Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722-0.773) and an average precision of 0.233 (95% CI 0.194-0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.

Impact of antibiotic treatment for pulmonary exacerbations on bacterial diversity in cystic fibrosis.

BACKGROUND: A diverse array of bacterial species is present in the CF airways, in addition to those recognised as clinically important. Here, we investigated the relative impact of antibiotics, used predominantly to target Pseudomonas aeruginosa during acute exacerbations, on other non-pseudomonal species. METHODS: The relative abundance of viable P. aeruginosa and non-pseudomonal species was determined in sputa from 12 adult CF subjects 21, 14, and 7 days prior to antibiotics, day 3 of treatment, the final day of treatment, and 10-14 days afterwards, by T-RFLP profiling. RESULTS: Overall, relative P. aeruginosa abundance increased during antibiotic therapy compared to other bacterial species; mean abundance pre-antibiotic 51.0±36.0% increasing to 71.3±30.4% during antibiotic (ANOVA: F(1,54)=5.16; P<0.027). Further, the number of non-pseudomonal species detected fell; pre-antibiotic 6.0±3.3 decreasing to 3.7±3.3 during treatment (ANOVA: F(1,66)=5.11; P<0.027). CONCLUSIONS: Antibiotic treatment directed at P. aeruginosa has an additional significant impact on non-pseudomonal, co-colonising species.