目的 构建并验证主动脉夹层B型（TBAD）患者急性期预后的列线图预测模型,帮助临床医生在急性期内更准确地评估TBAD患者的死亡风险,并制定更合适的治疗策略。方法 回顾性分析从重症监护医学信息数据库v2.2 中提取的399例 TBAD患者的人口学资料和临床资料,结局为TBAD患者急性期（≤14 d）内死亡。先采用最小绝对收缩选择算法回归筛选特征变量,再采用多因素分析确定独立预后因素,并据此构建预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析（DCA）评价列线图预测模型的性能和临床适用性。结果 APS Ⅲ评分、二氧化碳总量、红细胞分布宽度为TBAD患者14 d内死亡的独立预测因素。列线图预测模型在内部验证中的受试者工作特征曲线下面积为0.776（95% CI：0.691 ~ 0.860）,Hosmer-Lemeshow 检验P=0.604,校准曲线和标准曲线高度重合,表明该模型具有良好的区分度和校准度。同时,DCA曲线显示,预测模型在大部分的阈值概率范围内提供了显著的净收益。结论 本研究基于APS Ⅲ评分、二氧化碳总量、红细胞分布宽度构建的列线图预测模型可以较准确地预测TBAD患者14 d内的死亡风险,有助于临床医生制定更合适的个体化治疗策略。

Objective To develop and verify a nomogram for predicting acute phase outcomes in patients with type B aortic dissection（TBAD）,enabling clinicians to more precisely evaluate mortality risk in TBAD patients during the acute stage and to devise better treatment plans．Methods This retrospective study analyzed demographic and clinical data of 399 TBAD patients from the Medical Information Mart for Intensive Care IV v2.2,focusing on mortality within 14 days of the acute phase in TBAD patients． Initially,the Least Absolute Shrinkage and Selection Operator regression was employed for feature variable selection,and then multivariate analysis was used to identify independent prognostic factors for constructing the predictive model．The nomogram predictive model's effectiveness and clinical applicability were assessed via the Receiver Operating Characteristic curve,calibration curve,and Decision Curve Analysis（DCA）．Results Acute Physidogy Score Ⅲ score,total carbon dioxide,and red blood cell distribution width emerged as independent predictors of 14-day mortality in TBAD patients．The internal validation of the nomogram predictive model showed an area under the curve of 0.776（95%CI：0.691-0.860）,with a Hosmer-Lemeshow test P-value of 0.604． The close alignment of the calibration and standard curves suggested the model's strong discriminative power and calibration． Furthermore,the DCA curve revealed that the predictive model offered substantial net benefits within a wide range of threshold probabilities．Conclusions This study's nomogram,developed using APS Ⅲ score,total carbon dioxide,and red blood cell distribution width,accurately predicts the 14-day mortality risk in TBAD patients,assisting clinicians in creating better personalized treatment plans．

       目的   构建并验证主动脉夹层B型（TBAD）患者急性期预后的列线图预测模型，帮助临床医生在急性期内更准确地评估TBAD患者的死亡风险，并制定更合适的治疗策略。方法   回顾性分析从重症监护医学信息数据库v2.2 中提取的399例 TBAD患者的人口学资料和临床资料，结局为TBAD患者急性期（≤14 d）内死亡。先采用最小绝对收缩选择算法回归筛选特征变量，再采用多因素分析确定独立预后因素，并据此构建预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析（DCA）评价列线图预测模型的性能和临床适用性。结果  APS Ⅲ评分、二氧化碳总量、红细胞分布宽度为TBAD患者14 d内死亡的独立预测因素。列线图预测模型在内部验证中的受试者工作特征曲线下面积为0.776（95% CI：0.691 ~ 0.860），Hosmer-Lemeshow 检验P=0.604，校准曲线和标准曲线高度重合，表明该模型具有良好的区分度和校准度。同时，DCA曲线显示，预测模型在大部分的阈值概率范围内提供了显著的净收益。结论   本研究基于APS Ⅲ评分、二氧化碳总量、红细胞分布宽度构建的列线图预测模型可以较准确地预测TBAD患者14 d内的死亡风险，有助于临床医生制定更合适的个体化治疗策略。

