included a population with a broader age and severity range; however, in a similar subgroup of patients, for those aged 65 years within the highest predicted mortality quartile, the frequency of readmission was similar. These findings are concordant with those from prior studies.[17, 19, 36, 37]
Among sepsis survivors, most readmissions were not for sepsis or infectious diagnoses, which is a novel finding with implications for designing approaches to reduce rehospitalization. The pattern in sepsis is similar to that seen in other common and costly hospital conditions.[17, 20, 23, 38, 39, 40] For example, between 18% and 25% of Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia were readmitted within 30 days; fewer than one‐third had the same diagnosis.[20] The timing of readmission in our sepsis cohort was also similar to that seen in other conditions.[20] For example, the median time of early readmission in this study was 11 days; it was between 10 and 12 days for patients with heart failure, pneumonia, and myocardial infarction.[20]
Krumholz and others suggest that the pattern of early rehospitalization after common acute conditions reflects a posthospital syndromean acquired, transient period of vulnerabilitythat could be the byproduct of common hospital factors.[20, 41] Such universal impairments might result from new physical and neurocognitive disability, nutritional deficiency, and sleep deprivation or delirium, among others.[41] If this construct were also true in sepsis, it could have important implications on the design of postsepsis care. However, prior studies suggest that sepsis patients may be particularly vulnerable to the sequelae of hospitalization.[2, 42, 43, 44, 45]
Among Medicare beneficiaries, Iwashyna and others reported that hospitalizations for severe sepsis resulted in significant increases in physical limitations and moderate to severe cognitive impairment.[1, 14, 46] Encephalopathy, sleep deprivation, and delirium are also frequently seen in sepsis patients.[47, 48] Furthermore, sepsis patients frequently need intensive care, which is also associated with increased patient disability and injury.[16, 46, 49, 50] We found that severity of illness and the need for intensive care were both predictive of the need for early readmission following sepsis. We also confirmed the results of prior studies suggesting that sepsis outcomes are strongly modified by age.[16, 19, 43, 51]
However, we found that the dominant factors contributing to patients' health trajectories were conditions present prior to admission. This finding is in accord with prior suggestions that acute severity of illness only partially predicts patients facing adverse posthospital sequelae.[23, 41, 52] Among sepsis patients, prior work demonstrates that inadequate consideration for presepsis level of function and utilization can result in an overestimation of the impact of sepsis on postdischarge health.[52, 53] Further, we found that the need for intensive care was not independently associated with an increased risk of high postsepsis utilization after adjusting for illness severity, a finding also seen in prior studies.[17, 23, 38, 51]
Taken together, our findings might suggest that an optimal approach to posthospital care in sepsis should focus on treatment approaches that address disease‐specific problems within the much larger context of common hospital risks. However, further study is necessary to clearly define the mechanisms by which age, severity of illness, and intensive care affect subsequent healthcare utilization. Furthermore, sepsis patients are a heterogeneous population in terms of severity of illness, site and pathogen of infection, and underlying comorbidity whose posthospital course remains incompletely characterized, limiting our ability to draw strong inferences.
These results should be interpreted in light of the study's limitations. First, our cohort included patients with healthcare insurance within a community‐based healthcare system. Care within the KPNC system, which bears similarities with accountable care organizations, is enhanced through service integration and a comprehensive health information system. Although prior studies suggest that these characteristics result in improved population‐based care, it is unclear whether there is a similar impact in hospital‐based conditions such as sepsis.[54, 55] Furthermore, care within an integrated system may impact posthospital utilization patterns and could limit generalizability. However, prior studies demonstrate the similarity of KPNC members to other patients in the same region in terms of age, socioeconomics, overall health behaviors, and racial/ethnic diversity.[56] Second, our study did not characterize organ dysfunction based on diagnosis coding, a common feature of sepsis studies that lack detailed physiologic severity data.[4, 5, 6, 8, 26] Instead, we focused on using granular laboratory and vital signs data to ensure accurate risk adjustment using a validated system developed in >400,000 hospitalizations.[30] Although this method may hamper comparisons with existing studies, traditional methods of grading severity by diagnosis codes can be vulnerable to biases resulting in wide variability.[10, 23, 26, 57, 58] Nonetheless, it is likely that characterizing preexisting and acute organ dysfunction will improve risk stratification in the heterogeneous sepsis population. Third, this study did not include data regarding patients' functional status, which has been shown to strongly predict patient outcomes following hospitalization. Fourth, this study did not address the cost of care following sepsis hospitalizations.[19, 59] Finally, our study excluded patients with incomplete utilization records, a choice designed to avoid the spurious inferences that can result from such comparisons.[53]
In summary, we found that sepsis exacted a considerable toll on patients in the hospital and in the year following discharge. Sepsis patients were frequently rehospitalized within a month of discharge, and on average had a 3‐fold increase in their subsequent time spent in healthcare facilities. Although age, severity of illness, and the need for ICU care impacted postsepsis utilization, the dominant contributing factorscomorbid disease burden or presepsis utilizationwere present prior to sepsis hospitalization. Early readmission patterns in sepsis appeared similar to those seen in other important hospital conditions, suggesting a role for shared posthospital, rather than just postsepsis, care approaches.
Disclosures
The funding for this study was provided by The Permanente Medical Group, Inc. and Kaiser Foundation Hospitals. The authors have no conflict of interests to disclose relevant to this article.
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Sepsis, the systemic inflammatory response to infection, is a major public health concern.[1] Worldwide, sepsis affects millions of hospitalized patients each year.[2] In the United States, it is the single most expensive cause of hospitalization.[3, 4, 5, 6] Multiple studies suggest that sepsis hospitalizations are also increasing in frequency.[3, 6, 7, 8, 9, 10]
Improved sepsis care has dramatically reduced in‐hospital mortality.[11, 12, 13] However, the result is a growing number of sepsis survivors discharged with new disability.[1, 9, 14, 15, 16] Despite being a common cause of hospitalization, little is known about how to improve postsepsis care.[15, 17, 18, 19] This contrasts with other, often less common, hospital conditions for which many studies evaluating readmission and postdischarge care are available.[20, 21, 22, 23] Identifying the factors contributing to high utilization could lend critical insight to designing interventions that improve long‐term sepsis outcomes.[24]
We conducted a retrospective study of sepsis patients discharged in 2010 at Kaiser Permanente Northern California (KPNC) to describe their posthospital trajectories. In this diverse community‐hospitalbased population, we sought to identify the patient‐level factors that impact the posthospital healthcare utilization of sepsis survivors.
METHODS
This study was approved by the KPNC institutional review board.
