Nirav Haribhakti1, Pallak Agarwal2, Julia Vida3, Pamela Panahon2, Farsha Rizwan2, Sarah Orfanos2, Jonathan Stoll2, Saqib Baig4, Javier Cabrera5,6, John B Kostis6, Cande V Ananth6,7,8, William J Kostis6, Anthony T Scardella2. 1. Division 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. 2. Division of Pulmonary and Critical Care Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, Suite 5200B, New Brunswick, NJ, 08901, USA. 3. Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA. 4. Division of Pulmonary, Allergy, and Critical Care, Thomas Jefferson University Hospitals, Philadelphia, PA, USA. 5. Department of Statistics and Biostatistics, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA. 6. Cardiovascular Institute, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA. 7. Division of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA. 8. Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
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.
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.
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