Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model

BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.

Establishment of a nomogram model for predicting therapy complications in patients with polycythemia and deep venous thrombosis

BACKGROUND Patients with deep venous thrombosis(DVT)residing at high altitudes can only rely on anticoagulation therapy,missing the optimal window for surgery or thrombolysis.Concurrently,under these conditions,patient outcomes can be easily complicated by high-altitude polycythemia(HAPC),which increases the difficulty of treatment and the risk of recurrent thrombosis.To prevent reaching this point,effective screening and targeted interventions are crucial.Thus,this study analyzes and provides a reference for the clinical prediction of thrombosis recurrence in patients with lower-extremity DVT combined with HAPC.AIM To apply the nomogram model in the evaluation of complications in patients with HAPC and DVT who underwent anticoagulation therapy.METHODS A total of 123 patients with HAPC complicated by lower-extremity DVT were followed up for 6-12 months and divided into recurrence and non-recurrence groups according to whether they experienced recurrence of lower-extremity DVT.Clinical data and laboratory indices were compared between the groups to determine the influencing factors of thrombosis recurrence in patients with lowerextremity DVT and HAPC.This study aimed to establish and verify the value of a nomogram model for predicting the risk of thrombus recurrence.RESULTS Logistic regression analysis showed that age,immobilization during follow-up,medication compliance,compliance with wearing elastic stockings,and peripheral blood D-dimer and fibrin degradation product levels were indepen-dent risk factors for thrombosis recurrence in patients with HAPC complicated by DVT.A Hosmer-Lemeshow goodness-of-fit test demonstrated that the nomogram model established based on the results of multivariate logistic regression analysis was effective in predicting the risk of thrombosis recurrence in patients with lowerextremity DVT complicated by HAPC(χ^(2)=0.873;P>0.05).The consistency index of the model was 0.802(95%CI:0.799-0.997),indicating its good accuracy and discrimination.CONCLUSION The column chart model for the personalized prediction of thrombotic recurrence risk has good application value in predicting thrombotic recurrence in patients with lower-limb DVT combined with HAPC after discharge.

Prognostic value of a nomogram model for postoperative liver metastasis of colon cancer

BACKGROUND Colon cancer is one of the most common malignant tumors of the digestive system.Liver metastasis after colon cancer surgery is the primary cause of death in patients with colon cancer.AIM To construct a novel nomogram model including various factors to predict liver metastasis after colon cancer surgery.METHODS We retrospectively analyzed 242 patients with colon cancer who were admitted and underwent radical resection for colon cancer in Zhejiang Provincial People’s Hospital from December 2019 to December 2022.Patients were divided into liver metastasis and non-liver metastasis groups.Sex,age,and other general and clinicopathological data(preoperative blood routine and biochemical test indexes)were compared.The risk factors for liver metastasis were analyzed using singlefactor and multifactorial logistic regression.A predictive model was then constructed and evaluated for efficacy.RESULTS Systemic inflammatory index(SII),C-reactive protein/albumin ratio(CAR),red blood cell distribution width(RDW),alanine aminotransferase,preoperative carcinoembryonic antigen level,and lymphatic metastasis were different between groups(P<0.05).SII,CAR,and RDW were risk factors for liver metastasis after colon cancer surgery(P<0.05).The area under the curve was 0.93 for the column-line diagram prediction model constructed based on these risk factors to distinguish whether liver metastasis occurred postoperatively.The actual curve of the column-line diagram predicting the risk of postoperative liver metastasis was close to the ideal curve,with good agreement.The prediction model curves in the decision curve analysis showed higher net benefits for a larger threshold range than those in extreme cases,indicating that the model is safer.CONCLUSION Liver metastases after colorectal cancer surgery could be well predicted by a nomogram based on the SII,CAR,and RDW.

