Predictive value of QT dispersion for acute heart failure after autologous and allogeneic hematopoietic stem cell transplantation

The aim of our study was to evaluate whether corrected QT dispersion (QTc dispersion), an electrocardiographic marker, is a good predictor of the development of acute heart failure after high-dose chemotherapy followed by autologous or allogeneic hematopoietic stem cell transplantation. We enrolled 50 consecutive patients, from age 15 to 63 years, with hematopoietic diseases scheduled to undergo autologous or allogeneic hematopoietic stem cell transplantation, and compared QTc dispersion with other markers before transplantation conditioning. In univariate logistic analysis, QTc dispersion was a significant factor for acute heart failure after hematopoietic stem cell transplantation (odds ratio, 3.7 per 10 msec; confidence interval, 1.6-8.5; P = 0.002). There were no significant differences as age, sex, systolic or diastolic echocardiographic function markers, cumulative anthracycline dose, or QTc before transplantation between patients with and without acute heart failure. After multiple adjustments for left ventricular ejection fraction, cumulative anthracycline dose, cyclophosphamide conditioning dose, QTc dispersion was a significant and independent factor for acute heart failure after hematopoietic stem cell transplantation (odds ratio, 48.0 per 10 msec; confidence interval, 1.4-1666.3; P = 0.03). This study demonstrated that QTc dispersion could be used as a powerful noninvasive predictor of the development of acute heart failure after hematopoietic stem cell transplantation.     

Differential Association Between HDL Subclasses and the Development of Type 2 Diabetes in a Prospective Study of Japanese Americans

OBJECTIVERecent studies have suggested that HDL cholesterol is inversely associated with the development of type 2 diabetes. However, little is known about the association between different HDL subclasses and the risk for future type 2 diabetes.RESULTSIn univariate analysis, total HDL and HDL2 cholesterol were inversely associated with the incidence of type 2 diabetes, but HDL3 cholesterol was not. In multivariate analysis, total HDL cholesterol (odds ratio per 1-SD increment, 0.72 [95% CI 0.52–0.995], P = 0.047) and HDL2 cholesterol (odds ratio per 1-SD increment, 0.64 [95% CI 0.44–0.93], P = 0.018) were inversely associated with the risk for type 2 diabetes independent of age, sex, BMI, waist circumference, family history of diabetes, lifestyle factors, systolic blood pressure, lipid-lowering medication use, triglyceride level, HOMA-insulin resistance, and 2-h glucose; however, HDL3 cholesterol was not associated with diabetes risk. The association between diabetes risk and total HDL and HDL2 cholesterol became insignificant after adjustment for VAT area.CONCLUSIONSSubjects with higher HDL2 cholesterol were at lower risk for incident type 2 diabetes, but this association was confounded by and not independent of VAT. Higher HDL3 cholesterol was not associated with diabetes risk.     

Long-term safety of subcutaneous immunotherapy with TO-204 in Japanese patients with house dust mite-induced allergic rhinitis and allergic bronchial asthma: Multicenter,open label clinical trial

Background

To evaluate the long-term safety of subcutaneous immunotherapy with TO-204, a standardized house dust mite (HDM) allergen extracts, we conducted a multicenter, open label clinical trial.

Prediction of the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks and chest radiography

BackgroundWe aimed to predict the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks (CNNs) and chest radiography.MethodsMedical records were searched for eligible patients with culture-confirmed active pulmonary tuberculosis. The eligible patients were randomly assigned to the training dataset group (N = 180) and the validation dataset group (N = 59). Posteroanterior X-ray radiographs in the standing position were obtained at diagnosis. The image data were augmented by a factor of 10 by randomly shifting and rotating the original image. Thus, 1800 images (112 × 112 pixels, 8-bit grayscale) from 180 patients in the training dataset group were used for training the CNN model. The model performance was evaluated on the validation dataset.ResultsThe values predicted by the CNN model were significantly associated with the actual values (Pearson's correlation coefficient 0.392, p = 0.002). The mean absolute error was 18.0. The visualization of the layer outputs suggested that the CNN model recognized some of the chest radiographic findings that were useful in predicting the duration needed to achieve culture negativity.ConclusionsThe CNN model was useful for predicting the duration needed to achieve culture negativity in active pulmonary tuberculosis, although the accuracy was unsatisfactory. This study suggests that chest radiography findings are as important as other clinical factors for prediction and could be learned by the machine.