社区2型糖尿病视网膜病变风险预测模型的构建与验证

背景　糖尿病视网膜病变（DR）发病率高,危害性大,是导致成人失明的主要原因,但社区实施眼底筛查较为困难,而DR风险预测模型可帮助全科医生早期识别DR高危人群。目的　探讨社区门诊就诊的2型糖尿病患者发生DR的危险因素,构建并验证DR风险预测模型,进一步提高DR患者筛查效率。方法　收集2018年6月-2019年6月于方庄社区卫生服务中心门诊就诊的421例2型糖尿病患者,采用随机数字表法分为模型组336例,检验组85例。收集患者的一般资料,血压以及空腹血糖（FBG）、餐后2 h血糖、糖化血红蛋白（HbA1c）、血肌酐、尿素氮、总胆固醇（TC）、三酰甘油（TG）、低密度脂蛋白胆固醇（LDL-C）及尿蛋白,均行非散瞳眼底彩色照相（NMFCS）。模型组采用单因素Logistic回归分析计算出发生DR的相关因素,得出的相关因素用多因素Logistic回归分析进一步探讨,在此基础上构建DR风险预测模型,并由检验组评估DR风险预测模型的可行性。结果　在421例2型糖尿病患者中DR患者共87例,其中模型组69例,检验组18例。多因素Logistic回归分析结果显示,DR的危险因素有病程（β=0.196,OR=1.217,P<0.001）,收缩压（SBP）（β=0.028,OR=1.028,P=0.038）,FBG（β=0.409,OR=1.506,P=0.003）,HbA1c（β=0.594,OR=1.811,P=0.001）,LDL-C（β=0.360,OR=1.434,P=0.038）。据此,构建的DR风险预测模型为Y=1/〔1+e-（0.196X1+0.028X2+0.409X3+0.594X4+0.360X5-16.482）〕,其中,Y指DR发生概率,X1指病程,X2指SBP,X3指FBG,X4指HbA1c,X5指LDL-C。DR风险预测模型预测模型组发生DR的ROC曲线下面积是0.884,诊断临界值是0.192。DR风险预测模型预测检验组发生DR的ROC曲线下面积是0.803,灵敏度为72.2%,特异度为79.1%。结论　糖尿病患者的病程、SBP、FBG、HbA1c、LDL-C与DR显著相关,DR风险预测模型对DR有一定的预测价值。     