Setting
We conducted a retrospective study of sepsis patients aged 18 years admitted to KPNC hospitals in 2010 whose hospitalizations included an overnight stay, began in a KPNC hospital, and was not for peripartum care. We identified sepsis based on International Classification of Disease, 9th Edition principal diagnosis codes used at KPNC, which capture a similar population to that from the Angus definition (see Supporting Appendix, Table 1, in the online version of this article).[7, 25, 26] We denoted each patient's first sepsis hospitalization as the index event.
| Predicted Hospital Mortality Quartiles (n=1,586 for Each Group) | |||||
|---|---|---|---|---|---|
| Overall | 1 | 2 | 3 | 4 | |
| |||||
| Baseline | |||||
| Age, y, mean | 71.915.7 | 62.317.8 | 71.214.2 | 75.612.7 | 78.612.2 |
| By age category | |||||
| <45 years | 410 (6.5) | 290 (18.3) | 71 (4.5) | 25 (1.6) | 24 (1.5) |
| 4564 years | 1,425 (22.5) | 539 (34.0) | 407 (25.7) | 292 (18.4) | 187 (11.8) |
| 6584 years | 3,036 (47.9) | 601 (37.9) | 814 (51.3) | 832 (52.5) | 789 (49.8) |
| 85 years | 1,473 (23.2) | 156 (9.8) | 294 (18.5) | 437 (27.6) | 586 (37.0) |
| Male | 2,973 (46.9) | 686 (43.3) | 792 (49.9) | 750 (47.3) | 745 (47.0) |
| Comorbidity | |||||
| COPS2 score | 5143 | 2627 | 5441 | 6445 | 6245 |
| Charlson score | 2.01.5 | 1.31.2 | 2.11.4 | 2.41.5 | 2.41.5 |
| Hospitalization | |||||
| LAPS2 severity score | 10742 | 6621 | 9020 | 11423 | 15928 |
| Admitted via emergency department | 6,176 (97.4) | 1,522 (96.0) | 1,537 (96.9) | 1,539 (97.0) | 1,578 (99.5) |
| Direct ICU admission | 1,730 (27.3) | 169 (10.7) | 309 (19.5) | 482 (30.4) | 770 (48.6) |
| ICU transfer, at any time | 2,206 (34.8) | 279 (17.6) | 474 (29.9) | 603 (38.0) | 850 (53.6) |
| Hospital mortality | |||||
| Predicted, % | 10.513.8 | 1.00.1 | 3.40.1 | 8.32.3 | 29.415.8 |
| Observed | 865 (13.6) | 26 (1.6) | 86 (5.4) | 197 (12.4) | 556 (35.1) |
| Hospital length of stay, d | 5.86.4 | 4.43.8 | 5.45.7 | 6.68.0 | 6.66.9 |
We linked hospital episodes with existing KPNC inpatient databases to describe patient characteristics.[27, 28, 29, 30] We categorized patients by age (45, 4564, 6584, and 85 years) and used Charlson comorbidity scores and Comorbidity Point Scores 2 (COPS2) to quantify comorbid illness burden.[28, 30, 31, 32] We quantified acute severity of illness using the Laboratory Acute Physiology Scores 2 (LAPS2), which incorporates 15 laboratory values, 5 vital signs, and mental status prior to hospital admission (including emergency department data).[30] Both the COPS2 and LAPS2 are independently associated with hospital mortality.[30, 31] We also generated a summary predicted risk of hospital mortality based on a validated risk model and stratified patients by quartiles.[30] We determined whether patients were admitted to the intensive care unit (ICU).[29]
Outcomes
We used patients' health insurance administrative data to quantify postsepsis utilization. Within the KPNC integrated healthcare delivery system, uniform information systems capture all healthcare utilization of insured members including services received at non‐KPNC facilities.[28, 30] We collected utilization data from the year preceding index hospitalization (presepsis) and for the year after discharge date or until death (postsepsis). We ascertained mortality after discharge from KPNC medical records as well as state and national death record files.
We grouped services into facility‐based or outpatient categories. Facility‐based services included inpatient admission, subacute nursing facility or long‐term acute care, and emergency department visits. We grouped outpatient services as hospice, home health, outpatient surgery, clinic, or other (eg, laboratory). We excluded patients whose utilization records were not available over the full presepsis interval. Among these 1211 patients (12.5% of total), the median length of records prior to index hospitalization was 67 days, with a mean value of 117 days.
Statistical Analysis
Our primary outcomes of interest were hospital readmission and utilization in the year after sepsis. We defined a hospital readmission as any inpatient stay after the index hospitalization grouped within 1‐, 3‐, 6‐, and 12‐month intervals. We designated those within 30 days as an early readmission. We grouped readmission principal diagnoses, where available, by the 17 Healthcare Cost and Utilization Project (HCUP) Clinical Classifications Software multilevel categories with sepsis in the infectious category.[33, 34] In secondary analysis, we also designated other infectious diagnoses not included in the standard HCUP infection category (eg, pneumonia, meningitis, cellulitis) as infection (see Supporting Appendix in the online version of this article).
We quantified outpatient utilization based on the number of episodes recorded. For facility‐based utilization, we calculated patient length of stay intervals. Because patients surviving their index hospitalization might not survive the entire year after discharge, we also calculated utilization adjusted for patients' living days by dividing the total facility length of stay by the number of living days after discharge.
Continuous data are represented as mean (standard deviation [SD]) and categorical data as number (%). We compared groups with analysis of variance or 2 testing. We estimated survival with Kaplan‐Meier analysis (95% confidence interval) and compared groups with log‐rank testing. We compared pre‐ and postsepsis healthcare utilization with paired t tests.
To identify factors associated with early readmission after sepsis, we used a competing risks regression model.[35] The dependent variable was time to readmission and the competing hazard was death within 30 days without early readmission; patients without early readmission or death were censored at 30 days. The independent variables included age, gender, comorbid disease burden (COPS2), acute severity of illness (LAPS2), any use of intensive care, total index length of stay, and percentage of living days prior to sepsis hospitalization spent utilizing facility‐based care. We also used logistic regression to quantify the association between these variables and high postsepsis utilization; we defined high utilization as 15% of living days postsepsis spent in facility‐based care. For each model, we quantified the relative contribution of each predictor variable to model performance based on differences in log likelihoods.[35, 36] We conducted analyses using STATA/SE version 11.2 (StataCorp, College Station, TX) and considered a P value of <0.05 to be significant.
RESULTS
Cohort Characteristics
Our study cohort included 6344 patients with index sepsis hospitalizations in 2010 (Table 1). Mean age was 72 (SD 16) years including 1835 (28.9%) patients aged <65 years. During index hospitalizations, higher predicted mortality was associated with increased age, comorbid disease burden, and severity of illness (P<0.01 for each). ICU utilization increased across predicted mortality strata; for example, 10.7% of patients in the lowest quartile were admitted directly to the ICU compared with 48.6% in the highest quartile. In the highest quartile, observed mortality was 35.1%.
One‐Year Survival
A total of 5479 (86.4%) patients survived their index sepsis hospitalization. Overall survival after living discharge was 90.5% (range, 89.6%91.2%) at 30 days and 71.3% (range, 70.1%72.5%) at 1 year. However, postsepsis survival was strongly modified by age (Figure 1). For example, 1‐year survival was 94.1% (range, 91.2%96.0%) for <45 year olds and 54.4% (range, 51.5%57.2%) for 85 year olds (P<0.01). Survival was also modified by predicted mortality, however, not by ICU admission during index hospitalization (P=0.18) (see Supporting Appendix, Figure 1, in the online version of this article).