A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding:A Two-center Retrospective Study

Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

老年急性ST段抬高型心肌梗死患者左心室血栓形成的影响因素及其Nomogram风险预测模型构建

目的探讨老年急性ST段抬高型心肌梗死(STEMI)患者左心室血栓(LVT)形成的影响因素,并构建Nomogram风险预测模型。方法将华中科技大学同济医学院附属同济医院2020年1月至2023年12月收治的873例急性STEMI患者以7∶3分为训练集(n=611)和验证集(n=262)。根据训练集患者LVT发生情况分为非LVT组(n=549)和LVT组(n=62)。多因素logistic回归分析急性STEMI患者发生LVT的影响因素;利用R软件rms程序包构建预测急性STEMI患者LVT发生风险的Nomogram模型,并通过ROC及Hosmer-Lemeshow检验验证其预测效能。结果与非LVT组比较,LVT组血红蛋白(Hb)[(131.15±15.99)g/L比(143.28±16.71)g/L]和左心室射血分数(LVEF)[(39.99±5.01)%比(45.24±6.69)%]明显较低(t=5.441、5.991,P<0.05),合并室壁瘤(37.10%比20.40%)、基线心肌梗死溶栓治疗(TIMI)血流分级0级(59.68%比44.81%)、未形成冠状动脉侧支循环(88.71%比72.13%)以及未进行急诊经皮冠状动脉介入治疗(PCI)或药物溶栓的占比(69.35%比48.82%)明显较高(χ^(2)=9.022、4.953、7.921、9.403,P<0.05)。多因素Logistic回归显示,Hb(OR=0.948)和LVEF(OR=0.868)为急性STEMI患者发生LVT的保护因素(P<0.05),合并室壁瘤(OR=6.812)、基线TIMI血流分级0级(OR=6.884)、未形成冠状动脉侧支循环(OR=7.273)、未进行急诊PCI或药物溶栓(OR=4.173)为其独立危险因素(P<0.05)。ROC分析显示,训练集AUC为0.871(95%CI 0.830~0.912),验证集为0.838(95%CI 0.731~0.945);Hosmer-Lemeshow检验中训练集χ^(2)=11.379,P=0.181,验证集χ^(2)=11.261,P=0.187。结论急性STEMI患者LVT的发生与Hb、LVEF、室壁瘤、基线TIMI分级、冠状动脉侧支循环形成及是否接受急诊PCI或药物溶栓密切相关,基于这些因素构建的Nomogram预测模型区分度及一致性良好,有助于临床预测。

维持性血液透析尿毒症患者动静脉内瘘急性血栓形成的危险因素分析及Nomogram预测模型构建

目的探讨维持性血液透析(MHD)尿毒症患者动静脉内瘘急性血栓形成的危险因素及Nomogram预测模型构建。方法选择2020年12月至2022年12月于新乡医学院第三附属医院门诊行MHD治疗的418例尿毒症患者为研究对象。根据动静脉内瘘是否形成急性血栓将患者分为急性血栓组(n=32)与非急性血栓组(n=386)。应用单因素及多因素logistic回归分析影响行MHD的尿毒症患者动静脉内瘘急性血栓形成的影响因素;根据尿毒症患者动静脉内瘘急性血栓形成的独立危险因素建立Nomogram预测模型,并应用Bootstrap来验证Nomogram模型的有效性。结果急性血栓组患者合并糖尿病患、合并低血压、透析时穿刺失败占比、钙磷乘积、超敏C反应蛋白(hs-CRP)、总胆固醇(TC)、低密度脂蛋白胆固醇(LDL-C)水平均显著高于非急性血栓组(P<0.05);logistic回归分析显示,糖尿病、低血压、透析时穿刺失败、钙磷乘积升高、高水平hs-CRP、高水平LDL-C是影响行MHD的尿毒症患者动静脉内瘘急性血栓形成的独立危险因素(P<0.05);依据6个独立危险因素构建了Nomogram预测模型,该模型的一致性指数为0.893(95%置信区间:0.833~0.928);且其校正曲线和标准曲线拟合较好,曲线下面积为0.918。结论糖尿病、低血压、透析时穿刺失败、钙磷乘积升高、高水平hs-CRP和LDL-C是行MHD的尿毒症患者动静脉内瘘急性血栓形成的危险因素,依据独立危险因素构建的Nomogram模型预测行MHD的尿毒症患者动静脉内瘘急性血栓形成的效果较好,有助于临床急性血栓形成患者的早期筛查。