抑郁症自杀行为简易预测模型构建及其预测价值研究

背景　抑郁症作为最常见的心境障碍之一,具有患病率高、复发率高、致残率高和致死率高等特点,给患者造成巨大的疾病负担,甚至出现自杀行为。但是,目前快速筛查抑郁症患者自杀行为的手段相对有限。目的　调查影响抑郁症患者出现自杀行为的心理社会因素,建立抑郁症患者自杀行为简易预测模型,为抑郁症患者自杀防治工作提供参考依据。方法　采用整群抽样方法选择2018年1-12月在南昌大学第二附属医院和南昌大学第一附属医院就诊的抑郁症患者为调查对象,采用一般情况问卷、抑郁自评量表（SDS）、焦虑自评量表（SAS）和Landeiman社会支持量表进行调查,采用多因素Logistic回归分析探讨抑郁症患者出现自杀行为的影响因素,Risk score法构建抑郁症患者自杀行为简易预测模型,并检测其预测效果。结果　共发放问卷2 233份,回收有效问卷2 090份,问卷有效回收率为93.60%。2 090例抑郁症患者中,142例（6.79%）出现自杀行为。经常吸烟、重度饮酒、既往抑郁发作次数≥1次、既往因抑郁症住院次数≥1次、伴焦虑症状、伴精神病性症状、伴自杀意念、有精神障碍家族史、正在用抗抑郁药物、有其他内外科疾病的抑郁症患者自杀行为比例高（P<0.05）。多因素Logistic回归分析结果显示,既往抑郁发作次数≥1次〔OR=4.308,95%CI（3.547,5.232）〕、伴焦虑症状〔OR=2.329,95%CI（1.201,4.518）〕、伴精神病性症状〔OR=2.492,95%CI（1.448,4.287）〕、伴自杀意念〔OR=4.044,95%CI（2.305,7.096）〕、SAS标准分高〔OR=1.036,95%CI（1.003,1.071）〕均是抑郁症患者自杀行为的危险因素（P<0.05）,正在用抗抑郁药物〔OR=0.110,95%CI（0.057,0.212）〕是抑郁症患者自杀行为的保护因素（P<0.05）。基于Logistic回归建立的Risk score预测模型为：Risk score=40.56×既往抑郁发作次数+23.50×伴焦虑症状+25.36×伴精神病性症+38.81×伴自杀意念-61.25×正在用抗抑郁药物+1.00×SAS标准分。按照Risk score预测模型绘制的受试者工作特征曲线（ROC）下面积（AUC）为0.920〔95%CI（0.907,0.931）〕,Youden指数最大时为0.7,截断值为193.23分,灵敏度为76.8%,特异度为94.2%。结论　抑郁症患者自杀行为发生率较高,既往抑郁发作次数≥1次、伴焦虑症状、伴精神病性症状、伴自杀意念、SAS标准分高均为抑郁症患者自杀行为的危险因素。基于Logistic回归建立的Risk score预测模型预测抑郁症患者自杀行为的灵敏度为76.8%,特异度为94.2%。     

季节性指数平滑法在预测某院儿科门诊人次中的应用

目的应用季节性指数平滑法预测某院儿科门诊人次,为儿科合理调配医疗资源提供科学依据。方法基于2013年1月1日-2018年12月31日某院儿科门诊人次数据,使用SPSS22.0软件建立季节性指数平滑模型,采用2016年1月1日-2018年12月31日各季度儿科门诊人次数据进行验证,并对2019年1月1日-2020年12月31日儿科门诊人次进行预测。结果季节性指数平滑法的最优预测模型为Winters相加模型,该模型在Gamma(趋势)、Delta(季节)均有统计学意义,平稳的R^2分别为0.76,R^2值为0.79,标准化的BIC为20.69,模型残差为白噪声序列,平均相对误差为8.85%;2019年1月1日-2020年12月31日某院儿科门诊人次的预测仍呈现出持续上升的季节性和周期性趋势。结论季节性指数平滑法的Winters相加模型能够较好的拟合该院儿科门诊人次的实际值,可用于儿科门诊量及变化趋势的预测,值得推广应用。     

北方汉族人Tanaka-Johnston预测方程研究

目的分析北方汉族青少年下颌切牙总宽度与尖牙、双尖牙总宽度之间的相关性,探寻适合目标人群的Tanaka-Johnston预测方程。方法从就诊于首都医科大学口腔医学院正畸科的病例中选取记存模型127副。研究下颌切牙总宽度与尖牙、双尖牙总宽度之间的相关性,建立北方汉族人的Tanaka-Johnston预测方程,并评价不同预测方程的准确性。结果男性组下颌切牙总宽度与上、下颌尖牙双尖牙总宽度之间相关系数r为0.71、0.77,女性组r为0.50、0.56。除本研究得出的方程外,其他预测方程普遍存在低估倾向。男性组实测值与预测值之差小于1mm的以北方汉族人方程上颌84%、下颌88%为最高;女性组以成都汉族人方程上颌74%,下颌81%为最高。结论与女性相比,男性下颌切牙总宽度与尖牙、双尖牙总宽度存在较强的正相关关系。预测准确度最高的方程分别为:男性上颌Y=11.8+0.5X,下颌Y=10.9+0.5X;女性上颌Y=11.1+0.5X,下颌Y=10.0+0.5X。     

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

Objective

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

Methods

A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject’s pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a “leave-n-out” randomized permutation cross-validation procedure.

