Dataset Information

Use of height-independent baseline creatinine imputation method with renal angina index.

ABSTRACT: BACKGROUND:The Renal Angina Index (RAI) is a validated screening tool used at 12 h of pediatric intensive care unit (PICU) admission to predict severe acute kidney injury (AKI) on day 3 of PICU stay. A measured or height-imputed baseline serum creatinine (SCr) is required for AKI diagnosis and RAI calculation, yet these are often lacking. We assessed an age-based, height-independent baseline SCr calculation and compared the RAI values employing this method to their historical counterpart. METHODS:An electronic algorithm was implemented to generate RAI score for patients admitted to our PICU. We reviewed 157 consecutive patient records from May 2017, until we cumulated 100 with a valid RAI calculation. We compared RAI scores using the age-based SCr imputation method of Pottel to the historical RAI. Our primary outcome was a difference in the rate of RAI fulfillment (??8) reclassification between methods. RESULTS:Of the first 100 patients, 27 had measured baseline SCr and 73 used height imputation. Only two patients had RAI reclassified with the Pottel method (one in each direction). Being small for age or older were associated with ??25% overestimation of the baseline SCr in 20 patients with the Pottel method compared with height imputation. 15/157 patients had a falsely positive RAI due to lack of measured baseline SCr and height. CONCLUSION:The age-based method to estimate baseline SCr offers a viable height-independent alternative for RAI calculation. While less precise than a height-based approach, this lack of precision rarely leads to reclassification of patient RAI status.

SUBMITTER: Roy JP

PROVIDER: S-EPMC6776697 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of height-independent baseline creatinine imputation method with renal angina index.

Roy Jean-Philippe JP Johnson Catherine C Towne Bryan B Menke Frank F Kiger Samuel S Young William W Basu Rajit R Chima Ranjit R Fei Lin L Krallman Kelli K Goldstein Stuart L SL

Pediatric nephrology (Berlin, Germany) 20190708 10

<h4>Background</h4>The Renal Angina Index (RAI) is a validated screening tool used at 12 h of pediatric intensive care unit (PICU) admission to predict severe acute kidney injury (AKI) on day 3 of PICU stay. A measured or height-imputed baseline serum creatinine (SCr) is required for AKI diagnosis and RAI calculation, yet these are often lacking. We assessed an age-based, height-independent baseline SCr calculation and compared the RAI values employing this method to their historical counterpart ...[more]

PMID: 31286243

Similar Datasets

Project description:ObjectiveObesity is the prominent modifiable risk factor known to influence the occurrence and progression of diabetes other than age, and the objective of this study was to evaluate and compare the predictive value of three simple baseline anthropometric indicators of obesity, body mass index (BMI), waist circumference (WC), and waist-height ratio (WHtR), for the occurrence of diabetes at different time points in the future.MethodsThe study subjects were 12,823 individuals with normoglycemic at baseline who underwent health screening and had measurements of BMI, WC, and WHtR. The outcome of interest was new-onset diabetes during follow-up. Time-dependent receiver operator characteristics (ROC) curves of baseline BMI, WC, and WHtR for predicting the risk of diabetes in the next 2 to 12 years were constructed and their area under the ROC curves (AUCs) and corresponding optimal threshold values were calculated for each time point, which were used to compare the accuracy and stability of the above three indicators for predicting the occurrence of diabetes in different future periods.ResultsDuring a median follow-up period of 7.02 years, with a maximum follow-up of 13 years, 320 new-onset diabetes were recorded. After adjusting for confounders and comparing standardized hazard ratios (HRs), WC was shown to be the best simple anthropometric indicator of obesity reflecting diabetes risk in all models, followed by WHtR. Time-dependent ROC analysis showed that WC had the highest AUC in predicting the occurrence of diabetes in the short term (2-5 years), and WHtR had the highest AUC in predicting the occurrence of diabetes in the medium to long term (6-12 years), while in any time point, both WC and WHtR had higher AUC than BMI in predicting future diabetes. In addition, we found relatively larger fluctuations in the thresholds of BMI and WC for predicting diabetes over time, while the thresholds of WHtR consistently remained between 0.47-0.50; comparatively speaking, WHtR may have greater application value in predicting future diabetes.ConclusionsOur analysis sustained that central obesity is a more important predictor of diabetes, and in clinical practice, we proposed measuring WHtR as a useful tool for predicting future diabetes.

Project description:Background and objectivesNovel AKI biomarkers carry variable performance for prediction of AKI in patients with heterogeneous illness. Until utility is demonstrated in critically ill patients outside of the cardiopulmonary bypass population, AKI biomarkers are unlikely to gain widespread implementation. Operationalization of an AKI risk stratification methodology, termed renal angina, was recently reported to enhance prediction at the time of intensive care unit admission for persistent severe AKI. The renal angina index (RAI) was developed to provide the clinical context to direct AKI biomarker testing. This study tested the hypothesis that incorporation of AKI biomarkers in patients fulfilling renal angina improves the prediction of persistent severe AKI.Design, setting, participants, & measurementsIn a multicenter study of 214 patients admitted to the pediatric intensive care unit with sepsis, the discrimination of plasma neutrophil gelatinase-associated lipocalin (NGAL), matrix metalloproteinase-8 (MMP-8), and neutrophil elastase-2 (Ela-2) were determined individually and in combination with the RAI for severe AKI. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated.ResultsIndividual biomarkers demonstrated marginal discrimination for severe AKI (area under curve [AUC]: NGAL, 0.72; MMP-8, 0.68; Ela-2, 0.72), inferior to prediction by the clinical model of the RAI (AUC=0.80). Incorporation of each biomarker significantly added to the renal angina model AKI prediction (AUC=0.80, increased to 0.84-0.88; P<0.05 for each). The inclusion of each biomarker with the RAI demonstrated NRI (0.512, 0.428, and 0.545 for NGAL, MMP-8, and Ela-2, respectively; all P<0.03) and IDI (0.075 for Ela-2). The inclusion of both Ela-2 and NGAL with RAI demonstrated an NRI of 0.871 (P<0.001) and an IDI of 0.1 (P=0.01).ConclusionsThis study shows that incorporation of AKI biomarkers into the RAI improves discrimination for severe AKI. The RAI optimizes the utility of AKI biomarkers in a heterogeneous, critically ill patient population.

Project description:BackgroundConsiderable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC.MethodsFiles containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis.ResultsVia differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients' survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways.ConclusionIn general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

Dataset Information

Use of height-independent baseline creatinine imputation method with renal angina index.

Publications

Use of height-independent baseline creatinine imputation method with renal angina index.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets