Machine Learning Applied to Routine Blood Tests and Clinical Metadata to Identify and Classify Heart failure

Abstract

AbstractIntroductionWe applied machine learning (ML) to routine bloods, then to advanced haematology data from a full blood count (rawFBC) plus biochemistry, to build predictive models for heart failure, which were then used at population scale.MethodsRoutine blood results from 8,031 patients with heart failure, with equal number of controls, were used in ML training and testing datasets (Split 80:20). NT-proBNP was used for diagnostic comparison. rawFBC metadata was used in a dataset of 698 patients, 314 of whom had heart failure, to train and test ML models (Split 70:30) from rawFBC, rawFBC plus biochemistry and routine bloods. The rawFBC model was used to predict heart failure in a validation dataset of 69,492 FBCs (2.3% heart failure prevalence).ResultsHeart failure was predicted from rawFBC and biochemistry versus rawFBC AUROC 0.93 versus 0.91, 95% CI -0.023 to 0.048, P = 0.5, and predicted from routine bloods and NT-proBNP, AUROC 0.87 versus 0.81, 95% CI 0.004 to 0.097, P = 0.03. In the validation cohort heart failure was predicted from rawFBC with AUROC 0.83, 95% CI 0.83 to 0.84, P < 0.001, sensitivity 75%, specificity 76%, PPV 7%, NPV 99.2% (Figure 2). Elevated NT-proBNP (≥ 34 pmol/L) was predicted from rawFBC with AUROC 0.97, 95% CI 0.93 to 0.99, P < 0.0001. Common predictive features included markers of erythropoiesis (red cell distribution width, haemoglobin, haematocrit).ConclusionHeart failure can be predicted from routine bloods with accuracy equivalent to NT-proBNP. Predictive features included markers of erythropoiesis, with therapeutic monitoring implications.