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A validation study comparing existing prediction models of acute kidney injury in patients with acute heart failure.

ABSTRACT: Acute kidney injury (AKI) is a common complication in acute heart failure (AHF) and is associated with prolonged hospitalization and increased mortality. The aim of this study was to externally validate existing prediction models of AKI in patients with AHF. Data for 10,364 patients hospitalized for acute heart failure between 2008 and 2018 were extracted from the Chang Gung Research Database and analysed. The primary outcome of interest was AKI, defined according to the KDIGO definition. The area under the receiver operating characteristic (AUC) curve was used to assess the discrimination performance of each prediction model. Five existing prediction models were externally validated, and the Forman risk score and the prediction model reported by Wang et al. showed the most favourable discrimination and calibration performance. The Forman risk score had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.696, 0.829, and 0.817, respectively. The Wang et al. model had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.73, 0.858, and 0.845, respectively. The Forman risk score and the Wang et al. prediction model are simple and accurate tools for predicting AKI in patients with AHF.

SUBMITTER: Lee TH

PROVIDER: S-EPMC8159983 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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A validation study comparing existing prediction models of acute kidney injury in patients with acute heart failure.

Lee Tao Han TH Fan Pei-Chun PC Chen Jia-Jin JJ Wu Victor Chien-Chia VC Lee Cheng-Chia CC Yen Chieh-Li CL Kuo George G Hsu Hsiang-Hao HH Tian Ya-Chung YC Chang Chih-Hsiang CH

Scientific reports 20210527 1

Acute kidney injury (AKI) is a common complication in acute heart failure (AHF) and is associated with prolonged hospitalization and increased mortality. The aim of this study was to externally validate existing prediction models of AKI in patients with AHF. Data for 10,364 patients hospitalized for acute heart failure between 2008 and 2018 were extracted from the Chang Gung Research Database and analysed. The primary outcome of interest was AKI, defined according to the KDIGO definition. The ar ...[more]

PMID: 34045629

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Project description:Rationale & objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.Study designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.Setting & participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.PredictorsKDIGO-based measures of AKI severity and comorbidities specified in the original models.OutcomesDeath or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.Analytical approachDecision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).Results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.LimitationsBoth original models overstated risks, indicating a need for regular updating.ConclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.

Project description:ImportanceSome patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up.ObjectiveTo derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.Design, setting, and participantsData from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013).ExposuresDemographic, laboratory, and comorbidity variables measured prior to discharge.Main outcomes and measuresAdvanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year.ResultsThe participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%).Conclusions and relevanceA multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research.

Project description:BackgroundMachine learning (ML) has been used to build high performance prediction model. Patients with congestive heart failure (CHF) are vulnerable to acute kidney injury (AKI) which makes treatment difficult. We aimed to establish an ML-based prediction model for the early identification of AKI in patients with CHF.MethodsPatients data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database, and patients with CHF were selected. Comparisons between several common ML classifiers were conducted to select the best prediction model. Recursive feature elimination (RFE) was used to select important prediction features. The model was improved using hyperparameters optimization (HPO). The final model was validated using an external validation set from the eICU Collaborative Research Database. The area under the receiver operating characteristic curve (AUROC), accuracy, calibration curve and decision curve analysis were used to evaluate prediction performance. Additionally, the final model was used to predict renal replacement therapy (RRT) requirement and to assess the short-term prognosis of patients with CHF. Finally, a software program was developed based on the selected features, which could intuitively report the probability of AKI.ResultsA total of 8,580 patients with CHF were included, among whom 2,364 were diagnosed with AKI. The LightGBM model showed the best prediction performance (AUROC = 0.803) among the 13 ML-based models. After RFE and HPO, the final model was established with 18 features including serum creatinine (SCr), blood urea nitrogen (BUN) and urine output (UO). The prediction performance of LightGBM was better than that of measuring SCr, UO or SCr combined with UO (AUROCs: 0.809, 0.703, 0.560 and 0.714, respectively). Additionally, the final model could accurately predict RRT requirement in patients with (AUROC = 0.954). Moreover, the participants were divided into high- and low-risk groups for AKI, and the 90-day mortality in the high-risk group was significantly higher than that in the low-risk group (log-rank p < 0.001). Finally, external validation using the eICU database comprising 9,749 patients with CHF revealed satisfactory prediction outcomes (AUROC = 0.816).ConclusionA prediction model for AKI in patients with CHF was established based on LightGBM, and the prediction performance of this model was better than that of other models. This model may help in predicting RRT requirement and in identifying the population with poor prognosis among patients with CHF.

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A validation study comparing existing prediction models of acute kidney injury in patients with acute heart failure.

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A validation study comparing existing prediction models of acute kidney injury in patients with acute heart failure.

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