A risk prediction score for acute kidney injury in the intensive care unit

Malhotra, Rakesh; Kashani, Kianoush; Macedo, Etienne; Kim, Jihoon; Bouchard, Josée; Wynn, Susan; Li, Guangxi; Ohno-Machado, Lucila; Mehta, Ravindra L.

doi:10.1093/ndt/gfx026

Cited by 150 publications

(158 citation statements)

References 72 publications

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“…Feature selection has been extensively studied for many years and has found applications in many domains, especially for problems involving high dimensional data 25–27 . However, stability is a major issue for feature selection, especially in the context of sample variation.…”

Section: Discussionmentioning

confidence: 99%

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Liu

et al. 2018

Sci Rep

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Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.

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Section: Discussionmentioning

confidence: 99%

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Liu

et al. 2018

Sci Rep

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“…UHC predicted mortality is a well-validated predictor of mortality among inpatients, and since one component of the MAKE30 outcome is in-hospital mortality, it is logical that UHC predicted mortality performs well at identifying patients likely to experience the MAKE30 endpoint. Similarly, those patients with AKI or renal dysfunction at ICU admission have been shown in prior studies to have increased risk for progression to RRT[21, 22], another component of the MAKE30 outcome. CKD defined by baseline creatinine was not independently predictive of MAKE30 in our study, but the presence of the Elixhauser renal failure variable and other covariates may have reduced CKD’s independent impact in the model.…”

Section: Discussionmentioning

confidence: 99%

Predicting Major Adverse Kidney Events among Critically Ill Adults Using the Electronic Health Record

et al. 2017

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Introduction Prediction of major adverse kidney events in critically ill patients may help target therapy, allow risk adjustment, and facilitate the conduct of clinical trials. Methods In a cohort comprised of all critically ill adults admitted to five intensive care units at a single tertiary care center over one year, we developed a logistic regression model for the outcome of Major Adverse Kidney Events within 30 days (MAKE30), the composite of persistent renal dysfunction, new renal replacement therapy (RRT), and in-hospital mortality. Proposed risk factors for the MAKE30 outcome were selected a priori and included age, race, gender, University Health System Consortium (UHC) expected mortality, baseline creatinine, volume of isotonic crystalloid fluid received in the prior 24 hours, admission service, intensive care unit (ICU), source of admission, mechanical ventilation or receipt of vasopressors within 24 hours of ICU admission, renal replacement therapy prior to ICU admission, acute kidney injury, chronic kidney disease as defined by baseline creatinine value, and renal failure as defined by the Elixhauser index. Results Among 10,983 patients in the study population, 1489 patients (13.6%) met the MAKE30 endpoint. The strongest independent predictors of MAKE30 were UHC expected mortality (OR 2.32 [95%CI 2.06-2.61]) and presence of acute kidney injury at ICU admission (OR 4.98 [95%CI 4.12-6.03]). The model had strong predictive properties including excellent discrimination with a bootstrap-corrected area-under-the-curve (AUC) of 0.903, and high precision of calibration with a mean absolute error prediction of 1.7%. Conclusions The MAKE30 composite outcome can be reliably predicted from factors present within 24 hours of ICU admission using data derived from the electronic health record.

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“…Malhotra et al [13] developed a clinical risk prediction model for AKI in the ICU using 2 independent cohorts: a discovery cohort from the University of California San Diego, and an external validation cohort from Mayo Clinic. They used forward elimination multiple logistic regression to select the variables for inclusion in their model and developed a scoring system using regression coefficients.…”

Section: What Has Been Done In Relation To Clinical Risk Prediction Tmentioning

confidence: 99%

“…Further, even among the variables that predicted AKI in more than 1 study, definitions used for these variables differed from study to study. For example, CKD was determined by Kashani et al [11] based on the patient’s medical history, while it was defined as baseline estimated glomerular filtration rate < 60 mL/min/1.73 m 2 by Malhotra et al[13] In contrast, Flechet et al[14] used baseline SCr rather than CKD in their risk prediction model. Similarly, the definition of nephrotoxin exposure varied widely across studies (Table 1b).…”

Section: Comparison Of Clinical Risk Prediction Modelsmentioning

confidence: 99%

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu

Neyra

Leaf

2018

Nephron

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Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality. Among critically ill patients admitted to intensive care units (ICUs), the incidence of AKI is as high as 50% and is associated with dismal outcomes. Thus, the development and validation of clinical risk prediction tools that accurately identify patients at high risk for AKI in the ICU is of paramount importance. We provide a comprehensive review of 3 clinical risk prediction tools that have been developed for incident AKI occurring in the first few hours or days following admission to the ICU. We found substantial heterogeneity among the clinical variables that were examined and included as significant predictors of AKI in the final models. The area under the receiver operating characteristic curves was ∼0.8 for all 3 models, indicating satisfactory model performance, though positive predictive values ranged from only 23 to 38%. Hence, further research is needed to develop more accurate and reproducible clinical risk prediction tools. Strategies for improved assessment of AKI susceptibility in the ICU include the incorporation of dynamic (time-varying) clinical parameters, as well as biomarker, functional, imaging, and genomic data.

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A risk prediction score for acute kidney injury in the intensive care unit

Cited by 150 publications

References 72 publications

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Predicting Major Adverse Kidney Events among Critically Ill Adults Using the Electronic Health Record

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu

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