羊水板层小体计数预测胎儿肺成熟的可行性及其临床意义

目的:探讨羊水板层小体计数预测胎肺成熟度的临床应用价值。方法:选择156例单胎孕妇作为研究对象,孕28～38周,每份羊水分3份分别进行LBC计数、L/S比值测定及泡沫试验。结果:羊水LBC与L/S比值成正线性相关,相关系数(r)为0.623,有极显著统计学意义(P<0.001);LBC与泡沫试验呈等级相关,相关系数为(r)为0.781,有极显著统计学意义(P<0.001)。以LBC≤10×109/L作为判断胎儿肺不成熟的临界值,以LBC≥35×109/L作为判断胎儿肺成熟的临界值,比较羊水LBC计数各预测值率与L/S比值差异无统计学意义(P>0.05);与泡沫试验相比,羊水LBC计数的各预测值明显高于泡沫试验,差异有统计学意义(P<0.01)。结论:羊水LBC计数在预测胎肺成熟度方面优于传统的泡沫试验,较L/S比值省时、省力,是临床中用于判断胎儿肺成熟方法的首选。     

羊水板层小体计数预测胎肺生化成熟

目的:探讨羊水板层小体计数(LBC)诊断胎肺生化成熟的价值。方法:测定118例妊娠≥28周羊水标本的LBC,用ROC曲线分析LBC诊断L/S比值、磷脂酰甘油(PG)成熟的界值和价值。结果:LBC与L/S比值、PG、泡沫试验、OD 650密切相关(P<0.001);LBC诊断L/S比值≥2.0或PG阳性的ROC曲线下面积为0.959(95%C I=0.929~0.989),最佳界值为≥86×109/L,诊断L/S比值<2.0及PG阴性的最佳界值为<40×109/L;LBC以≥86×109/L为界,诊断胎肺生化成熟的灵敏度为81.08%,特异度为100%,阳性预测价值100%,阴性预测价值为75.86%;以≥40×109/L为界,其灵敏度为100%,阴性预测价值为100%。结论:羊水板层小体计数对胎肺生化成熟有较高的诊断价值,可作为首选的胎肺成熟度快速筛选试验。     

Significance of the Modified NUTRIC Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia

Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.     

妊娠期糖尿病发病风险预测模型构建与评价

目的 分析妊娠期糖尿病（GDM）的相关危险因素,建立妊娠期糖尿病发病风险预测模型,为个体GDM发病风险预测及早期干预提供依据。方法 回顾性分析919名患妊娠期糖尿病孕妇与同期分娩的949名未患妊娠期糖尿病孕妇的既往病历资料,采用单因素分析和多因素logistic回归分析孕妇孕早期GDM的独立危险因素,构建GDM发病风险预测模型,采用Homser-Lemeshow拟合优度检验、拟合优度校正图、受试者工作特征曲线 （ROC）对该模型进行评估,采用deLong’s test进行AUC显著性差异检验。结果 基于巨大儿史、糖化血红蛋白、空腹血糖、血红蛋白、白细胞计数、活化部分凝血活酶时间及胚胎移植GDM发病风险模型,拟合程度检验 P =0.443＞0.05,验证集ROC曲线下面积（ AUC ）为0.836（95% CI :0.8019~0.838）,灵敏度为78.8%,特异度为78.6%,与训练集ROC曲线下面积无显著性差异（ P ＞0.05）。结论 基于危险因素构建的妊娠期糖尿病风险预测模型有较好的预测性能和泛化能力,能早期预测孕妇GDM发病风险并实施早期干预措施,从而降低GDM发病率。     

