Predictors of early postdischarge mortality in critically ill patients: a retrospective cohort study from the California Intensive Care Outcomes project.

PURPOSE: Existing intensive care unit (ICU) mortality measurement systems address in-hospital mortality only. However, early postdischarge mortality contributes significantly to overall 30-day mortality. Factors associated with early postdischarge mortality are unknown. METHODS: We performed a retrospective study of 8484 ICU patients. Our primary outcome was early postdischarge mortality: death after hospital discharge and 30 days or less from ICU admission. Cox regression models assessed the association between patient, hospital, and utilization factors and the primary outcome. RESULTS: In multivariate analyses, the hazard for early postdischarge mortality increased with rising severity of illness and decreased with full-code status (hazard ratio [HR], 0.33; 95% confidence interval [CI], 0.21-0.49). Compared with discharges home, early postdischarge mortality was highest for acute care transfers (HR, 3.18; 95% CI, 2.45-4.12). Finally, patients with very short ICU length of stay (<1 day) had greater early postdischarge mortality (HR, 1.86; 95% CI; 1.32-2.61) than those with longest stays (≥7 days). CONCLUSIONS: Early postdischarge mortality is associated with patient preferences (full-code status) and decisions regarding timing and location of discharge. These findings have important implications for anyone attempting to measure or improve ICU performance and who rely on in-hospital mortality measures to do so.

The effect of race and ethnicity on outcomes among patients in the intensive care unit: a comprehensive study involving socioeconomic status and resuscitation preferences.

OBJECTIVE: We sought to determine whether race or ethnicity is independently associated with mortality or intensive care unit length of stay among critically ill patients after accounting for patients' clinical and demographic characteristics including socioeconomic status and resuscitation preferences. DESIGN: Historical cohort study of patients hospitalized in intensive care units. SETTING: Adult intensive care units in 35 California hospitals during the years 2001-2004. PATIENTS: A total of 9,518 intensive care unit patients (6,334 white, 655 black, 1,917 Hispanic, and 612 Asian/Pacific Islander patients). MEASUREMENTS AND MAIN RESULTS: The primary outcome was risk-adjusted mortality and a secondary outcome was risk-adjusted intensive care unit length of stay. Crude hospital mortality was 15.9% among the entire cohort. Asian patients had the highest crude hospital mortality at 18.6% and black patients had the lowest at 15.0%. After adjusting for age and gender, Hispanic and Asian patients had a higher risk of death compared to white patients, but these differences were not significant after additional adjustment for severity of illness. Black patients had more acute physiologic derangements at intensive care unit admission and longer unadjusted intensive care unit lengths of stay. Intensive care unit length of stay was not significantly different among racial/ethnic groups after adjustment for demographic, clinical, and socioeconomic factors and do-not-resuscitate status. In an analysis restricted only to those who died, decedent black patients averaged 1.1 additional days in the intensive care unit (95% confidence interval, 0.26-2.6) compared to white patients who died, although this was not statistically significant. CONCLUSIONS: Hospital mortality and intensive care unit length of stay did not differ by race or ethnicity among this diverse cohort of critically ill patients after adjustment for severity of illness, resuscitation status, socioeconomic status, insurance status, and admission type. Black patients had more acute physiologic derangements at intensive care unit admission and were less likely to have a do-not-resuscitate order. These results suggest that among intensive care unit patients, there are no racial or ethnic differences in mortality within individual hospitals. If disparities in intensive care unit care exist, they may be explained by differences in the quality of care provided by hospitals that serve high proportions of minority patients.

Relationship between discharge practices and intensive care unit in-hospital mortality performance: evidence of a discharge bias.

CONTEXT: Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased. OBJECTIVE: Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model. DESIGN, SETTING, AND PARTICIPANTS: Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004. MEASURES: We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality-30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR. RESULTS: In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of -0.021 (-0.040-0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of -0.049 (-0.142-0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance. CONCLUSIONS: Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.

Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV.

BACKGROUND: To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS: Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS: The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p

Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders.

BACKGROUND: Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. METHODS: A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM(0) III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. RESULTS: Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM(0) III (AUC, 0.809), and SAPS II (AUC, 0.873; p < 0.001). The models differed substantially in data abstraction times, as follows: MPM(0)III, 11.1 min (95% confidence interval [CI], 8.7 to 13.4); SAPS II, 19.6 min (95% CI, 17.0 to 22.2); and APACHE IV, 37.3 min (95% CI, 28.0 to 46.6). CONCLUSIONS: We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM(0) III model offers a viable alternative without a substantial loss in accuracy.

Differences in mortality among patients with community-acquired pneumonia in California by ethnicity and hospital characteristics.

PURPOSE: To determine ethnic disparities in mortality for patients with community-acquired pneumonia, and the potential effects of hospital characteristics on disparities, we compared the risk-adjusted mortality of white, African American, Hispanic, and Asian American patients hospitalized for community-acquired pneumonia. METHODS: We studied patients discharged with community-acquired pneumonia in 1996 from an acute care hospital in California (n = 54,874). Logistic regression models were used to examine the association between ethnicity and hospital characteristics and 30-day mortality after adjusting for clinical characteristics. RESULTS: The overall 30-day mortality was 12.2%. After adjustment for demographic, clinical, and hospital characteristics, Hispanic (odds ratio [OR] = 0.81; 95% confidence interval [CI]: 0.73 to 0.90) and Asian American patients (OR = 0.88; 95% CI: 0.77 to 1.00) had lower mortality than did white patients, whereas African Americans had a similar mortality to whites (OR = 0.93; 95% CI: 0.83 to 1.06). There were no overall differences in mortality by hospital characteristics (i.e., teaching status, rural location, and public or district hospital). CONCLUSION: Hispanics and Asian Americans have a lower risk of death from community-acquired pneumonia than whites in California. No overall differences in mortality were observed by hospital characteristics.

The best of both worlds? Potential of hybrid prospective/concurrent risk adjustment.

BACKGROUND: There remains considerable uncertainty about whether prospective or concurrent risk adjustment (RA) is preferable. Although concurrent models have better predictive power than prospective models, the large payments associated with concurrent RA create incentives for fraudulent coding. A hybrid strategy--in which prospective payments were used for patients with low expected costs and concurrent payments were available upon the diagnosis of a small number of common, expensive conditions--might improve predictive performance while requiring less auditing than fully concurrent RA. In addition, within-condition RA (using clinical data) for the selected conditions could further improve predictive power. OBJECTIVES: To assess how such a hybrid strategy might perform, focusing on a small number of chronic, expensive conditions that are verifiable (hence auditable). SUBJECTS AND MEASURES: All patients from seven health plans who had two complete years of utilization data were considered. RA models were estimated among patients younger than 65 (n = 319,209) using the Hierarchical Coexisting Conditions (HCC) model with and without stratification of the sample based on the presence of one or more of 100 verifiable, expensive, predictive conditions (VEP100). R2 and predictive ratios were calculated for each model studied. RESULTS: Patients with a VEP100 condition (9.3% of the population) accounted for 84.3% of the variation in cost. R2 was 0.08 using a prospective HCC model on the entire population, but increased to 0.26 for a hybrid using prospective HCCs on the 90.7% of the sample without a VEP100 condition and a simple concurrent model consisting of dummy variables for each of the VEP100 conditions. CONCLUSION: Combined with targeted auditing, a hybrid approach to RA could improve our ability to match payments to costs. However, because this would require additional, costly data collection, more research is needed to determine whether this benefit justifies the data collection and auditing burden.