#DigitalSurgery: The Robot Will Assist the Surgeon Now. @ShafiAhmed5 on the convergence of #AR, #VR #AI & #Robotics on augmenting the clinician of the future. https://t.co/hpWqv5D2Yw Join us next week for #xMed 2019. https://t.co/La9S00SM8Z #MedEd #hcldr #surgery #digitalHealth— Exponential Medicine (@ExponentialMed) October 30, 2019
Critically ill patients are a highly heterogenous population who tend to have many comorbidities. Often, patients admitted to intensive-care units (ICUs) with the same diagnosis and similar risk profiles according to available risk prediction scores have completely different clinical trajectories and outcomes. Even with increasingly large amounts of electronic health record data available, including clinical notes, vital sign measurements, laboratory data, and imaging data, the goal of unravelling complex disease mechanisms to better forecast patient outcomes remains largely unattained in critical care.
Motivated by this problem, in The Lancet Digital Health, Annelaura Nielsen and colleagues present the results of an innovative, exploratory analysis predicting in-hospital, 30-day, and 90-day mortality on the basis of a large and uniquely detailed cohort of patients in ICUs. In addition to laboratory data and other clinical parameters obtained during the first 24 h of an ICU stay for more than 10 000 patients, this dataset also included detailed, 10-year medical histories before ICU admission for more than 230 000 individuals. Factors present before ICU admission, such as comorbidities and medical history, have long been known to affect the risk of future complications or chance of survival. However, even previous machine learning efforts that included broad health record data paid insufficient attention to these factors, and Nielsen and colleagues’ study is the first to link detailed medical history data from a highly heterogeneous patient population to clinical parameters measured during ICU stays. Remarkably, the authors concluded that a simple feed-forward neural network model including only age, sex, and patients’ previous 10-year disease history performed similarly (in terms of prediction of mortality risk) to the two most commonly used ICU risk scores (the Simplified Acute Physiology Score II and the Acute Physiologic Assessment and Chronic Health Evaluation II), and that the combination of medical history and comorbidities with high-frequency ICU data outperformed both scores (Matthews correlation coefficient 0·391 for in-hospital mortality vs 0·347 with the Simplified Acute Physiology Score II and 0·300 with the Acute Physiologic Assessment and Chronic Health Evaluation II).