Sydney E. S. Brown

The Epidemiology of Intensive Care Unit Readmissions in the United States

RATIONALE The incidence of intensive care unit (ICU) readmissions across the United States is unknown. OBJECTIVES To determine incidence of ICU readmissions in United States hospitals, and describe the distribution of time between ICU discharges and readmissions. METHODS This retrospective cohort study used 196,202 patients in 156 medical and surgical ICUs in 106 community and academic hospitals participating in Project IMPACT from April 1, 2001, to December 31, 2007. We used mixed-effects logistic regression, adjusting for patient and hospital characteristics, to describe how ICU readmission rates differed across patient types, ICU models, and hospital types. MEASUREMENTS AND MAIN RESULTS Measurements consisted of 48- and 120-hour ICU readmission rates and time to readmission. A total of 3,905 patients (2%) were readmitted to the ICU within 48 hours, and 7,171 (3.7%) within 120 hours. In adjusted analysis, there was no difference in ICU readmissions across patient types or ICU models. Among medical patients, those in academic hospitals had higher odds of 48- and 120-hour readmission than patients in community hospitals without residents (1.51 [95% confidence interval, 1.12-2.02] and 1.63 [95% confidence interval, 1.24-2.16]). Median time to ICU readmission was 3.07 days (interquartile range, 1.27-6.58). Closed ICUs had the longest times to readmission (3.55 d [interquartile range, 1.42-7.50]). CONCLUSIONS Approximately 2% and 4% of ICU patients discharged to the ward are readmitted within 48 and 120 hours, within a median time of 3 days. Medical patients in academic hospitals are more likely to be readmitted than patients in community hospitals without residents. ICU readmission rates could be useful for policy makers and investigations into their causes and consequences.

Outcomes among patients discharged from busy intensive care units.

BACKGROUND Strains on the capacities of intensive care units (ICUs) may influence the quality of ICU-to-floor transitions. OBJECTIVE To determine how 3 metrics of ICU capacity strain (ICU census, new admissions, and average acuity) measured on days of patient discharges influence ICU length of stay (LOS) and post-ICU discharge outcomes. DESIGN Retrospective cohort study from 2001 to 2008. SETTING 155 ICUs in the United States. PATIENTS 200 730 adults discharged from ICUs to hospital floors. MEASUREMENTS Associations between ICU capacity strain metrics and discharged patient ICU LOS, 72-hour ICU readmissions, subsequent in-hospital death, post-ICU discharge LOS, and hospital discharge destination. RESULTS Increases in the 3 strain variables on the days of ICU discharge were associated with shorter preceding ICU LOS (all P < 0.001) and increased odds of ICU readmissions (all P < 0.050). Going from the 5th to 95th percentiles of strain was associated with a 6.3-hour reduction in ICU LOS (95% CI, 5.3 to 7.3 hours) and a 1.0% increase in the odds of ICU readmission (CI, 0.6% to 1.5%). No strain variable was associated with increased odds of subsequent death, reduced odds of being discharged home from the hospital, or longer total hospital LOS. LIMITATION Long-term outcomes could not be measured. CONCLUSION When ICUs are strained, triage decisions seem to be affected such that patients are discharged from the ICU more quickly and, perhaps consequentially, have slightly greater odds of being readmitted to the ICU. However, short-term patient outcomes are unaffected. These results suggest that bed availability pressures may encourage physicians to discharge patients from the ICU more efficiently and that ICU readmissions are unlikely to be causally related to patient outcomes. PRIMARY FUNDING SOURCE Agency for Healthcare Research and Quality; National Heart, Lung, and Blood Institute; and Society of Critical Care Medicine.

Racial/Ethnic Differences in Concerns About Current and Future Medications Among Patients With Type 2 Diabetes

OBJECTIVE—To evaluate ethnic differences in medication concerns (e.g., side effects and costs) that may contribute to ethnic differences in the adoption of and adherence to type 2 diabetes treatments. RESEARCH DESIGN AND METHODS—We conducted face-to-face interviews from May 2004 to May 2006 with type 2 diabetic patients ≥18 years of age (N = 676; 25% Latino, 34% non-Hispanic Caucasian, and 41% non-Hispanic African American) attending Chicago-area clinics. Primary outcomes of interest were concerns regarding medications and willingness to take additional medications. RESULTS—Latinos and African Americans had higher A1C levels than Caucasians (7.69 and 7.54% vs. 7.18%, respectively; P < 0.01). Latinos and African Americans were more likely than Caucasians to worry about drug side effects (66 and 49% vs. 39%, respectively) and medication dependency (65 and 52% vs. 39%, respectively; both P < 0.01). Ethnic minorities were also more likely to report reluctance to adding medications to their regimen (Latino 12%, African American 18%, and Caucasian 7%; P < 0.01). In analyses adjusted for demographics, income, education, and diabetes duration, current report of pain/discomfort with pills (odds ratio 2.43 [95% CI 1.39–4.27]), concern regarding disruption of daily routine (1.97 [1.14–3.42]), and African American ethnicity (2.48 [1.32–4.69]) emerged as major predictors of expressed reluctance to adding medications. CONCLUSIONS—Latinos and African Americans had significantly more concerns regarding the quality-of-life effects of diabetes-related medications than Caucasians. Whether these medication concerns contribute significantly to differences in treatment adoption and disparities in care deserves further exploration.

The Cost Consequences of Improving Diabetes Care: The Community Health Center Experience

BACKGROUND Despite significant interest in the business case for quality improvement (QI), there are few evaluations of the impact of QI programs on outpatient organizations. The financial impact of the Health Disparities Collaboratives (HDC), a national QI program conducted in community health centers (HCs), was examined. METHODS Chief executive officers (CEOs) from health centers in two U.S. regions that participated in the Diabetes HDC (N = 74) were surveyed. In case studies of five selected centers, program costs/revenues, clinical costs/revenues, overall center financial health, and indirect costs/benefits were assessed. RESULTS CEOs were divided on the HDCs overall effect on finances (38%, worsened; 48%, no change; 14%, improved). Case studies showed that the HDC represented a new administrative cost (

Sustaining quality improvement in community health centers: perceptions of leaders and staff.

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Perceptions of quality-of-life effects of treatments for diabetes mellitus in vulnerable and nonvulnerable older patients.

22/patient, year 1) without a regular revenue source. In centers with billing data, the balance of diabetes-related clinical costs/revenues and payor mix did not clearly worsen or improve with the programs start. The most commonly mentioned indirect benefits were improved chronic illness care and enhanced staff morale. DISCUSSION CEO perceptions of the overall financial impact of the HDC vary widely; the case studies illustrate the numerous factors that may influence these perceptions. Whether the identified balance of costs and benefits is generalizable or sustainable will have to be addressed to optimally design financial reimbursement and incentives.

An empirical derivation of the optimal time interval for defining ICU readmissions.

The Health Disparities Collaboratives are the largest national quality improvement (QI) initiatives in community health centers. This article identifies the incentives and assistance personnel believe are necessary to sustain QI. In 2004, 1006 survey respondents (response rate 67%) at 165 centers cited lack of resources, time, and staff burnout as common barriers. Release time was the most desired personal incentive. The highest funding priorities were direct patient care services (44% ranked no. 1), data entry (34%), and staff time for QI (26%). Participants also needed help with patient self-management (73%), information systems (77%), and getting providers to follow guidelines (64%).

The allocation of intensivists' rounding time under conditions of intensive care unit capacity strain.

OBJECTIVES: To assess whether patient perceptions of treatments for diabetes mellitus differ according to clinical criteria such as limited life expectancy and functional decline (i.e., vulnerability).