Hospital Readmission
Overall, 978 (17.9%) patients had early readmission after index discharge (Table 2); nearly half were readmitted at least once in the year following discharge. Rehospitalization frequency was slightly lower when including patients with incomplete presepsis data (see Supporting Appendix, Table 2, in the online version of this article). The frequency of hospital readmission varied based on patient age and severity of illness. For example, 22.3% of patients in the highest predicted mortality quartile had early readmission compared with 11.6% in the lowest. The median time from discharge to early readmission was 11 days. Principal diagnoses were available for 78.6% of all readmissions (see Supporting Appendix, Table 3, in the online version of this article). Between 28.3% and 42.7% of those readmissions were for infectious diagnoses (including sepsis).
| Predicted Mortality Quartile | |||||
|---|---|---|---|---|---|
| Readmission | Overall | 1 | 2 | 3 | 4 |
| Within 30 days | 978 (17.9) | 158 (11.6) | 242 (17.7) | 274 (20.0) | 304 (22.3) |
| Within 90 days | 1,643 (30.1) | 276 (20.2) | 421 (30.8) | 463 (33.9) | 483 (35.4) |
| Within 180 days | 2,061 (37.7) | 368 (26.9) | 540 (39.5) | 584 (42.7) | 569 (41.7) |
| Within 365 days | 2,618 (47.9) | 498 (36.4) | 712 (52.1) | 723 (52.9) | 685 (50.2) |
| Variable | Hazard Ratio for Early Readmission | Odds Ratio for High Utilization | ||
|---|---|---|---|---|
| HR (95% CI) | Relative Contribution | OR (95% CI) | Relative Contribution | |
| ||||
| Age category | 1.2% | 11.1% | ||
| <45 years | 1.00 [reference] | 1.00 [reference] | ||
| 4564 years | 0.86 (0.64‐1.16) | 2.22 (1.30‐3.83)a | ||
| 6584 years | 0.92 (0.69‐1.21) | 3.66 (2.17‐6.18)a | ||
| 85 years | 0.95 (0.70‐1.28) | 4.98 (2.92‐8.50)a | ||
| Male | 0.99 (0.88‐1.13) | 0.0% | 0.86 (0.74‐1.00) | 0.1% |
| Severity of illness (LAPS2) | 1.08 (1.04‐1.12)a | 12.4% | 1.22 (1.17‐1.27)a | 11.3% |
| Comorbid illness (COPS2) | 1.16 (1.12‐1.19)a | 73.9% | 1.13 (1.09‐1.17)a | 5.9% |
| Intensive care | 1.21 (1.05‐1.40)a | 5.2% | 1.02 (0.85‐1.21) | 0.0% |
| Hospital length of stay, day | 1.01 (1.001.02)b | 6.6% | 1.04 (1.03‐1.06)a | 6.9% |
| Prior utilization, per 10% | 0.98 (0.95‐1.02) | 0.7% | 1.74 (1.61‐1.88)a | 64.2% |
Healthcare Utilization
The unadjusted difference between pre‐ and postsepsis healthcare utilization among survivors was statistically significant for most categories but of modest clinical significance (see Supporting Appendix, Table 4, in the online version of this article). For example, the mean number of presepsis hospitalizations was 0.9 (1.4) compared to 1.0 (1.5) postsepsis (P<0.01). After adjusting for postsepsis living days, the difference in utilization was more pronounced (Figure 2). Overall, there was roughly a 3‐fold increase in the mean percentage of living days spent in facility‐based care between patients' pre‐ and postsepsis phases (5.3% vs 15.0%, P<0.01). Again, the difference was strongly modified by age. For patients aged <45 years, the difference was not statistically significant (2.4% vs 2.9%, P=0.32), whereas for those aged 65 years, it was highly significant (6.2% vs 18.5%, P<0.01).
Factors associated with early readmission included severity of illness, comorbid disease burden, index hospital length of stay, and intensive care (Table 3). However, the dominant factor explaining variation in the risk of early readmission was patients' prior comorbid disease burden (73.9%), followed by acute severity of illness (12.4%), total hospital length of stay (6.6%), and the need for intensive care (5.2%). Severity of illness and age were also significantly associated with higher odds of high postsepsis utilization; however, the dominant factor contributing to this risk was a history of high presepsis utilization (64.2%).
DISCUSSION
In this population‐based study in a community healthcare system, the impact of sepsis extended well beyond the initial hospitalization. One in 6 sepsis survivors was readmitted within 30 days, and roughly half were readmitted within 1 year. Fewer than half of rehospitalizations were for sepsis. Patients had a 3‐fold increase in the percentage of living days spent in hospitals or care facilities after sepsis hospitalization. Although age and acute severity of illness strongly modified healthcare utilization and mortality after sepsis, the dominant factors contributing to early readmission and high utilization ratescomorbid disease burden and presepsis healthcare utilizationwere present prior to hospitalization.
Sepsis is the single most expensive cause of US hospitalizations.[3, 4, 5] Despite its prevalence, there are little contemporary data identifying factors that impact healthcare utilization among sepsis survivors.[9, 16, 17, 19, 24, 36, 37] Recently, Prescott and others found that in Medicare beneficiaries, following severe sepsis, healthcare utilization was markedly increased.[17] More than one‐quarter of survivors were readmitted within 30 days, and 63.8% were readmitted within a year. Severe sepsis survivors also spent an average of 26% of their living days in a healthcare facility, a nearly 4‐fold increase compared to their presepsis phase. The current study included a population with a broader age and severity range; however, in a similar subgroup of patients, for those aged 65 years within the highest predicted mortality quartile, the frequency of readmission was similar. These findings are concordant with those from prior studies.[17, 19, 36, 37]
Among sepsis survivors, most readmissions were not for sepsis or infectious diagnoses, which is a novel finding with implications for designing approaches to reduce rehospitalization. The pattern in sepsis is similar to that seen in other common and costly hospital conditions.[17, 20, 23, 38, 39, 40] For example, between 18% and 25% of Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia were readmitted within 30 days; fewer than one‐third had the same diagnosis.[20] The timing of readmission in our sepsis cohort was also similar to that seen in other conditions.[20] For example, the median time of early readmission in this study was 11 days; it was between 10 and 12 days for patients with heart failure, pneumonia, and myocardial infarction.[20]
Krumholz and others suggest that the pattern of early rehospitalization after common acute conditions reflects a posthospital syndromean acquired, transient period of vulnerabilitythat could be the byproduct of common hospital factors.[20, 41] Such universal impairments might result from new physical and neurocognitive disability, nutritional deficiency, and sleep deprivation or delirium, among others.[41] If this construct were also true in sepsis, it could have important implications on the design of postsepsis care. However, prior studies suggest that sepsis patients may be particularly vulnerable to the sequelae of hospitalization.[2, 42, 43, 44, 45]
Among Medicare beneficiaries, Iwashyna and others reported that hospitalizations for severe sepsis resulted in significant increases in physical limitations and moderate to severe cognitive impairment.[1, 14, 46] Encephalopathy, sleep deprivation, and delirium are also frequently seen in sepsis patients.[47, 48] Furthermore, sepsis patients frequently need intensive care, which is also associated with increased patient disability and injury.[16, 46, 49, 50] We found that severity of illness and the need for intensive care were both predictive of the need for early readmission following sepsis. We also confirmed the results of prior studies suggesting that sepsis outcomes are strongly modified by age.[16, 19, 43, 51]
However, we found that the dominant factors contributing to patients' health trajectories were conditions present prior to admission. This finding is in accord with prior suggestions that acute severity of illness only partially predicts patients facing adverse posthospital sequelae.[23, 41, 52] Among sepsis patients, prior work demonstrates that inadequate consideration for presepsis level of function and utilization can result in an overestimation of the impact of sepsis on postdischarge health.[52, 53] Further, we found that the need for intensive care was not independently associated with an increased risk of high postsepsis utilization after adjusting for illness severity, a finding also seen in prior studies.[17, 23, 38, 51]
Taken together, our findings might suggest that an optimal approach to posthospital care in sepsis should focus on treatment approaches that address disease‐specific problems within the much larger context of common hospital risks. However, further study is necessary to clearly define the mechanisms by which age, severity of illness, and intensive care affect subsequent healthcare utilization. Furthermore, sepsis patients are a heterogeneous population in terms of severity of illness, site and pathogen of infection, and underlying comorbidity whose posthospital course remains incompletely characterized, limiting our ability to draw strong inferences.
