Internal and emergency medicine
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Observational Study
Rate of hepatocellular carcinoma diagnosis in cirrhotic patients with ultrasound-detected liver nodules.
Ultrasound (US) detection of liver nodules in cirrhotic patients requires further radiological examinations and often a follow-up with repeated short-term evaluations to verify the presence of hepatocellular carcinoma (HCC). Aims of the study were to assess the rate of HCC diagnosis and to identify HCC predictors in a cohort of cirrhotics followed-up after US detection of the liver nodule(s). One-hundred-eighty-eight consecutive cirrhotic patients (124 males, mean age 64.2 years) with liver nodule(s) detected by US were enrolled. ⋯ US-detected liver nodules are not neoplastic in more than half of cirrhotic patients. A definite diagnosis may be obtained at the time of the first radiologic evaluation after US in the vast majority of the cases. Patients in whom nodules are found not to be tumoral may return to the US surveillance program routinely applied to all cirrhotics.
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Chronic kidney disease (CKD) significantly increases the rate of adverse cardiovascular events in patients with coronary artery disease. In this study, we aimed to establish a risk score (RS) model to predict in-hospital mortality risk in patients with end-stage renal disease (ESRD) and acute myocardial infarction (AMI). A total of 113 consecutive patients with ESRD and AMI were retrospectively enrolled between January 1, 2015 and December 31, 2019. ⋯ The present RS model had a sensitivity of 85.7%, the specificity of 84%, and an accuracy of 78.1%. In ROC curve analysis, the model demonstrated a good discriminate power in predicting in-hospital mortality (AUC = 0.895, 95% CI 0.814-0.96; P < 0.001), which was significantly better than the predictive power of the Global Registry of Acute Coronary Events risk score (GRACE RS) (AUC = 0.754, 95% CI 0.641-0.868; P < 0.001 after Z test). A novel RS model, which was established to help predict in-hospital mortality of patients with ESRD and AMI, was easy to use and had higher accuracy than the GRACE RS.
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Pulmonary hypertension (PH) is defined as an elevated mean pulmonary artery pressure at rest (mPAP ≥ 25 mmHg), evaluated by right heart catheterization (RHC). The aim of the present study was to evaluate HRCT findings in relation to transthoracic echocardiographic data to better characterize PH in IPF patients and to identify a non-invasive composite index with high predictive value for PH in these patients. 37 IPF patients were enrolled in this retrospective study. All patients underwent a complete assessment for PH, including transthoracic Doppler echocardiography, HRCT scan and right heart catheterization. ⋯ Multivariate regression showed that the combination of sPAP, PA area measured by HRCT and the ratio of the diameter of the segmental artery to that of the adjacent bronchus in the apicoposterior segment of the left upper lobe was strongly correlated with mPAP (R2 = 0.53; p = 0.0009). The ROC analysis showed that 931.6 was the ULN for PA area, with 86% sensitivity and 61% specificity (0.839 AUC); 20.34 was the ULN for the ratio of PA area to ascending aorta diameter, with 100% sensitivity and 50% specificity (0.804 AUC). The composite index proposed in the present study could help early detection of IPF patients suspected of PH requiring confirmation by RHC (if deemed clinically necessary).
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There were good clinical outcomes of drug-eluting balloon (DEB) use in de novo lesions and in-stent restenosis (ISR) lesions. However, few studies focused on DEB use in patients with acute myocardial infarction (AMI). The aim of this study was to retrospective evaluate the efficacy of DEB for patients of AMI with de novo small coronary artery disease. ⋯ Late lumen loss was similar between the two groups (DEB 0.14 ± 0.13 mm, DES 0.19 ± 0.12 mm, P = 0.442). DEB is a reasonable strategy for AMI with small coronary artery. Compared with DES, DEB is an alternative strategy which had similar 24-month clinical outcomes.
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As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. ⋯ Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.