脓毒症患者并发急性肾损伤的危险因素分析及Nomogram预测模型的构建

目的分析脓毒症患者并发急性肾损伤的危险因素,并构建列线图(Nomogram)预测模型。方法选取2020年3月至2022年9月就诊的180例脓毒症患者为研究对象,根据患者是否并发急性肾损伤将其分为急性肾损伤组(n=75)和非急性肾损伤组(n=105)。采用受试者工作特征(ROC)曲线分析连续性变量的预测价值;采用logistic回归分析影响脓毒症患者并发急性肾损伤的危险因素;采用内部数据验证Nomogram模型临床效能。结果两组患者在年龄等一般资料的比较中,差异无统计学意义(P>0.05)。与非急性肾损伤组相比,急性肾损伤组患者男性较多,肺部感染者较多,平均动脉压(MAP)、前降钙素水平较低,乳酸、白细胞计数、多器官功能衰竭(MODS)评分水平较高(P<0.05)。MAP、乳酸、前降钙素、白细胞计数、MODS评分的ROC曲线下面积(AUC)为0.823、0.921、0.663、0.948、0.946,最佳截断值为89 mmHg、2.13 mmol/L、13.22μg/L、15×10^(9)·L^(-1)、6分。性别(男)、肺部感染(是)、MAP(≤89 mmHg)、乳酸(>2.13 mmol/L)、白细胞计数(>15×10^(9)·L^(-1))、MODS评分(>6分)均为影响脓毒症患者并发急性肾损伤的危险因素(P<0.05)。内部验证结果显示,Nomogram模型预测脓毒症患者并发急性肾损伤的风险C-index为0.763(95%CI:0.651~0.832);模型预测脓毒症患者并发急性肾损伤的风险阈值>0.07,Nomogram模型提供临床净收益。结论性别(男)、肺部感染(是)、MAP(≤89 mmHg)、乳酸(>2.13 mmol/L)、白细胞计数(>15×10^(9)·L^(-1))、MODS评分(>6分)均为影响脓毒症患者并发急性肾损伤的危险因素,且基于以上变量构建的Nomogram模型可以对患者并发急性肾损伤进行较好的预测。

股骨粗隆间骨折术后下肢深静脉血栓并发症影响因素分析及Nomogram模型建立

目的探讨股骨粗隆间骨折手术后早期发生下肢深静脉血栓的危险因素,并进行Nomogram模型构建。方法选取2018年1月—2020年12月于华北石油管理局总医院行股骨粗隆间骨折手术的120例患者,术后早期发生下肢深静脉血栓13例。按照术后住院期间是否发生下肢深静脉血栓分为并发症组和无并发症组。采用多因素Logistics分析术后发生并发症的风险并构建Nomogram模型。结果并发症组患者住院时长、术后住院时间、治疗总费用均高于对照组,差异有统计学意义(P<0.05)。单因素分析显示,骨折类型、合并慢性病种类、贫血输血、骨折发病时长是术后下肢深静脉血栓的潜在影响因素,差异有统计学意义(P<0.05)。将单因素有统计学差异的变量进行Logistics多因素分析,合并慢性病种类、贫血输血、骨折发病时长是股骨粗隆间骨折术后下肢深静脉血栓的独立危险因素(P<0.05)。采用内部数据进行验证,C-index为0.746(95%CI:0.596~0.935),该Nomogram模型预测与实际观测结果吻合度较好。结论术前合并慢性病≥3种、贫血输血、骨折发病时长>1 d是股骨粗隆间骨折术后下肢深静脉血栓的独立危险因素。本研究构建的Nomogram模型有助于指导医师完善个性化围手术期治疗方案。

Nomogram for predicting the risk of anxiety and depression in patients with non-mild burns

BACKGROUND Post-burn anxiety and depression affect considerably the quality of life and recovery of patients;however,limited research has demonstrated risk factors associated with the development of these conditions.AIM To predict the risk of developing post-burn anxiety and depression in patients with non-mild burns using a nomogram model.METHODS We enrolled 675 patients with burns who were admitted to The Second Affiliated Hospital,Hengyang Medical School,University of South China between January 2019 and January 2023 and met the inclusion criteria.These patients were randomly divided into development(n=450)and validation(n=225)sets in a 2:1 ratio.Univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with post-burn anxiety and depression dia-gnoses,and a nomogram model was constructed.RESULTS Female sex,age<33 years,unmarried status,burn area≥30%,and burns on the head,face,and neck were independent risk factors for developing post-burn anxiety and depression in patients with non-mild burns.The nomogram model demonstrated predictive accuracies of 0.937 and 0.984 for anxiety and 0.884 and 0.923 for depression in the development and validation sets,respectively,and good predictive per-formance.Calibration and decision curve analyses confirmed the clinical utility of the nomogram.CONCLUSION The nomogram model predicted the risk of post-burn anxiety and depression in patients with non-mild burns,facilitating the early identification of high-risk patients for intervention and treatment.

