Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002-03/08/2007. All data regarding patients' medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934-0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896-0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911-0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients' level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation

Clinical Method Applied to Focused Ultrasound: The Case of Wells’ Score and Echocardiography in the Emergency Department: A Systematic Review and a Meta-Analysis

Background and Objectives: bedside cardiac ultrasound is a widely adopted method in Emergency Departments (ED) for extending physical examination and refining clinical diagnosis. However, in the setting of hemodynamically-stable pulmonary embolism, the diagnostic role of echocardiography is still the subject of debate. In light of its high specificity and low sensitivity, some authors suggest that echocardiographic signs of right ventricle overload could be used to rule-in pulmonary embolism. In this study, we aimed to clarify the diagnostic role of echocardiographic signs of right ventricle overload in the setting of hemodynamically-stable pulmonary embolism in the ED. Materials and Methods: we performed a systematic review of literature in PubMed, Web of Science and Cochrane databases, considering the echocardiographic signs for the diagnosis of pulmonary embolism in the ED. Studies considering unstable or shocked patients were excluded. Papers enrolling hemodynamically stable subjects were selected. We performed a diagnostic test accuracy meta-analysis for each sign, and then performed a critical evaluation according to pretest probability, assessed with Wells’ score for pulmonary embolism. Results: 10 studies were finally included. We observed a good specificity and a low sensitivity of each echocardiographic sign of right ventricle overload. However, once stratified by the Wells’ score, the post-test probability only increased among high-risk patients. Conclusions: signs of echocardiographic right ventricle overload should not be used to modify the clinical behavior in low- and intermediate- risk patients according to Wells’ score classification. Among high-risk patients, however, echocardiographic signs could help a physician in detecting patients with the highest probability of pulmonary embolism, necessitating a confirmation by computed tomography with pulmonary angiography. However, a focused cardiac and thoracic ultrasound investigation is useful for the differential diagnosis of dyspnea and chest pain in the ED

Clusters of Comorbidities in the Short-Term Prognosis of Acute Heart Failure among Elderly Patients: A Retrospective Cohort Study

Background and Objectives: Elderly patients affected by acute heart failure (AHF) often show different patterns of comorbidities. In this paper, we aimed to evaluate how chronic comorbidities cluster and which pattern of comorbidities is more strongly related to in-hospital death in AHF. Materials and Methods: All patients admitted for AHF to an Internal Medicine Department (01/2015–01/2019) were retrospectively evaluated; the main outcome of this study was in-hospital death during an admission for AHF; age, sex, the Charlson comorbidity index (CCI), and 17 different chronic pathologies were investigated; the association between the comorbidities was studied with Pearson’s bivariate test, considering a level of p ≤ 0.10 significant, and considering p < 0.05 strongly significant. Thus, we identified the clusters of comorbidities associated with the main outcome and tested the CCI and each cluster against in-hospital death with logistic regression analysis, assessing the accuracy of the prediction with ROC curve analysis. Results: A total of 459 consecutive patients (age: 83.9 ± 8.02 years; males: 56.6%). A total of 55 (12%) subjects reached the main outcome; the CCI and 16 clusters of comorbidities emerged as being associated with in-hospital death from AHF. Of these, CCI and six clusters showed an accurate prediction of in-hospital death. Conclusions: Both the CCI and specific clusters of comorbidities are associated with in-hospital death from AHF among elderly patients. Specific phenotypes show a greater association with a worse short-term prognosis than a more generic scale, such as the CCI