Evaluation of a fast‐and‐frugal clinical decision algorithm (‘pathways’) on clinical outcomes in hospitalised patients with COVID‐19 treated with anticoagulants
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
AbstractRationale, Aims and ObjectivesCritics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion.MethodsAn observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree (‘algorithm’). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion.ResultsIn an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings.ConclusionsWhen delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes.