These results should be interpreted in light of the study's limitations. First, our cohort included patients with healthcare insurance within a community‐based healthcare system. Care within the KPNC system, which bears similarities with accountable care organizations, is enhanced through service integration and a comprehensive health information system. Although prior studies suggest that these characteristics result in improved population‐based care, it is unclear whether there is a similar impact in hospital‐based conditions such as sepsis.[54, 55] Furthermore, care within an integrated system may impact posthospital utilization patterns and could limit generalizability. However, prior studies demonstrate the similarity of KPNC members to other patients in the same region in terms of age, socioeconomics, overall health behaviors, and racial/ethnic diversity.[56] Second, our study did not characterize organ dysfunction based on diagnosis coding, a common feature of sepsis studies that lack detailed physiologic severity data.[4, 5, 6, 8, 26] Instead, we focused on using granular laboratory and vital signs data to ensure accurate risk adjustment using a validated system developed in >400,000 hospitalizations.[30] Although this method may hamper comparisons with existing studies, traditional methods of grading severity by diagnosis codes can be vulnerable to biases resulting in wide variability.[10, 23, 26, 57, 58] Nonetheless, it is likely that characterizing preexisting and acute organ dysfunction will improve risk stratification in the heterogeneous sepsis population. Third, this study did not include data regarding patients' functional status, which has been shown to strongly predict patient outcomes following hospitalization. Fourth, this study did not address the cost of care following sepsis hospitalizations.[19, 59] Finally, our study excluded patients with incomplete utilization records, a choice designed to avoid the spurious inferences that can result from such comparisons.[53]
In summary, we found that sepsis exacted a considerable toll on patients in the hospital and in the year following discharge. Sepsis patients were frequently rehospitalized within a month of discharge, and on average had a 3‐fold increase in their subsequent time spent in healthcare facilities. Although age, severity of illness, and the need for ICU care impacted postsepsis utilization, the dominant contributing factorscomorbid disease burden or presepsis utilizationwere present prior to sepsis hospitalization. Early readmission patterns in sepsis appeared similar to those seen in other important hospital conditions, suggesting a role for shared posthospital, rather than just postsepsis, care approaches.
Disclosures
The funding for this study was provided by The Permanente Medical Group, Inc. and Kaiser Foundation Hospitals. The authors have no conflict of interests to disclose relevant to this article.
Sepsis, the systemic inflammatory response to infection, is a major public health concern.[1] Worldwide, sepsis affects millions of hospitalized patients each year.[2] In the United States, it is the single most expensive cause of hospitalization.[3, 4, 5, 6] Multiple studies suggest that sepsis hospitalizations are also increasing in frequency.[3, 6, 7, 8, 9, 10]
Improved sepsis care has dramatically reduced in‐hospital mortality.[11, 12, 13] However, the result is a growing number of sepsis survivors discharged with new disability.[1, 9, 14, 15, 16] Despite being a common cause of hospitalization, little is known about how to improve postsepsis care.[15, 17, 18, 19] This contrasts with other, often less common, hospital conditions for which many studies evaluating readmission and postdischarge care are available.[20, 21, 22, 23] Identifying the factors contributing to high utilization could lend critical insight to designing interventions that improve long‐term sepsis outcomes.[24]
We conducted a retrospective study of sepsis patients discharged in 2010 at Kaiser Permanente Northern California (KPNC) to describe their posthospital trajectories. In this diverse community‐hospitalbased population, we sought to identify the patient‐level factors that impact the posthospital healthcare utilization of sepsis survivors.
METHODS
This study was approved by the KPNC institutional review board.
Setting
We conducted a retrospective study of sepsis patients aged 18 years admitted to KPNC hospitals in 2010 whose hospitalizations included an overnight stay, began in a KPNC hospital, and was not for peripartum care. We identified sepsis based on International Classification of Disease, 9th Edition principal diagnosis codes used at KPNC, which capture a similar population to that from the Angus definition (see Supporting Appendix, Table 1, in the online version of this article).[7, 25, 26] We denoted each patient's first sepsis hospitalization as the index event.
| Predicted Hospital Mortality Quartiles (n=1,586 for Each Group) | |||||
|---|---|---|---|---|---|
| Overall | 1 | 2 | 3 | 4 | |
| |||||
| Baseline | |||||
| Age, y, mean | 71.915.7 | 62.317.8 | 71.214.2 | 75.612.7 | 78.612.2 |
| By age category | |||||
| <45 years | 410 (6.5) | 290 (18.3) | 71 (4.5) | 25 (1.6) | 24 (1.5) |
| 4564 years | 1,425 (22.5) | 539 (34.0) | 407 (25.7) | 292 (18.4) | 187 (11.8) |
| 6584 years | 3,036 (47.9) | 601 (37.9) | 814 (51.3) | 832 (52.5) | 789 (49.8) |
| 85 years | 1,473 (23.2) | 156 (9.8) | 294 (18.5) | 437 (27.6) | 586 (37.0) |
| Male | 2,973 (46.9) | 686 (43.3) | 792 (49.9) | 750 (47.3) | 745 (47.0) |
| Comorbidity | |||||
| COPS2 score | 5143 | 2627 | 5441 | 6445 | 6245 |
| Charlson score | 2.01.5 | 1.31.2 | 2.11.4 | 2.41.5 | 2.41.5 |
| Hospitalization | |||||
| LAPS2 severity score | 10742 | 6621 | 9020 | 11423 | 15928 |
| Admitted via emergency department | 6,176 (97.4) | 1,522 (96.0) | 1,537 (96.9) | 1,539 (97.0) | 1,578 (99.5) |
| Direct ICU admission | 1,730 (27.3) | 169 (10.7) | 309 (19.5) | 482 (30.4) | 770 (48.6) |
| ICU transfer, at any time | 2,206 (34.8) | 279 (17.6) | 474 (29.9) | 603 (38.0) | 850 (53.6) |
| Hospital mortality | |||||
| Predicted, % | 10.513.8 | 1.00.1 | 3.40.1 | 8.32.3 | 29.415.8 |
| Observed | 865 (13.6) | 26 (1.6) | 86 (5.4) | 197 (12.4) | 556 (35.1) |
| Hospital length of stay, d | 5.86.4 | 4.43.8 | 5.45.7 | 6.68.0 | 6.66.9 |
We linked hospital episodes with existing KPNC inpatient databases to describe patient characteristics.[27, 28, 29, 30] We categorized patients by age (45, 4564, 6584, and 85 years) and used Charlson comorbidity scores and Comorbidity Point Scores 2 (COPS2) to quantify comorbid illness burden.[28, 30, 31, 32] We quantified acute severity of illness using the Laboratory Acute Physiology Scores 2 (LAPS2), which incorporates 15 laboratory values, 5 vital signs, and mental status prior to hospital admission (including emergency department data).[30] Both the COPS2 and LAPS2 are independently associated with hospital mortality.[30, 31] We also generated a summary predicted risk of hospital mortality based on a validated risk model and stratified patients by quartiles.[30] We determined whether patients were admitted to the intensive care unit (ICU).[29]
Outcomes
We used patients' health insurance administrative data to quantify postsepsis utilization. Within the KPNC integrated healthcare delivery system, uniform information systems capture all healthcare utilization of insured members including services received at non‐KPNC facilities.[28, 30] We collected utilization data from the year preceding index hospitalization (presepsis) and for the year after discharge date or until death (postsepsis). We ascertained mortality after discharge from KPNC medical records as well as state and national death record files.