肺癌术后化疗患者癌因性疲乏的Nomogram模型构建

目的分析影响肺癌术后化疗患者癌因性疲乏程度的危险因素并构建Nomogram预测模型。方法收集2021年10月至2023年9月江西中医药大学附属医院172例肺癌术后化疗患者的临床资料。采用Piper疲乏修正量表(revised Piper fatigue scale,RPFS)进行调查,评估患者癌因性疲乏程度,Logistic多元回归模型分析影响肺癌术后化疗患者癌因性疲乏程度的危险因素并构建Nomogram预测模型,评估模型预测效能。结果患者文化程度、肿瘤淋巴结转移分类(tumornede metastasis classification,TNM)分期、抑郁、不良反应、白细胞计数等的相关因素的RPFS评分,差异有统计学意义(P<0.05)。肺癌术后化疗患者中/重度癌因性疲乏中,高中及以上文化程度比例、TNM分期Ⅲ/Ⅳ期比例、有抑郁比例、中/重度不良反应比例、白细胞低于正常值下限比例均明显高于无/轻度癌因性疲乏的肺癌术后化疗患者,差异有统计学意义(P<0.05)。Logistic多元回归分析表明文化程度(高中以上)、TNM分期(Ⅲ/Ⅳ期)、抑郁(有)、不良反应程度(中/重度)、白细胞计数(低于正常值下限)是影响肺癌术后化疗患者癌因性疲乏程度的独立危险因素(P<0.05)。内部验证结果显示C指数为0.899(0.842~0.955),一致性较好。Nomogram模型的阈值>0.21,Nomogram模型提供的临床净收益均高于文化程度、TNM分期、抑郁、不良反应程度、白细胞计数单一因子预测结果。结论本研究基于肺癌术后化疗患者癌因疲乏程度的危险因素构建Nomogram模型,该模型可为临床肺癌因术后化疗患者癌性疲乏进行较好预测。

基于25⁃羟基维生素D、血清学因子等对老年类风湿性关节炎合并间质性肺疾病Nomogram预测模型的构建和评价

目的基于25-羟基维生素D[25-(OH)D]、血清学因子等构建老年类风湿性关节炎合并间质性肺疾病(RA-ILD)的Nomogram预测模型,并进行模型评价。方法选取2020年5月—2022年10月收治的老年类风湿性关节炎(RA)220例,根据是否合并间质性肺疾病将其分为RA-ILD组(51例)和单纯RA组(169例)2组,比较2组一般资料和实验室相关指标[类风湿因子(RF)、抗环瓜氨酸抗体(anti-CCP)、抗角蛋白抗体(AKA)、类风湿关节炎活动度评分(DAS28)]、25-(OH)D、血清学因子[白细胞介素-33(IL-33)、白细胞介素-35(IL-35)、赖氨酰氧化酶样蛋白-2(LOXL-2)、涎液化糖链抗原-6(KL-6)、基质金属蛋白酶-8(MMP-8)]水平,分析老年RA患者25-(OH)D与各血清学因子的相关性,探讨老年RA-ILD发生的影响因素,根据影响因素、25-(OH)D及血清学因子构建老年RA-ILD的Nomogram预测模型,并对该模型进行评价。结果RA-ILD组和单纯RA组RF、DAS28比较差异有统计学意义(P<0.01);RA-ILD组25-(OH)D、IL-35、KL-6低于单纯RA组,IL-33、LOXL-2、MMP-8高于单纯RA组(P<0.05,P<0.01)。老年RA患者25-(OH)D与IL-35、KL-6呈正相关,与IL-33、LOXL-2、MMP-8呈负相关(P<0.05)。25-(OH)D、IL-35、KL-6、IL-33、LOXL-2、MMP-8、RF和DAS28均为老年RA-ILD发生的影响因素(P<0.01)。在Nomogram预测模型中直接获取各预测因素对应得分,得分之和对应的预测概率即为该老年患者RA-ILD发生的风险概率,该模型对老年RA-ILD发生具有良好预测效能,且具有良好校准度。结论基于25-(OH)D、血清学因子等构建老年RA-ILD发生的Nomogram预测模型,预测效能较高、校准度良好。

