Background: Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients. Method: This is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix. Results: The cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p < 0.001; ICU LOS 3.8 days vs. 1.9 days, p < 0.001). 9.8% of patients in the VTE group required more advanced oxygen support, compared to 2.7% of patients in the non-VTE group (p < 0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes, and inflammatory markers were predictors for in-hospital VTE in COVID-19 patients. Conclusions: Patients with COVID-19 have a high risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making a clinical judgment on empirical dosages of anticoagulation.

PURPOSE: Studies have investigated the early use of liquid biopsy (LBx) during the diagnostic workup of patients presenting with clinical evidence of advanced lung cancer, but real-world adoption and impact has not been characterized. The aim of this study was to determine whether the use of LBx before diagnosis (Dx; LBx-Dx) enables timely comprehensive genomic profiling (CGP) and shortens time until treatment initiation for advanced non–small-cell lung cancer (aNSCLC). MATERIALS AND METHODS: This study used the Flatiron Health-Foundation Medicine electronic health record-derived deidentified clinicogenomic database of patients with aNSCLC from approximately 280 US cancer clinics. RESULTS: Of 1,076 patients with LBx CGP ordered within 30 days prediagnosis/postdiagnosis, we focused on 56 (5.2%) patients who ordered LBx before diagnosis date (median 8 days between order and diagnosis, range, 1-28). Compared with 1,020 patients who ordered LBx after diagnosis (Dx-LBx), LBx-Dx patients had similar stage and ctDNA tumor fraction (TF). LBx-Dx patients received CGP results a median of 1 day after Dx versus 25 days for Dx-LBx patients. Forty-three percent of LBx-Dx were positive for an National Comprehensive Cancer Network driver, and 32% had ctDNA TF >1% but were driver negative (presumed true negatives). In 748 patients with previously untreated aNSCLC, median time from Dx to therapy was shorter in the LBx-Dx versus Dx-LBx group (21 v 35 days; P < .001). CONCLUSION: Early LBx in anticipation of pathologic diagnosis of aNSCLC was uncommon in this real-world cohort, yet this emerging paradigm was associated with an abbreviated time to CGP results and faster therapy initiation. Forthcoming prospective studies will clarify the utility of LBx in parallel with biopsy for diagnostic confirmation for patients presenting with suspected advanced lung cancer.

Hypercoagulability is a recognized feature in SARS-CoV-2 infection. There exists a need for a dedicated risk assessment model (RAM) that can risk-stratify hospitalized COVID-19 patients for venous thromboembolism (VTE) and guide anticoagulation. We aimed to build a simple clinical model to predict VTE in COVID-19 patients. This large-cohort, retrospective study included adult patients admitted to four hospitals with PCR-confirmed SARS-CoV-2 infection. Model training was performed on 3531 patients hospitalized between March and December 2020 and validated on 2508 patients hospitalized between January and September 2021. Diagnosis of VTE was defined as acute deep vein thrombosis (DVT) or pulmonary embolism (PE). The novel RAM was based on commonly available parameters at hospital admission. LASSO regression and logistic regression were performed, risk scores were assigned to the significant variables, and cutoffs were derived. Seven variables with assigned scores were delineated as: DVT History = 2; High D-Dimer (>500–2000 ng/mL) = 2; Very High D-Dimer (>2000 ng/mL) = 5; PE History = 2; Low Albumin (<3.5 g/dL) = 1; Systolic Blood Pressure <120 mmHg = 1, Tachycardia (heart rate >100 bpm) = 1. The model had a sensitivity of 83% and specificity of 53%. This simple, robust clinical tool can help individualize thromboprophylaxis for COVID-19 patients based on their VTE risk category.