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Background: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. Methods: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. Results: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. Conclusions: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD. [ABSTRACT FROM AUTHOR]

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html. [ABSTRACT FROM AUTHOR]

Introduction: Endoscopic ultrasound (EUS) may play a role in evaluating treatment response after definitive chemoradiation therapy (dCRT) for esophageal squamous cell carcinoma (ESCC). This study explored the prognostic markers of EUS with biopsies and developed two nomograms for survival prediction. Methods: A total of 821 patients newly diagnosed with ESCC between January 2015 and December 2019 were reviewed. We investigated the prognostic value of the changes in tumor imaging characteristics and histopathological markers by an interim response evaluation, including presence of stenosis, ulceration, tumor length, tumor thickness, lumen involvement, and tumor remission. Independent prognostic factors of progression-free survival (PFS) and overall survival (OS) were determined using Cox regression analysis and further selected to build two nomogram models for survival prediction. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to respectively assess its discriminatory capacity, predictive accuracy, and clinical usefulness. Results: A total of 155 patients were enrolled in this study and divided into the training (109 cases) and testing (46 cases) cohorts. Tumor length, residual tumor thickness, reduction in tumor thickness, lumen involvement, and excellent remission (ER) of spatial luminal involvement in ESCC (ER/SLI) differed significantly between responders and non-responders. For patients undergoing dCRT, tumor stage (P = 0.001, 0.002), tumor length (P = 0.013, 0.008), > 0.36 reduction in tumor thickness (P = 0.004, 0.004) and ER/SLI (P = 0.041, 0.031) were independent prognostic markers for both PFS and OS. Time-dependent ROC curves, calibration curves, and DCA indicated that the predicted survival rates of our two established nomogram models were highly accurate. Conclusion: Our nomogram showed high accuracy in predicting PFS and OS for ESCC after dCRT. External validation and complementation of other biomarkers are needed in further studies. [ABSTRACT FROM AUTHOR]