Estimating costs of quality improvement for outpatient healthcare organisations: a practical methodology

Background:Intensive care unit (ICU) readmission rates are commonly viewed as indicators of ICU quality. However, definitions of ICU readmissions vary, and it is unknown which, if any, readmissions are associated with ICU quality. Objective:Empirically derive the optimal interval between ICU discharge and readmission for purposes of considering ICU readmission as an ICU quality indicator. Research Design:Retrospective cohort study. Subjects:A total of 214,692 patients discharged from 157 US ICUs participating in the Project IMPACT database, 2001–2008. Measures:We graphically examined how patient characteristics and ICU discharge circumstances (eg, ICU census) were related to the odds of ICU readmissions as the allowable interval between ICU discharge and readmission was lengthened. We defined the optimal interval by identifying inflection points where these relationships changed significantly and permanently. Results:A total of 2242 patients (1.0%) were readmitted to the ICU within 24 hours; 9062 (4.2%) within 7 days. Patient characteristics exhibited stronger associations with readmissions after intervals >48–60 hours. By contrast, ICU discharge circumstances and ICU interventions (eg, mechanical ventilation) exhibited weaker relationships as intervals lengthened, with inflection points at 30–48 hours. Because of the predominance of afternoon readmissions regardless of time of discharge, using intervals defined by full calendar days rather than fixed numbers of hours produced more valid results. Discussion:It remains uncertain whether ICU readmission is a valid quality indicator. However, having established 2 full calendar days (not 48 h) after ICU discharge as the optimal interval for measuring ICU readmissions, this study will facilitate future research designed to determine its validity.

Intensive care unit capacity strain and adherence to prophylaxis guidelines.

