Impact of acute kidney injury on in-hospital outcomes among patients hospitalized with acute heart failure - A propensity-score matched analysis

Incidence of clinical outcomes in heart failure patients with and without advanced chronic kidney disease

AIMS

Chronic kidney disease (CKD) is a well-established risk factor for heart failure (HF); however, patients with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 have been systematically excluded from clinical trials. This study investigated the incidence of HF and kidney outcomes in HF patients with and without advanced CKD, that is, eGFR < 30.

METHODS

From nationwide registries, HF patients were identified from 2014 to 2018 and categorized into three groups according to baseline eGFR (eGFR ≥ 60, 60 > eGFR ≥ 30 and eGFR < 30). The incidence of primary outcomes (all-cause mortality, HF hospitalization, end-stage kidney disease and sustained 50% eGFR decline) was estimated using cumulative incidence functions.

RESULTS

Of the 21 959 HF patients included, the median age was 73.9 years, and 30% of patients had an eGFR between 30 and 60 and 7% had an eGFR < 30. The 4 year incidence of all-cause mortality was highest for patients with eGFR < 30 (28.3% for patients with eGFR ≥ 60, 51.6% for patients with 60 > eGFR ≥ 30 and 72.2% for patients with eGFR < 30). The 4 year incidence of HF hospitalization was comparable between the groups (25.8%, 29.8% and 26.1% for patients with eGFR ≥ 60, 60 > eGFR ≥ 30 and eGFR < 30, respectively). For patients with eGFR < 30, kidney outcomes were four times more often the first event than patients with eGFR > 30 (4 year incidence of kidney outcome as the first event was 5.0% for eGFR ≥ 60, 4.8% for 60 > eGFR ≥ 30 and 20.1% for eGFR < 30).

CONCLUSIONS

Patients with advanced CKD had a higher incidence of mortality and poorer kidney outcomes than those without advanced CKD, but a similar incidence of HF hospitalizations.

Incidence, mortality, and predictors of acute kidney injury in patients with heart failure: a systematic review

Acute kidney injury (AKI) is common in patients with heart failure (HF), but studies have been inconsistent about the incidence of AKI in patients with HF. We conducted a meta-analysis to examine the incidence of AKI and its impact on mortality in patients with HF. We also looked at inpatient variables that could predict the development of AKI to identify potential risk factors, so that these can be used as a starting point for intervention and prevention in this group. The Embase, Medline, PubMed, Cochrane libraries, and Web of Science databases were used for searching articles from the inception of the database to October 2022. The EndNote software was used for screening. Meta-analysis was performed using Stata 16.0 software to combine effect sizes. A total of 37 studies were included. Of all the 3 533 583 patients with HF, 774 887 had AKI, with a pooled incidence of 33% [95% confidence interval (CI): 32-35%]. The incidence rate of AKI in acute HF and chronic HF was 36% (95% CI: 31-40%) and 30% (95% CI: 24-35%), respectively. Eleven studies found that AKI patients had higher in-hospital mortality than non-AKI patients [risk ratio (RR): 3.65; 95% CI: 3.04-4.39, P < 0.001]. Mortality was assessed in five studies, and it was found that mortality remained high at 1-year follow-up after onset of AKI (RR: 1.85, 95% CI: 1.54-2.22, P < 0.001). Fifteen admission variables were included and analysed in 13 studies. The combined results showed that diabetes, hypertension, history of chronic kidney disease, chronic HF systolic, age, N-terminal pro-B-type natriuretic peptide, creatinine > 1.0 mg/dL, index estimated glomerular filtration rate < 60 mL/min/1.73 m2 , blood urea nitrogen > 24 mg/dL, intravenous dobutamine, and serum albumin were predictor factors for HF patients with AKI (P < 0.05). In this meta-analysis, AKI occurred in approximately 33% of HF patients during hospitalization and the risk of dying in the hospital was tripled. Even during 1-year long-term follow-up, the risk of death remained high, and multiple inpatient variables showed that HF patients tended to have AKI. Early intervention and treatment are important to reduce the incidence of AKI and improve the prognosis.

A Knowledge-based and Data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083608 DOI: 10.1109/embc40787.2023.10340347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.

