Clinical and echocardiographic risk score predicts need for hospitalization among patients with COVID-19

Abstract

Abstract Background Age and medical co-morbidities are known predictors of disease severity in coronavirus disease-2019 (COVID-19). Whether baseline transthoracic echocardiographic (TTE) abnormalities could refine risk-stratification in this context remains unknown. Purpose To analyze performance of a risk score combining clinical and pre-morbid TTE features in predicting risk of hospitalization among patients with COVID-19. Methods Adult patients testing positive for COVID-19 between March 1st and October 31st, 2020 with pre-infection TTE (within 15–180 days) were selected. Those with severe valvular disease, acute cardiac events between TTE and COVID-19, or asymptomatic carriers of virus (on employment screening/nursing home placement) were excluded. Baseline demographic, clinical co-morbidities, and TTE findings were extracted from electronic health records and compared between groups stratified by hospital admission. Total sample was randomly split into training (≈70%) and validation (≈30%) sets. Age was transformed into ordered categories based on cubic spline regression. Regression model was developed on the training set. Variables found significant (at p<0.10) on univariate analysis were selected for multivariate analysis with hospital admission as outcome. β-coefficients were obtained from 5000 bootstrapped samples after forced entry of significant variables, and scores assigned using Schneeweiss's scoring system. Final risk score performance was compared between training/validation cohorts using receiver-operating curve (ROC) and calibration curve analyses. Results 192 patients were included, 83 (43.2%) were admitted. Clinical/TTE characteristics stratified by hospitalization are in Table 1. Moderate or worse pulmonary hypertension and left atrial enlargement were only TTE parameters with coefficients deserving a score (Table 1). The risk score had excellent discrimination in training and validation sets (figure 1 left panel; AUC 0.785 versus 0.836, p=0.452). Calibration curves showed strong linear correlation between predicted and observed probabilities of hospitalization in both training and validation sets (Figure 1, middle and right panels, respectively). ROC analysis revealed a score ≥7 as having best overall quality with sensitivity and specificity of 70–75% in both training and validation sets. A score ≥12 had 98% and 97% specificity and ≥14 had 100% specificity. Conclusion A combined clinical and echocardiographic risk score shows promise in predicting risk of hospitalization among patients with COVID-19, and hence help anticipate resource utilization. External validation and comparison against clinical risk score alone is worth further investigation. Funding Acknowledgement Type of funding sources: None.