老年骨折术后手术部位感染Logistic预测模型建立及其价值

目的运用Logistic多元回归分析建立老年骨折术后手术部位感染(SSI)数学预测模型并评估其预测感染的效能。方法选取2015年1月-2019年12月江南大学附属医院就诊的1034例老年骨折手术患者为研究对象,根据术后感染情况将患者分为感染组和对照组,利用Logistic多元回归分析术后SSI的危险因素并建立预测模型,通过受试者工作特征曲线(ROC)评价预测模型的效能。结果1034例患者中有60例患者术后发生SSI,发生率为5.80%;多元Logistic回归分析结果显示:年龄≥65岁、手术时间≥3 h、II/III类手术切口、受伤至手术时间≥8 h、引流管留置时间≥5 d、住院时间≥7 d是术后SSI的独立危险因素(P<0.05);老年骨折患者术后SSI的预测模型为:Logit(P)=2.438+0.641×(年龄)+0.581×(手术时间)+0.504×(II/III类手术切口)+0.482×(受伤至手术时间)+0.653×(引流管留置时间)+0.543×(住院时间),Hosmer-lemeshow拟合优度检验,模型的预测概率和实际发生率比较无统计学差异;验证试验ROC曲线下面积为0.840,95%CI为0.760~0.919,随机抽取100例进行Logistic回归预测模型验证其总准确率为76.00%。结论年龄≥65岁、手术时间≥3 h、II/III类手术切口、受伤至手术时间、引流管留置时间≥5 d、住院时间≥7 d是老年骨折患者术后SSI的危险因素,Logistic回归分析构建的预测模型对术后SSI的预测效能较好。     

四种与冠心病相关指标联合诊断冠心病价值评价

目的：探讨klgistic多指标联合诊断试验ROC分析中的应用，评价4种与冠心病发病有关指标在冠心病诊断及联合诊断中的效果。方法：根据疾病状态建立klgistic回归模型，通过形成的预测概率或联合预测因子为分析指标，并结合双正态模型建立ROC曲线。结果：通过实例阐述了整个分析过程，并说明了指标对诊断冠心病的有效性，确定了基于联合诊断的晟佳工作点。结论：ROC分析中结合logistic回归模型简单有效，尤其适用于有协变量或多指标联合诊断试验的分析评价。     

脑出血手术患者肺部感染风险预测评分模型的构建与验证

目的 构建脑出血手术患者肺部感染风险预测评分模型,识别肺部感染的高危人群,为临床医务人员早期采取有效预防与控制措施提供依据.方法 前瞻性收集山东省某医院2016—2018年脑出血手术患者的临床资料,将患者按照7:3的比例随机分为建模组和验证组,利用建模组数据建立logistic回归模型,依据β值对危险因素进行赋分,构建...     

Validation of logistic regression models in small samples: application to calvarial lesions diagnosis

We have used the leave-one-out (LOO) method and the area under the receiver operating characteristic (ROC) curve to validate logistic models with a sample of 167 patients with calvarial lesions. Seven logistic regression models were developed from 12 clinical and radiological variables to predict the most common diagnoses separately. The LOO method was used to test the validity of the equations. The discriminant power of every model was assessed by means of the area under the ROC curve (Az). The model with the greatest discrimination ability for the whole data set was the osteoma equation (Az = 0.951). The discriminatory ability of the statistical models decreased significantly with the LOO procedure, having the malignancy model the highest value (Az = 0.931). The LOO method can obtain a high benefit from small samples in order to validate prediction rules. In studies with small samples, resampling techniques such as the LOO should be routinely used in predictive modeling. This method may improve the forecast of infrequent diseases, such as calvarial lesions.     

