Development and Validation of a Prediction Model for Acute Kidney Injury Among Patients With Acute Decompensated Heart Failure.
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SUBMITTER: Wang L
PROVIDER: S-EPMC8634389 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIn acute decompensated heart failure (ADHF) the risk of acute kidney injury (AKI) is high. Early detection of patients at risk for AKI is important. We tested urinary [TIMP-2] × [IGFBP7], a new US Food and Drug Administration-cleared test to assess AKI risk, in a cohort of hospitalized ADHF patients.HypothesisIn patients with ADHF, urinary [TIMP-2] × [IGFBP7] is associated with moderate to severe AKI and related to increased mortality.MethodsWe enrolled 400 patients in the emergency department at Robert-Bosch Hospital, Stuttgart, Germany. We examined the predictive ability of urinary [TIMP-2] × [IGFBP7] (units: [ng/mL]2 /1000) for development of AKI stage 2 or 3 within 24 hours of sample collection in patients with ADHF. Operating characteristics were determined for the validated cutoffs of 0.3 and 2.0.ResultsForty patients had ADHF upon presentation and sufficient data for AKI staging. 27.5% developed AKI stage 2-3 within 7 days. Urinary [TIMP-2] × [IGFBP7] discriminated for AKI stage 2-3 over the first day with an area under the ROC curve of 0.84 (95% confidence interval: 0.72-0.93) and over 7 days with an AUC of 0.77 (95% confidence interval: 0.65-0.88). For the first day, sensitivity was 86% at the 0.3 cutoff and specificity was 95% at the 2.0 cutoff for prediction of AKI stage 2-3. There was a trend (P = 0.08) for higher mortality in patients with urinary [TIMP-2] × [IGFBP7] >2.0 and AKI 2-3.ConclusionsUrinary [TIMP-2] × [IGFBP7] is a promising marker for AKI risk assessment in patients with ADHF.
| S-EPMC6490429 | biostudies-literature
Project description:Although several risk factors for acute kidney injury (AKI) have been identified, early detection of AKI in acute decompensated heart failure patients remains a challenge. The aim of this study was to develop and validate a risk score for early prediction of AKI in acute decompensated heart failure patients. A total of 676 consecutive acute decompensated heart failure participants were prospectively enrolled from 6 regional central hospitals. Data from 507 participants were analyzed. Participants from 4 of the 6 hospitals (n=321) were used to develop a risk score and conduct internal validation. External validation of the developed risk score was conducted in participants from the other 2 hospitals (n=186). Sequential logistic regression was used to develop and validate the risk score. The c statistic and calibration plot were used to assess the discrimination and calibration of the proposed risk score. The overall occurrence of AKI was 33.1% (168/507). The risk score, ranging from 0 to 55, demonstrated good discriminative power with an optimism-corrected c statistic of 0.859. Similar results were obtained from external validation with c statistic of 0.847 (95% CI 0.819-0.927). The risk score had good calibration with no apparent over- or under-prediction observed from calibration plots. The novel risk score is a simple and accurate tool that can help clinicians assess the risk of AKI in acute decompensated heart failure patients, which in turn helps them plan and initiate the most appropriate disease management for patients in time.
| S-EPMC5210339 | biostudies-literature
Project description:Background: Irreversible worsening of cardiac function is an adverse event associated with significant morbidity among patients with acute decompensated heart failure (ADHF). We aimed to develop a parsimonious model which is simple to use in clinical settings for the prediction of the risk of irreversible worsening of cardiac function. Methods: A total of 871 ADHF patients were enrolled in this study. Data for each patient were collected from the medical records. Irreversible worsening of cardiac function included cardiac death within 30-days of patient hospitalization, implantation of a left ventricular assistance device, or emergency heart transplantation. We performed LASSO regression for variable selection to derive a multivariable logistic regression model. Five candidate predictors were selected to derive the final prediction model. The prediction model was verified using C-statistics, calibration curve, and decision curve. Results: Irreversible worsening of cardiac function occurred in 7.8% of the patients. Advanced age, NYHA class, high blood urea nitrogen, hypoalbuminemia, and vasopressor use were its strongest predictors. The prediction model showed good discrimination C-statistic value, 0.866 (95% CI, 0.817-0.907), which indicated good identical calibration and clinical efficacy. Conclusion: In this study, we developed a prediction model and nomogram to estimate the risk of irreversible worsening of cardiac function among ADHF patients. The findings may provide a reference for clinical physicians for detection of irreversible worsening of cardiac function and enable its prompt management.