基于多中心的老年OSAHS合并冠心病患者不良预后的Nomogram预测模型构建

目的基于多中心分析老年阻塞性睡眠呼吸暂停低通气综合征(OSAHS)合并冠心病患者不良预后的影响因素并构建Nomogram预测模型。方法于2020年3月至2022年3月选取江苏省中医院共357例老年OSAHS合并冠心病患者,按照7∶3比例将纳入患者分为建模组(n=250)及验证组(n=107)。对患者进行为期1年的随访,根据患者预后将建模组分为预后良好组(n=215)和预后不良组(n=35)。单因素及多因素Logistic回归分析影响老年OSAHS合并冠心病患者不良预后的因素,并根据此结果构建Nomogram预测模型,再以H-L拟合度曲线评估模型的有效性,以受试者工作特征(ROC)曲线评估模型的区分度。结果单因素分析结果显示,体重指数、睡眠呼吸暂停低通气指数(AHI)、睡眠平均氧饱和度(SaO_(2))、超敏C反应蛋白(hs-CRP)及白细胞介素-6(IL-6)为老年OSAHS合并冠心病患者不良预后的影响因素(P<0.05)。多因素Logistic回归分析显示,体重指数较高、AHI水平较高、hs-CRP水平较高、低水平的睡眠平均SaO_(2)为老年OSAHS合并冠心病患者不良预后的影响因素(P<0.05)。验证模型显示,建模组χ^(2)=6.125,P=0.421,ROC曲线下面积AUC为0.958(95%CI:0.926~0.980),敏感度及特异度分别为82.86%、96.28%;验证组χ^(2)=5.754,P=0.311,AUC为0.932(95%CI:0.893~0.960),敏感度及特异度分别为85.70%、88.40%。结论体重指数较高、AHI水平较高、hs-CRP水平较高、低水平的睡眠平均SaO_(2)为老年OSAHS合并冠心病患者不良预后的影响因素,以此构建的Nomogram预测模型具有较好的区分度及有效性,可帮助临床预测患者不良预后的发生风险,具有较高的临床价值。

创伤性胫骨平台骨折术后感染的危险因素分析及Nomogram预测模型构建

目的 分析创伤性胫骨平台骨折术后感染的危险因素,并构建Nomogram预测模型。方法 2019年10月~2021年8月行手术治疗的创伤性胫骨平台骨折病人148例,根据术后是否出现感染分为感染组(20例)和未感染组(128例)。比较两组病人的一般资料;采用单因素分析及多因素Logistic回归分析影响创伤性胫骨平台骨折术后感染的危险因素;采用ROC分析有统计学意义的连续性变量的预测价值;采用内部数据验证Nomogram模型预测效能。结果 两组病人年龄、性别等一般资料比较,差异无统计学意义(P>0.05),与未感染组相比,感染组病人糖尿病、开放型骨折类型、骨筋膜室综合征占比较高,手术时间和住院时间较长(P<0.05);糖尿病(是)、骨折类型(开放型)、骨筋膜室综合征(有)、手术时间(>3小时)是影响创伤性胫骨平台骨折术后感染的危险因素;手术时间和住院时间的AUC为0.792、0.651;最佳截断值为3小时、13天(P<0.05);Nomogram模型预测创伤性胫骨平台骨折术后感染风险的C-index为0.744(0.651~0.807);模型预测创伤性胫骨平台骨折术后感染风险的阈值>0.09。结论 糖尿病(是)、骨折类型(开放型)、骨筋膜室综合征(有)、手术时间(>3小时)是影响创伤性胫骨平台骨折术后感染的危险因素,且基于以上变量构建的Nomogram模型有较好的预测价值。