We grouped services into facility‐based or outpatient categories. Facility‐based services included inpatient admission, subacute nursing facility or long‐term acute care, and emergency department visits. We grouped outpatient services as hospice, home health, outpatient surgery, clinic, or other (eg, laboratory). We excluded patients whose utilization records were not available over the full presepsis interval. Among these 1211 patients (12.5% of total), the median length of records prior to index hospitalization was 67 days, with a mean value of 117 days.
Statistical Analysis
Our primary outcomes of interest were hospital readmission and utilization in the year after sepsis. We defined a hospital readmission as any inpatient stay after the index hospitalization grouped within 1‐, 3‐, 6‐, and 12‐month intervals. We designated those within 30 days as an early readmission. We grouped readmission principal diagnoses, where available, by the 17 Healthcare Cost and Utilization Project (HCUP) Clinical Classifications Software multilevel categories with sepsis in the infectious category.[33, 34] In secondary analysis, we also designated other infectious diagnoses not included in the standard HCUP infection category (eg, pneumonia, meningitis, cellulitis) as infection (see Supporting Appendix in the online version of this article).
We quantified outpatient utilization based on the number of episodes recorded. For facility‐based utilization, we calculated patient length of stay intervals. Because patients surviving their index hospitalization might not survive the entire year after discharge, we also calculated utilization adjusted for patients' living days by dividing the total facility length of stay by the number of living days after discharge.
Continuous data are represented as mean (standard deviation [SD]) and categorical data as number (%). We compared groups with analysis of variance or 2 testing. We estimated survival with Kaplan‐Meier analysis (95% confidence interval) and compared groups with log‐rank testing. We compared pre‐ and postsepsis healthcare utilization with paired t tests.
To identify factors associated with early readmission after sepsis, we used a competing risks regression model.[35] The dependent variable was time to readmission and the competing hazard was death within 30 days without early readmission; patients without early readmission or death were censored at 30 days. The independent variables included age, gender, comorbid disease burden (COPS2), acute severity of illness (LAPS2), any use of intensive care, total index length of stay, and percentage of living days prior to sepsis hospitalization spent utilizing facility‐based care. We also used logistic regression to quantify the association between these variables and high postsepsis utilization; we defined high utilization as 15% of living days postsepsis spent in facility‐based care. For each model, we quantified the relative contribution of each predictor variable to model performance based on differences in log likelihoods.[35, 36] We conducted analyses using STATA/SE version 11.2 (StataCorp, College Station, TX) and considered a P value of <0.05 to be significant.
RESULTS
Cohort Characteristics
Our study cohort included 6344 patients with index sepsis hospitalizations in 2010 (Table 1). Mean age was 72 (SD 16) years including 1835 (28.9%) patients aged <65 years. During index hospitalizations, higher predicted mortality was associated with increased age, comorbid disease burden, and severity of illness (P<0.01 for each). ICU utilization increased across predicted mortality strata; for example, 10.7% of patients in the lowest quartile were admitted directly to the ICU compared with 48.6% in the highest quartile. In the highest quartile, observed mortality was 35.1%.
One‐Year Survival
A total of 5479 (86.4%) patients survived their index sepsis hospitalization. Overall survival after living discharge was 90.5% (range, 89.6%91.2%) at 30 days and 71.3% (range, 70.1%72.5%) at 1 year. However, postsepsis survival was strongly modified by age (Figure 1). For example, 1‐year survival was 94.1% (range, 91.2%96.0%) for <45 year olds and 54.4% (range, 51.5%57.2%) for 85 year olds (P<0.01). Survival was also modified by predicted mortality, however, not by ICU admission during index hospitalization (P=0.18) (see Supporting Appendix, Figure 1, in the online version of this article).
Hospital Readmission
Overall, 978 (17.9%) patients had early readmission after index discharge (Table 2); nearly half were readmitted at least once in the year following discharge. Rehospitalization frequency was slightly lower when including patients with incomplete presepsis data (see Supporting Appendix, Table 2, in the online version of this article). The frequency of hospital readmission varied based on patient age and severity of illness. For example, 22.3% of patients in the highest predicted mortality quartile had early readmission compared with 11.6% in the lowest. The median time from discharge to early readmission was 11 days. Principal diagnoses were available for 78.6% of all readmissions (see Supporting Appendix, Table 3, in the online version of this article). Between 28.3% and 42.7% of those readmissions were for infectious diagnoses (including sepsis).
| Predicted Mortality Quartile | |||||
|---|---|---|---|---|---|
| Readmission | Overall | 1 | 2 | 3 | 4 |
| Within 30 days | 978 (17.9) | 158 (11.6) | 242 (17.7) | 274 (20.0) | 304 (22.3) |
| Within 90 days | 1,643 (30.1) | 276 (20.2) | 421 (30.8) | 463 (33.9) | 483 (35.4) |
| Within 180 days | 2,061 (37.7) | 368 (26.9) | 540 (39.5) | 584 (42.7) | 569 (41.7) |
| Within 365 days | 2,618 (47.9) | 498 (36.4) | 712 (52.1) | 723 (52.9) | 685 (50.2) |
| Variable | Hazard Ratio for Early Readmission | Odds Ratio for High Utilization | ||
|---|---|---|---|---|
| HR (95% CI) | Relative Contribution | OR (95% CI) | Relative Contribution | |
| ||||
| Age category | 1.2% | 11.1% | ||
| <45 years | 1.00 [reference] | 1.00 [reference] | ||
| 4564 years | 0.86 (0.64‐1.16) | 2.22 (1.30‐3.83)a | ||
| 6584 years | 0.92 (0.69‐1.21) | 3.66 (2.17‐6.18)a | ||
| 85 years | 0.95 (0.70‐1.28) | 4.98 (2.92‐8.50)a | ||
| Male | 0.99 (0.88‐1.13) | 0.0% | 0.86 (0.74‐1.00) | 0.1% |
| Severity of illness (LAPS2) | 1.08 (1.04‐1.12)a | 12.4% | 1.22 (1.17‐1.27)a | 11.3% |
| Comorbid illness (COPS2) | 1.16 (1.12‐1.19)a | 73.9% | 1.13 (1.09‐1.17)a | 5.9% |
| Intensive care | 1.21 (1.05‐1.40)a | 5.2% | 1.02 (0.85‐1.21) | 0.0% |
| Hospital length of stay, day | 1.01 (1.001.02)b | 6.6% | 1.04 (1.03‐1.06)a | 6.9% |
| Prior utilization, per 10% | 0.98 (0.95‐1.02) | 0.7% | 1.74 (1.61‐1.88)a | 64.2% |
Healthcare Utilization
The unadjusted difference between pre‐ and postsepsis healthcare utilization among survivors was statistically significant for most categories but of modest clinical significance (see Supporting Appendix, Table 4, in the online version of this article). For example, the mean number of presepsis hospitalizations was 0.9 (1.4) compared to 1.0 (1.5) postsepsis (P<0.01). After adjusting for postsepsis living days, the difference in utilization was more pronounced (Figure 2). Overall, there was roughly a 3‐fold increase in the mean percentage of living days spent in facility‐based care between patients' pre‐ and postsepsis phases (5.3% vs 15.0%, P<0.01). Again, the difference was strongly modified by age. For patients aged <45 years, the difference was not statistically significant (2.4% vs 2.9%, P=0.32), whereas for those aged 65 years, it was highly significant (6.2% vs 18.5%, P<0.01).
Factors associated with early readmission included severity of illness, comorbid disease burden, index hospital length of stay, and intensive care (Table 3). However, the dominant factor explaining variation in the risk of early readmission was patients' prior comorbid disease burden (73.9%), followed by acute severity of illness (12.4%), total hospital length of stay (6.6%), and the need for intensive care (5.2%). Severity of illness and age were also significantly associated with higher odds of high postsepsis utilization; however, the dominant factor contributing to this risk was a history of high presepsis utilization (64.2%).
DISCUSSION
In this population‐based study in a community healthcare system, the impact of sepsis extended well beyond the initial hospitalization. One in 6 sepsis survivors was readmitted within 30 days, and roughly half were readmitted within 1 year. Fewer than half of rehospitalizations were for sepsis. Patients had a 3‐fold increase in the percentage of living days spent in hospitals or care facilities after sepsis hospitalization. Although age and acute severity of illness strongly modified healthcare utilization and mortality after sepsis, the dominant factors contributing to early readmission and high utilization ratescomorbid disease burden and presepsis healthcare utilizationwere present prior to hospitalization.
Sepsis is the single most expensive cause of US hospitalizations.[3, 4, 5] Despite its prevalence, there are little contemporary data identifying factors that impact healthcare utilization among sepsis survivors.[9, 16, 17, 19, 24, 36, 37] Recently, Prescott and others found that in Medicare beneficiaries, following severe sepsis, healthcare utilization was markedly increased.[17] More than one‐quarter of survivors were readmitted within 30 days, and 63.8% were readmitted within a year. Severe sepsis survivors also spent an average of 26% of their living days in a healthcare facility, a nearly 4‐fold increase compared to their presepsis phase. The current study included a population with a broader age and severity range; however, in a similar subgroup of patients, for those aged 65 years within the highest predicted mortality quartile, the frequency of readmission was similar. These findings are concordant with those from prior studies.[17, 19, 36, 37]
Among sepsis survivors, most readmissions were not for sepsis or infectious diagnoses, which is a novel finding with implications for designing approaches to reduce rehospitalization. The pattern in sepsis is similar to that seen in other common and costly hospital conditions.[17, 20, 23, 38, 39, 40] For example, between 18% and 25% of Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia were readmitted within 30 days; fewer than one‐third had the same diagnosis.[20] The timing of readmission in our sepsis cohort was also similar to that seen in other conditions.[20] For example, the median time of early readmission in this study was 11 days; it was between 10 and 12 days for patients with heart failure, pneumonia, and myocardial infarction.[20]
Krumholz and others suggest that the pattern of early rehospitalization after common acute conditions reflects a posthospital syndromean acquired, transient period of vulnerabilitythat could be the byproduct of common hospital factors.[20, 41] Such universal impairments might result from new physical and neurocognitive disability, nutritional deficiency, and sleep deprivation or delirium, among others.[41] If this construct were also true in sepsis, it could have important implications on the design of postsepsis care. However, prior studies suggest that sepsis patients may be particularly vulnerable to the sequelae of hospitalization.[2, 42, 43, 44, 45]
Among Medicare beneficiaries, Iwashyna and others reported that hospitalizations for severe sepsis resulted in significant increases in physical limitations and moderate to severe cognitive impairment.[1, 14, 46] Encephalopathy, sleep deprivation, and delirium are also frequently seen in sepsis patients.[47, 48] Furthermore, sepsis patients frequently need intensive care, which is also associated with increased patient disability and injury.[16, 46, 49, 50] We found that severity of illness and the need for intensive care were both predictive of the need for early readmission following sepsis. We also confirmed the results of prior studies suggesting that sepsis outcomes are strongly modified by age.[16, 19, 43, 51]
However, we found that the dominant factors contributing to patients' health trajectories were conditions present prior to admission. This finding is in accord with prior suggestions that acute severity of illness only partially predicts patients facing adverse posthospital sequelae.[23, 41, 52] Among sepsis patients, prior work demonstrates that inadequate consideration for presepsis level of function and utilization can result in an overestimation of the impact of sepsis on postdischarge health.[52, 53] Further, we found that the need for intensive care was not independently associated with an increased risk of high postsepsis utilization after adjusting for illness severity, a finding also seen in prior studies.[17, 23, 38, 51]
Taken together, our findings might suggest that an optimal approach to posthospital care in sepsis should focus on treatment approaches that address disease‐specific problems within the much larger context of common hospital risks. However, further study is necessary to clearly define the mechanisms by which age, severity of illness, and intensive care affect subsequent healthcare utilization. Furthermore, sepsis patients are a heterogeneous population in terms of severity of illness, site and pathogen of infection, and underlying comorbidity whose posthospital course remains incompletely characterized, limiting our ability to draw strong inferences.
These results should be interpreted in light of the study's limitations. First, our cohort included patients with healthcare insurance within a community‐based healthcare system. Care within the KPNC system, which bears similarities with accountable care organizations, is enhanced through service integration and a comprehensive health information system. Although prior studies suggest that these characteristics result in improved population‐based care, it is unclear whether there is a similar impact in hospital‐based conditions such as sepsis.[54, 55] Furthermore, care within an integrated system may impact posthospital utilization patterns and could limit generalizability. However, prior studies demonstrate the similarity of KPNC members to other patients in the same region in terms of age, socioeconomics, overall health behaviors, and racial/ethnic diversity.[56] Second, our study did not characterize organ dysfunction based on diagnosis coding, a common feature of sepsis studies that lack detailed physiologic severity data.[4, 5, 6, 8, 26] Instead, we focused on using granular laboratory and vital signs data to ensure accurate risk adjustment using a validated system developed in >400,000 hospitalizations.[30] Although this method may hamper comparisons with existing studies, traditional methods of grading severity by diagnosis codes can be vulnerable to biases resulting in wide variability.[10, 23, 26, 57, 58] Nonetheless, it is likely that characterizing preexisting and acute organ dysfunction will improve risk stratification in the heterogeneous sepsis population. Third, this study did not include data regarding patients' functional status, which has been shown to strongly predict patient outcomes following hospitalization. Fourth, this study did not address the cost of care following sepsis hospitalizations.[19, 59] Finally, our study excluded patients with incomplete utilization records, a choice designed to avoid the spurious inferences that can result from such comparisons.[53]
In summary, we found that sepsis exacted a considerable toll on patients in the hospital and in the year following discharge. Sepsis patients were frequently rehospitalized within a month of discharge, and on average had a 3‐fold increase in their subsequent time spent in healthcare facilities. Although age, severity of illness, and the need for ICU care impacted postsepsis utilization, the dominant contributing factorscomorbid disease burden or presepsis utilizationwere present prior to sepsis hospitalization. Early readmission patterns in sepsis appeared similar to those seen in other important hospital conditions, suggesting a role for shared posthospital, rather than just postsepsis, care approaches.