| S-EPMC8702716 | biostudies-literature
Project description:One-quarter of patients with acute decompensated heart failure (ADHF) experience acute kidney injury (AKI)-an abrupt reduction or loss of kidney function associated with increased long-term mortality. There is a critical need to identify early and real-time markers of AKI in ADHF; however, to date, no protein biomarkers have exhibited sufficient diagnostic or prognostic performance for widespread clinical uptake. We aimed to identify novel protein biomarkers of AKI associated with ADHF by quantifying changes in protein abundance in the kidneys that occur during ADHF development and recovery in an ovine model. Relative quantitative protein profiling was performed using sequential window acquisition of all theoretical fragment ion spectra-mass spectrometry (SWATH-MS) in kidney cortices from control sheep (n = 5), sheep with established rapid-pacing-induced ADHF (n = 8), and sheep after ~4 weeks recovery from ADHF (n = 7). Of the 790 proteins quantified, we identified 17 candidate kidney injury markers in ADHF, 1 potential kidney marker of ADHF recovery, and 2 potential markers of long-term renal impairment (differential abundance between groups of 1.2-2.6-fold, adjusted p < 0.05). Among these 20 candidate protein markers of kidney injury were 6 candidates supported by existing evidence and 14 novel candidates not previously implicated in AKI. Proteins of differential abundance were enriched in pro-inflammatory signalling pathways: glycoprotein VI (activated during ADHF development; adjusted p < 0.01) and acute phase response (repressed during recovery from ADHF; adjusted p < 0.01). New biomarkers for the early detection of AKI in ADHF may help us to evaluate effective treatment strategies to prevent mortality and improve outcomes for patients.
| S-EPMC8778509 | biostudies-literature
Project description:A quarter of patients with acute decompensated heart failure (ADHF) experience acute kidney injury (AKI), an abrupt loss of kidney function that doubles the risk of death at one-year. There is a critical need to identify early markers for AKI in ADHF, however, no protein candidates have been validated as diagnostic or prognostic biomarkers in this setting. We aimed to identify novel candidate protein biomarkers by quantifying changes in protein expression in the kidney that occur during ADHF development and recovery in an ovine model. Relative quantitative protein profiling was performed using Sequential Window Acquisition of All Theoretical Mass Spectrometry (SWATH-MS) in kidney cortex from healthy control sheep (n=5), sheep with established rapid pacing-induced ADHF (n=8) and sheep after ~4 weeks recovery from ADHF (n=7). Of 790 proteins quantified, we identified 17 candidate kidney injury markers, one potential marker of kidney recovery and two markers of long-term renal impairment that were differentially expressed between groups (1.2-2.6 fold-change, p<0.05). Differentially expressed proteins were enriched in pro-inflammatory signaling pathways: Glycoprotein VI (activated during ADHF development, adjusted p<0.01) and acute phase response (repressed during recovery from ADHF, p<0.01). This research identified 20 candidate protein markers of kidney injury, including 6 promising candidates supported by existing evidence and 14 novel candidates never implicated in AKI. Early awareness of AKI in ADHF through the use of biomarkers has the potential to reduce mortality and improve outcomes for these at-risk patients.
2022-01-19 | PXD028983 | Pride
Project description: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.
| S-EPMC8159983 | biostudies-literature
Project description:Heart failure (HF) inpatient mortality prediction models can help clinicians make treatment decisions and researchers conduct observational studies; however, published models have not been validated in external populations.We compared the performance of 7 models that predict inpatient mortality in patients hospitalized with acute decompensated heart failure: 4 HF-specific mortality prediction models developed from 3 clinical databases (ADHERE [Acute Decompensated Heart Failure National Registry], EFFECT study [Enhanced Feedback for Effective Cardiac Treatment], and GWTG-HF registry [Get With the Guidelines-Heart Failure]); 2 administrative HF mortality prediction models (Premier, Premier+); and a model that uses clinical data but is not specific for HF (Laboratory-Based Acute Physiology Score [LAPS2]). Using a multihospital, electronic health record-derived data set (HealthFacts [Cerner Corp], 2010-2012), we identified patients ?18 years admitted with HF. Of 13?163 eligible patients, median age was 74 years; half were women; and 27% were black. In-hospital mortality was 4.3%. Model-predicted mortality ranges varied: Premier+ (0.8%-23.1%), LAPS2 (0.7%-19.0%), ADHERE (1.2%-17.4%), EFFECT (1.0%-12.8%), GWTG-Eapen (1.2%-13.8%), and GWTG-Peterson (1.1%-12.8%). The LAPS2 and Premier models outperformed the clinical models (C statistics: LAPS2 0.80 [95% confidence interval 0.78-0.82], Premier models 0.81 [95% confidence interval 0.79-0.83] and 0.76 [95% confidence interval 0.74-0.78], and clinical models 0.68 to 0.70).Four clinically derived, inpatient, HF mortality models exhibited similar performance, with C statistics near 0.70. Three other models, 1 developed in electronic health record data and 2 developed in administrative data, also were predictive, with C statistics from 0.76 to 0.80. Because every model performed acceptably, the decision to use a given model should depend on practical concerns and intended use.
