Predicting 30-Day hospital readmission using Hospital score and Lace index in a tertiary care hospital in Pakistan: A cross-sectional study

Objectives Unplanned hospital readmissions not only negatively impact patients' quality of life but also pose a substantial burden on both the hospital and the individual. Hence, there is a growing need to identify and prevent avoidable readmissions, aiming to reduce mortality and enhance the quality of care. LACE and HOSPITAL scores are widely used tools to assess the risk of readmission. This study was conducted to determine if the HOSPITAL score and LACE index are fair predictors of 30-day readmission in adult patients admitted to the internal medicine service at a tertiary care centre in a Lower-Middle Income Country (LMIC) like Pakistan. Design This study employed a cross-sectional study design. Setting The study includes records of all patients admitted to the internal medicine service at a tertiary care hospital in Karachi, Pakistan during 5 years (2015–2020). Participants This study involved 38,602 patients, aged 18 and above who were admitted to internal medicine service through the emergency room or electively. Outcome measures Readmission to hospital within 30 days Results The study included a total of 38,602 unique patients, among whom 7.1% (2,822) were readmitted within 30 days of their index admission. The mean age of all patients was 55.8 years, (S.D. = 18.9), with males constituting 46.1% (n = 17,797). Patients readmitted within 30 days demonstrated a higher mean LACE index compared to those not readmitted (9.1 vs. 7.58, p < 0.001). Additionally, the mean HOSPITAL score was higher in patients readmitted within 30 days compared to those not readmitted (3.37 vs. 2.69, p < 0.001). An ROC (Receiver operating characteristic curve) evaluation of the LACE index and HOSPITAL score for this population showed a C statistic of 0.657 (95% CI) and 0.655, respectively, for hospital readmission Conclusions The LACE index and HOSPITAL scores demonstrated fair discrimination in predicting readmissions, suggesting their potential for improving early detection and intervention strategies to mitigate such occurrences. This study underscores the importance of developing and validating predictive models across various hospitals, taking into account the complex healthcare landscape of LMICs.