开颅手术后颅内感染风险预测模型构建及效果评价

 目的 运用logistic回归分析构建神经外科开颅手术后颅内感染风险预测模型并进行效果评价。方法 选取某院神经外科2019年1月—2021年6月行开颅手术的患者为研究对象,根据术后是否发生颅内感染分为病例组和对照组,采用logistic回归分析开颅手术后颅内感染发生的危险因素并构建风险预测模型,通过Hosmer-Lemeshow拟合优度检验和受试者工作特征(ROC)曲线对其效果进行综合评价。结果 共纳入778例开颅手术患者,121例发生术后颅内感染,发病率为15.55%;logistic多因素回归分析结果显示,幕下手术、脑室引流时间≥3 d、使用明胶海绵≥3片、出血量≥300 mL、切口脑脊液漏是开颅手术后颅内感染的独立危险因素(均P<0.05);开颅手术后颅内感染的风险预测模型为:logit (P)=5.408＋0.833×(幕下手术)＋0.083×(脑室引流时间)＋1.059×(使用明胶海绵)＋0.456×(出血量)＋2.821×(切口脑脊液漏);Hosmer-Lemeshow拟合优度检验结果显示颅内感染的预测概率和实际发病率比较,差异无统计学意义(P=0.768);logistic回归风险预测模型验证准确率为86.00%,ROC曲线下面积为0.847,95%CI为0.814~0.878。结论 幕下手术、脑室引流时间≥3 d、使用明胶海绵≥3片、出血量≥300 mL、切口脑脊液漏是神经外科开颅手术后颅内感染的独立危险因素,运用logistic回归分析构建的风险预测模型对术后颅内感染的预测效果较好。     

Logistics回归分析剖宫产瘢痕妊娠治疗后再入院治疗的影响因素及不同治疗方案的最佳获益人群

目的采用Logistics回归分析剖宫产瘢痕妊娠治疗后再入院治疗的影响因素及不同治疗方案的最佳获益人群。方法收集湖北省妇幼保健院2016年4月至2018年4月收治的430例剖宫产瘢痕妊娠患者临床资料,根据患者治疗后是否再次入院治疗分为未再次入院组(389例)和再次入院组(41例)。采用复方米非司酮联合米索前列醇进行药物流产后联合B超引导下刮宫术治疗的患者为A方法组,共240例;采用双侧子宫动脉栓塞术联合甲氨蝶呤动脉灌注化疗联合B超引导下刮宫术为B方法组,共190例。分别采用单因素方差分析、Logistic回归多因素分析对年龄、孕周、孕次、囊胚大小、瘢痕厚度、瘢痕妊娠至剖宫产时间、术前血人绒毛膜促性腺激素水平(human chorionic gonadotropin,h CG)、术后h CG下降速率、治疗方法、术中出血量、术后并发症发生率、住院时间、住院费用等因素进行分析并筛选相关危险因素;采用ROC曲线分析对危险因素进行评估,并分析不同治疗方法的最佳获益人群。结果单因素方差分析结果显示未再次入院组和再次入院组间孕周、孕次、胎囊大小、瘢痕厚度、术中出血量比较,差异均有统计学意义(P<0.05)。对上述5项因素进行多因素Logistics回归分析,结果显示孕周≥7周、囊胚大小≥2.5 cm、瘢痕厚度≤3 mm是影响剖宫产瘢痕妊娠治疗后再入院治疗的独立危险因素;采用ROC曲线确定孕周临界值为7.12周,囊胚大小临界值为2.52 cm,瘢痕厚度临界值为2.98 mm。B方法组患者中孕周≥7.12周,囊胚≥2.52 cm,瘢痕厚度≥2.98 mm患者比例均高于A方法组患者,差异有统计学意义(P<0.05),再入院率比较,差异无统计学意义(P>0.05)。结论孕周、胎囊大小、瘢痕厚度是影响剖宫产瘢痕妊娠治疗后再入院治疗的独立危险因素,当孕周≥7.12周,胎囊大小≥2.52 cm,瘢痕厚度≤2.98 mm时发生率最高。对这部分患者建议采用双侧子宫动脉栓塞术联合甲氨蝶呤动脉灌注化疗联合B超引导下刮宫术进行治疗。     

剖宫产术后瘢痕子宫再次妊娠经阴道分娩可行性的预测模型构建

目的 构建剖宫产术后瘢痕子宫再次妊娠经阴道分娩预测模型并探讨其可行性.方法 回顾性分析2020年2月至2021年3月在苏州市第九人民医院和苏州市立医院接受剖宫产术后瘢痕子宫再次妊娠经阴道试产的294例孕妇的临床资料.采用Logistic回归分析筛选瘢痕子宫阴道分娩可行性的影响因素,基于筛选结果建立列线图预测模型.采用R...     

对数几率回归模型构建联合预测因子对慢性乙型肝炎患者早期肝硬化诊断的临床应用

目的探讨对数几率回归模型(Logistic)构建联合预测因子在慢性乙型肝炎患者(CHB)早期肝硬化(LC)诊断中的临床应用。方法选择本院2018年1月-2019年10月的CHB患者179例、CHB/LC患者179例及测试集57例,检测血常规、血清肝纤维化、凝血功能、血清生化等实验室指标。单因素Logistic回归筛选并用多因素Logistic回归拟合得到联合预测因子,生成受试者工作曲线(ROC),在测试集中进行测试,评价模型预测效果。结果透明质酸(HA)、部分活化凝血酶原时间(APTT)、谷丙转氨酶(ALT)和血小板(PLT)纳入拟合得到新的联合预测因子表达式Logit(X)=39.621+1.365HA+1.213APTT+1.011ALT-0.723PLT。ROC曲线显示X的cut-off值取308.80时,YI指数最大为0.538,AUC为0.815,分别高于单个协变量因子。在模型准确率为70.12%的前提下,CHB合并LC患者测试组的预测LC准确率为72.41%,CHB患者测试组的预测为非LC的准确率为96.42%。结论对数几率回归模型构建的联合预测因子ROC曲线能对CHB患者的早期LC诊疗有更好的预测价值。     

Affirmative actions: can the discriminant accuracy of a test be determined in the face of selection bias?

Clinical estimates of test efficacy can be distorted by the differential referral of positive and negative test responders for outcome verification. Accordingly, a series of computer simulations was performed to quantify the effects of various degrees of this selection bias on the observed true-positive rate, false-positive rate, and discriminant accuracy of a hypothetical test. The error in observed true- and false-positive rates was positive with respect to diagnosis, and negative with respect to prognosis. The magnitude of error was highly correlated with the magnitude of bias associated with the test response (primary selection bias), but not with the magnitude of bias associated with additional independent factors (secondary selection bias). Mathematical correction for preferential referral based on the test response using a previously published algorithm completely removed the correlation with primary selection bias for both diagnosis and prognosis. Although a significant correlation with secondary selection bias persisted at intermediate base rates, its magnitude was small. Discriminant accuracy was assessed in terms of area under a receiver operating characteristic (ROC) curve. Biased values of true- and false-positive rates were distributed along the curve defined by the actual true- and false-positive rates of the test for both diagnosis and prognosis. As a result, the areas under ROC curves calculated from biased true- and false-positive rates were within 2% of the areas calculated from the actual rates. Only when the primary and secondary observations were independent with respect to one outcome and dependent with respect to the other outcome did a systematic error appear in ROC area.(ABSTRACT TRUNCATED AT 250 WORDS)     

Medical diagnostic testing models for multifactorial disease classifications

In this note we extend the logistic regression receiver operating characteristic (ROC) analysis framework to diagnosing disease that can be classified in a 2 x 2 factorial setting. Similar to the standard ROC curve analysis these new models accommodate prediction of cross-classifications for treatment success and diagnosis of disease. A didactic example is given for jointly diagnosing malaria positive and respiratory distress in children in Ghana.     

A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets

The receiver operating characteristic (ROC) curve is a statistical tool for evaluating the accuracy of diagnostic tests. Investigators often compare the validity of two tests based on the estimated areas under the respective ROC curves. However, the traditional way of comparing entire areas under two ROC curves is not sensitive when two ROC curves cross each other. Also, there are some cutpoints on the ROC curves that are not considered in practice because their corresponding sensitivities or specificities are unacceptable. For the purpose of comparing the partial area under the curve (AUC) within a specific range of specificity for two correlated ROC curves, a non-parametric method based on Mann-Whitney U-statistics has been developed. The estimation of AUC along with its estimated variance and covariance is simplified by a method of grouping the observations according to their cutpoint values. The method is used to evaluate alternative logistic regression models that predict whether a subject has incident breast cancer based on information in Medicare claims data.     