       Objective  To develop and verify a nomogram for predicting acute phase outcomes in patients with type B aortic dissection（TBAD），enabling clinicians to more precisely evaluate mortality  risk in TBAD patients during the acute stage and to devise better treatment plans．Methods  This retrospective study analyzed demographic and clinical data of 399 TBAD patients from the Medical Information Mart for Intensive Care IV v2.2，focusing on mortality within 14 days of the acute phase in TBAD patients．Initially，the Least Absolute Shrinkage and Selection Operator regression was employed for feature variable selection，and then multivariate analysis was used to identify independent prognostic factors for constructing the predictive model．The nomogram predictive model’s effectiveness and clinical applicability were assessed via the Receiver Operating Characteristic curve，calibration curve，and Decision Curve Analysis（DCA）．Results  Acute Physidogy Score Ⅲ score，total carbon dioxide，and red blood cell distribution width emerged as independent predictors of 14-day mortality in TBAD patients．The internal validation of the nomogram predictive model showed an area under the curve of 0.776（95%CI：0.691-0.860），with a Hosmer-Lemeshow test P-value of 0.604．The close alignment of the calibration and standard curves suggested the model’s strong discriminative power and calibration．Furthermore，the DCA curve  revealed that the predictive model offered substantial net benefits within a wide  range of threshold probabilities．Conclusions  This study's nomogram，developed using APS Ⅲ score，total carbon dioxide，and  red blood cell distribution width，accurately predicts the 14-day mortality risk in TBAD patients，assisting clinicians in creating better personalized treatment plans．

       目的   基于Nomogram初步构建膝骨关节炎（KOA）患者术前衰弱的风险预测模型。方法   便利选取172例于2021年12月—2022年8月在广州市某三甲医院关节外科接受择期膝关节置换术的KOA患者为研究对象，依据衰弱的发生与否分为衰弱组（n=111）和非衰弱组（n=61），通过单因素分析筛选变量，纳入Logistic回归分析，并构建列线图模型。结果   单因素分析结果显示年龄、BMI、膝关节疼痛年限、合并症、抑郁、焦虑、疼痛、睡眠障碍、营养状况等在不同组间比较差异存在统计学的意义（P＜0.05）。多因素Logistic回归分析表明，BMI异常（OR=3.360）、膝关节疼痛年限＞5年（OR=14.188）、抑郁（OR=5.608）、睡眠障碍（OR=25.480）是KOA患者术前衰弱的独立危险因素（P＜0.05）。基于此，建立了预测膝骨关节炎患者术前衰弱风险的列线图预测模型。结果显示C-index为0.915，校正曲线接近理想曲线，ROC曲线下面积（AUC）为0.919（95%CI：0.878~0.961），可见该预测模型具有较好的区分度和准确度。结论   根据BMI、膝关节疼痛年限、抑郁以及睡眠障碍这四个独立危险因素，可以准确地预测膝骨关节炎患者术前衰弱的风险。

    Objective  To develop a nomogram for predicting the risk of preoperative frailty in knee osteoarthritis patients．Methods  A convenience sample of 172 patients who underwent elective knee arthroplasty at a Grade-A hospital in Guangzhou from December 2021 to August 2022 was selected．The patients were divided into two groups based on the presence of preoperative frailty：frailty group（n=111）and non-frailty group（n=61）．The variables with statistical differences were screened by univariate analysis for multivariate logistic regression analysis，and the nomogram prediction model was established．Results  Univariate analysis identified significant differences between the groups in age，BMI，years of knee pain，complications，depression，anxiety，pain，sleep disturbance，and nutrition（P＜0.05）．Multivariate logistic regression showed that abnormal BMI（OR=3.360），years of knee pain ＞ 5（OR=14.188），depression（OR=5.608），and sleep disorders（OR=25.480）were independent  risk factors for preoperative frailty in knee osteoarthritis patients（P＜0.05）．Based on these findings，a nomogram prediction model was established．Model verification results demonstrated that the nomogram had good differentiation and accuracy in predicting the risk of preoperative frailty，with a C-index of 0.915，an area under the ROC curve of 0.919（95% CI：0.878~0.961），and a calibration curve slope close to 1．Conclusions  The nomogram，based on four independent risk factors（BMI，years of knee pain，depression，and sleep disturbance），effectively predicts the risk of preoperative frailty in knee osteoarthritis patients．