| 张瀚文,王 鸿,陈效友,张 丹,林 虎.基于血清阴离子间隙、白蛋白及APACHEⅡ评分预测重症肺结核患者预后的模型构建与评价[J].,2024,(8):1455-1459 |
| 基于血清阴离子间隙、白蛋白及APACHEⅡ评分预测重症肺结核患者预后的模型构建与评价 |
| Construction and Evaluation of the Model Based on Serum Anion Gap, Albumin and APACHE II Score to Predict the Prognosis of Patients with Severe Pulmonary Tuberculosis |
| 投稿时间：2023-10-06 修订日期：2023-10-28 |
| DOI：10.13241/j.cnki.pmb.2024.08.009 |
| 中文关键词: 重症肺结核 阴离子间隙 白蛋白 APACHEⅡ评分 预后 预测模型 |
| 英文关键词: Severe pulmonary tuberculosis Anion gap Albumin APACHEⅡ score Prognosis Prediction model |
| 基金项目:北京市科技计划项目（D181100000418003）；黑龙江省卫生健康委科研课题（2019-118） |
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| 中文摘要: |
| 摘要 目的：分析基于血清阴离子间隙（AG）、白蛋白（Alb）及急性生理和慢性健康评估Ⅱ（APACHEⅡ）评分构建的预测模型对重症肺结核（PTB）患者的价值。方法：选取2020年10月～2023年1月我院收治的60例重症PTB患者纳入重症PTB组，根据院内生存状态分为死亡组21例和存活组39例，另选取同期我院60名体检健康者纳入对照组。检测血清Alb，AG水平，并计算APACHEⅡ评分。采用多因素Logistic回归模型分析重症PTB患者预后影响因素并基于影响因素构建预测模型。采用受试者工作特征（ROC）曲线分析血清AG、Alb、APACHEⅡ评分和预测模型对重症PTB患者预后的预测价值。结果：与对照组比较，重症PTB组血清AG水平升高，Alb水平降低（P＜0.05）。60例重症PTB患者院内死亡率为35.00%（21/60）。多因素Logistic回归模型分析显示，AG升高、APACHEⅡ评分增加为重症PTB患者预后的独立危险因素，Alb升高为独立保护因素（P＜0.05）。重症PTB患者预后预测模型方程Ln(P/1-P)＝-0.173 +0.105×AG -0.057×Alb +0.057×APACHEⅡ评分，Hosmer-Lemeshow检验（P＞0.05）。ROC曲线分析显示，预测模型预测重症PTB患者预后的曲线下面积（AUC）为0.859，大于血清AG、Alb、APACHEⅡ评分单独预测。结论：血清AG、Alb及APACHEⅡ评分与重症PTB患者预后独立相关，基于血清AG、Alb及APACHEⅡ评分构建的预测模型对重症PTB患者预后预测价值较高。 |
| 英文摘要: |
| ABSTRACT Objective: To analysis the value of prediction model based on serum anion gap (AG), albumin (Alb) and acute physiology and chronic health evaluation II (APACHE II) score in patients with severe pulmonary tuberculosis (PTB). Methods: 60 severe PTB patients admitted to our hospital from October 2020 to January 2023 were included in severe PTB group, patients were divided into death group (21 cases) and survival group (39 cases) according to the in-hospital survival status, and 60 healthy subjects in our hospital during the same period were selected as control group. The levels of serum Alb and AG were detected, and APACHE II score was calculated. The prognostic influence factors of severe PTB patients were analyzed by multivariate logistic regression model and the prediction model was constructed based on these influence factors. The predictive value of serum AG, Alb, APACHE II score and prediction model for the prognosis of severe PTB patients were analyzed by receiver operating characteristic (ROC) curve. Results: Compared with control group, the serum AG level was increased and the Alb level was decreased in severe PTB group (P<0.05). The in-hospital mortality of 60 severe PTB patients was 35.00% (21/60). Multivariate logistic regression model analysis showed that, the increase of AG and APACHE II score were independent risk factors for the prognosis of severe PTB patients, and the increase of Alb was an independent protective factor (P<0.05). The prognostic prediction model equation of severe PTB patients was Ln (P/1-P) =-0.173+0.105×AG-0.057×Alb+0.057×APACHE II score, Hosmer-Lemeshow test (P>0.05). ROC curve analysis showed that, the area under the curve (AUC) of the prediction model for predicting the prognosis of severe PTB patients was 0.859, which was greater than the independent prediction of serum AG, Alb and APACHE II score. Conclusion: Serum AG, Alb and APACHE II score were independently associate with the prognosis of severe PTB patients, the prediction model base on serum AG, Alb and APACHE II score had a high predictive value for the prognosis of severe PTB patients. |
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