目的基于血清学标志物构建早产儿视网膜病变(ROP)患儿预后的Nomogram预测模型,并验证模型的预测价值。方法选取2022年1月至2024年1月河北医科大学第二医院收治的195例(390眼)ROP患儿,治疗后随访3个月,根据预后情况分为预后不良组(n=41)...目的基于血清学标志物构建早产儿视网膜病变(ROP)患儿预后的Nomogram预测模型,并验证模型的预测价值。方法选取2022年1月至2024年1月河北医科大学第二医院收治的195例(390眼)ROP患儿,治疗后随访3个月,根据预后情况分为预后不良组(n=41)、预后良好组(n=154)。比较两组患儿一般资料、治疗前血清学标志物[血管内皮生长因子(VEGF)、胰岛素样生长因子-1(IGF-1)、谷氨酸(Glu)、信号转导和转录激活因子3(STAT3)、低氧诱导因子3α(HIF-3α)]水平,通过LASSO-Logistic回归分析ROP患儿预后不良的影响因素,根据影响因素构建ROP患儿预后不良的Nomogram预测模型,通过受试者工作特征曲线、校准曲线及决策曲线验证模型的预测价值。结果预后不良组患儿1 min Apgar、5 min Apgar、病情程度重度占比、支气管肺发育不良占比、败血症占比及治疗前血清VEGF、Glu、STAT3、HIF-3α水平均高于预后良好组,胎龄、出生体重、血清IGF-1水平均低于预后良好组(均为P<0.05);LASSO-Logistic回归分析显示,胎龄、病情程度、支气管肺发育不良、败血症及治疗前血清VEGF、IGF-1、Glu、STAT3、HIF-3α水平均为ROP患儿预后不良的影响因素(均为P<0.05);根据影响因素构建ROP患儿预后不良的Nomogram预测模型,该模型预测ROP患儿预后不良的曲线下面积为0.943(95%CI:0.907~0.978),具有较高预测效能,该模型的校准度良好,预测结果与实际观测结果有较好的一致性,且在预测ROP患儿预后不良方面拥有良好的临床效用。结论血清VEGF、IGF-1、Glu、STAT3、HIF-3α水平均为ROP患儿预后的影响因素,基于以上血清学标志物构建的ROP患儿预后的Nomogram预测模型具有较高应用价值。展开更多
BACKGROUND As the global population ages,the number of elderly patients with acute coronary syndrome(ACS)rises.However,prognostic assessment tools for elderly patients with ACS remain lacking,particularly in the Chine...BACKGROUND As the global population ages,the number of elderly patients with acute coronary syndrome(ACS)rises.However,prognostic assessment tools for elderly patients with ACS remain lacking,particularly in the Chinese population.This study aimed to develop and validate a nomogram to predict 2-year major adverse cardiovascular and cerebrovascular events(MACCE)in elderly Chinese patients with ACS.METHODS A retrospective analysis was conducted using two independent cohorts of ACS patients aged≥65 years who underwent percutaneous coronary intervention:the derivation cohort(n=1674)and the validation cohort(n=2333).Candidate predictors were selected using multivariable Cox proportional hazards regression and the Akaike information criterion.A final nomogram incorporating ten variables was constructed.Model performance was evaluated in terms of discrimination[concordance index(C-index)and area under the receiver operating characteristic curve(AUC)]and calibration(calibration plots).RESULTS The 2-year incidence of MACCE was 12.5%(n=210)in the derivation cohort and 15.6%(n=364)in the validation cohort.The nomogram demonstrated good discrimination,with C-index values of 0.727 and 0.661 and AUCs of 0.723 and 0.699 in the derivation cohort and the validation cohort,respectively;significantly outperforming the GRACE risk score(P<0.001).Calibration plots showed good agreement between the predicted and observed outcomes.Patients classified as the high-risk group by the nomogram had a significantly higher MACCE incidence compared to that of the low-risk group(log-rank P<0.001).CONCLUSIONS This newly developed nomogram provides a reliable tool for individualized prediction of the 2-year MACCE risk in elderly Chinese patients with ACS who underwent percutaneous coronary intervention.It outperformed the GRACE score in both discrimmination and calibration and may help improve clinical decision-making and personalized risk stratification in this vulnerable population.展开更多
