Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

Abstract

Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

“…In the context of COVID-19, these systems provide tools for early diagnosis and have the potential to decrease the number of fatalities [17] [29] . Furthermore, storing the monitored parameters and patients’ health history enables analyses of the efficacy of treatment plans and the identification of risk factors for disease outcomes that are required for timely and effective clinical decision-making [53] .…”

Sociodemographic, laboratory, image data and predictors of gravity risk in patients with COVID-19

“…Important changes related to vital signs have been documented during the admission of patients infected with COVID-19, such as an increase in baseline temperature as well as heart and respiratory rates. This assessment is indispensable, since reduced SPO 2 and increased blood pressure, heart rate, and respiratory rate are independent risk factors for mortality in patients with COVID -19, with emphasis on SPO 2 and high blood pressure [ 36 , 37 ]. A study published in The Journal of the American Medical Association (JAMA) revealed that at the time of screening, 30.7% of patients were febrile and 17.3% tachypneic [ 13 ].…”

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

“…As the number of cases increases and more data becomes available, various researches [1] , [2] , [3] , [4] , [5] , [6] , [7] develop a range of mathematical models or employ machine learning algorithms to forecast the transmission of SARS-CoV-2. Previous studies have also employed LSTM [8] , [9] , [10] , [11] , [12] or XGBoost [13] , [14] , [15] , [16] , [17] , [18] , [19] models to forecast the spread of COVID-19 and identify the most influential COVID-19 indicators. Chimmua et al [8] adopted LSTM algorithm to forecast confirmed cases in Canada within next two weeks and emphasized the significant role of social distance regular.…”

“…Models developed on data from early COVID infected nations such as Italy and the United States are used to predict the development of the disease in other nations. The machine learning algorithm XGBoost was employed to build the models to predict the criticality [13] , mortality [14] , [15] and survival [16] in COVID-19 patients. Li et al [17] constructed an XGBoost-based classification algorithm to distinguish between influenza and COVID-19 patients.…”