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Obstructive sleep apnea syndrome (OSAS) is an important risk factor in cardiovascular disorders. Although the precise mechanism remains to be described, hypercoagulability seems to be involved. To test the presence of decreased fibrinolysis, we measured plasma levels of plasminogen activator inhibitor-1 (PAI-1) in 96 male subjects (32 subjects with OSAS, 32 subjects with both OSAS and hypertension, and a 32 subjects in the control group). All subjects completed a sleep questionnaire and underwent a general physical examination, biochemistry test, and polysomnography study. Patients and control subjects had similar baseline clinical characteristics for age, gender, smoking habit, and levels of cholesterol and triglycerides. Patients with OSAS presented significantly higher circulating levels of PAI-1 compared with the control group, and the difference was even more marked in patients with both OSAS and hypertension. OSAS patients presented a significant inverse correlation between PAI-1 levels and apnea-hypopnea index (r= -0.71, p<0.001). In conclusion, OSAS patients presented higher circulating levels of PAI than the control group, which was even greater when patients had associated hypertension.

The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6–25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

INTRODUCTION:Diagnosis of pleural infection (PI) may be challenging. The purpose of this paper is to develop and validate a clinical prediction model for the diagnosis of PI based on pleural fluid (PF) biomarkers.METHODS:A prospective study was conducted on pleural effusion. Logistic regression was used to estimate the likelihood of having PI. Two models were built using PF biomarkers. The power of discrimination (area under the curve) and calibration of the two models were evaluated.RESULTS:The sample was composed of 706 pleural effusion (248 malignant; 28 tuberculous; 177 infectious; 48 miscellaneous exudates; and 212 transudates). Areas under the curve for Model 1 (leukocytes, percentage of neutrophils, and C-reactive protein) and Model 2 (the same markers plus interleukin-6 [IL-6]) were 0.896 and 0.909, respectively (not significant differences). However, both models showed higher capacity of discrimination than their biomarkers when used separately (P < 0.001 for all). Rates of correct classification for Models 1 and 2 were 88.2% (623/706: 160/177 [90.4%] with infectious pleural effusion [IPE] and 463/529 [87.5%] with non-IPE) and 89.2% (630/706: 153/177 [86.4%] of IPE and 477/529 [90.2%] of non-IPE), respectively.CONCLUSIONS:The two predictive models developed for IPE showed a good diagnostic performance, superior to that of any of the markers when used separately. Although IL-6 contributes a slight greater capacity of discrimination to the model that includes it, its routine determination does not seem justified.