Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation
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RESEARCH LETTER
Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation Adeel Abbasi1, Yasmin Karasu2, Cindy Li2, Neel R. Sodha3, Carsten Eickhoff4,5 and Corey E. Ventetuolo1,6*
Hemorrhage and thrombosis are major causes of morbidity and mortality during extracorporeal membrane oxygenation (ECMO). Even in a controlled setting, bleeding occurs frequently—almost half (46%) of the patients randomized to ECMO in the EOLIA trial had hemorrhage requiring transfusion [1]. The pathophysiology of these complications during ECMO is complex, dynamic and not fully understood [2]. This may explain why standard approaches to monitor coagulation are imperfect and studies that employ traditional biostatistical methods do not consistently identify common risk factors. We applied machine learning to an ECMO dataset to predict hemorrhage and thrombosis. Our hypothesis was that machine learning would accurately predict these events and identify novel factors not anticipated clinically or identified by traditional biostatistical methods. We used a preexisting, manually extracted, adult ECMO dataset established to study anticoagulation practices and ECMO complications [3]. The dataset was first cleaned. Data were condensed to one row per patient. The mean and range were used to create new variables from continuous variables. Categorical variables were encoded as binary variables using one-hot encoding. Missingness was handled by first dropping variables’ missing values for all patients. Some missing data were recovered by reviewing the electronic health record. Seven variables were dropped to limit the potential of reverse causation artificially *Correspondence: [email protected] 6 Department of Health Services, Policy and Practice, Brown School of Public Health, Providence, RI, USA Full list of author information is available at the end of the article
enhancing outcome prediction. Remaining variables still missing values (thromboelastography, anti-factor Xa levels) were dropped. Hemorrhage was defined as bleeding during ECMO requiring a transfusion and/or intervention, thrombosis as deep vein thrombosis, pulmonary embolism, ischemic stroke during or following ECMO, or ECMO circuitry change. The study cohort included 44 consecutive patients supported with ECMO. The average age was 42 years; 66% were men. The most common indication for ECMO was acute respiratory distress syndrome (59%), and 66% were supported with veno-venous ECMO. There were a total of 19 hemorrhage events, most commonly cannulation site bleeding (42%), and 16 thrombotic events, most commonly deep vein thrombosis (81%). We compared chi-square to five supervised classification and regression machine learning models: random forest, recursive feature elimination, decision trees, k-nearest neighbors and logistic regression. Leave-oneout cross-validation maximized the training cohort size, which allowed each patient to be used to train and test the models to minimize sample bias [4]. The models to predict hemo
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