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A Simple Scoring Tool to Predict Medical Intensive Care Unit Readmissions Based on Both Patient and Process Factors
Nirav Haribhakti1, Pallak Agarwal2, Julia Vida3
1Division of Pulmonary and Critical Care Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, Suite 5200B, New Brunswick, NJ, 08901, USA. nharibhakti@lifespan.org.
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.
Insights
A new, simple scoring tool helps clinicians predict medical intensive care unit (MICU) readmissions using seven key factors. This tool aids efficient discharge planning and identifies high-risk patients, including those with sepsis.
Area of Science:
- Critical Care Medicine
- Health Services Research
- Predictive Analytics
Background:
- Existing medical intensive care unit (MICU) readmission predictive models are often complex and not practical for clinical use during discharge planning.
- There is a need for a straightforward scoring system that integrates patient, system, and process factors to predict MICU readmissions effectively.
Purpose of the Study:
- To develop a simple, comprehensive scoring tool for predicting medical intensive care unit (MICU) readmissions.
- To incorporate both patient-specific and system-level factors into a single predictive model.
Main Methods:
- A retrospective chart review was conducted on patients admitted to the MICU.
- Logistic regression models were used to develop a scoring tool on a training set (n=409) and validated on an independent set (n=474).
- Key variables identified included sepsis diagnosis, intubation, mechanical ventilation duration, tracheostomy, admission source, discharge timing, and length of stay.
Main Results:
- The developed seven-variable scoring tool demonstrated good predictive performance with an Area Under the Curve of 0.76 in the validation set.
- Readmission rates varied significantly across scoring ranges, from 3.95% in the lowest to 50% in the highest.
- The study identified an association between MICU admission diagnosis of sepsis and increased readmission risk.
Conclusions:
- A simple, seven-variable scoring tool has been developed for efficient clinical use at MICU discharge to assess readmission risk.
- This tool can aid clinicians in identifying patients at higher risk for readmission, facilitating targeted interventions.
- The study highlights the significant association between sepsis as a MICU admission diagnosis and subsequent readmissions.
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