Centre for Implementation Science, Faculty of Health Sciences, University of Southampton, Southampton, SO17 1BJ, UK.
College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Penryn, Cornwall,, TR10 9FE, UK.
BMC Nephrol. 2019 Feb 14;20(1):56. doi: 10.1186/s12882-019-1237-x.
The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.
Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).
Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64-0.77); FLS II (AUC 0.77, 95% CI: 0.69-0.85) and MLR II (AUC 0.74, 95% CI: 0.65-0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92-0.98).
FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.
急性肾损伤（AKI）的发病率在英国持续上升，相关死亡率仍然很高。大约五分之一的住院患者与 AKI 有关，大约三分之一的住院 AKI 患者在住院期间会发生 AKI。其中五分之一的病例被认为是可以避免的。早期风险检测仍然是减少医院 AKI 的关键，在这些医院中，近一半发生 AKI 的患者的护理质量都不理想。
对 2015 年 3 月 18 日至 12 月 31 日期间入住皇家康沃尔医院信托基金（RCHT）的成年患者的电子匿名数据进行修剪，仅保留住院 24 小时内收集的数据。这些数据集根据三个不同的时间段进行划分：用于训练 Takagi-Sugeno 模糊逻辑系统（FLS）和多变量逻辑回归（MLR）模型的数据；用于测试的数据；以及用于验证的后续患者数据。开发了三个模糊逻辑模型和三个 MLR 模型来关联在入院后 7 天内被诊断为最大分期 AKI 的患者的特征：第一个模型用于识别任何 AKI 分期（FLS I、MLR I），第二个模型用于识别 AKI 分期 2 或 3 的模式（FLS II、MLR II），第三个模型用于识别 AKI 分期 3（FLS III、MLR III）。模型准确性用曲线下面积（AUC）表示。
内部验证时每个模型的准确性为：FLS I 和 MLR I（AUC 0.70，95%CI：0.64-0.77）；FLS II（AUC 0.77，95%CI：0.69-0.85）和 MLR II（AUC 0.74，95%CI：0.65-0.83）；FLS III 和 MLR III（AUC 0.95，95%CI：0.92-0.98）。
FLS II 和 FLS III（以及相应的 MLR 模型）可以高度准确地识别出有发生医院 AKI 风险的高危患者。这两个模型不能与之前的研究进行适当的比较，因为这是首次尝试量化广泛的内科和外科住院患者发生特定 AKI 分期的风险。FLS I 和 MLR I 的性能与其他现有模型相当。
