Article(id=1261728808432952130, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778749189718, onlineDateStr=2026-05-14, pubDate=1774195200000, pubDateStr=2026-03-23, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778749189718, onlineIssueDateStr=2026-05-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778749189718, creator=13701087609, updateTime=1778749189718, updator=13701087609, issue=Issue{id=1261728796072358112, tenantId=1146029695717560320, journalId=1146119944283992078, year='2026', volume='4', issue='6', pageStart='1', pageEnd='234', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778749186771, creator=13701087609, updateTime=1778750183088, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1261732975067148479, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1261732975071342784, tenantId=1146029695717560320, journalId=1146119944283992078, issueId=1261728796072358112, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=70, endPage=76, ext={CN=ArticleExt(id=1261728811771618141, articleId=1261728808432952130, tenantId=1146029695717560320, journalId=1146119944283992078, language=CN, title=基于多维度常规指标的机器学习模型早期预测急性冠脉综合征, columnId=1151957373175820401, journalTitle=实验室检测, columnName=创新应用, runingTitle=null, highlight=null, articleAbstract=
目的 基于检验、影像及临床特征等多维度常规指标，构建机器学习模型早期预测急性冠脉综合征。
方法 回顾性分析吉林市化工医院2022年1月—2025年1月期间578例急性冠脉综合征（acute coronary syndrome, ACS）确诊患者电子病例数据，提取入院24 h内常规检验指标（血脂、心肌损伤标志物、炎症标志物等）、影像学特征（超声心动图、胸部X线）及临床资料共103项变量。通过单变量分析、最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）-随机森林重要性评估、Bootstrap稳定性验证筛选确定18项核心预测因子，采用极端梯度提升（eXtreme Gradient Boosting, XGBoost）等5种机器学习算法构建模型，用独立测试集（n=174）评估性能，并通过沙普利加性解释（shapley additive explanations, SHAP）解析预测机制。
结果 XGBoost模型预测ACS的效能最优，测试集受试者工作特征曲线下面积（area under the curve, AUC）为0.901 ［95%置信区间（confidence interval, CI）：0.872~0.927］，敏感性83.2%、特异性88.7%，显著优于其他机器学习模型及传统评分（DeLong检验P<0.05）。SHAP可解释性分析显示：左心室射血分数（left ventricular ejection fraction, LVEF）是最关键预测因子（SHAP均值绝对值｜0.32｜），当LVEF<50%时风险显著增加（SHAP值Δ=-0.41）；超敏C反应蛋白（high-sensitivity C-reactive protein, hs-CRP）>8 mg/L与肌钙蛋白I （cardiac troponin I, cTnI）>120 ng/L存在协同作用，共同显著增加ACS风险；高密度脂蛋白胆固醇（high-density lipoprotein cholesterol, HDL-C）>1.42 mmol/L联合估算肾小球滤过率（estimated glomerular filtration rate, eGFR）>75 mL/min，可降低35%~48%的ACS风险。
结论 本研究构建的18项常规指标XGBoost模型具有优异判别能力（AUC>0.90）和临床可解释性，并首次量化HDL-C与eGFR协同保护效应，为ACS早期风险分层提供实用工具。
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