Elsevier

Journal of Critical Care

Volume 26, Issue 1, February 2011, Pages 65-75
Journal of Critical Care

Prognosis and Prediction
Predictors of early postdischarge mortality in critically ill patients: A retrospective cohort study from the California Intensive Care Outcomes project,☆☆,

https://doi.org/10.1016/j.jcrc.2010.06.010Get rights and content

Abstract

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.

Introduction

Intensive care unit (ICU) quality reporting systems in the United States currently focus exclusively on in-hospital mortality [1], [2] and do not account for deaths that occur soon after discharge, even though 30-day mortality timed from admission (or a procedure) is used for most other conditions [3], [4]. Almost 15% of ICU survivors die within 6 months of discharge [5], and as efforts intensify to reduce the average ICU length of stay (LOS) and in-hospital mortality [1], [6], it is conceivable that a significant number of deaths may occur in the early postdischarge period. This is especially relevant given the initiation of public reporting of ICU in-hospital mortality [1].

Understanding factors associated with early postdischarge mortality has clinical and policy implications. From a clinical perspective, hospitals and physicians often do not receive feedback regarding mortality rates among successfully discharged patients and do not have information about factors that place patients at risk for adverse outcomes after discharge [7]. From a health policy perspective, public reporting of in-hospital mortality may be subject to measurement bias [8]. This would be true if hospitals' patient populations were weighted toward any currently unmeasured patient, hospital, or utilization (eg, LOS) factor associated with higher or lower risk for early postdischarge mortality.

There has been no prior published research designed to identify factors associated with early postdischarge mortality among survivors after a hospitalization that includes an ICU admission. Some patient characteristics, including severity of illness, have been shown to be associated with in-hospital mortality and may, likewise, be associated with early postdischarge mortality [9]. Similarly, hospital characteristics (eg, teaching status) may be important [10], [11], [12]. Finally, utilization decisions, such as discharge location and ICU LOS, may influence whether a patient dies within the hospital or after discharge [5], [8], [13].

To assess the relationship between patient, hospital, and key utilization factors and early postdischarge mortality, we examined patients from the California Intensive Care Outcomes (CALICO) project who survived to hospital discharge.

Section snippets

Hospital selection

All California hospitals in the United States (n = 308) with at least 50 hospital beds were sent a recruitment packet. Nurses from 35 volunteer hospitals attended a training session, abstracted sample charts, and received feedback on their interrater reliability [12].

Patient selection

We collected data between 2001 and 2004. Inclusion criteria were age 18 years or older and ICU stay of 4 hours or more [12]. In addition, because CALICO was designed to compare 3 validated risk adjustment methods, the study only

Hospital characteristics

Characteristics of the 35 CALICO hospitals were similar to all California hospitals. Full details are published elsewhere [12]. Briefly, 35 participating hospitals included 57% not-for-profit institutions, 29% teaching hospitals, 9% hospitals with less than 100 licensed beds and 41% with more than 300 licensed beds.

Patient characteristics

Among the 9178 patients who met our inclusion criteria, we excluded 647 (7.0%) patients for which NDI data were not available (92% of these did not have social security numbers) and

Discussion

This is the first study, to our knowledge, to explore risk factors for early death after hospital discharge among ICU survivors. We found that patient, hospital, and utilization factors were independently associated with early postdischarge mortality. We found that short ICU LOS and discharge to locations other than home were associated with increased hazard of early postdischarge death. These associations persisted after excluding patients whose early postdischarge deaths were more likely to

Acknowledgments

We acknowledge Teresa Chipps, BS, and Christine Bass, MPH, Department of Medicine (General Internal Medicine and Public Health), Center for Health Services Research, Vanderbilt University, Nashville, TN, for their administrative and editorial assistance in the preparation of this manuscript. This work was supported by the California Office of Statewide Health Planning and Development, the Agency for Healthcare Research and Quality (R01 HS13919‑01). Dr Dudley's work was also supported by an

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    Work was performed at the University of California, San Francisco, San Francisco, CA.

    ☆☆

    Conflicts of Interest: The authors have no conflicts of interest to disclose.

    Disclaimers: The views expressed in this article are those of the authors and do not necessarily represent the views of the US Department of Veterans Affairs.

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