女性压力性尿失禁风险预测的研究进展

综述女性压力性尿失禁(stress urinary incontinence,SUI)风险预测的研究进展。指出女性SUI风险预测相关文献普遍存在风险预测工具和指标缺乏临床大样本验证,指标、纳入人群不统一的现象,风险预测模型存在构建过程未按报告规范进行,未进行完整的模型验证以及模型性能评价指标不规范的情况。未来研究可以在现有风险预测工具或模型的基础上进行改进、完善,以期为女性SUI风险预测提供参考。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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连续性血液净化治疗患儿静脉留置导管感染风险预测体系的构建及应用

目的:探讨连续性血液净化治疗患儿静脉留置导管感染风险因素,据此构建风险预测体系,并检验其实际应用效果,以期为临床预防护理提供依据。方法:选取医院2018年4月—2020年4月收治的400例连续性血液净化治疗患儿,按两组基础资料具有匹配性原则将其分为构建组300例、验证组100例,统计构建组中静脉留置导管感染患儿例数,通过单因素分析、多因素Logistic回归分析筛选静脉留置导管感染的独立危险因素,据此构建风险预测体系,并检验其在验证组中的应用效果。结果:经统计得到,构建组中静脉留置导管感染患儿共66例,感染发生率为22.00%;单因素分析得到,连续性血液净化治疗患儿静脉留置导管感染风险因素有穿刺部位、导管留置时间、插管次数、血流速度、血红蛋白、遵医依从性、抗生素使用时间、操作人员手卫生(P<0.05);多因素Logistic回归分析得到,连续性血液净化治疗患儿静脉留置导管感染独立风险因素有股静脉置管、导管留置时间>7 d、血流速度>180 mL/min、血红蛋白<100 g/L、遵医依从性差、抗生素使用时间>7 d(P<0.05);构建得到连续性血液净化治疗患儿静脉留置导管感染风险预测体系为P=1/[1+e^(-(-1.935+1.635×股静脉置管+1.740×导管留置时间>7 d+1.725×血流速度>180 mL/min+2.241×血红蛋白<100 g/L+2.089×遵医依从性差+1.331×抗生素使用时间>7 d))],ROC曲线分析显示,曲线下面积AUC=0.881,灵敏度为86.67%,特异性为97.14%,准确率为94.00%。结论:连续性血液净化治疗患儿静脉留置导管感染风险大,且风险因素复杂,研究构建的静脉留置导管感染风险预测体系灵敏度高、特异性强,评估准确率高。     

Performance of risk prediction models for post-operative mortality in patients undergoing liver resection

BackgroundLiver resection is commonly performed for hepatic tumors, however preoperative risk stratification remains challenging. We evaluated the performance of contemporary prediction models for short-term mortality after liver resection in patients with and without cirrhosis.MethodsThis retrospective cohort study examined National Surgical Quality Improvement Program data. We included patients who underwent liver resections from 2014 to 2019. VOCAL-Penn, MELD, MELD-Na, ALBI, and Mayo risk scores were evaluated in terms of model discrimination and calibration for 30-day post-operative mortality.ResultsA total 15,198 patients underwent liver resection, of whom 249 (1.6%) experienced 30-day post-operative mortality. The VOCAL-Penn score had the highest discrimination (area under the ROC curve [AUC] 0.74) compared to all other models. The VOCAL-Penn score similarly outperformed other models in patients with (AUC 0.70) and without (AUC 0.74) cirrhosis.ConclusionThe VOCAL-Penn score demonstrated superior predictive performance for 30-day post-operative mortality after liver resection as compared to existing clinical standards.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.     

Prognostic Model and Nomogram for Estimating Survival of Small Breast Cancer: A SEER-based Analysis

BackgroundDifferent clinicopathologic characteristics could contribute to inconsistent prognoses of small breast neoplasms (T1a/T1b). This study was done to conduct a retrospective analysis and establish a clinical prediction model to predict individual survival outcomes of patients with small carcinomas of the breast.Materials and MethodsBased on the Surveillance, Epidemiology, and End Results (SEER) database, eligible patients with small breast carcinomas were analyzed. Univariate analysis and multivariate analysis were performed to clarify the indicators of overall survival. Pooling risk factors enabled nomograms to be constructed and further predicted 3-year, 5-year, and 10-year survival of patients with small breast cancer. The model was internally validated for discrimination and calibration.ResultsA total of 17,543 patients with small breast neoplasms diagnosed between 2013 and 2016 were enrolled. Histologic grade, lymph node stage, estrogen receptor or progesterone receptor status, and molecular subtypes of breast cancer were regarded as the risk factors of prognosis in a Cox proportional hazards model (P < .05). A nomogram was constructed to give predictive accuracy toward individual survival rate of patients with small breast neoplasms.ConclusionsThis prognostic model provided a robust and effective method to predict the prognosis of patients with small breast cancer.