Nomogram Model to Predict Acute Kidney Injury in Hospitalized Patients with Heart Failure
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SUBMITTER: Xu R
PROVIDER: S-EPMC11367008 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Project description:BackgroundPatients with congestive heart failure (CHF) are vulnerable to contrast-induced kidney injury (CI-AKI), but few prediction models are currently available. Therefore, we aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography.MethodsA total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48-72 hours following the procedure. Predictors for the simple nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer-Lemeshow test and 1000 bootstrap samples.ResultsThe incidence of CI-AKI was 9.06% (170) in the total sample, 8.64% (n = 109) in the development cohort, and 9.92% (n = 61) in the validation cohort (P=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction, and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs. 0.75, P=0.061), as well as a well-fitted calibration curve.ConclusionsThe present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed.
| S-EPMC8009707 | biostudies-literature
Project description:Background Heart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients. Materials and methods The data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms. Results A total of 2,678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO2), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95. Conclusion Using the ML model could accurately predict the development of AKI in HF patients.
| S-EPMC9512707 | biostudies-literature
Project description:OBJECTIVES:Acute kidney injury (AKI) is a common complication in hospitalized patients with cirrhosis which contributes to morbidity and mortality. Improved prediction of AKI in this population is needed for prevention and early intervention. We developed a model to identify hospitalized patients at risk for AKI. METHODS:Admission data from a prospective cohort of hospitalized patients with cirrhosis without AKI on admission (n = 397) was used for derivation. AKI development in the first week of admission was captured. Independent predictors of AKI on multivariate logistic regression were used to develop the prediction model. External validation was performed on a separate multicenter cohort (n = 308). RESULTS:In the derivation cohort, the mean age was 57 years, the Model for End-Stage Liver Disease score was 17, and 59 patients (15%) developed AKI after a median of 4 days. Admission creatinine (OR: 2.38 per 1 mg/dL increase [95% CI: 1.47-3.85]), international normalized ratio (OR: 1.92 per 1 unit increase [95% CI: 1.92-3.10]), and white blood cell count (OR: 1.09 per 1 × 10/L increase [95% CI: 1.04-1.15]) were independently associated with AKI. These variables were used to develop a prediction model (area underneath the receiver operator curve: 0.77 [95% CI: 0.70-0.83]). In the validation cohort (mean age of 53 years, Model for End-Stage Liver Disease score of 16, and AKI development of 13%), the area underneath the receiver operator curve for the model was 0.70 (95% CI: 0.61-0.78). DISCUSSION:A model consisting of admission creatinine, international normalized ratio, and white blood cell count can identify patients with cirrhosis at risk for in-hospital AKI development. On further validation, our model can be used to apply novel interventions to reduce the incidence of AKI among patients with cirrhosis who are hospitalized.
| S-EPMC6775340 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) and chronic kidney disease (CKD) have become worldwide public health problems, but little information is known about the epidemiology of acute kidney disease (AKD)-a state in between AKI and CKD. We aimed to explore the incidence and outcomes of hospitalized patients with AKD after AKI, and investigate the prognostic value of AKD in predicting 30-day and one-year adverse outcomes.MethodsA total of 2,556 hospitalized AKI patients were identified from three tertiary hospitals in China in 2015 and followed up for one year.AKD and AKD stage were defined according to the consensus report of the Acute Disease Quality Initiative 16 workgroup. Multivariable regression analyses adjusted for confounding variables were used to examine the association of AKD with adverse outcomes.ResultsAKD occurred in 45.4% (1161/2556) of all AKI patients, 14.5% (141/971) of AKI stage 1 patients, 44.6% (308/691) of AKI stage 2 patients and 79.6% (712/894) of AKI stage 3 patients. AKD stage 1 conferred a greater risk of Major Adverse Kidney Events within 30 days (MAKE30) (odds ratio [OR], 2.36; 95% confidence interval 95% CI [1.66-3.36]) than AKD stage 0 but the association only maintained in AKI stage 3 when patients were stratified by AKI stage. However, compared with AKD stage 0, AKD stage 2-3 was associated with higher risks of both MAKE30 and one-year chronic dialysis and mortality independent of the effects of AKI stage with OR being 31.35 (95% CI [23.42-41.98]) and 2.68 (95% CI [2.07-3.48]) respectively. The association between AKD stage and adverse outcomes in 30 days and one year was not significantly changed in critically ill and non-critically ill AKI patients. The results indicated that AKD is common among hospitalized AKI patients. AKD stage 2-3 provides additional information in predicting 30-day and one-year adverse outcomes over AKI stage. Enhanced follow-up of renal function of these patients may be warranted.
| S-EPMC8158174 | biostudies-literature
Project description:Background: Acute kidney injury is an adverse event that carries significant morbidity among patients with acute decompensated heart failure (ADHF). We planned to develop a parsimonious model that is simple enough to use in clinical practice to predict the risk of acute kidney injury (AKI) occurrence. Methods: Six hundred and fifty patients with ADHF were enrolled in this study. Data for each patient were collected from medical records. We took three different approaches of variable selection to derive four multivariable logistic regression model. We selected six candidate predictors that led to a relatively stable outcome in different models to derive the final prediction model. The prediction model was verified through the use of the C-Statistics and calibration curve. Results: Acute kidney injury occurred in 42.8% of the patients. Advanced age, diabetes, previous renal dysfunction, high baseline creatinine, high B-type natriuretic peptide, and hypoalbuminemia were the strongest predictors for AKI. The prediction model showed moderate discrimination C-Statistics: 0.766 (95% CI, 0.729-0.803) and good identical calibration. Conclusion: In this study, we developed a prediction model and nomogram to estimate the risk of AKI among patients with ADHF. It may help clinical physicians detect AKI and manage it promptly.
