Subjective score and outcomes after discharge from the intensive care unit: a prospective observational study

Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

BACKGROUND

Deaths can occur after a patient has survived treatment for a serious illness in an intensive care unit (ICU). Mortality rates after leaving the ICU can be considered indicators of health care quality. This study aims to describe risk factors and mortality of surviving patients discharged from an ICU in a university hospital.

METHODS

Retrospective cohort study carried out from January 2017 to December 2018. Data on age, sex, length of hospital stay, diagnosis on admission to the ICU, hospital discharge outcome, presence of infection, and Simplified Acute Physiology Score (SAPS) III prognostic score were collected. Infected patients were considered as those being treated for an infection on discharge from the ICU. Patients were divided into survivors and non-survivors on leaving the hospital. The association between the studied variables was performed using the logistic regression model.

RESULTS

A total of 1,025 patients who survived hospitalization in the ICU were analyzed, of which 212 (20.7%) died after leaving the ICU. When separating the groups of survivors and non-survivors according to hospital outcome, the median age was higher among non-survivors. Longer hospital stays and higher SAPS III values were observed among non-survivors. In the logistic regression, the variables age, length of hospital stay, SAPS III, presence of infection, and readmission to the ICU were associated with hospital mortality.

CONCLUSIONS

Infection on ICU discharge, ICU readmission, age, length of hospital stay, and SAPS III increased risk of death in ICU survivors.

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes.

DATA SOURCES

PubMed, Web of Science, Cochrane, and Embase.

STUDY SELECTION

Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021.

DATA EXTRACTION

Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships.

DATA SYNTHESIS

Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time.

CONCLUSIONS

Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.