Results

A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].

Conclusions

These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.

Significance

The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.     

Value of adipokines in predicting the severity of acute pancreatitis: Comprehensive review

AIM: To analyze the prognostic value of adipokines in predicting the course, complications and fatal outcome of acute pancreatitis (AP).METHODS: We performed the search of PubMed database and the systemic analysis of the literature for both experimental and human studies on prognostic value of adipokines in AP for period 2002-2012. Only the papers that described the use of adipokines for prediction of severity and/or complications of AP were selected for further analysis. Each article had to contain information about the levels of measured adipokines, diagnosis and verification of AP, to specify presence of pancreatic necrosis, organ dysfunction and/or mortality rates. From the very beginning, study was carried out adhering to the PRISMA checklist and flowchart for systemic reviews. To assess quality of all included human studies, the Quality Assessment of Diagnostic Accuracy Studies tool was used. Because of the high heterogeneity between the studies, it was decided to refrain from the statistical processing or meta-analysis of the available data.RESULTS: Nine human and three experimental studies were included into review. In experimental studies significant differences between leptin concentrations at 24 and 48 h in control, acute edematous and acute necrotizing pancreatitis groups were found (P = 0.027 and P < 0.001). In human studies significant differences between leptin and resitin concentrations in control and acute pancreatitis groups were found. 1-3 d serum adiponectin threshold of 4.5 μg/mL correctly classified the severity of 81% of patients with AP. This threshold yielded a sensitivity of 70%, specificity 85%, positive predictive value 64%, negative predictive value88% (area under curve 0.75). Resistin and visfatin concentrations differ significantly between mild and severe acute pancreatitis groups, they correlate with severity of disease, need for interventions and outcome. Both adipokines are good markers for parapancreatic necrosis and the cut-off values of 11.9 ng/mL and 1.8 ng/mL respectively predict the high ranges of radiological scores. However, the review revealed that all nine human studies with adipokines are very different in terms of methodology and objectives, so it is difficult to generalize their results. It seems that concentrations of the leptin and resistin increases significantly in patients with acute pancreatitis compared with controls. Serum levels of adiponectin, visfatin and especially resitin (positive correlation with Acute Physiology and Chronic Health Evaluation II, Ranson and C-reactive protein) are significantly different in mild acute pancreatitis and severe acute pancreatitis patients, so, they can serve as a markers for the disease severity prediction. Resistin and visfatin can also be used for pancreatic and parapancreatic necrosis prediction, interventions needs and possible, outcome.CONCLUSION: High levels of adipokines could allow for prediction of a severe disease course and outcome even in small pancreatic lesions on computed tomography scans.     