Nomogram模型在新生儿耐碳青霉烯类肺炎克雷伯菌肠道定植风险评估中的应用

目的建立一种有效的Nomogram模型,用以评估新生儿耐碳青霉烯类肺炎克雷伯菌(CRKP)肠道定植风险,运用建立的Nomogram模型得出新生儿CRKP具体的定植概率大小,采取个体化的预防策略,减少定植的发生,降低新生儿CRKP继发感染的可能性。方法选取入院后48 h新生儿进行直肠拭子/大便培养及药敏鉴定为CRKP定植患者187例,在同期入院的经检测为非CRKP定植患儿中随机抽取187例为非定植组,共374例进行回顾性研究。通过R语言(R 4.2.1)的caret软件包对374例总样本按建模组∶验证组=3∶1进行随机分组。模型建立:对建模组数据运用R 4.2.1的glmnet包进行LASSO回归分析确定最终用于建模的预测因素,rms软件包进行Nomogram模型的构建。模型验证:R 4.2.1 pROC和rms软件包对建模组和验证组数据分别进行一致性指数(Cindex)、受试者工作特征曲线(ROC)及其曲线下面积(AUC)、校准曲线对Nomogram模型的效能进行内部验证和外部验证。结果运用LASSO回归分析,从研究的35个可能影响新生儿CRKP定植的项目中确定了8个预测因子,分别是性别、剖宫产、母乳喂养、鼻胃管、灌肠、碳青霉烯类、益生菌和总住院时间。使用这8个预测变量构建的Nomogram模型显示出中等预测能力,ROC曲线下面积为建模组0.835、验证组0.800。Hos-mer-Lemeshow检验显示,预测概率与实际概率高度一致(建模组,P=0.678>0.05;验证组,P=0.208>0.05)显示出非常好的拟合度。结论在Nomogram模型中引入性别、剖宫产、母乳喂养、鼻胃管、灌肠、使用碳青霉烯类抗生素、口服益生菌、总住院时间可以提高其预测新生儿CRKP定植风险的能力。根据Nomogram模型预测的定植概率的大小不同,采取个体化的预防措施,减少新生儿CRKP定植的发生,降低新生儿CRKP继发感染的发生率。

预测宫颈癌合并糖尿病患者术后切口感染风险的Nomogram模型建立

目的旨在构建预测宫颈癌合并糖尿病患者根治性子宫切除术术后切口感染风险的列线图模型,并进行模型评估。方法选取在本院接受根治性子宫切除术治疗的宫颈癌合并糖尿病患者267例作为研究对象,其中发生术后切口感染(感染组)97例,未发生术后切口感染(未感染组)170例。采用单因素和多因素Logistic回归筛选与术后切口感染相关的独立影响因素。采用R软件和相关程序包绘制列线图模型。结果与未感染组相比,感染组年龄≥55岁、体重指数(body mass index,BMI)≥25、引流管留置时间≥7 d、住院时间≥14 d、血清白蛋白<30 g/L的患者比例明显增加(P<0.05)。与未感染组相比,感染组血清白细胞介素6(interleukin-6,IL-6)、肿瘤坏死因子α(tumor necrosis factor-α,TNF-α)、C反应蛋白(C-reactive protein,CRP)和降钙素原(procalcitonin,PCT)水平显著升高(P<0.05)。多变量Logistic回归分析表明,年龄≥55岁、BMI≥25、引流管留置时间≥7 d、住院时间≥14 d,以及血清IL-6、TNF-α、CRP和PCT水平升高均是术后切口感染发生的独立危险因素(P<0.05)。用于评估宫颈癌合并糖尿病患者术后切口感染发生风险的列线图具有良好的预测准确性(C指数为0.947)、区分度(受试者工作特征曲线下面积为0.947)、一致性(Hosmer-Lemeshow拟合优度检验平均绝对误差为0.011)和临床效能。结论本研究基于围手术期特征构建了预测宫颈癌合并糖尿病患者术后切口感染的列线图模型,该模型可能有助于加强感染控制意识,为根治性子宫切除术后高危患者的管理提供参考。