Purpose: Acinetobacter baumannii is the most common microorganism in sputum cultures from long-term hospitalized patients and is often the cause of hospital-acquired pneumonia (HAP), which is usually associated with poor prognosis and high mortality. It is sometimes difficult to distinguish between A. baumannii infection and colonization. This study aimed to evaluate factors that differentiate infection from colonization and predict mortality in patients with nosocomial pneumonia caused by A. baumannii. Patients and Methods: The data used in this study were collected in our hospital between January 2018 and December 2020 from patients whose sputum cultures were positive for A. baumannii. Results: A total of 714 patients were included, with 571 in the infection group and 143 in the colonization group. The in-hospital mortality rate in the infection group was 20.5%. Univariate and multivariate logistic regression analyses showed that age, total number of inpatient departments, absolute neutrophil count, and C-reactive protein (CRP) level helped distinguish between infection and colonization. The area under the receiver operating characteristic curve (ROC) of the identification model was 0.694. In the infection group, age, Charlson comorbidity score, neutrophil-to-lymphocyte ratio, blood urea nitrogen/albumin ratio, CRP level, presence of multidrug resistance, and clinical pulmonary infection score (≥ 6) ratio were associated with in-hospital mortality. The area under the ROC curve for the prediction model was 0.828. The top three drug resistance rates in the infection group were 100% (cefazolin), 98.77% (ceftriaxone), and 71.8% (cefuroxime). Conclusion: The combination of common parameters helps identify A. baumannii respiratory tract infection or colonization. Several novel predictors can be used to predict the risk of death from A. baumannii pneumonia to reduce mortality. The drug resistance of A. baumannii remains high. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT). Methods: A retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, n = 387) and the testing set (30%, n = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance of the nomogram was assessed based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA). Results: After multivariable logistic regression analysis, certain factors, such as smoking, National Institutes of Health of Stroke Scale (NIHSS) score, blood urea nitrogen-to-creatinine ratio (BUN/Cr), and neutrophil-to-lymphocyte ratio (NLR), were found to be independent predictors of ICH and were used to construct a nomogram. The AUC-ROC values of the nomogram were 0.887 (95% CI: 0.842–0.933) and 0.776 (95% CI: 0.681–0.872) in the training and testing sets, respectively. The AUC-ROC of the nomogram was higher than that of the Multicenter Stroke Survey (MSS), Glucose, Race, Age, Sex, Systolic blood Pressure, and Severity of stroke (GRASPS), and stroke prognostication using age and NIH Stroke Scale-100 positive index (SPAN-100) scores for predicting ICH in both the training and testing sets (p < 0.05). The calibration plot demonstrated good agreement in both the training and testing sets. DCA indicated that the nomogram was clinically useful. Conclusions: The new nomogram, which included smoking, NIHSS, BUN/Cr, and NLR as variables, had the potential for predicting the risk of ICH in patients with AIS after IVT. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is a common disease in the intensive care unit (ICU). AKI patients with nonrecovery of renal function have a markedly increased risk of death compared with patients with recovery. The current study aimed to explore and validate the utility of urinary cell cycle arrest biomarkers for predicting nonrecovery in patients who developed AKI after ICU admission. Methods: We prospectively and consecutively enrolled 379 critically ill patients who developed AKI after admission to the ICU, which were divided into a derivation cohort (194 AKI patients) and a validation cohort (185 AKI patients). The biomarkers of urinary tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) were detected at inclusion immediately after AKI diagnosis (day 0) and 24 h later (day 1). The optimal cut-off values of these biomarkers for predicting nonrecovery were estimated in the derivation cohort, and their predictive accuracy was assessed in the validation cohort. The primary endpoint was nonrecovery from AKI (within 7 days). Results: Of 379 patients, 159 (41.9%) patients failed to recover from AKI onset, with 79 in the derivation cohort and 80 in the validation cohort. Urinary [TIMP-2]*[IGFBP7] on day 0 showed a better prediction ability for nonrecovery than TIMP-2 and IGFBP7 alone, with an area under the reciever operating characteristic curve (AUC) of 0.751 [95% confidence interval (CI) 0.701–0.852, p < 0.001] and an optimal cut-off value of 1.05 ((ng/mL)2/1000). When [TIMP-2]*[IGFBP7] was combined with the clinical factors of AKI diagnosed by the urine output (UO) criteria, AKI stage 2–3 and nonrenal SOFA score for predicting nonrecovery, the AUC was significantly improved to 0.852 (95% CI 0.750–0.891, p < 0.001), which achieved a sensitivity and specificity of 88.8% (72.9, 98.7) and 92.6% (80.8, 100.0), respectively. However, urine [TIMP-2]*[IGFBP7], TIMP-2 alone, and IGFBP7 alone on day 1 performed poorly for predicting AKI recovery. Conclusion: Urinary [TIMP-2]*[IGFBP7] on day 0 showed a fair performance for predicting nonrecovery from AKI. The predictive accuracy can be improved when urinary [TIMP-2]*[IGFBP7] is combined with the clinical factors of AKI diagnosed by the UO criteria, AKI stage 2–3 and nonrenal SOFA score. [ABSTRACT FROM AUTHOR]

Introduction: Patients with impaired citrate metabolism may experience citrate accumulation (CA), which causes life-threatening metabolic acidosis and hypocalcemia. CA poses a challenge for clinicians when deciding on the use of regional citrate anticoagulation (RCA) for patients with liver dysfunction. This study aimed to develop a prediction model integrating multiple clinical variables to assess the risk of CA in liver transplant patients. Methods: This single-center prospective cohort study included postoperative liver transplant patients who underwent continuous renal replacement therapy (CRRT) with RCA. The study end point was CA. A prediction model was developed using a generalized linear mixed-effect model based on the Akaike information criterion. The predictive values were assessed using the receiver operating characteristic curve and bootstrap resampling (times = 500) to estimate the area under the curve (AUC) and the corresponding 95% confidence interval (CI). A nomogram was used to visualize the model. Results: This study included 32 patients who underwent 133 CRRT sessions with RCA. CA occurred in 46 CRRT sessions. The model included lactate, norepinephrine >0.1 μg/kg/min, alanine aminotransferase, total bilirubin, and standard bicarbonate, which were tested before starting each CRRT session and body mass index, diabetes mellitus, and chronic kidney disease as predictors. The AUC of the model was 0.867 (95% CI 0.786–0.921), which was significantly higher than that of the single predictor (p < 0.05). A nomogram visualized the prediction model. Conclusions: The prediction model integrating multiple clinical variables showed a good predictive value for CA. A nomogram visualized the model for easy application in clinical practice. [ABSTRACT FROM AUTHOR]

