The risk factors of the 72-h unscheduled return visit admission to emergency department in adults below 50 years old

Abstract

Background An unscheduled return visit (URV) to the emergency department (ED) within 72-h is an indicator of ED performance. An unscheduled return revisit (URV) within 72-h was used to monitor adverse events and medical errors in a hospital quality improvement program. The study explores the potential factors that contribute to URV to the ED within 72-h and the unscheduled return revisit admission (URVA) in adults below 50 years old. Methods The case–control study enrolled 9483 URV patients during 2015–2020 in National Cheng-Kung University Hospital. URVA and URV non-admission (URVNA) patients were analyzed. The Gini impurity index was calculated by decision tree (DT) to split the variables capable of partitioning the groups into URVA and URVNA. Logistic regression is applied to calculate the odds ratio (OR) of candidate variables. The α level was set at 0.05. Results Among patients under the age of 50, the percentage of females in URVNA was 55.05%, while in URVA it was 53.25%. Furthermore, the average age of URVA patients was 38.20 ± 8.10, which is higher than the average age of 35.19 ± 8.65 observed in URVNA. The Charlson Comorbidity Index (CCI) of the URVA patients (1.59 ± 1.00) was significantly higher than that of the URVNA patients (1.22 ± 0.64). The diastolic blood pressure (DBP) of the URVA patients was 85.29 ± 16.22, which was lower than that of the URVNA (82.89 ± 17.29). Severe triage of URVA patients is 21.1%, which is higher than the 9.7% of URVNA patients. The decision tree suggests that the factors associated with URVA are “severe triage,” “CCI higher than 2,” “DBP less than 86.5 mmHg,” and “age older than 34 years”. These risk factors were verified by logistic regression and the OR of CCI was 2.42 (1.50–3.90), the OR of age was 1.84 (1.50–2.27), the OR of DBP less than 86.5 was 0.71 (0.58–0.86), and the OR of severe triage was 2.35 (1.83–3.03). Conclusions The results provide physicians with a reference for discharging patients and could help ED physicians reduce the cognitive burden associated with the diagnostic errors and stress.

Prediction Models Using Decision Tree and Logistic Regression Method for Predicting Hospital Revisits in Peritoneal Dialysis Patients

“…Numerous studies in the past have developed prediction models for forecasting patient hospital revisits [ 8 , 9 , 10 , 11 ]. Different from previous research, our study incorporates a broader range of clinical parameters in our analysis, including laboratory tests, vital signs, GCS scores, and comorbidities as indicated in Table 1 and Table 3 .…”