| S-EPMC8634389 | 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:The significance of minimal increases in serum creatinine below the levels indicative of the acute kidney injury (AKI) stage is not well established. We aimed to investigate the influence of pre-stage AKI (pre-AKI) on clinical outcomes. We enrolled a total of 21,261 patients who were admitted to the Seoul National University Bundang Hospital from January 1, 2013 to December 31, 2013. Pre-AKI was defined as a 25-50% increase in peak serum creatinine levels from baseline levels during the hospital stay. In total, 5.4% of the patients had pre-AKI during admission. The patients with pre-AKI were predominantly female (55.0%) and had a lower body weight and lower baseline levels of serum creatinine (0.63 ± 0.18 mg/dl) than the patients with AKI and the patients without AKI (P < 0.001). The patients with pre-AKI had a higher prevalence of diabetes mellitus (25.1%) and malignancy (32.6%). The adjusted hazard ratio of in-hospital mortality for pre-AKI was 2.112 [95% confidence interval (CI), 1.143 to 3.903]. In addition, patients with pre-AKI had an increased length of stay (7.7 ± 9.7 days in patients without AKI, 11.4 ± 11.4 days in patients with pre-AKI, P < 0.001) and increased medical costs (4,061 ± 4,318 USD in patients without AKI, 4,966 ± 5,099 USD in patients with pre-AKI, P < 0.001) during admission. The adjusted hazard ratio of all-cause mortality for pre-AKI during the follow-up period of 2.0 ± 0.6 years was 1.473 (95% CI, 1.228 to 1.684). Although the adjusted hazard ratio of pre-AKI for overall mortality was not significant among the patients admitted to the surgery department or who underwent surgery, pre-AKI was significantly associated with mortality among the non-surgical patients (adjusted HR 1.542 [95% CI, 1.330 to 1.787]) and the patients admitted to the medical department (adjusted HR 1.384 [95% CI, 1.153 to 1.662]). Pre-AKI is associated with increased mortality, longer hospital stay, and increased medical costs during admission. More attention should be paid to the clinical significance of pre-AKI.
| S-EPMC5132306 | biostudies-literature
Project description:BackgroundGalectin-3, a biomarker of inflammation and fibrosis, can be associated with renal and myocardial damage and dysfunction in patients with acute heart failure (AHF).Methods and resultsWe retrospectively analyzed 790 patients with AHF who were enrolled in the AKINESIS study. During hospitalization, patients with galectin-3 elevation (> 25.9 ng/mL) on admission more commonly had acute kidney injury (assessed by KDIGO criteria), renal tubular damage (peak urine neutrophil gelatinase-associated lipocalin [uNGAL] > 150 ng/dL) and myocardial injury (≥ 20% increase in the peak high-sensitivity cardiac troponin I [hs-cTnI] values compared to admission). They less commonly had ≥ 30% reduction in B-type natriuretic peptide from admission to last measured value. In multivariable linear regression analysis, galectin-3 was negatively associated with estimated glomerular filtration rate and positively associated with uNGAL and hs-cTnI. Higher galectin-3 was associated with renal replacement therapy, inotrope use and mortality during hospitalization. In univariable Cox regression analysis, higher galectin-3 was associated with increased risk for the composite of death or rehospitalization due to HF and death alone at 1 year. After multivariable adjustment, higher galectin-3 levels were associated only with death.ConclusionsIn patients with AHF, higher galectin-3 values were associated with renal dysfunction, renal tubular damage and myocardial injury, and they predicted worse outcomes.
| S-EPMC11427130 | biostudies-literature
Project description:To identify patients who are likely to develop contrast-induced acute kidney injury (CI-AKI) in patients with acute myocardial infarction (AMI), a nomogram was developed in AMI patients. Totally 920 patients with AMI were enrolled in our study. The discrimination and calibration of the model were validated. External validations were also carried out in a cohort of 386 AMI patients. Our results showed in the 920 eligible AMI patients, 114 patients (21.3%) developed CI-AKI in the derivation group (n = 534), while in the validation set (n = 386), 50 patients (13%) developed CI-AKI. CI-AKI model included the following six predictors: hemoglobin, contrast volume >100 ml, hypotension before procedure, eGFR, log BNP, and age. The area under the curve (AUC) was 0.775 (95% confidence interval [CI]: 0.732-0.819) in the derivation group and 0.715 (95% CI: 0.631-0.799) in the validation group. The Hosmer-Lemeshow test has a p value of 0.557, which confirms the model's goodness of fit. The AUC of the Mehran risk score was 0.556 (95% CI: 0.498-0.615) in the derivation group. The validated nomogram provided a useful predictive value for CI-AKI in patients with AMI.
| S-EPMC6021383 | biostudies-literature
Project description:BackgroundThis study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients.MethodsEligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA).ResultsA total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities.ConclusionsThe dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.
| S-EPMC11351080 | biostudies-literature