To the Editor: With rising demand for critical care, intensivists’ time must increasingly be divided among patients (1–6). Recent studies suggest that increased strain at intensive care unit (ICU) admission leads to higher mortality in closed ICUs (7) and that increased strain at discharge leads to increases in ICU readmissions (8). These relationships between strain and outcomes could be mediated by strain-induced changes in the time intensivists devote to patients during patient care rounds (7–9). We therefore examined how the allocation of intensivists’ time during rounds changes at times of low versus high ICU strain and whether intensivists preferentially allocate time away from certain patient groups as strain increases. Some results have been previously reported in the form of an abstract (10). Methods We conducted a prospective study of patient care rounds in the 24-bed medical ICU of the Hospital of the University of Pennsylvania in 2012. Time spent performing various rounding activities was recorded in real time by trained data collectors, using a tablet computer. Methods for assessing interrater reliability can be found in the online supplement. Data collection was randomly assigned to one of two intensivist-led medical ICU teams each day and was not performed on weekends. Variables describing patient characteristics, staffing, and ICU strain were obtained from the electronic medical record and as part of a separate clinical trial (11). Our analysis focused on “cognitive rounding time” (time spent on the patient’s assessment and plan) and on total rounding time (presentation of events and data, assessment and plan, and teaching related to that patient). Three validated strain variables (5) were considered: team census (“census”), representing the number of patients rounded on by the observed team each day; number of new admissions (“admissions”) since the end of rounds the previous day; and average severity of illness (“acuity”) of patients on the team, using Acute Physiology and Chronic Health Evaluation III (APACHE III) scores (12). We constructed explanatory linear mixed-effects models for cognitive and total rounding time for each patient-day. Patients were treated as random clusters, cumulative days hospitalized in the ICU were included as a random slope and as a linear term, and attending was treated as an indicator variable (13). Patient race was obtained from the electronic medical record and could be reported by either patient or provider. We considered three race categories: black, nonblack (white or Asian), and unknown. Table 1 and Table E1 in the online supplement describe all evaluated covariates. Table 1. Descriptive Statistics We constructed separate models to determine whether time was allocated away from specific patient groups as strain increased by exploring interactions between the three strain variables and the following six patient variables: admission status (new admission vs. follow-up), patient race (black vs. nonblack), age (continuous), severity of illness (continuous), family presence on rounds, and patient sex. We then constructed a fully adjusted model with all interactions having a P value < 0.2 and used backward selection, removing nonsignificant terms (14). We used Holm tests of conditional significance given the multiple comparisons made (15). Additional details regarding the statistical analyses are available in the online supplement. Results Rounds were observed for 566 patients over the course of 114 noncontiguous weekdays, for a total of 1,295 patient-days. Intensivists rounded on a median of 11 patients (interquartile range [IQR], 10–13) each day, including two new admissions (IQR, 1–3). Median daily rounding time was 188.6 minutes (IQR, 164.8–212.6 min); 91.9 minutes (IQR, 77.9–107.3 min) were spent on cognitive rounding time (Table 1). Daily rounding time increased as census (6.3 min; 95% confidence interval [CI], 2.4–10.1 min; P = 0.002) and admissions (6.0 min; 95% CI, 0.6–11.4 min; P = 0.031) increased (Figure E1 in the online supplement); cognitive rounding time increased as census increased (2.5 min; 95% CI, 0.1–4.9 min; P = 0.045). In fully adjusted models, with increasing daily admissions, newly admitted patients received 1.38 fewer minutes (95% CI, −2.43 to −0.33 min; PHolm = 0.002) total rounding time (interaction P value = 0.01) and 0.73 fewer minutes (95% CI, −1.42 to −0.07 min; PHolm = 0.0113) cognitive rounding time per additional admission. No significant changes occurred among follow-up patients (interaction P value = 0.030; Figure E2). As census increased, each unit increase led to a 0.5-minute (95% CI, 0.87–0.13 min; PHolm = 0.0135) decrease in cognitive rounding time among new admissions, with no decrement among follow-ups (interaction P value = 0.028). The effect of census on total rounding time was modified by new admission status and race. A three-way interaction (P = 0.04) revealed that among follow-ups, nonblack patients received 3.4 minutes (95% CI, −5.6 to −1.2 min; PHolm = 0.02) more than blacks at low census (eight patients); however, the excess time spent with nonblacks disappeared as census increased (P < 0.01). In contrast, no significant differences in strain-induced decrements in rounding time were observed between black and nonblack new admissions (P = 0.22; Figure 1). These relationships persisted in two sensitivity analyses, excluding patients of indeterminate race or excluding Asians from the nonblack group (Table E2). Figure 1. Total rounding time. Models are adjusted for acuity, severity of illness measured on Day 1 of the first intensive care unit (ICU) stay, day number in ICU course, attending, data collector, order patient was rounded on (inverse), maximum team size, attending’s ... Neither patient age, sex, acuity, and severity of illness nor the presence of family on rounds affected the allocation of rounding time. Discussion This study provides the first description of how ICU physicians allocate rounding time among patients and how this allocation changes as ICUs become strained. Daily rounding time increased with increases in census and admissions, but less time was spent per patient, primarily affecting new admissions and nonblack follow-up patients. These findings are consistent with studies showing that clinicians perceive their time to be highly constrained (1, 5, 6). The observation that strain preferentially affected new admissions and nonblack follow-ups may reflect the fact that these patients received more time in general, such that further reductions were challenging. Importantly, we found that increases in ICU strain did not result in disproportionate decreases in the time allocated to other patient subgroups, suggesting that ICU physicians generally ration their time equitably. Although total rounding time was allocated away from nonblack follow-ups as census increased, the facts that nonblacks received more time overall and that similar patterns were not observed among new admissions casts doubt on this finding, representing a true racial disparity. This study had several limitations. First, data were not collected outside of morning rounds, and therefore we could not assess how strain affected time allocation at other times. Second, although a differential effect of census on time allocation was not found between newly admitted black and nonblack patients, future research should determine whether a larger sample would reveal a significant disparity. Third, severity of illness was assessed only at ICU admission, limiting severity adjustment on subsequent days; however, bias introduced by inadequate severity adjustment is unlikely to be differential across different levels of census, and therefore it is unlikely to have affected the results. Residual confounding could still be present, as severity of illness may be indirectly affected by census. Finally, data capture was not formally evaluated; however, interrater reliability was excellent. In summary, this study provides a description of how intensivists allocate their time among patients as their workloads increase, providing objective confirmation of the common perception that time is a scarce resource. However, as a single-center study, these results may not generalize to other ICUs. In addition, because we often lacked data on rounding time on the same patient over contiguous days, we could not address whether observed decreases in rounding time mediated previously observed relationships between strain and outcomes or whether they represent improved efficiency. Future research is needed to explore these questions (7, 8).