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| 非糖尿病急性心肌梗死患者发生应激性高血糖的风险列线图模型建立 |
| Establishment of a nomogram model for predicting risk of stress hyperglycemia in non-diabetic patients with acute myocardial infarction |
| 投稿时间：2021-02-28 |
| DOI：10.3969/j.issn.1000-0399.2021.10.009 |
| 中文关键词: 急性心肌梗死 应激性高血糖 危险因素 列线图 |
| 英文关键词: Acute myocardial infarction Stress hyperglycemia Risk factors Nomogram |
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| 中文摘要: |
| 目的 构建非糖尿病急性心肌梗死（AMI）患者发生应激性高血糖（SHG）的风险列线图模型。方法 选取2018年1月至2020年1月阜阳市第二人民医院初次发生ST段抬高AMI且在12 h内接受急诊经皮冠状动脉介入治疗的314例非糖尿病住院患者作为研究对象，采用留一法，将其分为训练集（n=233）与验证集（n=81），回顾性分析训练集患者的临床资料，分别使用单因素和logistic回归多因素分析发生SHG的危险因素，并建立相关列线图预测模型。结果 女性（OR=2.301，95% CI：1.097～4.826）、年龄≥60岁（OR=3.088，95% CI：1.410～6.761）、身体质量指数≥28 kg/m2（OR=2.178，95% CI：1.012～4.686）、心功能Killip分级≥Ⅱ级（OR=5.549，95% CI：2.750～11.195）和左心室射血分数<50%（OR=4.162，95% CI：2.162～8.014）是非糖尿病AMI患者发生SHG的危险因素（P<0.05）。基于此建立列线图模型，并对该模型进行验证，结果显示，训练集和验证集的一致性指数分别为0.807和0.794，模型预测概率均与实际发生率相吻合，受试者工作特征曲线的曲线下面积分别为0.811（95% CI：0.782～0.841）和0.804（95% CI：0.767～0.829）。结论 非糖尿病AMI患者发生SHG的危险因素较多，本研究建立的列线图模型具有准确的预测能力，可为临床甄别SHG高危患者和改善AMI患者预后提供参考依据。 |
| 英文摘要: |
| Objective To construct a nomogram model for stress hyperglycemia(SHG) in patients with non-diabetic acute myocardia l infarction(AMI),and to verify the predictive efficacy of the model. Methods From January 2018 to January 2020,314 non-diabetic inpatients in Fuyang Second People's Hospital who had STEMI for the first time and received PCI within 12 h were selected as the research objects. They were divided into training set(n=233) and validation set(n=81) using the leave-one method. The clinical data of the patients in the training set were retrospectively analyzed. Single factor and logistic regression were used to analyze independent risk factors for SHG, and the related nomogram prediction model was established. Results Female (OR=2.301,95%CI:1.097~4.826), age ≥ 60 years (OR=3.088,95%CI:1.410~6.761),BMI ≥ 28 kg/m2 (OR=2.178,95%CI:1.012~4.686),Killip grade of cardiac function ≥ Grade II (OR=5.549,95%CI:2.750~11.195) and LVEF<50% (OR=4.162,95%CI:2.162~8.014) were independent risk factors of SHG in non-diabetic AMI patients (P<0.05). Based on this a nomogram model was established and verified. The results showed that the consistency index of the training set and the validation set was 0.807 and 0.794, respectively. The predicted probabilities of the model were consistent with the actual occurrence rate. The AUC of the ROC curve was 0.811 (95%CI:0.782~0.841) and 0.804 (95%CI:0.767~0.829). Conclusions There are many risk factors for SHG in non-diabetic AMI patients. The nomogram model established in this study has accurate predictive ability and can provide a reference for clinical screening of SHG high-risk patients and improving the prognosis of AMI patients. |
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