Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models

Abstract

BackgroundPulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes.Materials and methodsIn this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality.ResultsA total number of 1,017 patients were finally enrolled in the study, including 465 women and 552 men. Overall incidence of study main endpoint was 9.6%, (7.2% in men and 12.4% in women; p-value = 0.05). The overall performance of the GB model is better than the other two models (AUC: 0.94 for GB vs. 0.88 and 0.90 for DL and LR models respectively). Based on GB model, lower O2 saturation and right ventricle dilation and dysfunction were among the strongest adverse event predictors.ConclusionML-based models have notable prediction ability in PE patients. These algorithms may help physicians to detect high-risk patients earlier and take appropriate preventive measures.

Association between Controlling Nutritional Status (CONUT) Score and Body Composition, Inflammation and Frailty in Hospitalized Elderly Patients

“…However, static physical performance is also associated with malnutrition to the extent that the reduction in muscle strength has been observed as a predictor of nutritional status [ 58 ]. Cognitive and functional impairment are important risk factors for unfavorable clinical outcomes in hospitalized patients [ 59 , 60 , 61 ]. Considering the high prevalence of patients with higher CONUT scores presenting with impaired cognitive and physical function, as highlighted in our study population, the CONUT score could be helpful in identifying patients with alterations in these clinical domains and who are at risk for unfavorable clinical outcomes.…”