Rationale & Objective

Risk prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk stratification of mortality and major adverse kidney events (MAKE) in critically ill patients with incident AKI.

Study Design

Multicenter cohort study.

Setting & Participants

9,587 adult patients admitted to heterogeneous intensive care units (ICUs; March 2009 to February 2017) who experienced AKI within the first 3 days of their ICU stays.

Predictors

Multimodal clinical data consisting of 71 features collected in the first 3 days of ICU stay.

Outcomes

(1) Hospital mortality and (2) MAKE, defined as the composite of death during hospitalization or within 120 days of discharge, receipt of kidney replacement therapy in the last 48 hours of hospital stay, initiation of maintenance kidney replacement therapy within 120 days, or a ≥50% decrease in estimated glomerular filtration rate from baseline to 120 days from hospital discharge.

Analytical Approach

Four machine-learning algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) and the SHAP (Shapley Additive Explanations) framework were used for feature selection and interpretation. Model performance was evaluated by 10-fold cross-validation and external validation.

Results

One developed model including 15 features outperformed the SOFA (Sequential Organ Failure Assessment) score for the prediction of hospital mortality, with areas under the curve of 0.79 (95% CI, 0.79-0.80) and 0.71 (95% CI, 0.71-0.71) in the development cohort and 0.74 (95% CI, 0.73-0.74) and 0.71 (95% CI, 0.71-0.71) in the validation cohort (P < 0.001 for both). A second developed model including 14 features outperformed KDIGO (Kidney Disease: Improving Global Outcomes) AKI severity staging for the prediction of MAKE: 0.78 (95% CI, 0.78-0.78) versus 0.66 (95% CI, 0.66-0.66) in the development cohort and 0.73 (95% CI, 0.72-0.74) versus 0.67 (95% CI, 0.67-0.67) in the validation cohort (P < 0.001 for both).

Limitations

The models are applicable only to critically ill adult patients with incident AKI within the first 3 days of an ICU stay.

Conclusions

The reported clinical models exhibited better performance for mortality and kidney recovery prediction than standard scoring tools commonly used in critically ill patients with AKI in the ICU. Additional validation is needed to support the utility and implementation of these models.

Plain-Language Summary

Acute kidney injury (AKI) occurs commonly in critically ill patients admitted to the intensive care unit (ICU) and is associated with high morbidity and mortality rates. Prediction of mortality and recovery after an episode of AKI may assist bedside decision making. In this report, we describe the development and validation of a clinical model using data from the first 3 days of an ICU stay to predict hospital mortality and major adverse kidney events occurring as long as 120 days after hospital discharge among critically ill adult patients who experienced AKI within the first 3 days of an ICU stay. The proposed clinical models exhibited good performance for outcome prediction and, if further validated, could enable risk stratification for timely interventions that promote kidney recovery.

中文翻译：

---

急性肾损伤危重患者死亡率和主要肾脏不良事件的预测

理性客观

用于协助急性肾损伤 (AKI) 管理的风险预测工具主要关注 AKI 发病，但很少关注肾脏恢复问题。我们开发了针对发生 AKI 的危重患者的死亡率和主要不良肾脏事件 (MAKE) 风险分层的临床模型。

学习规划

多中心队列研究。

背景及参与者

9,587 名成年患者入住异构重症监护病房（ICU；2009 年 3 月至 2017 年 2 月），他们在入住 ICU 的前 3 天内经历了 AKI。

预测因素

多模态临床数据由 ICU 入住前 3 天收集的 71 个特征组成。

结果

(1) 医院死亡率和 (2) MAKE，定义为住院期间或出院 120 天内死亡、住院最后 48 小时内接受肾脏替代治疗、120 天内开始维持性肾脏替代治疗的综合死亡，或出院后 120 天期间估计肾小球滤过率较基线下降 ≥50%。

分析法

使用四种机器学习算法（逻辑回归、随机森林、支持向量机和极限梯度提升）和 SHAP（Shapley Additive Explanations）框架进行特征选择和解释。通过 10 倍交叉验证和外部验证来评估模型性能。

结果

一种包含 15 个特征的开发模型在预测医院死亡率方面优于 SOFA（序贯器官衰竭评估）评分，曲线下面积为 0.79（95% CI，0.79-0.80）和 0.71（95% CI，0.71-0.71）在开发队列中为 0.74 (95% CI, 0.73-0.74) 和 0.71 (95% CI, 0.71-0.71) 在验证队列中（ 两者P < 0.001）。第二个开发的模型包括 14 个特征，在 MAKE 预测方面优于 KDIGO（肾脏疾病：改善全球结果）AKI 严重程度分期：0.78（95% CI，0.78-0.78）对比开发中的 0.66（95% CI，0.66-0.66）验证队列中的这一比例为 0.73 (95% CI, 0.72-0.74) 与 0.67 (95% CI, 0.67-0.67)（ 两者P < 0.001）。

局限性

该模型仅适用于入住 ICU 的前 3 天内发生 AKI 的危重成年患者。

结论

与 ICU 重症 AKI 患者常用的标准评分工具相比，所报告的临床模型在死亡率和肾脏恢复预测方面表现出更好的性能。需要额外的验证来支持这些模型的实用性和实施​​。

通俗易懂的语言总结

急性肾损伤（AKI）通常发生在重症监护病房（ICU）的重症患者中，并且与高发病率和死亡率相关。预测 AKI 发作后的死亡率和康复情况可能有助于临床决策。在本报告中，我们描述了临床模型的开发和验证，该模型使用 ICU 住院前 3 天的数据来预测住院死亡率和出院后 120 天内发生的重大不良肾脏事件，这些事件发生在危重成年患者中入住 ICU 后的前 3 天内出现 AKI。所提出的临床模型在结果预测方面表现出良好的性能，如果进一步验证，可以实现风险分层，以便及时采取干预措施，促进肾脏恢复。