Predicting AKI in critical patients: An interpretable model based on albumin and fluid balance.

IF 1.1 4区 医学 Q3 UROLOGY & NEPHROLOGY Clinical nephrology Pub Date : 2024-11-28 DOI:10.5414/CN111510
Yifan Xu, Zengyu Zhang, Bai Xu, Lan Sun, Lei Zhong, Yuqi Chen, Siyu Tang, Yan Qu, Xianghong Yang
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Abstract

Background: Acute kidney injury (AKI) is a clinically complex syndrome with a high incidence and mortality rate in the intensive care unit (ICU). Early identification of high-risk patients and timely intervention are crucial.

Objective: A local database was used to construct a model that predicts the incidence of AKI in ICU patients within 48 hours.

Materials and methods: We conducted a study involving 9,628 critically ill patients at Zhejiang Provincial People's Hospital and divided the cohort into derivation and validation groups. We collected and analyzed demographic data, vital signs, laboratory tests, medications, clinical interventions, and other information for all patients, resulting in a total of 232 variables. Six different machine learning algorithms were employed to construct models, and the optimal model was selected and validated.

Results: A total of 2,441 patients were included, of whom 1,138 (46.62%) met the AKI criteria. A model was derived that included 16 variables such as albumin transfusion, fluid balance, diastolic blood pressure (DBP), partial pressure of oxygen (PO2), blood glucose (GLU), platelet (PLT), baseline serum creatinine (bSCr), serum sodium, age, epinephrine, proton pump inhibitor (PPI), intra-abdominal infection, anemia, diabetes, glycerin fructose, and nutritional pathway. The area under the receiver operating characteristic curve (AUC) was 0.822. Subgroup analysis revealed the impact of blood pressure fluctuations on AKI. Additionally, the study demonstrated a bidirectional effect of albumin and fluid balance on AKI.

Conclusion: This model is highly accurate and may facilitate the early diagnosis of and interventions for AKI.

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预测危重病人的 AKI:基于白蛋白和体液平衡的可解释模型。
背景:急性肾损伤(AKI)是一种临床复杂的综合征,在重症监护病房(ICU)中发病率和死亡率都很高。早期识别高危患者并及时干预至关重要:利用本地数据库构建了一个模型,该模型可预测重症监护室患者 48 小时内 AKI 的发生率:我们对浙江省人民医院的 9628 名重症患者进行了研究,并将队列分为推导组和验证组。我们收集并分析了所有患者的人口统计学数据、生命体征、实验室检查、用药、临床干预和其他信息,共得出 232 个变量。我们采用了六种不同的机器学习算法来构建模型,并选择和验证了最佳模型:共纳入 2,441 名患者,其中 1,138 人(46.62%)符合 AKI 标准。得出的模型包括 16 个变量,如白蛋白输注、体液平衡、舒张压(DBP)、氧分压(PO2)、血糖(GLU)、血小板(PLT)、基线血清肌酐(bSCr)、血清钠、年龄、肾上腺素、质子泵抑制剂(PPI)、腹腔内感染、贫血、糖尿病、甘油果糖和营养途径。接收者操作特征曲线下面积(AUC)为 0.822。亚组分析显示了血压波动对 AKI 的影响。此外,该研究还证明了白蛋白和液体平衡对 AKI 的双向影响:结论:该模型非常准确,有助于对 AKI 进行早期诊断和干预。
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来源期刊
Clinical nephrology
Clinical nephrology 医学-泌尿学与肾脏学
CiteScore
2.10
自引率
9.10%
发文量
138
审稿时长
4-8 weeks
期刊介绍: Clinical Nephrology appears monthly and publishes manuscripts containing original material with emphasis on the following topics: prophylaxis, pathophysiology, immunology, diagnosis, therapy, experimental approaches and dialysis and transplantation.
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