Study design
We conducted a single centre, prospective cohort study to determine the efficacy of two models at predicting unplanned readmissions in those aged 75 and older who were initially admitted with an acute medical condition. Data collection took place from February to April 2019 at a large teaching hospital in central London. All patients 75 years of age and older admitted to the acute admissions ward during a continuous 30-day period were included in the study. Exclusion criteria included patients transferred from the acute admissions ward to another inpatient ward and those who died prior to 30-days post-discharge. Data collection was carried out Monday to Friday and as such patients both admitted and discharged within the same Saturday-Sunday period may have been omitted from collection. The primary outcome measure was the area under the receiver operating characteristic curve (AUROC), also termed the c-statistic, for the BOOST and LACE scoring systems. A power calculation was completed using the R-based web tool easyROC [12] with sample size determined using a type I error of 0.05, a power of 0.8, a c-statistic of 0.7, and an allocation ratio of 6. The suggested sample size was 152 with 19 positive and 133 negative cases. The allocation ratio was predicted from literature suggesting a readmission rate of 14% in the elderly [13]. Secondary outcomes included the significance of individual predictor model components, and the sensitivity, specificity, and odds of high and low risk LACE and BOOST patient groups.