Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers

Abstract

Introduction. Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before. Objective. We aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers. Patients and Methods. We obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period. The time of follow-up ranged from 12 to 72 months. A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission. Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset. Results. For in-hospital mortality, our model achieved a c-statistic of 0.89 while the GRACE score 2.0 achieved 0.90. For six-month mortality, the results of our model and the GRACE score on the test set were 0.77 and 0.73, respectively. Red cell distribution width (HR 1.23; 95% CL 1.16-1.30; P<0.001) and neutrophil to lymphocyte ratio (HR 1.08; 95% CL 1.05-1.10; P<0.001) showed independent association with all-cause mortality in multivariable Cox regression. Conclusions. Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome. A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

“…In the past decade, the incorporation of ML algorithms into prognostic models has increased. For example, multiple studies discuss the utility of ML in prognostic models for mortality following myocardial infarction 30–32 . ML has also been applied in cardiac diagnostics, to predict the occurrence of atrial fibrillation 33 .…”

“…For example, multiple studies discuss the utility of ML in prognostic models for mortality following myocardial infarction. [30][31][32] ML has also been applied in cardiac diagnostics, to predict the occurrence of atrial fibrillation. 33 Recent reviews emphasize the tremendous interest in combining these techniques for clinical guidance and the need for additional prognostic studies.…”

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma

“…17 In the medical literature, machine learning is being applied in both the clinical and basic science fields from medical imaging to genomic sequencing to predicting clinical outcomes. 13,18,19 An area where ML may be of additional value is in the analysis of large clinical data registries. Multi-institutional, national, and international data registries allow higher statistical power and open doors to address previously difficultto-answer questions.…”

Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation

“…For gradient boosted trees model using the XGboost implementation, we applied a similar approach as previously described by us, which involved applications of machine learning (ML) methods in predicting the outcomes of acute coronary syndrome and balloon cryoablation of pulmonary veins 17–19 …”