The Role of Gut-Derived, Protein-Bound Uremic Toxins in the Cardiovascular Complications of Acute Kidney Injury

Acute kidney injury (AKI) is a frequent disease encountered in the hospital, with a higher incidence in intensive care units. Despite progress in renal replacement therapy, AKI is still associated with early and late complications, especially cardiovascular events and mortality. The role of gut-derived protein-bound uremic toxins (PBUTs) in vascular and cardiac dysfunction has been extensively studied during chronic kidney disease (CKD), in particular, that of indoxyl sulfate (IS), para-cresyl sulfate (PCS), and indole-3-acetic acid (IAA), resulting in both experimental and clinical evidence. PBUTs, which accumulate when the excretory function of the kidneys is impaired, have a deleterious effect on and cause damage to cardiovascular tissues. However, the link between PBUTs and the cardiovascular complications of AKI and the pathophysiological mechanisms potentially involved are unclear. This review aims to summarize available data concerning the participation of PBUTs in the early and late cardiovascular complications of AKI.

Kidney Function According to Different Equations in Patients Admitted to a Cardiology Unit and Impact on Outcome

Background: This paper aims to evaluate the concordance between the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula and alternative equations and to assess their predictive power for all-cause mortality in unselected patients discharged alive from a cardiology ward. Methods: We retrospectively included patients admitted to our Cardiology Division independently of their diagnosis. The total population was classified according to Kidney Disease: Improving Global Outcomes (KDIGO) categories, as follows: G1 (estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m²); G2 (eGFR 89–60 mL/min/1.73 m²); G3a (eGFR 59–45 mL/min/1.73 m²); G3b (eGFR 44–30 mL/min/1.73 m²); G4 (eGFR 29–15 mL/min/1.73 m²); G5 (eGFR <15 mL/min/1.73 m²). Cockcroft-Gault (CG), CG adjusted for body surface area (CG-BSA), Modification of Diet in Renal Disease (MDRD), Berlin Initiative Study (BIS-1), and Full Age Spectrum (FAS) equations were also assessed. Results: A total of 806 patients were included. Good agreement was found between the CKD-EPI formula and CG-BSA, MDRD, BIS-1, and FAS equations. In subjects younger than 65 years or aged ≥85 years, CKD-EPI and MDRD showed the highest agreement (Cohen’s kappa (K) 0.881 and 0.588, respectively) while CG showed the lowest. After a median follow-up of 407 days, overall mortality was 8.2%. The risk of death was higher in lower eGFR classes (G3b HR4.35; 95%CI 1.05–17.80; G4 HR7.13; 95%CI 1.63–31.23; G5 HR25.91; 95%CI 6.63–101.21). The discriminant capability of death prediction tested with ROC curves showed the best results for BIS-1 and FAS equations. Conclusion: In our cohort, the concordance between CKD-EPI and other equations decreased with age, with the MDRD formula showing the best agreement in both younger and older patients. Overall, mortality rates increased with the renal function decreasing. In patients aged ≥75 years, the best discriminant capability for death prediction was found for BIS-1 and FAS equations.

Development and Validation of a Simple-to-Use Nomogram for Predicting In-Hospital Mortality in Patients With Acute Heart Failure Undergoing Continuous Renal Replacement Therapy

Background: Patients with acute heart failure (AHF) who require continuous renal replacement therapy (CRRT) have a high risk of in-hospital mortality. It is clinically important to screen high-risk patients using a model or scoring system. This study aimed to develop and validate a simple-to-use nomogram consisting of independent prognostic variables for the prediction of in-hospital mortality in patients with AHF undergoing CRRT. Methods: We collected clinical data for 121 patients with a diagnosis of AHF who underwent CRRT in an AHF unit between September 2011 and August 2020 and from 105 patients in the medical information mart for intensive care III (MIMIC-III) database. The nomogram model was created using a visual processing logistic regression model and verified using the standard method. Results: Patient age, days after admission, lactic acid level, blood glucose concentration, and diastolic blood pressure were the significant prognostic factors in the logistic regression analyses and were included in our model (named D-GLAD) as predictors. The resulting model containing the above-mentioned five factors had good discrimination ability in both the training group (C-index, 0.829) and the validation group (C-index, 0.740). The calibration and clinical effectiveness showed the nomogram to be accurate for the prediction of in-hospital mortality in both the training and validation cohort when compared with other models. The in-hospital mortality rates in the low-risk, moderate-risk, and high-risk groups were 14.46, 40.74, and 71.91%, respectively. Conclusion: The nomogram allowed the optimal prediction of in-hospital mortality in adults with AHF undergoing CRRT. Using this simple-to-use model, the in-hospital mortality risk can be determined for an individual patient and could be useful for the early identification of high-risk patients. An online version of the D-GLAD model can be accessed at https://ahfcrrt-d-glad.shinyapps.io/DynNomapp/. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT0751838.