基于递归分区分析的毒症患者急性损伤预测模型的开发和验证
Kunmei Lai1, Guo Lin2, Caiming Chen1,3,4
1Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Journal of intensive care medicine
|November 15, 2023
概括
这项研究使用临床数据开发了与败血症相关的急性损伤 (SA-AKI) 的预测模型. 该模型准确识别高风险患者,有助于早期干预,并改善SA-AKI的结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
背景情况:
- 败血症相关的急性损伤 (SA-AKI) 提出了重大的临床挑战.
- 目前缺乏SA-AKI风险的可靠预测模型.
- 这项研究通过开发一种新的预测工具来解决这一差距.
研究的目的:
- 创建和验证SA-AKI的预测模型.
- 为医疗保健提供者提供一个早期风险评估工具.
- 为了最终改善败血症患者的治疗结果.
主要方法:
- 一组615名败血症患者被分为训练 (n=435) 和验证 (n=180) 组.
- 为SA-AKI预后开发了一个LASSO后勤回归模型.
- SHAP分析确定了关键预测因素,以及递归分区分层的风险.
主要成果:
- 高血压,糖尿病,C反应蛋白,前素 (PCT),aPTT和血小板计数是独立的SA-AKI预测因素.
- 该模型的AUC为0.843 (培训) 和0.834 (验证).
- PCT是最有影响力的预测因素;风险分层与SA-AKI发生率和存活率相关.
结论:
- 一个包含五个关键SA-AKI预测器的复合预测模型已成功开发.
- 这种模型对识别患有SA-AKI高风险的患者有希望.
- 早期识别可以促进及时干预,并可能改善患者的预后.
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