Disclosures
The funding for this study was provided by The Permanente Medical Group, Inc. and Kaiser Foundation Hospitals. The authors have no conflict of interests to disclose relevant to this article.
- . The lingering consequences of sepsis: a hidden public health disaster? JAMA. 2010;304(16):1833–1834.
- , , , et al.; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med. 2013;41(2):580–637.
- , , . Costs for hospital stays in the United States, 2010. HCUP statistical brief #16. January 2013. Rockville, MD: Agency for Healthcare Research and Quality; 2013. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb146.pdf. Accessed October 1, 2013.
- , , , . The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348(16):1546–1554.
- , , , , , . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):1303–1310.
- , , , . Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35(5):1244–1250.
- , , . Septicemia in U.S. hospitals, 2009. HCUP statistical brief #122. October 2011. Rockville, MD: Agency for Healthcare Research and Quality; 2011. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb122.pdf. Accessed October 1, 2013.
- , , , , , . Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40(3):754–761.
- , , , . Population burden of long‐term survivorship after severe sepsis in older Americans. J Am Geriatr Soc. 2012;60(6):1070–1077.
- , , , . Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med. 2013;41(5):1167–1174.
- , , , et al. Outcomes of the Surviving Sepsis Campaign in intensive care units in the USA and Europe: a prospective cohort study. Lancet Infect Dis. 2012;12(12):919–924.
- , , , . Reducing mortality in severe sepsis: the Surviving Sepsis Campaign. Clin Chest Med. 2008;29(4):721–733, x.
- , , , et al.; Early Goal‐Directed Therapy Collaborative Group. Early goal‐directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345(19):1368–1377.
- , , , . Long‐term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):1787–1794.
- , , , , , . Long‐term mortality and quality of life in sepsis: a systematic review. Crit Care Med. 2010;38(5):1276–1283.
- , , , et al.; the Scottish Critical Care Trials Group and the Scottish Intensive Care Society Audit Group. Mortality and quality of life in the five years after severe sepsis. Crit Care. 2013;17(2):R70.
- , , , , . Post‐Discharge Health Care Use Is Markedly Higher in Survivors of Severe Sepsis. Am J Respir Crit Care Med 2013;187:A1573.
- , , , . Long‐term survival and function after suspected gram‐negative sepsis. JAMA. 1995;274(4):338–345.
- , , , , . Long‐term mortality and medical care charges in patients with severe sepsis. Crit Care Med. 2003;31(9):2316–2323.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
- , , , , . A systematic review and meta‐analysis of studies comparing readmission rates and mortality rates in patients with heart failure. Arch Intern Med. 2004;164(21):2315–2320.
- , . Hospitalizations for heart failure in the United States—a sign of hope. JAMA. 2011;306(15):1705–1706.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- , . Sepsis after Scotland: enough with the averages, show us the effect modifiers. Crit Care. 2013;17(3):148.
- , , , et al. Kaiser Permanente's performance improvement system, part 3: multisite improvements in care for patients with sepsis. Jt Comm J Qual Patient Saf. 2011;37(11): 483–493.
- , , , et al. Identifying patients with severe sepsis using administrative claims: patient‐level validation of the Angus implementation of the International Consensus Conference Definition of Severe Sepsis [published online ahead of print September 18, 2012]. Med Care. doi: 10.1097/MLR.0b013e318268ac86. Epub ahead of print.
- . Linking automated databases for research in managed care settings. Ann Intern Med. 1997;127(8 pt 2):719–724.
- , , , , , . Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care. 2008;46(3):232–239.
- , , , , . An electronic Simplified Acute Physiology Score‐based risk adjustment score for critical illness in an integrated healthcare system. Crit Care Med. 2013;41(1):41–48.
- , , , , . Risk‐adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system. Med Care. 2013;51(5):446–453.
- , , , , , . Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7(5):388–395.
- , , , . The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2009;63(7):798–803.
- , , , , , . Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108–1113.
- Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. Clinical Classifications Software (CCS) for ICD‐9‐CM Fact Sheet. Available at: http://www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp. Accessed January 20, 2013.
- , . A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1997;94(446):496–509.
- , , . Severe sepsis in managed care: analysis of incidence, one‐year mortality, and associated costs of care. J Manag Care Pharm. 2004;10(6):521–530.
- , , , , , . Detailed cost analysis of care for survivors of severe sepsis. Crit Care Med. 2004;32(4):981–985.
- , , , , et al. Readmissions among patients with severe sepsis/septic shock among inner‐city minority New Yorkers. Chest. 2012;142:286A.
- , , . Readmission and late mortality after pediatric severe sepsis. Pediatrics. 2009;123(3):849–857.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102.
- , , , et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):1644–1655.
- , , . The effect of age on the development and outcome of adult sepsis. Crit Care Med. 2006;34(1):15–21.
- , . Multiple systems organ failure: failure of host defense homeostasis. Crit Care Clin. 1989;5(2):199–220.
- . Pathophysiology of sepsis. Am J Pathol. 2007;170(5):1435–1444.
- , . Surviving intensive care: a report from the 2002 Brussels Roundtable. Intensive Care Med. 2003;29(3):368–377.
- , , . The encephalopathy in sepsis. Crit Care Clin. 2008;24(1):67–82, viii.
- , . Sepsis‐associated encephalopathy. Nat Rev Neurol. 2012;8(10):557–566.
- , , , et al. Improving long‐term outcomes after discharge from intensive care unit: report from a stakeholders' conference. Crit Care Med. 2012;40(2):502–509.
- , , , , . The association between sepsis and potential medical injury among hospitalized patients. Chest. 2012;142(3):606–613.
- , , , , , . Three‐year outcomes for Medicare beneficiaries who survive intensive care. JAMA. 2010;303(9):849–856.
- , , , , , . Does acute organ dysfunction predict patient‐centered outcomes? Chest. 2002;121(6):1963–1971.
- , , , . Spurious inferences about long‐term outcomes: the case of severe sepsis and geriatric conditions. Am J Respir Crit Care Med. 2012;185(8):835–841.
- , , , , , . Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010;362(23):2155–2165.
- , , , et al., Outpatient electronic health records and the clinical care and outcomes of patients with diabetes mellitus. Ann Intern Med. 2012;157(7):482–489.
- . Similarity of the adult Kaiser Permanente membership in Northern California to the insured and general population in Northern California: statistics from the 2009 California Health Interview Survey. Internal Division of Research Report. Oakland, CA: Kaiser Permanente Division of Research; January 24, 2012. Available at: http://www.dor.kaiser.org/external/chis_non_kp_2009. Accessed January 20, 2013.
- , , , , . Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003–2009. JAMA. 2012;307(13):1405–1413.
- , . Finding pure and simple truths with administrative data. JAMA. 2012;307(13):1433–1435.
- , , , . Cost savings attributable to reductions in intensive care unit length of stay for mechanically ventilated patients. Med Care. 2008;46(12):1226–1233.
- . The lingering consequences of sepsis: a hidden public health disaster? JAMA. 2010;304(16):1833–1834.
- , , , et al.; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med. 2013;41(2):580–637.
- , , . Costs for hospital stays in the United States, 2010. HCUP statistical brief #16. January 2013. Rockville, MD: Agency for Healthcare Research and Quality; 2013. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb146.pdf. Accessed October 1, 2013.
- , , , . The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348(16):1546–1554.
- , , , , , . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):1303–1310.
- , , , . Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35(5):1244–1250.
- , , . Septicemia in U.S. hospitals, 2009. HCUP statistical brief #122. October 2011. Rockville, MD: Agency for Healthcare Research and Quality; 2011. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb122.pdf. Accessed October 1, 2013.
- , , , , , . Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40(3):754–761.
- , , , . Population burden of long‐term survivorship after severe sepsis in older Americans. J Am Geriatr Soc. 2012;60(6):1070–1077.
- , , , . Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med. 2013;41(5):1167–1174.
- , , , et al. Outcomes of the Surviving Sepsis Campaign in intensive care units in the USA and Europe: a prospective cohort study. Lancet Infect Dis. 2012;12(12):919–924.
- , , , . Reducing mortality in severe sepsis: the Surviving Sepsis Campaign. Clin Chest Med. 2008;29(4):721–733, x.
- , , , et al.; Early Goal‐Directed Therapy Collaborative Group. Early goal‐directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345(19):1368–1377.
- , , , . Long‐term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):1787–1794.
- , , , , , . Long‐term mortality and quality of life in sepsis: a systematic review. Crit Care Med. 2010;38(5):1276–1283.
- , , , et al.; the Scottish Critical Care Trials Group and the Scottish Intensive Care Society Audit Group. Mortality and quality of life in the five years after severe sepsis. Crit Care. 2013;17(2):R70.
- , , , , . Post‐Discharge Health Care Use Is Markedly Higher in Survivors of Severe Sepsis. Am J Respir Crit Care Med 2013;187:A1573.
- , , , . Long‐term survival and function after suspected gram‐negative sepsis. JAMA. 1995;274(4):338–345.
- , , , , . Long‐term mortality and medical care charges in patients with severe sepsis. Crit Care Med. 2003;31(9):2316–2323.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
- , , , , . A systematic review and meta‐analysis of studies comparing readmission rates and mortality rates in patients with heart failure. Arch Intern Med. 2004;164(21):2315–2320.
- , . Hospitalizations for heart failure in the United States—a sign of hope. JAMA. 2011;306(15):1705–1706.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
- , . Sepsis after Scotland: enough with the averages, show us the effect modifiers. Crit Care. 2013;17(3):148.
- , , , et al. Kaiser Permanente's performance improvement system, part 3: multisite improvements in care for patients with sepsis. Jt Comm J Qual Patient Saf. 2011;37(11): 483–493.
- , , , et al. Identifying patients with severe sepsis using administrative claims: patient‐level validation of the Angus implementation of the International Consensus Conference Definition of Severe Sepsis [published online ahead of print September 18, 2012]. Med Care. doi: 10.1097/MLR.0b013e318268ac86. Epub ahead of print.
- . Linking automated databases for research in managed care settings. Ann Intern Med. 1997;127(8 pt 2):719–724.
- , , , , , . Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care. 2008;46(3):232–239.
- , , , , . An electronic Simplified Acute Physiology Score‐based risk adjustment score for critical illness in an integrated healthcare system. Crit Care Med. 2013;41(1):41–48.
- , , , , . Risk‐adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system. Med Care. 2013;51(5):446–453.
- , , , , , . Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7(5):388–395.
- , , , . The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2009;63(7):798–803.
- , , , , , . Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108–1113.
- Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. Clinical Classifications Software (CCS) for ICD‐9‐CM Fact Sheet. Available at: http://www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp. Accessed January 20, 2013.
- , . A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1997;94(446):496–509.
- , , . Severe sepsis in managed care: analysis of incidence, one‐year mortality, and associated costs of care. J Manag Care Pharm. 2004;10(6):521–530.
- , , , , , . Detailed cost analysis of care for survivors of severe sepsis. Crit Care Med. 2004;32(4):981–985.
- , , , , et al. Readmissions among patients with severe sepsis/septic shock among inner‐city minority New Yorkers. Chest. 2012;142:286A.
- , , . Readmission and late mortality after pediatric severe sepsis. Pediatrics. 2009;123(3):849–857.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102.
- , , , et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):1644–1655.
- , , . The effect of age on the development and outcome of adult sepsis. Crit Care Med. 2006;34(1):15–21.
- , . Multiple systems organ failure: failure of host defense homeostasis. Crit Care Clin. 1989;5(2):199–220.
- . Pathophysiology of sepsis. Am J Pathol. 2007;170(5):1435–1444.
- , . Surviving intensive care: a report from the 2002 Brussels Roundtable. Intensive Care Med. 2003;29(3):368–377.
- , , . The encephalopathy in sepsis. Crit Care Clin. 2008;24(1):67–82, viii.
- , . Sepsis‐associated encephalopathy. Nat Rev Neurol. 2012;8(10):557–566.
- , , , et al. Improving long‐term outcomes after discharge from intensive care unit: report from a stakeholders' conference. Crit Care Med. 2012;40(2):502–509.
- , , , , . The association between sepsis and potential medical injury among hospitalized patients. Chest. 2012;142(3):606–613.
- , , , , , . Three‐year outcomes for Medicare beneficiaries who survive intensive care. JAMA. 2010;303(9):849–856.
- , , , , , . Does acute organ dysfunction predict patient‐centered outcomes? Chest. 2002;121(6):1963–1971.
- , , , . Spurious inferences about long‐term outcomes: the case of severe sepsis and geriatric conditions. Am J Respir Crit Care Med. 2012;185(8):835–841.
- , , , , , . Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010;362(23):2155–2165.
- , , , et al., Outpatient electronic health records and the clinical care and outcomes of patients with diabetes mellitus. Ann Intern Med. 2012;157(7):482–489.
- . Similarity of the adult Kaiser Permanente membership in Northern California to the insured and general population in Northern California: statistics from the 2009 California Health Interview Survey. Internal Division of Research Report. Oakland, CA: Kaiser Permanente Division of Research; January 24, 2012. Available at: http://www.dor.kaiser.org/external/chis_non_kp_2009. Accessed January 20, 2013.
- , , , , . Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003–2009. JAMA. 2012;307(13):1405–1413.
- , . Finding pure and simple truths with administrative data. JAMA. 2012;307(13):1433–1435.
- , , , . Cost savings attributable to reductions in intensive care unit length of stay for mechanically ventilated patients. Med Care. 2008;46(12):1226–1233.
© 2014 Society of Hospital Medicine
