哮喘患者疲劳症状的临床特征及影响因素分析

目的 探讨哮喘患者疲劳症状的临床特点及影响因素。方法 选取哮喘患者198例,根据疲劳严重度量 表（FSS）评分将患者分为疲劳组（≥4分,126例）和非疲劳组（＜4分,72例）。分析2组临床基本特征、肺功能、呼出气 一氧化氮（FeNO）、呼吸困难分级（mMRC）、哮喘控制测试评分（ACT）、日常生活能力（MBI）评分、6 min 步行测试 （6MWT）、汉密尔顿抑郁量表17项（HAMD-17）、匹兹堡睡眠质量指数量表（PSQI）、过去1年急性发作次数等方面的 差异。二分类Logistic回归分析哮喘患者疲劳症状的影响因素。根据筛选后的指标构建列线图预测模型,通过受试 者工作特征（ROC）曲线和校准曲线评价模型的预测价值。结果 疲劳组 ACT 评分低于非疲劳组,mMRC 分级、 HAMD-17评分、PSQI评分及上1年急性发作次数高于非疲劳组（P＜0.01）,2组余指标差异无统计学意义（P＞0.05）。 二分类 Logistic 回归分析显示,ACT 评分（OR=0.644,95%CI：0.508~0.816）、mMRC 分级（OR=2.313,95%CI：1.349～ 3.966）、HAMD-17评分（OR=1.561,95%CI：1.273~1.913）及PSQI评分（OR=1.932,95%CI：1.506～2.479）是哮喘患者发 生疲劳的影响因素。基于4项影响因素构建列线图预测模型,预测模型的ROC曲线下面积为0.935（95%CI：0.902~ 0.967）。内部验证显示,C-index 为 0.929,校准曲线表明列线图的预测结果与实际的观测结果一致性良好。结论 哮喘患者中疲劳的发生率较高,哮喘控制不佳、高mMRC分级、负性情绪和睡眠障碍是发生疲劳的重要影响因素,根 据影响因素构建的列线图具有较高的预测价值。     

Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

ObjectiveOur aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).MethodsWe selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.ResultsSix independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.ConclusionsThe novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.     

老年脓毒性休克患者进展为慢重症的列线图预测模型的构建及验证

目的 探讨老年脓毒性休克患者进展为慢重症的相关危险因素,并在此基础上构建与验证预测慢重症发生风险的列线图模型。方法 将纳入研究的252例年龄≥65岁的脓毒性休克患者作为训练集,并根据是否进展为慢重症将其分为慢重症组（86例）和非慢重症组（166例）。统计2组患者入EICU 24 h内一般资料、查尔森合并症指数（CCI）评分、序贯器官衰竭评估（SOFA）评分、腹内压（IAP）、机械通气（MV）和连续肾脏替代治疗（CRRT）比例以及血清乳酸（Lac）、降钙素原（PCT）水平的差异。通过多因素Logistic回归确定老年脓毒性休克患者进展为慢重症的独立危险因素,并以此构建预测慢重症发生风险的列线图模型。分别通过校准曲线和受试者工作特征（ROC）曲线验证模型的校准度和区分度,并采用决策曲线分析法（DCA）确定模型的临床实用性。另外选取74例老年脓毒性休克患者作为验证集对预测模型进行外部验证。结果 训练集老年脓毒性休克患者慢重症发生率为34.13%。与非慢重症组相比,慢重症组年龄≥75岁,CCI评分≥3分,CRRT比例、MV比例、SOFA评分、IAP水平较高（P<0.05）。多因素Logi...     

Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance

BackgroundNeurocognitive disorders (NCDs) and sleep disturbance are highly prevalent in the perioperative period and intensive care unit (ICU). There has been a lack of individualized evaluation tools designed for the high‐risk NCDs in critically ill patients with sleep disturbance.ObjectivesThe aim of this study was to develop and validate prediction models for NCDs among adult patients with sleep disturbance.MethodsThe R software was used to analyze the dataset of adult patients admitted to the ICU with sleep disturbance, who were diagnosed following the codes of the International Classification of Diseases, 9th Revision (ICD‐9) and 10th Revision (ICD‐10) using the MIMIC‐IV database. We used logistic regression and LASSO analyses to identify important risk factors associated with NCDs and develop nomograms for NCDs predictions. We measured the performances of the nomograms using the bootstrap resampling procedure, sensitivity, specificity of the receiver operating characteristic (ROC), area under the ROC curves (AUC), and decision curve analysis (DCA).ResultsThe prediction models shared the 10 risk factors (age, gender, midazolam, morphine, glucose, diabetes diseases, potassium, international normalized ratio, partial thromboplastin time, and respiratory rate). Cardiovascular diseases were included in the logistic regression, the sensitivity was 74.1%, and specificity was 64.6%. When platelet and Glasgow Coma Score (GCS) were included and cardiovascular diseases were removed in the LASSO prediction model, the sensitivity was 86.1% and specificity was 82.8%. Discriminative abilities of the logistic prediction and LASSO prediction models for NCDs in the validation set were evaluated as the AUC scores, which were 0.730 (95% CI 0.716–0.743) and 0.920 (95% CI 0.912–0.927). Net benefits of the prediction models were observed at threshold probabilities of 0.567 and 0.914.ConclusionsThe LASSO prediction model showed better performance than the logistic prediction model and should be preferred for nomogram‐assisted decisions on clinical risk management of NCDs among adult patients with sleep disturbance in the ICU.     

Nomograms Predict Cancer‐Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

To date, there have been no data to predict the survival of patients with leiomyosarcoma from soft limb tissue because of the rarity of this disease. Nomograms have been widely applied in clinical oncology to precisely predict the survival of individual patients. This was a retrospective study to construct and validate nomograms to predict the cancer‐specific survival (CSS) and overall survival (OS) of patients with primary limb leiomyosarcoma (PL‐LMS). A total of 1,208 patients with LMS from limb soft tissue were collected from the Surveillance, Epidemiology, and End Results database from 1975 to 2015. We identified independent prognostic factors using univariate and multivariate Cox analyses. These prognostic factors were then included in the nomograms to predict 3‐ and 5‐year CSS and OS rates. Finally, we validated the nomograms internally and externally. A total of 1208 patients were collected and divided into validation (N = 604) and training (N = 604) groups. Age, race, grade, tumor size, stage, and surgical types were demonstrated as independent prognostic factors for CSS and OS (all p < 0.05) and further used to construct the nomograms. The concordance index (C‐index) for CSS was 0.857 for internal validation and 0.727 for external validation. The C‐index for OS and CSS both demonstrated that the nomogram prediction agreed perfectly with actual survival. We developed nomograms to predict CSS and OS in PL‐LMS patients and can benefit from using them to identify patients’ mortality risk and make more precise assessments regarding survival. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1649–1657, 2019.