Division of Nephrology, University Medicine Cluster, National University Hospital, Level 10 Medicine Office, NUHS Tower Block, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Renal Unit, Department of Medicine, Ng Teng Fong General Hospital, Singapore, Singapore.
BMC Nephrol. 2019 Jan 31;20(1):32. doi: 10.1186/s12882-019-1206-4.
Electronic health records (EHR) detect the onset of acute kidney injury (AKI) in hospitalized patients, and may identify those at highest risk of mortality and renal replacement therapy (RRT), for earlier targeted intervention.
Prospective observational study to derive prediction models for hospital mortality and RRT, in inpatients aged ≥18 years with AKI detected by EHR over 1 year in a tertiary institution, fulfilling modified KDIGO criterion based on serial serum creatinine (sCr) measures.
We studied 3333 patients with AKI, of 77,873 unique patient admissions, giving an AKI incidence of 4%. KDIGO AKI stages at detection were 1(74%), 2(15%), 3(10%); corresponding peak AKI staging in hospital were 61, 20, 19%. 392 patients (12%) died, and 174 (5%) received RRT. Multivariate logistic regression identified AKI onset in ICU, haematological malignancy, higher delta sCr (sCr rise from AKI detection till peak), higher serum potassium and baseline eGFR, as independent predictors of both mortality and RRT. Additionally, older age, higher serum urea, pneumonia and intraabdominal infections, acute cardiac diseases, solid organ malignancy, cerebrovascular disease, current need for RRT and admission under a medical specialty predicted mortality. The AUROC for RRT prediction was 0.94, averaging 0.93 after 10-fold cross-validation. Corresponding AUROC for mortality prediction was 0.9 and 0.9 after validation. Decision tree analysis for RRT prediction achieved a balanced accuracy of 70.4%, and identified delta-sCr ≥ 148 μmol/L as the key factor that predicted RRT.
Case fatality was high with significant renal deterioration following hospital-wide AKI. EHR clinical model was highly accurate for both RRT prediction and for mortality; allowing excellent risk-stratification with potential for real-time deployment.
电子健康记录 (EHR) 可检测住院患者急性肾损伤 (AKI) 的发病情况，并可能识别出死亡率和肾脏替代治疗 (RRT) 风险最高的患者，以便更早地进行靶向干预。
前瞻性观察研究，旨在为在一家三级医疗机构中通过 EHR 在 1 年内检测到的年龄≥18 岁的 AKI 住院患者中，基于连续血清肌酐 (sCr) 测量的改良 KDIGO 标准，分别建立医院死亡率和 RRT 的预测模型。
我们研究了 3333 例 AKI 患者，这些患者来自 77873 例独特的患者入院记录，AKI 的发病率为 4%。EHR 检测到的 AKI 分期为 1 期（74%）、2 期（15%）、3 期（10%）；相应的院内 AKI 高峰分期分别为 61 期、20 期和 19 期。392 例（12%）患者死亡，174 例（5%）患者接受了 RRT。多变量逻辑回归确定 ICU 中 AKI 的发生、血液恶性肿瘤、更高的 delta sCr（AKI 检测到峰值时的 sCr 升高）、更高的血清钾和基线 eGFR 是死亡率和 RRT 的独立预测因素。此外，年龄较大、血清尿素升高、肺炎和腹腔内感染、急性心脏疾病、实体器官恶性肿瘤、脑血管疾病、当前需要 RRT 和内科专业入院是死亡率的预测因素。RRT 预测的 AUROC 为 0.94，经过 10 倍交叉验证后平均为 0.93。死亡率预测的 AUROC 分别为 0.9 和 0.9。RRT 预测的决策树分析达到了 70.4%的平衡准确率，并确定 delta-sCr≥148μmol/L 是预测 RRT 的关键因素。
全院 AKI 后死亡率高，肾功能明显恶化。EHR 临床模型对 RRT 预测和死亡率均具有高度准确性；可以进行极好的风险分层，并具有实时部署的潜力。