血液标志物与胰腺外炎症CT评分对急性胰腺炎严重性早期预测的比较

目的:比较血液标志物及胰腺外炎症CT评分(extrapancreatic inflammation on CT score,EPIC)对急性胰腺炎(acute pancreatitis,AP)严重性的早期预测价值.方法:对2010-09/2011-09住院的96例AP患者首个24h内的临床、实验室及CT资料进行分析.临床上重症急性胰腺炎(severe acute pancreatitis,SAP)的标准为:死亡或持续器官衰竭及/或入住ICU,及/或手术治疗.对重症急性胰腺炎组及轻症急性胰腺炎(mild acute pancreatitis,MAP)组患者血液标志物及胰腺外炎症CT评分进行t检验,血液标志物及EPIC预测AP严重性的相关性检验及预测AP严重性的ROC分析,并计算预测敏感性、阳性预测值及准确度.结果:MAP76例,SAP20例.重症患者的血液标志物及胰腺外炎症CT评分均明显较轻症患者的大[白细胞:(15.16±5.06)×109/Lvs(11.05±1.76)×109/L,中性粒细胞与淋巴细胞比值:18.95±12.13vs6.63±3.44,高敏C-反应蛋白:58.35mg/L±20.47mg/Lvs28.59mg/L±12.92mg/L,D-二聚体:1596.95μg/L±1409.05μg/Lvs412.52μg/L±316.66μg/L,胰腺外炎症CT评分:3.30±0.86vs1.50±0.96,P=0.000].白细胞、中性粒细胞与淋巴细胞比值、高敏C-反应蛋白、D-二聚体及胰腺外炎症CT评分与AP严重性的Spearman相关系数(rs)分别为0.419、0.571、0.568、0.434及0.61(P=0.000).白细胞、中性粒细胞与淋巴细胞比值、高敏C-反应蛋白、D-二聚体及胰腺外炎症CT评分对AP严重性预测的曲线下面积分别为0.798(0.670-0.925)、0.906(0.830-0.981)、0.904(0.838-0.970)、0.808(0.638-0.938)以及0.917(0.851-0.983);预测敏感性分别为70.00%、85.00%、85.00%、75.00%及85.00%;阳性预测值分别为58.33%、73.91%、51.52%、48.39%及72.00%;预测准确度分别为83.33%、90.63%、80.21%、78.13%及90.63%.结论:白细胞及D-二聚体对AP严重性的预测价值中等,中性粒细胞与淋巴细胞比值、高敏C-反应蛋白及胰腺外炎症CT评分的预测价值较高,其中中性粒细胞与淋巴细胞比值和胰腺外炎症CT评分预测的准确度最高,胰腺外炎症CT评分与AP严重性的相关系数最大,其预测AP严重性的受试者曲线下面积最大.     

Predicting length of stay in thermal burns: A systematic review of prognostic factors

Background

Continued improvement in all aspects of the management of thermal injury has resulted in marked improvements in the traditionally reported outcome of mortality. This has resulted in the search for alternative parameters that can be monitored to indicate the performance of burn services. Length of stay (LOS) in hospitalised burn patients has long been considered reflective of injury-associated morbidity, cost and the quality of care, which can be monitored consistently across services.

Aim

We undertook a systematic review of published literature pertaining to LOS prognostication in thermal burns to identify the relevant factors, quantify the risk associated with these factors and identify predictive prognostic models.

Methods

Electronic searches were performed on MEDLINE, CINHAL, EMBASE, Web of Science^®, the Cochrane collection and a general web search was performed using Google^®. The searches were complemented by a manual search of the contents of leading burns journals. Quality of the studies included in the review was evaluated against published standards for prognostic studies.

Results

Fourteen studies were included in the review after meeting the inclusion/exclusion criteria. Age and %TBSA were the strongest predictors of LOS in these studies. Other significant predictors included % full thickness burn, female gender, inhalation injury, surgery including escharotomy and the depth of burn. Nine studies reported multivariate models for predicting LOS in patients sustaining thermal injury. None of these models were validated and the goodness-of-fit statistic (R²) ranged from 0.15 to 0.75.

Conclusion

This review has demonstrated that %TBSA and age are the best predictors of LOS in published literature. Current prognostic models do not explain a significant proportion of variation in LOS.     

防治术后恶心呕吐的研究进展

背景 术后恶心呕吐(postoperative nausea and vomiting,PONV)是麻醉手术后最常见的并发症之一,其发生率可高达30％.虽然国际上已公布了防治PONV共识和指南,但尚存在缺陷的PONV预测模型和复杂的防治策略成为临床防治PONV最大的障碍. 目的 探究在临床工作中防治PONV的主要障碍,为临床防治PONV工作提供更有效的途径. 内容 尚存在缺陷的PONV预测模型和复杂的防治策略是临床防治PONV最大的障碍,多模式防治PONV是一种简单、有效的预防PONV发生的方法. 趋向 多模式防治PONV更容易被临床麻醉医生接受和应用,新型的抗PONV药物和传统经典的抗呕吐药物为临床多模式防治提供了更大的选择空间.