文摘目的基于血清学标志物构建早产儿视网膜病变(ROP)患儿预后的Nomogram预测模型,并验证模型的预测价值。方法选取2022年1月至2024年1月河北医科大学第二医院收治的195例(390眼)ROP患儿,治疗后随访3个月,根据预后情况分为预后不良组(n=41)、预后良好组(n=154)。比较两组患儿一般资料、治疗前血清学标志物[血管内皮生长因子(VEGF)、胰岛素样生长因子-1(IGF-1)、谷氨酸(Glu)、信号转导和转录激活因子3(STAT3)、低氧诱导因子3α(HIF-3α)]水平,通过LASSO-Logistic回归分析ROP患儿预后不良的影响因素,根据影响因素构建ROP患儿预后不良的Nomogram预测模型,通过受试者工作特征曲线、校准曲线及决策曲线验证模型的预测价值。结果预后不良组患儿1 min Apgar、5 min Apgar、病情程度重度占比、支气管肺发育不良占比、败血症占比及治疗前血清VEGF、Glu、STAT3、HIF-3α水平均高于预后良好组,胎龄、出生体重、血清IGF-1水平均低于预后良好组(均为P<0.05);LASSO-Logistic回归分析显示,胎龄、病情程度、支气管肺发育不良、败血症及治疗前血清VEGF、IGF-1、Glu、STAT3、HIF-3α水平均为ROP患儿预后不良的影响因素(均为P<0.05);根据影响因素构建ROP患儿预后不良的Nomogram预测模型,该模型预测ROP患儿预后不良的曲线下面积为0.943(95%CI:0.907~0.978),具有较高预测效能,该模型的校准度良好,预测结果与实际观测结果有较好的一致性,且在预测ROP患儿预后不良方面拥有良好的临床效用。结论血清VEGF、IGF-1、Glu、STAT3、HIF-3α水平均为ROP患儿预后的影响因素,基于以上血清学标志物构建的ROP患儿预后的Nomogram预测模型具有较高应用价值。
基金supported by the National High Level Hospital Clinical Research Funding(2025-GSP-QN-31&2022-GSP-QN-1)the Young Talent Program of the Academician Fund(YS-2022-002)the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(CIFMS)(2023-I2M-1-002)。
文摘BACKGROUND As the global population ages,the number of elderly patients with acute coronary syndrome(ACS)rises.However,prognostic assessment tools for elderly patients with ACS remain lacking,particularly in the Chinese population.This study aimed to develop and validate a nomogram to predict 2-year major adverse cardiovascular and cerebrovascular events(MACCE)in elderly Chinese patients with ACS.METHODS A retrospective analysis was conducted using two independent cohorts of ACS patients aged≥65 years who underwent percutaneous coronary intervention:the derivation cohort(n=1674)and the validation cohort(n=2333).Candidate predictors were selected using multivariable Cox proportional hazards regression and the Akaike information criterion.A final nomogram incorporating ten variables was constructed.Model performance was evaluated in terms of discrimination[concordance index(C-index)and area under the receiver operating characteristic curve(AUC)]and calibration(calibration plots).RESULTS The 2-year incidence of MACCE was 12.5%(n=210)in the derivation cohort and 15.6%(n=364)in the validation cohort.The nomogram demonstrated good discrimination,with C-index values of 0.727 and 0.661 and AUCs of 0.723 and 0.699 in the derivation cohort and the validation cohort,respectively;significantly outperforming the GRACE risk score(P<0.001).Calibration plots showed good agreement between the predicted and observed outcomes.Patients classified as the high-risk group by the nomogram had a significantly higher MACCE incidence compared to that of the low-risk group(log-rank P<0.001).CONCLUSIONS This newly developed nomogram provides a reliable tool for individualized prediction of the 2-year MACCE risk in elderly Chinese patients with ACS who underwent percutaneous coronary intervention.It outperformed the GRACE score in both discrimmination and calibration and may help improve clinical decision-making and personalized risk stratification in this vulnerable population.
