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Pulmonary embolism (PE) is a potentially life-threatening condition requiring prompt diagnosis and treatment. Recent advances have led to the development of newer techniques and drugs aimed at improving PE management, reducing its associated morbidity and mortality and the complications related to anticoagulation. This review provides an overview of the current knowledge and future perspectives on PE treatment. Anticoagulation represents the first-line treatment of hemodynamically stable PE, direct oral anticoagulants being a safe and effective alternative to traditional anticoagulation: these drugs have a rapid onset of action, predictable pharmacokinetics, and low bleeding risk. Systemic fibrinolysis is suggested in patients with cardiac arrest, refractory hypotension, or shock due to PE. With this narrative review, we aim to assess the state of the art of newer techniques and drugs that could radically improve PE management in the near future: (i) mechanical thrombectomy and pulmonary embolectomy are promising techniques reserved to patients with massive PE and contraindications or failure to systemic thrombolysis; (ii) catheter-directed thrombolysis is a minimally invasive approach that can be suggested for the treatment of massive or submassive PE, but the lack of large, randomized controlled trials represents a limitation to widespread use; (iii) novel pharmacological approaches, by agents inhibiting thrombin-activatable fibrinolysis inhibitor, factor Xia, and the complement cascade, are currently under investigation to improve PE-related outcomes in specific settings.

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.

Data regarding further risk stratification of intermediate-risk pulmonary embolism (IR-PE) are scanty. Whether transthoracic echocardiography may be helpful in further risk assessment of death in such population has still to be proven. Two-hundred fifty-four consecutive patients (51.6% females, age 63.7 ± 17.3 years) with IR-PE admitted to a tertiary regional referral center were enrolled. Patients underwent a complete transthoracic echocardiography within 36 h from hospital admission, on top of clinical assessment, physical examination, computer tomography pulmonary angiography (CTPA), and serum measurement of Troponin I (TnI) levels. The occurrence of 90 day mortality was chosen as primary outcome measure. When compared to survivors, non-surviving IR-PE patients had smaller left-ventricular end-diastolic volumes (39.8 ± 20.9 vs 49.4 ± 19.9 ml/m 2 , p = 0.006) with reduced stroke volume index (SVi) (24.7 ± 10.9 vs 30.9 ± 12.6 ml/m 2 , p: 0.004) and time-velocity integral at left-ventricular outflow tract (VTI LVOT ) (0.17 ± 0.03 vs 0.20 ± 0.04 m, p = 0.0001), whereas no differences were recorded regarding right heart parameters. Cox regression analysis revealed that right atrial enlargement (RAE) (HR 3.432, 5-95% CI 1.193-9.876, p: 0.022), the ratio between tricuspid annulus plane excursion and pulmonary arterial systolic pressure (TAPSE/PASp) (HR 4.833, 5-95% 1.230-18.986, p = 0.024), as well as SVi (HR 11.199, 5-95% CI 2.697-48.096, p = 0.001) and VTI LVOT (HR 4.212, 5-95% CI 1.384-12.820, p = 0.011) were powerful independent predictors of mortality. Neither CTPA RV/LV nor TnI resulted associated with impaired survival. In intermediate-risk pulmonary embolism, RAE, TAPSE/PASp ratio, SVi, and VTI LVOT predict independently prognosis to a greater extent than CTPA and TnI., (© 2021. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)

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., (© 2021. The Author(s).)