Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.     

自制简易脊柱手术床结合C型臂X线机粘贴标尺在基层医院行PKP术中的应用

目的 观察自制简易脊柱手术床结合C型臂X线机粘贴标尺在基层医院行经皮穿刺椎体后凸成形术(Percutaneous Kyphoplasty,PKP)中的运用效果.方法 回顾性分析2017年1月至2019年7月收治的46例行PKP术治疗的胸腰椎骨质疏松性椎体压缩性骨折患者资料.其中26例在普通手术床上进行手术,运用C型臂X...     

空气与二氧化碳气腹腹腔镜胆囊切除术的比较

目的 比较空气与CO2气腹腹腔镜胆囊切除术（1aparoscopic cholecystectomy,LC）的临床效果,探讨空气膨腹介质下LC的临床应用价值。方法2013年7～10月109例胆囊良性疾病按本科手术日分为2组,分别施行空气气腹或c0,气腹LC,前者除使用空气气腹外,余均使用常规的腹腔镜手术设备和操作器械,比较2组手术并发症、疼痛反应、术后住院时间、总住院费用等。结果 2组均顺利完成LC,无中转开腹、严重并发症发生。空气组无一例中转CO2气腹手术,与CO2组比较,空气组术后肩痛发生率低（X^2=4.097,P=0.043）、恶心呕吐发生率低（X^2=4.584,P=0.032）、视觉模拟评分低（t=-3.568,P=0.000）、术后排气时间短（Z=-4.287,P=0.000）、术后住院时间短（t=2.312,P=0.023）、住院费用低（t=-3.854,P=0.000）。结论 空气气腹LC安全可行,简易价廉,减少CO2排放,减轻CO2气腹术后并发症。     

Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms:sourced from retrospective studies

Although intracranial hemorrhage in moyamoya disease can occur repeatedly, predicting the disease is difficult. Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors, evaluating the weight of different factors, and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease. To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes, we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling, including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes. Another 500 hemispheres with normal vessel appearance were selected as negative samples. We used deep residual neural network(Res Net-152) algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery, then trained and validated the model. The accuracy, sensitivity, and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64 ± 0.87%, 96.55 ± 3.44%, and 98.29 ± 0.98%, respectively. The area under the receiver operating characteristic curve was 0.990. We used a combined multi-view conventional neural network algorithm to integrate age, sex, and hemorrhagic factors with features of the digital subtraction angiography. The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69 ± 1.58% and the sensitivity and specificity were 94.12 ± 2.75% and 89.86 ± 3.64%, respectively. The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, China(approved No. 2014-278) on January 12, 2015.     

Outcomes of Donation After Circulatory Death Heart Transplantation in Australia

Background

Transplantation of hearts retrieved from donation after circulatory death (DCD) donors is an evolving clinical practice.

人工智能在眼科疾病诊疗的应用现状

近年来，人工智能（artificial intelligence，AI）在医学领域的应用范围不断扩大，已成为医疗行业关注的焦点，在心血管疾病、乳腺癌、皮肤癌、糖尿病视网膜病变、白内障、青光眼及早产儿视网膜病变等诊疗中均有新进展。本综述旨在了解AI在眼科领域的应用现状、优势、进展及不足，为AI在眼科领域的进一步应用及推广提供更多信息。     

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.     

New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach

Objective

To identify key predictors and survival outcomes of new-onset diabetes after transplant (NODAT) in liver transplant (LT) recipients by using the Scientific Registry of Transplant Recipients.

A method for systematic discovery of adverse drug events from clinical notes

Objective Adverse drug events (ADEs) are undesired harmful effects resulting from use of a medication, and occur in 30% of hospitalized patients. The authors have developed a data-mining method for systematic, automated detection of ADEs from electronic medical records.Materials and Methods This method uses the text from 9.5 million clinical notes, along with prior knowledge of drug usages and known ADEs, as inputs. These inputs are further processed into statistics used by a discriminative classifier which outputs the probability that a given drug–disorder pair represents a valid ADE association. Putative ADEs identified by the classifier are further filtered for positive support in 2 independent, complementary data sources. The authors evaluate this method by assessing support for the predictions in other curated data sources, including a manually curated, time-indexed reference standard of label change events.Results This method uses a classifier that achieves an area under the curve of 0.94 on a held out test set. The classifier is used on 2 362 950 possible drug–disorder pairs comprised of 1602 unique drugs and 1475 unique disorders for which we had data, resulting in 240 high-confidence, well-supported drug-AE associations. Eighty-seven of them (36%) are supported in at least one of the resources that have information that was not available to the classifier.Conclusion This method demonstrates the feasibility of systematic post-marketing surveillance for ADEs using electronic medical records, a key component of the learning healthcare system.