Predictors of venous thromboembolism in COVID-19 patients: results of the COVID-19 Brazilian Registry

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Development of a Decision Support Tool for Anticoagulation in Critically Ill Patients Admitted for SARS-CoV-2 Infection: The CALT Protocol

Severe COVID-19 infections are at high risk of causing thromboembolic events (TEEs). However, the usual exams may be unavailable or unreliable in predicting the risk of TEEs at admission or during hospitalization. We performed a retrospective analysis of two centers (n = 124 patients) including severe COVID-19 patients to determine the specific risk factors of TEEs in SARS-CoV-2 infection at admission and during stays at the intensive care unit (ICU). We used stepwise regression to create two composite scores in order to predict TEEs in the first 48 h (H0-H48) and during the first 15 days (D1-D15) in ICU. We then evaluated the performance of our scores in our cohort. During the period H0-H48, patients with a TEE diagnosis had higher D-Dimers and ferritin values at day 1 (D1) and day 3 (D3) and a greater drop in fibrinogen between D1 and D3 compared with patients without TEEs. Over the period D1-D15, patients with a diagnosis of a TEE showed a more marked drop in fibrinogen and had higher D-Dimers and lactate dehydrogenase (LDH) values at D1 and D3. Based on ROC analysis, the COVID-related acute lung and deep vein thrombosis (CALT) 1 score, calculated at D1, had a diagnostic performance for TEEs at H0-H48, estimated using an area under the curve (AUC) of 0.85 (CI95%: 0.76-0.93, p < 10^-3). The CALT 2 score, calculated at D3, predicted the occurrence of TEEs over the period D1-D15 with an estimated AUC of 0.85 (CI95%: 0.77-0.93, p < 10^-3). These two scores were used as the basis for the development of the CALT protocol, a tool to assist in the decision to use anticoagulation during severe SARS-CoV-2 infections. The CALT scores showed good performances in predicting the risk of TEEs in severe COVID-19 patients at admission and during ICU stays. They could, therefore, be used as a decision support protocol on whether or not to initiate therapeutic anticoagulation.

COVID-19 and Pulmonary Thrombosis-An Unresolved Clinical Puzzle: A Single-Center Cohort Study

Pulmonary thrombosis (PT) is a frequent complication of COVID-19. However, the risk factors, predictive scores, and precise diagnostic guidelines on indications for CT pulmonary angiography (CTPA) are still lacking. This study aimed to analyze the clinical and laboratory characteristics associated with PT in patients with COVID-19. We conducted a cohort study of consecutively hospitalized adult patients with COVID-19 who underwent CTPA at the University Hospital for Infectious Diseases in Zagreb, Croatia between 1 April and 31 December 2021. Of 2078 hospitalized patients, 575 (27.6%) underwent CTPA. PT was diagnosed in 178 (30.9%) patients (69.6% males, median age of 61, IQR 50-69 years). The PT group had a higher CRP, LDH, D-dimer, platelets, and CHOD score. PT was more frequent in patients requiring ≥15 L O₂/min (25.0% vs. 39.7%). In multivariable analysis, only D-dimer ≥ 1.0 mg/L (OR 1.78, 95%CI 1.12-2.75) and O₂ ≥ 15 L (OR 1.89, 95%CI 1.26-2.84) were associated with PT. PT was not associated with in-hospital mortality. In conclusion, our data confirmed a high incidence of PT in hospitalized patients with COVID-19, however, no correlation with traditional risk factors and mortality was found. CTPA should be performed in patients requiring high-flow supplemental oxygen or those with increased D-dimer levels.