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Prediction of pulmonary intensive care unit readmissions with Stability and Workload Index for Transfer score
1Health Sciences University Atatürk Chest Diseases and Thoracic Surgery Education and Research Hospital, Department of Pulmonology, Ankara, Türkiye
2Alanya Alaaddin Keykubat University, Faculty of Medicine, Department of Pulmonology, Alanya, Antalya, Türkiye
Eurasian Journal of Pulmonology 2023; 25(2): 98-106 DOI: 10.14744/ejp.2022.4001
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Abstract


BACKGROUND AND AIM: Readmission of patients discharged from the intensive care unit (ICU) to the ICU is common and increases mortality. The Stability and Workload Index for Transfer (SWIFT) score is a scoring system developed and validated to predict the risk of readmission to the ICU. We evaluated the usability of this scoring system in patients with respiratory failure in a pulmonary intensive care unit (PICU).

METHODS: This study was a retrospective cross-sectional study that included patients hospitalized in the PICU between January 1, 2020, and December 31, 2020. Patients who were discharged to the clinic or home and readmitted in the first 7–30 days were included in the study. Patients referred to an upper-level ICU or another hospital and those who died in the hospital were excluded from the study.

RESULTS: A total of 442 patients received inpatient treatment during the study period, and 421 patients were included. Eight (1.9%) patients were readmitted within the first 7 days, and 25 (5.9%) patients were readmitted within 7–30 days. There was no significant difference between the SWIFT score, Acute Physiology and Chronic Health Evaluation II (APACHE II), and modified Charlson Comorbidity Index (mCCI) scores of the readmitted patients and those who were not. We calculated the area under the curve value for the SWIFT score as 0.548 (95% CI: 0.440–0.656).

CONCLUSIONS: For patients discharged from the PICU, neither the SWIFT score nor APACHE II
and mCCI were not sufficient to predict readmission. This study showed that existing scoring systems is insufficient to predict the readmission of patients with respiratory failure, and there is still a need for scoring systems to predict the readmission of these patients.