Biomarker-guided duration of antibiotic treatment in children hospitalised with confirmed or suspected bacterial infection: statistical analysis plan for the BATCH trial and PRECISE sub-study.

INTRODUCTION: The BATCH trial is a multi-centre randomised controlled trial to compare procalcitonin-guided management of severe bacterial infection in children with current management. PRECISE is a mechanistic sub-study embedded into the BATCH trial. This paper describes the statistical analysis plan for the BATCH trial and PRECISE sub-study. METHODS: The BATCH trial will assess the effectiveness of an additional procalcitonin test in children (aged 72 h to 18 years) hospitalised with suspected or confirmed bacterial infection to guide antimicrobial prescribing decisions. Participants will be enrolled in the trial from randomisation until day 28 follow-up. The co-primary outcomes are duration of intravenous antibiotic use and a composite safety outcome. Target sample size is 1942 patients, based on detecting a 1-day reduction in intravenous antibiotic use (90% power, two-sided) and on a non-inferiority margin of 5% risk difference in the composite safety outcome (90% power, one-sided), while allowing for up to 10% loss to follow-up. RESULTS: Baseline characteristics will be summarised overall, by trial arm, and by whether patients were recruited before or after the pause in recruitment due to the COVID-19 pandemic. In the primary analysis, duration of intravenous antibiotic use will be tested for superiority using Cox regression, and the composite safety outcome will be tested for non-inferiority using logistic regression. The intervention will be judged successful if it reduces the duration of intravenous antibiotic use without compromising safety. Secondary analyses will include sensitivity analyses, pre-specified subgroup analyses, and analysis of secondary outcomes. Two sub-studies, including PRECISE, involve additional pre-specified subgroup analyses. All analyses will be adjusted for the balancing factors used in the randomisation, namely centre and patient age. CONCLUSION: We describe the statistical analysis plan for the BATCH trial and PRECISE sub-study, including definitions of clinical outcomes, reporting guidelines, statistical principles, and analysis methods. The trial uses a design with co-primary superiority and non-inferiority endpoints. The analysis plan has been written prior to the completion of follow-up. TRIAL REGISTRATION: BATCH: ISRCTN11369832, registered 20 September 2017, doi.org/10.1186/ISRCTN11369832. PRECISE: ISRCTN14945050, registered 17 December 2020, doi.org/10.1186/ISRCTN14945050.

Short-term risk prediction after major lower limb amputation: PERCEIVE study.

BACKGROUND: The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. METHODS: The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. RESULTS: Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). CONCLUSION: Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation.