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.
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.
Retrospective chart review.
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.
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.
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.
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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Kramer AA, Higgins TL, Zimmerman JE. Intensive care unit readmissions in U.S. hospitals: patient characteristics, risk factors, and outcomes. Crit Care Med 2012;40(1):3–10.
Rosenberg AL, Hofer TP, Hayward RA, Strachan C, Watts CM. Who bounces back? Physiologic and other predictors of intensive care unit readmission. Crit Care Med. 2001;29(3):511–8.
Kramer AA, Higgins TL, Zimmerman JE. The association between ICU readmission rate and patient outcomes. Crit Care Med. 2013;41(1):24–33.
Jo YS, Lee YJ, Park JS, et al. Readmission to medical intensive care units: risk factors and prediction. Yonsei Med J. 2015;56(2):543–9.
Ponzoni CR, Corrêa TD, Filho RR, et al. Readmission to the intensive care unit: incidence, risk factors, resource use, and outcomes. A retrospective cohort study. Ann Am Thorac Soc. 2017;14(8):1312–1319.
Vollam S, Dutton S, Lamb S, Petrinic T, Young JD, Watkinson P. Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis. Intensive Care Med. 2018;44(7):1115–1129.
Laupland KB, Shahpori R, Kirkpatrick AW, Stelfox HT. Hospital mortality among adults admitted to and discharged from intensive care on weekends and evenings. J Crit Care. 2008;23(3):317–24.
Ofoma UR, Dong Y, Gajic O, Pickering BW. A qualitative exploration of the discharge process and factors predisposing to readmissions to the intensive care unit. BMC Health Serv Res. 2018;18(1):6.
Renton J, Pilcher DV, Santamaria JD, et al. Factors associated with increased risk of readmission to intensive care in Australia. Intensive Care Med. 2011;37(11):1800-8.
Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019;95:27–37.
Fialho A.S., Cismondi F., Vieira S.M., Reti S.R., Sousa J.M.C., Finkelstein SN. Data mining using clinical physiology at discharge to predict ICU readmissions. Exp Syst Appl. 2012;39(18):13158–65.
Rojas JC, Carey KA, Edelson DP, Venable LR, Howell MD, Churpek MM. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann Am Thorac Soc. 2018;15(7):846–853.
Badawi O, Breslow MJ. Readmissions and death after ICU discharge: development and validation of two predictive models. PLoS ONE. 2012;7(11):e48758.
Lin YW, Zhou Y, Faghri F, Shaw MJ, Campbell RH. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLoS ONE. 2019;14(7):e0218942.
Norman BC, Cooke CR, Ely EW, Graves JA. Sepsis-associated 30-day risk-standardized readmissions: analysis of a nationwide Medicare sample. Crit Care Med 2017;45(7):1130-1137.
Mayr FB, Talisa VB, Balakumar V, Chang CH, Fine M, Yende S. Proportion and cost of unplanned 30-day readmissions after sepsis compared with other medical conditions. JAMA. 2017;317(5):530–531.
Abusara AK, Nazer LH, Hawari FI. ICU readmission of patients with cancer: incidence, risk factors and mortality. J Crit Care. 2019;51:84–87.
Makris N, Dulhunty JM, Paratz JD, Bandeshe H, Gowardman JR. Unplanned early readmission to the intensive care unit: a case-control study of patient, intensive care and ward-related factors. Anaesth Intensive Care. 2010;38(4):723–31.
de Mestral C, Iqbal S, Fong N, et al. Impact of a specialized multidisciplinary tracheostomy team on tracheostomy care in critically ill patients. Can J Surg. 2011;54(3):167–172.
Spataro E, Durakovic N, Kallogjeri D, Nussenbaum B. Complications and 30-day hospital readmission rates of patients undergoing tracheostomy: a prospective analysis. Laryngoscope. 2017;127(12):2746–2753.
Simpson HK, Clancy M, Goldfrad C, Rowan K. Admissions to intensive care units from emergency departments: a descriptive study. Emerg Med J 2005;22(6):423–8.
Molina JA, Seow E, Heng BH, Chong WF, Ho B: Outcomes of direct and indirect medical intensive care unit admissions from the emergency department of an acute care hospital: a retrospective cohort study. BMJ Open. 2014;4(11):e005553
Flabouris A, Hart GK, George C. Outcomes of patients admitted to tertiary intensive care units after interhospital transfer: comparison with patients admitted from emergency departments. Crit Care Resusc. 2008;10(2):97–105.
James MT, Wald R, Bell CM, et al. Weekend hospital admission, acute kidney injury, and mortality. J Am Soc Nephrol. 2010;21(5):845–51.
Kramer AA, Zimmerman JE. A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak. 2010;10:27.
Markazi-moghaddam N, Fathi M, Ramezankhani A. Risk prediction models for intensive care unit readmission: A systematic review of methodology and applicability. Aust Crit Care. 2019.
Frost SA, Tam V, Alexandrou E, et al. Readmission to intensive care: development of a nomogram for individualising risk. Crit Care Resusc. 2010;12(2):83–9.
Magruder JT, Kashiouris M, Grimm JC, et al. A predictive model and risk score for unplanned cardiac surgery intensive care unit readmissions. J Card Surg. 2015;30(9):685–90.
Conflict of Interest
The authors declare that they do not have a conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Haribhakti, N., Agarwal, P., Vida, J. et al. A Simple Scoring Tool to Predict Medical Intensive Care Unit Readmissions Based on Both Patient and Process Factors. J GEN INTERN MED (2021). https://doi.org/10.1007/s11606-020-06572-w
- patient discharge
- patient readmission
- intensive care units
- risk assessment
- patient transfer