Dataset Information

Model to predict mortality in critically ill adults with acute kidney injury.

ABSTRACT: Acute kidney injury (AKI) requiring dialysis is associated with high mortality. Most prognostic tools used to describe case complexity and to project patient outcome lack predictive accuracy when applied in patients with AKI. In this study, we developed an AKI-specific predictive model for 60-day mortality and compared the model to the performance of two generic (Sequential Organ Failure Assessment [SOFA] and Acute Physiology and Chronic Health Evaluation II [APACHE II]) scores, and a disease specific (Cleveland Clinic [CCF]) score.Data from 1122 subjects enrolled in the Veterans Affairs/National Institutes of Health Acute Renal Failure Trial Network study; a multicenter randomized trial of intensive versus less intensive renal support in critically ill patients with AKI conducted between November 2003 and July 2007 at 27 VA- and university-affiliated centers.The 60-day mortality was 53%. Twenty-one independent predictors of 60-day mortality were identified. The logistic regression model exhibited good discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.85 (0.83 to 0.88), and a derived integer risk score yielded a value of 0.80 (0.77 to 0.83). Existing scoring systems, including APACHE II, SOFA, and CCF, when applied to our cohort, showed relatively poor discrimination, reflected by areas under the ROC curve of 0.68 (0.64 to 0.71), 0.69 (0.66 to 0.73), and 0.65 (0.62 to 0.69), respectively.Our new risk model outperformed existing generic and disease-specific scoring systems in predicting 60-day mortality in critically ill patients with AKI. The current model requires external validation before it can be applied to other patient populations.

SUBMITTER: Demirjian S

PROVIDER: S-EPMC3359007 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Model to predict mortality in critically ill adults with acute kidney injury.

Demirjian Sevag S Chertow Glenn M GM Zhang Jane Hongyuan JH O'Connor Theresa Z TZ Vitale Joseph J Paganini Emil P EP Palevsky Paul M PM

Clinical journal of the American Society of Nephrology : CJASN 20110901 9

<h4>Background and objectives</h4>Acute kidney injury (AKI) requiring dialysis is associated with high mortality. Most prognostic tools used to describe case complexity and to project patient outcome lack predictive accuracy when applied in patients with AKI. In this study, we developed an AKI-specific predictive model for 60-day mortality and compared the model to the performance of two generic (Sequential Organ Failure Assessment [SOFA] and Acute Physiology and Chronic Health Evaluation II [AP ...[more]

PMID: 21896828

Similar Datasets

Project description:BackgroundThe epidemiologic characteristics of children and young adults with acute kidney injury have been described in single-center and retrospective studies. We conducted a multinational, prospective study involving patients admitted to pediatric intensive care units to define the incremental risk of death and complications associated with severe acute kidney injury.MethodsWe used the Kidney Disease: Improving Global Outcomes criteria to define acute kidney injury. Severe acute kidney injury was defined as stage 2 or 3 acute kidney injury (plasma creatinine level ≥2 times the baseline level or urine output <0.5 ml per kilogram of body weight per hour for ≥12 hours) and was assessed for the first 7 days of intensive care. All patients 3 months to 25 years of age who were admitted to 1 of 32 participating units were screened during 3 consecutive months. The primary outcome was 28-day mortality.ResultsA total of 4683 patients were evaluated; acute kidney injury developed in 1261 patients (26.9%; 95% confidence interval [CI], 25.6 to 28.2), and severe acute kidney injury developed in 543 patients (11.6%; 95% CI, 10.7 to 12.5). Severe acute kidney injury conferred an increased risk of death by day 28 after adjustment for 16 covariates (adjusted odds ratio, 1.77; 95% CI, 1.17 to 2.68); death occurred in 60 of the 543 patients (11.0%) with severe acute kidney injury versus 105 of the 4140 patients (2.5%) without severe acute kidney injury (P<0.001). Severe acute kidney injury was associated with increased use of mechanical ventilation and renal-replacement therapy. A stepwise increase in 28-day mortality was associated with worsening severity of acute kidney injury (P<0.001 by log-rank test). Assessment of acute kidney injury according to the plasma creatinine level alone failed to identify acute kidney injury in 67.2% of the patients with low urine output.ConclusionsAcute kidney injury is common and is associated with poor outcomes, including increased mortality, among critically ill children and young adults. (Funded by the Pediatric Nephrology Center of Excellence at Cincinnati Children's Hospital Medical Center and others; AWARE ClinicalTrials.gov number, NCT01987921 .).

Project description:BACKGROUND:Mortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality. METHODS:Kidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden. RESULTS:In the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07-0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76-0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75-0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75-0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75-0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08-0.46). The model using AKI burden performed better (AUROC 0.77, 0.74-0.80) than the models using AKI presence (AUROC 0.75, 0.71-0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72-0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73-0.79, p = 0.009). CONCLUSION:AKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.

Project description:Background and Objectives: Critically ill surgical patients are susceptible to various postoperative complications, including acute kidney injury (AKI) and multiorgan distress syndrome (MODS). These complications intensify patient suffering and significantly increase morbidity and mortality rates. This study aimed to identify the biomarkers for predicting AKI and MODS in critically ill surgical patients. Materials and Methods: We prospectively enrolled critically ill surgical patients admitted to the intensive care unit via the emergency department between July 2022 and July 2023. A total of 83 patients were recruited, and their data were used to analyze MODS. Three patients who showed decreased creatinine clearance at the initial presentation were excluded from the analysis for AKI. Patient characteristics and laboratory parameters including white blood cell (WBC) count, neutrophil count, delta neutrophil index, urine and serum β2-microglobulin, and urine serum mitochondrial DNA copy number (mtDNAcn) were analyzed to determine the reliable biomarker to predict AKI and MODS. Results: The following parameters were independently correlated with MODS: systolic blood pressure (SBP), initial neutrophil count, and platelet count, according to a logistic regression model. The optimal cut-off values for SBP, initial neutrophil count, and platelet count were 113 mmHg (sensitivity 66.7%; specificity 73.9%), 8.65 (X3) (109/L) (sensitivity 72.2%; specificity 64.6%), and 195.0 (X3) (109/L) (sensitivity 66.7%; specificity 81.5%), respectively. According to the logistic regression model, diastolic blood pressure (DBP) and initial urine mtDNAcn were independently correlated with AKI. The optimal cut-off value for DBP and initial urine mtDNAcn were 68.5 mmHg (sensitivity 61.1%; specificity 79.5%) and 1225.6 copies/μL (sensitivity 55.6%; specificity 95.5%), respectively. Conclusions: SBP, initial neutrophil count, and platelet count were independent predictors of MODS in critically ill patients undergoing surgery. DBP and initial urine mtDNAcn levels were independent predictors of AKI in critically ill surgical patients. Large-scale multicenter prospective studies are needed to confirm our results.

Dataset Information

Model to predict mortality in critically ill adults with acute kidney injury.

Publications

Model to predict mortality in critically ill adults with acute kidney injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets