Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU). METHODS AND RESULTS: We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH. CONCLUSION: The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure.

Atrial Septal Defect Closure Device-Related Infective Endocarditis in a 20-Week Pregnant Woman.

With increasing atrial septal defect (ASD) repairs, more women of childbearing age will have ASD closure devices. Current ASD closure trials have excluded women planning pregnancy, making their management challenging. We present a pregnant woman, with a repaired ASD, who presented with device-related infective endocarditis. (Level of Difficulty: Beginner.).

Platelet-Derived Growth Factor in Heart Failure.

Defective vascular and cardiomyocyte function are implicated in the development and progression of both heart failure with reduced and preserved ejection fraction. Any treatment option that augments these myocardial processes may therefore be of significant value. The platelet-derived growth factor (PDGF) family is involved in a wide range of growth processes and plays a key role in both regulating angiogenesis and mesenchymal cell development. Thus, PDGF may serve as a potent therapy for heart failure. While numerous animal studies have demonstrated beneficial cardiovascular effects of growth factor therapy, promising laboratory data has not yet translated to effective therapies. In this review, we outline the biological role of PDGF and summarize previous studies that have focused on the cardiovascular effects of normal PDGF signaling, administration of PDGF, and the effects of PDGF on stem cell therapy.