肺癌化疗病人发生PICC相关上肢静脉血栓风险Nomogram模型的构建

目的:探究肺癌化疗病人发生经外周静脉置入中心静脉导管(PICC)相关上肢静脉血栓的危险因素,构建其风险Nomogram模型,并进一步验证该模型的预测价值。方法:回顾性分析2014年1月—2020年12月南昌大学第一附属医院呼吸内科收治的391例原发性肺癌化疗病人,比较病人的临床资料,并采用多因素Logistic回归模型分析肺癌化疗病人发生PICC相关上肢静脉血栓的独立危险因素,R软件整合独立危险因素并构建Nomogram模型。结果:89例(22.76%)肺癌化疗病人出现PICC相关上肢静脉血栓。体质指数(≥27.49 kg/m^(2))、糖尿病(是)、纤维蛋白原(FIB)(≥3.60 g/L)、活化部分凝血活酶时间(APTT)(≥33 s)、D-二聚体(≥0.90 mg/L)以及C反应蛋白(CRP)(≥5.80 mg/L)是肺癌化疗病人发生PICC相关上肢静脉血栓的独立危险因素(P<0.05)。该Nomogram模型预测与实际观测结果的吻合度较好,一致性指数(C-index)为0.832[95%CI(0.793,0.866)],当高风险阈值>0.05时,此Nomogram模型在预测肺癌化疗病人PICC相关上肢静脉血栓高风险的方面可提供高于单个独立危险因素的额外临床净收益。结论:基于体质指数、糖尿病、纤维蛋白原、活化部分凝血活酶时间、D-二聚体以及C反应蛋白独立危险因素构建的Nomogram模型在预测肺癌化疗病人发生PICC相关上肢静脉血栓具备可行性,可为临床风险分层及制定护理策略提供重要参考。

基于Nomogram模型鉴别肺腺癌病理亚型的临床价值

目的 探讨基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析构建Nomogram模型预测原位腺癌(AIS)、微浸润腺癌(MIA)及浸润性腺癌(IAC)的价值。方法 选取本院97例经手术病理证实且病理亚型明确的肺腺癌患者,将AIS和MIA归为第1组,IAC为第2组,比较两组患者年龄、性别、吸烟史、长径、短径及免疫组化Ki-67等临床医学特征差异,采用3D Slicer软件进行图像分割,特征提取与选择,通过LASSO算法对特征进行降维,筛选影像组学特征构建预测模型。再采用R软件的rms工具包构建Nomogram模型,计算ROC曲线下面积(AUC),以评价Nomogram模型鉴别肺磨玻璃结节病理亚型的效能。结果 1)性别、吸烟史、长径、短径及免疫组化Ki-67等临床医学特征均差异无统计学意义(P>0.05);2)筛选7个CT影像组学特征:平面度、大依赖低灰度强调、小波变换LHL第十百分位、小波变换HLL第十百分位、小波变换最小值、小波变换均值及小依赖低灰度强度比较,差异均有统计学意义(P均<0.05);3)基于CT影像组学特征建立预测肺磨玻璃结节病理亚型的Nomogram模型,训练集中AUC为0.863,准确率为87.9%,灵敏度为67.9%,特异度为91.1%;验证集中AUC为0.792,准确率为75.0%,灵敏度为66.7%,特异度为90.5%,可见此Nomogram模型具有较好的预测效能。结论 对于预测肺腺癌浸润程度,Nomogram模型具有明显优势,可作为一种鉴别手段。

老年重症肺炎患者病情转归不良的风险预测Nomogram模型建立及验证

目的构建老年重症肺炎(SP)患者病情转归不良的风险预测Nomogram模型,并验证其预测效能。方法回顾性分析2022年1月~2023年6月本院重症监护病房收治的432例老年SP患者,随机分为训练集(n=288)和验证集(n=144)。根据患者入住ICU后28天病情转归将其分为转归不良组和转归良好组。采用Logistic回归分析筛选患者病情转归不良的影响因素,构建其风险预测Nomogram模型,以受试者工作特征(ROC)曲线和校准曲线评估模型的区分度和准确性,以决策曲线分析(DCA)评估模型的临床净获益。结果老年SP患者病情转归不良发生率为24.77%;肺炎严重程度(PSI)评分、白细胞计数(WBC)、纤维蛋白原(FIB)、降钙素原(PCT)是患者病情转归不良的影响因素(OR=1.677、1.619、1.435、1.935,P均<0.05);训练集与验证集的一致性指数(C-index)分别为0.874、0.839;ROC曲线下面积(AUC)分别为0.929、0.891;决策曲线(DCA)显示在训练集及验证集中模型预测病情转归不良的最大净获益值阈值概率区间分别在0~0.91和0~0.90。结论PSI评分、WBC、FIB、PCT是老年SP患者病情转归不良的影响因素,以此为基础构建的风险预测Nomogram模型具有良好预测效能与临床应用价值。