A Simple Scoring Tool to Predict Medical Intensive Care Unit Readmissions Based on Both Patient and Process Factors

Abstract

BACKGROUND: Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge. OBJECTIVE: To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions. DESIGN: Retrospective chart review. PARTICIPANTS: We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge. MAIN MEASURES: Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients. KEY RESULTS: Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. The scoring tool derived from the training dataset included the following variables: MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU. The area under the curve of the scoring tool on the validation dataset was 0.76 (95% CI, 0.68-0.84), and the model fit the data well (Hosmer-Lemeshow p = 0.644). Readmission rate was 3.95% among cases in the lowest scoring range and 50% in the highest scoring range. CONCLUSION: We developed a simple seven-variable scoring tool that can be used by clinicians at MICU discharge to efficiently assess a patient's risk of MICU readmission. Additionally, this is one of the first studies to show an association between MICU admission diagnosis of sepsis and MICU readmissions.

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

“…The most common modeling technique was logistic regression. Twenty-three studies evaluated at least one logistic regression model (15–18, 23, 24, 26–29, 31–40, 42, 43, 46). Other model techniques included recurrent neural networks (22, 30), gradient boosting machines (14, 25), and artificial neural networks (23).…”

“…Thirty-three studies were included in the final analysis, as summarized in Supplemental Table 1 (http://links.lww.com/CCX/B130) (14–46). Twenty-five studies recruited patients from a single center; eight studies (14–19, 29, 42) reported results from two or more centers.…”

“…The importance of homogeneity was substantiated by several studies with a low risk of bias ( Supplemental Table 3 , http://links.lww.com/CCX/B130 ) ( 14 – 25 , 27 – 30 , 32 , 37 – 43 , 45 , 46 ). While developing distinct models for each patient subgroup may prove cumbersome if performed to the extreme, limiting future studies to a single surgery type (e.g., cardiac surgery) or a single initial complaint (e.g., respiratory distress) may lead to better overall model performance.…”