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Approaches to Predicting Outcomes in Patients with Acute Kidney Injury.

ABSTRACT: Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.

SUBMITTER: Saly D

PROVIDER: S-EPMC5266278 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Approaches to Predicting Outcomes in Patients with Acute Kidney Injury.

Saly Danielle D Yang Alina A Triebwasser Corey C Oh Janice J Sun Qisi Q Testani Jeffrey J Parikh Chirag R CR Bia Joshua J Biswas Aditya A Stetson Chess C Chaisanguanthum Kris K Wilson F Perry FP

PloS one 20170125 1

Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into trai ...[more]

PMID: 28122032

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Project description:It has been suggested that fluid accumulation may delay recognition of acute kidney injury. We sought to determine the impact of fluid balance on the incidence of nondialysis requiring acute kidney injury in patients with acute lung injury and to describe associated outcomes, including mortality.Analysis of the Fluid and Catheter Treatment Trial, a factorial randomized clinical trial of conservative vs. liberal fluid management and of management guided by a central venous vs. pulmonary artery catheter.Acute Respiratory Distress Syndrome Network hospitals.One thousand patients.None.The incidence of acute kidney injury, defined as an absolute rise in creatinine of ?0.3 mg/dL or a relative change of >50% over 48 hrs, was examined before and after adjustment of serum creatinine for fluid balance. The incidence of acute kidney injury before adjustment for fluid balance was greater in those managed with the conservative fluid protocol (57% vs. 51%, p = .04). After adjustment for fluid balance, the incidence of acute kidney injury was greater in those managed with the liberal fluid protocol (66% vs. 58%, p = .007). Patients who met acute kidney injury criteria after adjustment of creatinine for fluid balance (but not before) had a mortality rate that was significantly greater than those who did not meet acute kidney injury criteria both before and after adjustment for fluid balance (31% vs. 12%, p < .001) and those who had acute kidney injury before but not after adjustment for fluid balance (31% vs. 11%, p = .005). The mortality of those patients meeting acute kidney injury criteria after but not before adjustment for fluid balance was similar to patients with acute kidney injury both before and after adjustment for fluid balance (31% vs. 38%, p = .18).Fluid management influences serum creatinine and therefore the diagnosis of acute kidney injury using creatinine-based definitions. Patients with "unrecognized" acute kidney injury that is identified after adjusting for positive fluid balance have higher mortality rates, and patients who have acute kidney injury before but not after adjusting for fluid balance have lower mortality rates. Future studies of acute kidney injury should consider potential differences in serum creatinine caused by changes in fluid balance and the impact of these differences on diagnosis and prognosis.

Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Project description:Rationale & objectiveOutcomes of patients hospitalized with coronavirus disease 2019 (COVID-19) and acute kidney injury (AKI) are not well understood. The goal of this study was to investigate the survival and kidney outcomes of these patients.Study designRetrospective cohort study.Setting & participantsPatients (aged≥18 years) hospitalized with COVID-19 at 13 hospitals in metropolitan New York between March 1, 2020, and April 27, 2020, followed up until hospital discharge.ExposureAKI.OutcomesPrimary outcome: in-hospital death.Secondary outcomesrequiring dialysis at discharge, recovery of kidney function.Analytical approachUnivariable and multivariable time-to-event analysis and logistic regression.ResultsAmong 9,657 patients admitted with COVID-19, the AKI incidence rate was 38.4/1,000 patient-days. Incidence rates of in-hospital death among patients without AKI, with AKI not requiring dialysis (AKI stages 1-3), and with AKI receiving dialysis (AKI 3D) were 10.8, 31.1, and 37.5/1,000 patient-days, respectively. Taking those without AKI as the reference group, we observed greater risks for in-hospital death for patients with AKI 1-3 and AKI 3D (HRs of 5.6 [95% CI, 5.0-6.3] and 11.3 [95% CI, 9.6-13.1], respectively). After adjusting for demographics, comorbid conditions, and illness severity, the risk for death remained higher among those with AKI 1-3 (adjusted HR, 3.4 [95% CI, 3.0-3.9]) and AKI 3D (adjusted HR, 6.4 [95% CI, 5.5-7.6]) compared with those without AKI. Among patients with AKI 1-3 who survived, 74.1% achieved kidney recovery by the time of discharge. Among those with AKI 3D who survived, 30.6% remained on dialysis at discharge, and prehospitalization chronic kidney disease was the only independent risk factor associated with needing dialysis at discharge (adjusted OR, 9.3 [95% CI, 2.3-37.8]).LimitationsObservational retrospective study, limited to the NY metropolitan area during the peak of the COVID-19 pandemic.ConclusionsAKI in hospitalized patients with COVID-19 was associated with significant risk for death.

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Approaches to Predicting Outcomes in Patients with Acute Kidney Injury.

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Approaches to Predicting Outcomes in Patients with Acute Kidney Injury.

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