Using shapes of COVID-19 positive patient-specific trajectories for mortality prediction

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care

BEHRTDAY: dynamic mortality risk prediction using time-variant COVID-19 patient specific trajectories

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients’ hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients’ trajectories, and through masking, it learnt each variable’s context

Is Cardiac Diastolic Dysfunction a Part of Post-Menopausal Syndrome?

Post-menopausal women exhibit an exponential increase in the incidence of heart failure with preserved ejection fraction compared with men of the same age, which indicates a potential role of hormonal changes in subclinical and clinical diastolic dysfunction. This paper reviews the preclinical evidence that demonstrates the involvement of estrogen in many regulatory molecular pathways of cardiac diastolic function and the clinical data that investigates the effect of estrogen on diastolic function in post-menopausal women. Published reports show that estrogen deficiency influences both early diastolic relaxation via calcium homeostasis and the late diastolic compliance associated with cardiac hypertrophy and fibrosis. Because of the high risk of diastolic dysfunction and heart failure with preserved ejection fraction in post-menopausal women and the positive effects of estrogen on preserving cardiac function, further clinical studies are needed to clarify the role of endogenous estrogen or hormone replacement in mitigating the onset and progression of heart failure with preserved ejection fraction in women. (C) 2019 Published by Elsevier on behalf of the American College of Cardiology Foundation.Cardiolog

Combining stress-only myocardial perfusion imaging with coronary calcium scanning as a new paradigm for initial patient work-up: an exploratory analysis

BACKGROUND: We conducted an exploratory analysis to test whether the addition of a CAC scan can increase the applicability of stress-only SPECT-MPI. METHODS: We studied 162 patients referred for rest/stress SPECT-MPI who underwent a CAC scan. Each scan was interpreted by two readers in stepwise fashion: stress-only images; addition of clinical data; and addition of CAC data. At each step, the reader was asked if rest SPECT-MPI was necessary. RESULTS: Stress-only images were interpreted as normal in 62, probably normal in 42, equivocal in 15, probably abnormal in 5, and definitely abnormal in 38 patients. Rest SPECT-MPI imaging was considered necessary, in 0% of normal studies, but in 88% of probably normal studies, and 100% of those with equivocal/abnormal studies. Addition of the clinical data did not materially change this decision. Additional consideration of the CAC scan results did not influence the deemed lack of need for a rest SPECT-MPI with normal SPECT-MPI or the necessity of rest SPECT-MPI with abnormal SPECT-MPI. However, the CAC scan reduced the deemed need for a rest SPECT-MPI in 72% with a probably normal, 47% with an equivocal, and 40% of those with a probably abnormal SPECT-MPI. CONCLUSIONS: Our exploratory analysis indicates that addition of a CAC scan to stress SPECT-MPI tends to diminish experienced readers\u27 deemed need to perform rest SPECT-MPI studies among patients with probably normal or borderline stress-only SPECT-MPI studies. Thus, further study appears warranted to assess the utility of using CAC scanning as a means for increasing the percent of SPECT-MPI studies that can be performed as stress-only studies