OC 13.1 - Arterial Thromboembolism Risk Assessment Model in Hospitalized COVID-19 Patients

Sunday, July 10, 2022

10:45 AM – 11:00 AM

Location: ExCel Center, S11B

Presentation Format: In-Person

Presenting Author(s)

PL

Pin Li

Department of Public Health Sciences
Henry Ford Health System
DETROIT, Michigan, United States

Co-Author(s)

YL

Yi Lee

Department of Medicine
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
QJ

Qasim Jehangir

Department of Medicine
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
CL

Chun-Hui Lin

Department of Public Health Sciences
Henry Ford Health System
Detroit, Michigan, United States
GK

Geetha Krishnamoorthy

Department of Medicine
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
AS

Anupam A. Sule

Department of Medicine
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
DA

Dinesh Apala

Division of Cardiology
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
AH

Abdul R. Halabi

Division of Cardiology
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
KP

Kiritkumar Patel

Division of Cardiology
St. Joseph Mercy Oakland Hospital
Pontiac, Michigan, United States
DW

Dee Dee Wang

Center for Structural Heart Disease
Henry Ford Health System
Detroit, Michigan, United States
LP

Laila Poisson

Department of Public Health Sciences
Henry Ford Health System
Detroit, Michigan, United States
GN

Girish B. Nair

Division of Pulmonary and Critical Care Medicine
William Beaumont Hospital
Royal Oak, Michigan, United States

Background: Patients with SARS-CoV-2 infection are at an increased risk of cardiovascular and thrombotic complications portending an extremely poor prognosis. COVID-19 infection is known to be an independent risk factor for acute ischemic stroke (AIS) and myocardial infarction (MI).

Aims: We propose to develop risk assessment model (RAM) that can risk stratify hospitalized COVID-19 patients for arterial thromboembolism (ATE).

Methods: This multicenter, retrospective study included adult patients admitted with PCR proven SARS-CoV-2 infection between 3/1/2020 and 9/5/2021. The composite outcome was in-hospital ATE events, including AIS, MI, and other ATE identified by ICD-10 codes. 49 variables, including baseline demographics, past medical history, presenting vitals and laboratory values, were categorized with multiple imputation to impute missing values. Variables selected by LASSO regression were used to build the final RAM.

Results: Among 3531 patients from training cohort (admitted before 12/31/2020), 548 (15.5%) patients developed acute ATE, compared to 285 of 2508 (11.4%) in the validation cohort (admitted after 12/31/2020). The final score included 16 items: male gender (1); Non-African American race (1); Age 40-59 (2), Age 60+ (4); Systolic blood pressure >=160mmHg (1); History of cerebrovascular accident (1), Coronary artery disease (1), Smoking (1); Leukocytes>11 K/uL (1), B-type natriuretic peptide>100 pg/mL (1), Lactate dehydrogenase>192 U/L (1), Creatinine>1.4 mg/dL (1), Aspartate aminotransferase>41 U/L (1), Troponin-I>0.03 ng/mL (1), Troponin-I>0.09 ng/mL (3), Interleukin-6>5 pg/mL(1), Potassium < 3.5 mEq/L(1), Magnesium < 1.8 mg/mL (1). RAM had a good discrimination for ATE (training AUC 0.777, 95% CI 0.756–0.797; validation AUC 0.749, 95% CI 0.721–0.778). The validation cohort was stratified as low-risk (score 0-8), intermediate-risk (score 9-13) and high-risk groups (score 14+), with the incidence of ATE 2.1%, 11.3%, and 31.1%, respectively.

Conclusion(s): Our prediction model based on 16 parameters commonly available at hospital admission showed moderate performance in identifying hospitalized COVID-19 patients at low and high risk for ATE.