Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury.

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.

Identifying KDIGO Trajectory Phenotypes Associated with Increased Inpatient Mortality.

Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of renal and non-renal complications. Nearly 50% of patients in the ICU experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such measurement does not consider either the progression or duration of AKI, both of which are associated with adverse outcomes post-AKI. In this article, by leveraging a large volume of SCr temporal variabilities, we present a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is better than the existing trajectory subtyping methods on both the inpatient mortality stratification and the post-7-day AKI progression estimation. With TAKI, it is found that the trend of KDIGO trajectory appears to be more highly associated with inpatient mortality rates than the maximum KDIGO score.

Serial Diffusion Tensor Imaging In Vivo Predicts Long-Term Functional Recovery and Histopathology in Rats following Different Severities of Spinal Cord Injury.

The current study demonstrates the feasibility of using serial magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) in vivo to quantify temporally spinal cord injury (SCI) pathology in adult female Sprague-Dawley rats that were scanned prior to a moderate or severe upper lumbar contusion SCI. Injured rats were behaviorally tested for hind limb locomotion (Basso, Beattie, Bresnahan [BBB] scores) weekly for 4 weeks and scanned immediately after each session, ending with terminal gait analyses prior to euthanasia. As a measure of tissue integrity, fractional anisotropy (FA) values were significantly lower throughout the spinal cord in both injury cohorts at all time-points examined versus pre-injury. Moreover, FA values were significantly lower following severe versus moderate SCI at all time-points, and FA values at the injury epicenters at all time-points were significantly correlated with both spared white and gray matter volumes, as well as lesion volumes. Critically, quantified FA values at subacute (24 h) and all subsequent time-points were highly predictive of terminal behavior, reflected in significant correlations with both weekly BBB scores and terminal gait parameters. Critically, the finding that clinically relevant subacute (24 h) FA values accurately predict long-term functional recovery may obviate long-term studies to assess the efficacy of therapeutics tested experimentally or clinically. In summary, this study demonstrates a reproducible serial MRI procedure to predict the long-term impact of contusion SCI on both behavior and histopathology using subacute DTI metrics obtained in vivo to accurately predict multiple terminal outcome measures, which can be particularly valuable when comparing experimental interventions.