老年患者中央导管相关血流感染风险预测评分模型构建与验证

目的构建老年患者中央导管相关血流感染(CLABSI)风险预测评分模型,为筛选高危人群,有效预防与控制老年患者血流感染提供依据。方法依据纳入排除标准,收集2015年1月1日—2017年12月31日住院期间曾留置中央导管的老年患者病例资料,按照7∶3的比例随机分为建模组和验证组(随机种子为20180708),对建模组数据进行危险因素识别,构建logistic回归模型,根据β值赋予各危险因素相应的分值,建立感染风险评分模型,利用受试者工作特征(ROC)曲线评价模型的预测准确度;依据建立的感染风险评分模型对验证组病例进行评分,利用ROC曲线评价模型的预测准确度。利用R软件构建决策曲线。结果 logistic回归分析结果表明:本次住院手术次数≥3次、住ICU日数≥2 d、中心静脉置管日数≥7 d、使用抗菌药物等是老年患者发生CLABSI的独立危险因素;风险评分模型中相应的分值分别为3、4、4、9分,得分13～17分为高风险人群;评分模型在建模组数据中ROC曲线下面积(AUC)为0.74;依据验证组患者风险得分情况绘制ROC曲线,曲线下面积(AUC)为0.70。决策曲线显示,在阈值0.01～0.05区间内风险评分模型的净获益较高。结论建立的风险评分模型具有较好的判别效度和应用价值,可用于老年患者CLABSI的易感高危人群识别,做到早期预防与控制。     

A general regression methodology for ROC curve estimation

A method for applying generalized ordinal regression models to categorical rating data to estimate and analyze receiver operating characteristic (ROC) curves is presented. These models permit parsimonious adjustment of ROC curve parameters for relevant covariates through two regression equations that correspond to location and scale. Particular shapes of ROC curves are interpreted in relation to the kind of covariates included in the two regressions. The model is shown to be flexible because it is not restricted to the assumption of binormality that is commonly employed in smoothed ROC curve estimation, although the binormal model is one particular form of the more general model. The new method provides a mechanism for pinpointing the effect that interobserver variability has on the ROC curve. It also allows for the adjustment of ROC curves for temporal variation and case mix, and provides a way to assess the incremental diagnostic value of a test. The new methodology is recommended because it substantially improves the ability to assess diagnostic tests using ROC curves.     

胎儿肺成熟度与早产儿心力储备的相关性研究

目的探讨早产儿胎儿肺成熟度和心力储备（CCR）间的相关性。方法选择2008年7月至2009年8月于四川大学华西第二医院产科住院分娩的36例出生后2 h以内早产儿为研究对象,采用羊水泡沫试验及羊水板层小体计数（LBC）检测本组早产儿的胎儿肺成熟度;根据胎儿肺成熟度,将其分为胎儿肺不成熟组（n=17）,胎儿肺可疑成熟组（n=3）和胎儿肺成熟组（n=16）。采用心音图实验（PCGT）检测CCR指标,包括第一心音幅值（S1）与第二心音幅值（S2）的比值（S1/S2）、心脏舒张期时限（D）与收缩期时限（S）的比值（D/S）和应激后心力变化趋势（CCCTS）;采用线性回归分析各指标与早产儿胎儿肺成熟度之间的相关性（本研究遵循的程序符合四川大学华西第二医院人体试验委员会制定的伦理学标准,得到该委员会批准,分组征得受试对象监护人知情同意,并与监护人签署临床研究知情同意书）。各组早产儿出生体质量、身长等一般情况比较,差异无统计学意义（P〉0.05）。结果早产儿胎儿肺成熟度与孕龄成正相关（r=0.536,P=0.001）;3组间 S1/S2和 CCCTS 比较,其差异无统计学意义（H=0.013,1.651,均 P〉0.05）;3组间D/S比较,仅胎儿肺成熟早产儿组较胎儿肺不成熟早产儿组高,其差异有统计学意义（q=0.088,P=0.023）。CRR指标与胎儿肺成熟度的偏相关分析结果显示,早产儿胎儿肺成熟度与 S1/S2、CCCTS 不相关（r=0.1892,0.2890,均P〉0.05）;与D/S成正相关（r=0.4014,P=0.017）。结论成熟的胎儿肺发育可使早产儿心肌灌注时间延长,心肌获得较多氧气和营养,使其 CCR较高;胎儿肺不成熟早产儿CCR低,出生后应加强对心脏功能的保护。通过对 D/S 和胎儿肺成熟度相关关系的深入研究,有可能找到判断胎儿肺成熟度的新方法。     

超声预测巨大儿的临床价值

目的评价产前超声检查预测巨大儿的临床价值。方法回顾性分析禹州市人民医院146例巨大儿和292例正常足月儿的产前超声检测参数：胎儿腹围（Ac）、股骨长径（FL）、双顶径（BPD）、头围（HC）,Logistic回归建立巨大儿预测方程．对受试者工作特征曲线（ROC）、敏感性、特异性等指标评价预测效能。结果①巨大儿组AC（36．65±1．36）cm和FL（78．76±2．79）mm大于非巨大儿组AC（34．66±1．33）cm和FL（74．52±3．08）1T11TI,P。0．000。②AC（OR=3．454）和FL（OR=1．7381均入选以巨大儿为结果变量的回归方程,巨大儿预测方程：P=1／[1＋EXP（87．331-1．239AC-0．553FL）]。⑧万程预测巨大儿的ROC曲线下面积为0．941（95％CI：0．919-0．962）大于AC0．845（95％CI：0．807~0．883）和FL0．837（95％CI：0．797～0．877）的预测面积．P=O．000,方程、Ac和FL诊断巨大儿的敏感性分别是0．842、0．712和0．753,特异性分别是0．904、f）．853和0．812,一致率分别是0．884、0．806和0．792。结论超声测量的胎儿Ac和FL是预测巨大儿的显著参数,将这两个指标综合考虑可槎高巨大胎儿的诊断率。     

基于SEER数据库肝内胆管恶性肿瘤的Nomogram模型建立与验证

目的 探讨肝内胆管癌（ICC）行手术治疗后影响患者生存的危险因素，并依据此构建列线图预测模型。 方法 通过SEER*Stat软件从美国国立癌症研究所“监测、流行病学和结果数据库”(the Surveillance，Epidemiology，and End Results，SEER)数据库获取2004—2015年诊断为ICC患者的临床病理资料信息共2 100例，通过R软件中“rms”包将筛选过后的临床病理资料随机分成建模组和验证组，两组人数分别为1 470例和630例，通过使用R软件进行单因素Cox回归分析，根据处理后的结果筛出可能与ICC患者生存相关的危险因素，再将得到的危险因素进行多因素Cox回归分析，基于多因素Cox回归分析的结果构建ICC患者的列线图，模型的预测能力通过受试者工作特征曲线（ROC曲线）、C - index、和校准曲线等方式进行验证。 结果 建模组ICC患者的6、12和24月生存为分别为65.2%、47.5%和29.4%；验证组ICC的6、12和24月生存为分别为70.3%、51.3%和32.2%。多因素Cox分析显示年龄、性别、AJCC State Group分期、手术、肿瘤大小、化疗为影响ICC患者预后的危险因素，根据筛选出的6个独立危险因素所构建的预测模型，C - index为0.742（95%CI:0.729～0.757），建模组中患者6、12和24月的ROC曲线下面积分别为0.848、0.818和0.751。验证组据筛选出的6个独立危险因素所构建的预测模型，C - index为0.737（95%CI:0.715～0.760），验证组中患者6、12和24月的ROC曲线下面积分别为0.772、0.733和0.706。在两组队列中6、12和24月的校准曲线显示两个列线图的预测值和实际观察值高度一致。 结论 通过筛选出的独立危险因素能够很好的预测ICC患者的预后，为临床工作个体化治疗提供参考依据。