Risk Factors for 1-Year Mortality and Hospital Utilization Patterns in Critical Care Survivors: A Retrospective, Observational, Population-Based Data Linkage Study

Crit Care Med. 2019 Jan;47(1):15-22. doi: 10.1097/CCM.0000000000003424.

Abstract

Objectives: Clear understanding of the long-term consequences of critical care survivorship is essential. We investigated the care process and individual factors associated with long-term mortality among ICU survivors and explored hospital use in this group.

Design: Population-based data linkage study using the Secure Anonymised Information Linkage databank.

Setting: All ICUs between 2006 and 2013 in Wales, United Kingdom.

Patients: We identified 40,631 patients discharged alive from Welsh adult ICUs.

Interventions: None.

Measurements and main results: Primary outcome was 365-day survival. The secondary outcomes were 30- and 90-day survival and hospital utilization in the 365 days following ICU discharge. Kaplan-Meier curves were plotted to compare survival rates. Cox proportional hazards regression models were used to determine risk factors of mortality. Seven-thousand eight-hundred eighty-three patients (19.4%) died during the 1-year follow-up period. In the multivariable Cox regression analysis, advanced age and comorbidities were significant determinants of long-term mortality. Expedited discharge due to ICU bed shortage was associated with higher risk. The rate of hospitalization in the year prior to the critical care admission was 28 hospitalized days/1,000 d; post critical care was 88 hospitalized days/1,000 d for those who were still alive; and 57 hospitalized days/1,000 d and 412 hospitalized days/1,000 d for those who died by the end of the study, respectively.

Conclusions: One in five ICU survivors die within 1 year, with advanced age and comorbidity being significant predictors of outcome, leading to high resource use. Care process factors indicating high system stress were associated with increased risk. More detailed understanding is needed on the effects of the potentially modifiable factors to optimize service delivery and improve long-term outcomes of the critically ill.

Publication types

  • Observational Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Comorbidity
  • Hospitalization / statistics & numerical data*
  • Humans
  • Intensive Care Units*
  • Mortality*
  • Proportional Hazards Models
  • Retrospective Studies
  • Risk Factors
  • Survivors*
  • Wales / epidemiology