Background: The clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.Methods: A total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell's concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.Results: A nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.Conclusion: The established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG. [ABSTRACT FROM AUTHOR]

Background: To date, the missed diagnosis rate of pulmonary hypertension (PH) was high, and there has been limited development of a rapid, simple, and effective way to screen the disease. The purpose of this study is to develop a deep learning approach to achieve rapid detection of possible abnormalities in chest radiographs suggesting PH for screening patients suspected of PH. Methods: We retrospectively collected frontal chest radiographs and the pulmonary artery systolic pressure (PASP) value measured by Doppler transthoracic echocardiography from 762 patients (357 healthy controls and 405 with PH) from three institutes in China from January 2013 to May 2019. The wohle sample comprised 762 images (641 for training, 80 for internal test, and 41 for external test). We firstly performed a 8-fold cross-validation on the 641 images selected for training (561 for pre-training, 80 for validation), then decided to tune learning rate to 0.0008 according to the best score on validation data. Finally, we used all the pre-training and validation data (561+80 = 641) to train our models (Resnet50, Xception, and Inception V3), evaluated them on internal and external test dataset to classify the images as having manifestations of PH or healthy according to the area under the receiver operating characteristic curve (AUC/ROC). After that, the three deep learning models were further used for prediction of PASP using regression algorithm. Moreover, we invited an experienced chest radiologist to classify the images in the test dataset as having PH or not, and compared the prediction accuracy performed by deep learing models with that of manual classification. Results: The AUC performed by the best model (Inception V3) achieved 0.970 in the internal test, and slightly declined in the external test (0.967) when using deep learning algorithms to classify PH from normal based on chest X-rays. The mean absolute error (MAE) of the best model for prediction of PASP value was smaller in the internal test (7.45) compared to 9.95 in the external test. Manual classification of PH based on chest X-rays showed much lower AUCs compared to that performed by deep learning models both in the internal and external test. Conclusions: The present study used deep learning algorithms to classify abnormalities suggesting PH in chest radiographs with high accuracy and good generalizability. Once tested prospectively in clinical settings, the technology could provide a non-invasive and easy-to-use method to screen patients suspected of having PH. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the factors that affect 30-day mortality of patients with HAP. The data used in this study were collected from all HAP occurred in our hospital between January 2014 and December 2017. A total of 1158 cases were included. 150 (13.0%) of whom died within 30 days. This reported mortality identified by the univariate Cox regression analysis is found to have been affected by the following factors: age greater than 70 years, presence of diabetes mellitus and chronic obstructive pulmonary disease, gastric tube intubation, administration of proton-pump inhibitor, blood albumin level less than 30 g/l, elevated neutrophil-to-lymphocyte ratio, antibiotics therapy in the preceding 90 days, intensive care unit (ICU) admission, blood lymphocyte count less than 0.8 × 109/L, elevated blood urea nitrogen/albumin (BUN/ALB) level, and presence of multidrug-resistant (MDR) pathogens. In the second multivariate analysis, administration of proton-pump inhibitor, administration of antibiotics in the preceding 90 days, ICU admission, blood lymphocyte count less than 0.8 × 109/L, elevated BUN/ALB level, and presence of MDR pathogens were still associated with 30-day mortality. The area under the receiver operating characteristic curves in the BUN/ALB predicting 30-day mortality due to HAP was 0.685. A high BUN/ALB was significantly associated with a worse survival than a low BUN/ALB P<0.001. Therefore, an elevated BUN/ALB level is a risk factor for mortality on patients with HAP. [ABSTRACT FROM AUTHOR]