title: A risk prediction score for acute kidney injury in the intensive care unit.
abstract: BACKGROUND
  Acute kidney injury (AKI) is common in critically ill patients and is associated with high morbidity and mortality. Early identification of high-risk patients provides an opportunity to develop strategies for prevention, early diagnosis and treatment of AKI.
  
  
  METHODS
  We undertook this multicenter prospective cohort study to develop and validate a risk score for predicting AKI in patients admitted to an intensive care unit (ICU). Patients were screened for predictor variables within 48 h of ICU admission. Baseline and acute risk factors were recorded at the time of screening and serum creatinine was measured daily for up to 7 days. A risk score model for AKI was developed with multivariate regression analysis combining baseline and acute risk factors in the development cohort (573 patients) and the model was further evaluated on a test cohort (144 patients). Validation was performed on an independent prospective cohort of 1300 patients. The discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUROC) and model calibration was evaluated by Hosmer-Lemeshow statistic. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria (absolute change of 0.3 mg/dL or relative change of 50% from baseline serum creatinine in 48 h to 7 days, respectively).
  
  
  RESULTS
  AKI developed in 754 (37.2%) patients. In the multivariate model, chronic kidney disease, chronic liver disease, congestive heart failure, hypertension, atherosclerotic coronary vascular disease, pH ≤ 7.30, nephrotoxin exposure, sepsis, mechanical ventilation and anemia were identified as independent predictors of AKI and the AUROC for the model in the test cohort was 0.79 [95% confidence interval (CI) 0.70-0.89]. On the external validation cohort, the AUROC value was 0.81 (95% CI 0.78-0.83). The risk model demonstrated good calibration in both cohorts. Positive and negative predictive values for the optimal cutoff value of ≥ 5 points in test and validation cohorts were 22.7 and 96.1% and 31.8 and 95.4%, respectively.
  
  
  CONCLUSIONS
  A risk score model integrating chronic comorbidities and acute events at ICU admission can identify patients at high risk to develop AKI. This risk assessment tool could help clinicians to stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and outcomes of ICU patients.
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Rakesh
authors.initials: R
authors.lastname: Malhotra
authors.affiliation: Division of Nephrology and Hypertension and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Kianoush B
authors.initials: KB
authors.lastname: Kashani
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Etienne
authors.initials: E
authors.lastname: Macedo
authors.affiliation: Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kim
authors.affiliation: Service de Néphrologie, Département de médecine, Hôpital du Sacré-Coeur de Montréal, Université de Montréal, Montréal, Québec, Canada.
authors.forename: Josee
authors.initials: J
authors.lastname: Bouchard
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Wynn
authors.affiliation: Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.
authors.forename: Guangxi
authors.initials: G
authors.lastname: Li
authors.affiliation: Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA, USA.
authors.forename: Ravindra
authors.initials: R
authors.lastname: Mehta
mesh_terms: D058186:Acute Kidney Injury / Q000175:diagnosis* / Q000209:etiology; D000368:Aged; D019540:Area Under Curve; D016638:Critical Illness / Q000453:epidemiology*; D005260:Female; D006760:Hospitalization; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D014481:United States / Q000453:epidemiology
keywords: acute kidney injury; clinical prediction; intensive care; risk assessment; risk factors