Nodular sarcoidosis is a rare presentation of pulmonary sarcoidosis. It usually presents with multiple pulmonary masses that tend to be peripheral and are associated with mediastinal lymphadenopathy. Bronchoscopy with transbronchial biopsies has high diagnostic yield. Despite its ominous presentation, nodular sarcoidosis has favorable prognosis.

Background The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. Objectives To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients’ data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Results Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Conclusion Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

Right heart thrombus in transit is an increasingly recognized medical emergency with very high mortality rate. Echocardiography helps to establish the diagnosis and can differentiate between right heart thrombi that result from atrial fibrillation and those originating from deep venous thrombosis. We present two cases of right heart thrombus in transit diagnosed with echocardiography that were managed with different approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s13089-017-0069-9) contains supplementary material, which is available to authorized users.

BackgroundSarcoidosis is a chronic disease with different phenotypic manifestations. Health-related quality of life is an important aspect in sarcoidosis, yet difficult to measure. The objective of this study was to identify clinical markers predictive of poor quality of life in sarcoidosis patients that can be followed over time and targeted for intervention.MethodsWe assessed the quality of life of 162 patients with confirmed sarcoidosis in a prospective, cross-sectional study using the Sarcoidosis Health Questionnaire (SHQ) and Short Form-36 Health Survey (SF-36). We evaluated the validity of these questionnaires and sought to identify variables that would best explain the performance scores of the patients.ResultsOn multivariate regression analyses, the very best composite model to predict total scores from both surveys was a model containing the distance-saturation product and Borg Dyspnea Scale score at the end of a 6-min walk test. This model could better predict SF-36 scores (R2 = 0.33) than SHQ scores (R2 = 0.24). Substitution of distanced walked in 6 min for the distance-saturation product in this model resulted in a lesser ability to predict both scores (R2 = 0.26 for SF-36; R2 = 0.22 for SHQ).ConclusionsBoth the SHQ and SF-36 surveys are valuable tools in the assessment of health-related quality of life in sarcoidosis patients. The best model to predict quality of life among these patients, as determined by regression analyses, included the distance-saturation product and Borg score after the 6-min walk test. Both variables represent easily obtainable clinical parameters that can be followed over time and targeted for intervention.

Pulmonary arterial hypertension (PAH) is a subgroup of PH patients characterized hemodynamically by the presence of pre-capillary PH, defined by a pulmonary artery wedge pressure (PAWP) ≤15 mmHg and a PVR >3 Wood units (WU) in the absence of other causes of pre-capillary PH. According to the current classification, PAH can be associated with exposure to certain drugs or toxins such as anorectic agents, amphetamines, or selective serotonin reuptake inhibitors. With the improvement in awareness and recognition of the drug-induced PAH, it allowed the identification of additional drugs associated with an increased risk for the development of PAH. The supposed mechanism is an increase in the serotonin levels or activation of serotonin receptors that has been demonstrated to act as a growth factor for the pulmonary artery smooth muscle cells and cause progressive obliteration of the pulmonary vasculature. PAH remains a rare complication of several drugs, suggesting possible individual susceptibility, and further studies are needed to identify patients at risk of drug-induced PAH.

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