| S-EPMC4988343 | biostudies-literature
Project description:BackgroundPredictive models and clinical risk scores for hospital-acquired acute kidney injury (AKI) are mainly focused on critical and surgical patients. We have used the electronic clinical records from a tertiary care general hospital to develop a risk score for new-onset AKI in general inpatients that can be estimated automatically from clinical records.MethodsA total of 47 466 patients met inclusion criteria within a 2-year period. Of these, 2385 (5.0%) developed hospital-acquired AKI. Step-wise regression modelling and Bayesian model averaging were used to develop the Madrid Acute Kidney Injury Prediction Score (MAKIPS), which contains 23 variables, all obtainable automatically from electronic clinical records at admission. Bootstrap resampling was employed for internal validation. To optimize calibration, a penalized logistic regression model was estimated by the least absolute shrinkage and selection operator (lasso) method of coefficient shrinkage after estimation.ResultsThe area under the curve of the receiver operating characteristic curve of the MAKIPS score to predict hospital-acquired AKI at admission was 0.811. Among individual variables, the highest odds ratios, all >2.5, for hospital-acquired AKI were conferred by abdominal, cardiovascular or urological surgery followed by congestive heart failure. An online tool (http://www.bioestadistica.net/MAKIPS.aspx) will facilitate validation in other hospital environments.ConclusionsMAKIPS is a new risk score to predict the risk of hospital-acquired AKI, based on variables present at admission in the electronic clinical records. This may help to identify patients who require specific monitoring because of a high risk of AKI.
| S-EPMC7857831 | biostudies-literature
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.
| S-EPMC8931220 | biostudies-literature
Project description:AimsTo determine if newer criteria for diagnosing and staging acute kidney injury (AKI) during heart failure (HF) admission are more predictive of clinical outcomes at 30 days and 1 year than the traditional worsening renal function (WRF) definition.MethodsWe analyzed prospectively collected clinical data on 637 HF admissions with 30-day and 1-year follow-up. The incidence, stages, and outcomes of AKI were determined using the following four definitions: KDIGO, RIFLE, AKIN, and WRF (serum creatinine rise ?0.3 mg/dl). Receiver operating curves were used to compare the predictive ability of each AKI definition for the occurrence of adverse outcomes (death, rehospitalization, dialysis).ResultsAKI by any definition occurred in 38.3% (244/637) of cases and was associated with an increased incidence of 30-day (32.3 vs. 6.9%, ?(2) = 70.1; p < 0.001) and 1-year adverse outcomes (67.5 vs. 31.0%, ?(2) = 81.4; p < 0.001). Most importantly, there was a stepwise increase in primary outcome with increasing stages of AKI severity using RIFLE, KDIGO, or AKIN (p < 0.001). In direct comparison, there were only small differences in predictive abilities between RIFLE and KDIGO and WRF concerning clinical outcomes at 30 days (AUC 0.76 and 0.74 vs. 0.72, ?(2) = 5.6; p = 0.02) as well as for KDIGO and WRF at 1 year (AUC 0.67 vs. 0.65, ?(2) = 4.8; p = 0.03).ConclusionDuring admission for HF, the benefits of using newer AKI classification systems (RIFLE, AKIN, KDIGO) lie with the ability to identify those patients with more severe degrees of AKI who will go on to experience adverse events at 30 days and 1 year. The differences in terms of predictive abilities were only marginal.
| S-EPMC3678145 | biostudies-literature