Acute Kidney Disease After Acute Decompensated Heart Failure

Conclusion: AKD after ADHF is associated with adverse outcomes. Our model could help in the identification of patients at risk for AKD development, especially in those who did not have an index AKI episode.

CISTANCHE EXTRACT WITH 30% ECHINCAOSIDE FOR KIDNEY DISEASE

ADHF is a poor prognostic event in patients with congestive heart failure, with a 1-year death rate of >30% and a high readmission rate.1,2 The condition usually occurs alongside AKI. The incidence of AKI among those admitted for ADHF varies from 9.6% to 43%.3–5 AKI, as a common complication of ADHF (i.e., acute cardiorenal syndrome type 1 in ADHF), is associated with higher 1-year death and readmission rates.3,4,6 The poor prognostic effect is more significant in those who developed AKI or worse renal function but without effective decongestion.7 AKD represents a continuum of kidney injury or renal function nonrecovery after initial kidney insult/ stress. In addition, the transition from AKI to AKD, to chronic kidney disease (CKD) reflects a continuum of persistent kidney injury after initial kidney insult.8 The time course for AKD is described as >7 days but within 90 days of initiating AKI. The current AKD definition is based on the consensus of the Acute Disease Quality Initiative 16 Workgroup.8 Therefore, AKD could be considered as a condition that prolonged kidney dysfunction (in the presence or absence of AKI) occurs before a patient meets the 90-day criteria for CKD.8,9 Compared with such research on AKI, studies investigating the incidence and prognostic impact of AKD in patients admitted for ADHF are rare. Some studies evaluating clinical factors related to the development of AKI/CKD or prediction models for patients with ADHF have been published, but few of them have addressed the development of AKD after ADHF.4,10–18

Figure 1. The flowchart for (a) patient selection and (b) the distribution of different AKI and AKD stages. AKD, acute kidney disease; AKI, acute kidney injury; ECMO, extracorporeal membrane oxygenation; ESRD, end-stage renal disease; RRT, renal replacement therapy. 

In this study, we investigated the incidence, clinical factors, and prognostic impact of AKD after ADHF. We also developed a prediction model for AKD after ADHF to facilitate risk stratification and thus promote early AKD identification and intervention.

METHODS 

Data Source

This was a retrospective cohort study using electronic data from the Chang Gung Research Database(CGRD). The Chang Gung Medical Foundation is the largest medical system in Taiwan, comprising 7 hospitals spanning all of Taiwan. The CGRD is a multi-institutional electronic medical record database that provides more detailed clinical information, such as laboratory results and hemodynamic records, than claims databases and has high overall and disease-specific coverage of Taiwan.19,20 The diseases evaluated in this study were identified using the International Classification of Diseases (ICD), Ninth Revision, Clinical Modification diagnostic codes for records before 2015, and ICD, Tenth Revision, Clinical Modification diagnostic codes for those after 2016. The data structure and validation of the CGRD are discussed elsewhere.20–22 This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (institutional review board number: 202000915B0). The need for individual consent was waived because personal identification data are not included in the CGRD. This study was conducted according to the STROBE statement (Supplementary Material).

Study Population 

Patients who were admitted for ADHF (identified by ICD, Ninth Revision, Clinical Modification: 428 and ICD, Tenth Revision, Clinical Modification: I50 in the hospital primary and secondary diagnosis during hospitalization) between January 1, 2008, and December 31, 2018, and who had sufficient data to determine the presence of AKI and AKD were identified in the CGRD. The use of ICD, Ninth Revision, Clinical Modification: 428 and ICD, Tenth Revision, Clinical Modification: I50 for identified ADHF hospitalized population is verified in other studies23,24 and with positive predictive value >90%.25,26 For patients with multiple ADHF admissions during the study period, the first ADHF admission was used as the index admission. Patients were excluded if they were <18 years old, were diagnosed with having end-stage renal disease and already on maintenance dialysis, or were on extracorporeal membrane oxygenation during the index admission. Patients with anticipated cardiac transplantation, who were diagnosed with having sepsis or obstructive uropathy, were exposed to a nephrotoxic agent during admission (including iodine contrast media, a nonsteroidal anti-inflammatory drug, aminoglycosides, or vancomycin), or developed severe AKI requiring dialysis were also excluded (Figure 1a).

AKI and AKD Definitions 

The presence of AKI was determined according to the Kidney Disease: Improving Global Outcomes AKI criteria, which is by comparing a patient's baseline creatinine levels with their highest creatinine level during the first 7 days of their index admission.27 For the baseline creatinine level, we used the lowest creatinine level in the 3 months before the index admission or, if no creatinine level within 3 months of the index admission was available, the first creatinine level in the same index admission. The first AKI episode in the index admission is identified as index AKI. The presence of AKD was determined based on consensus from the Acute Disease Quality Initiative 16 Workgroup.8 AKD is defined by a condition in which persisted AKI is present $7 days after an AKI initiating event. The Acute Disease Quality Initiative workgroup also mentioned that an AKI-initiating event can usually be identified but is not required to diagnose AKD.8,9 For AKD staging, the baseline creatinine level was compared with the creatinine level nearest to 90 days after the index admission; AKD stages 1 and 2 were defined as serum creatinine levels 1.5 to 1.9 and 2.0 to 2.9 times baseline, respectively, whereas stage 3 was defined as a serum creatinine level 3.0 times baseline, serum creatinine increase of $4.0 mg/dl, or being on renal replacement therapy for 8 to 90 days after the index date. If >1 value was obtained during the 8 to 90 days after the index admission, the presence of AKD was determined based on the creatinine level taken most closely to the 90th day after the index admission.

Potential Predictors (Covariates)

Patient's clinical characteristics, AKI susceptibility factors,4,10–17,28–30, and renal nonrecovery factors for congestive heart failure or critical illness populations31–35 were identified according to previous studies or the availability in our data set. The risk factors extracted included age, sex, underlying disease (i.e., diabetes mellitus, hypertension, CKD, liver cirrhosis, and malignancy), heart function assessment by New York Heart Association functional class,36, and left ventricular ejection fraction. Hemodynamic parameters (systolic blood pressure, diastolic blood pressure, and heart rate) at the arrival of the emergency room or at the day of admission, first laboratory results during index admission (including hemoglobin [HB]; blood urea nitrogen [BUN]; serum creatinine, albumin, sodium, potassium, proteinuria, and B-type natriuretic peptide [BNP] levels), and related medication prescriptions (outpatient loop diuretics in the previous 3 months and cumulative loop diuretics dosage during ADHF admission, digoxin, inotropes, or dobutamine use during the index admission) were also extracted.

Outcome Definition 

There were 2 primary outcomes in this study, which are the following: (i) the development of any stage of AKD and (ii) a composite outcome of stage 3 AKD or all-cause death during the eighth and 90th days after the index admission. The secondary outcomes are all-cause death, MAKE, and HFH from the 91st day to the fifth year after the index admission. MAKE was composed of a new diagnosis of end-stage renal disease requiring long-term renal replacement therapy, new-onset CKD (defined by estimated glomerular filtration rate <60 ml/ min per 1.73 m2 according to the Modification of Diet in Renal Disease equation), and all-cause death.

Statistical Analysis 

The baseline characteristics of patients with and without AKI or AKD were compared using the independent sample t-test for continuous variables and the c2 test for categorical variables. Univariate logistic regression analysis was used for the initial screening of the possible association between baseline characteristics and the risks of outcomes. Covariates with a significance of <0.2 in the univariate logistic regression analyses were further introduced into a multivariable model with automatic backward elimination. In the multivariable model, continuous parameters (i.e., systolic blood pressure, diastolic blood pressure, heart rate, HB, BUN, creatinine, potassium, and albumin) were categorized based on previous reports or according to clinical definitions.4,10–17 The models for predicting any-stage AKD, the composite of stage 3 AKD and all-cause death, and all-cause death alone were developed separately. 

The clinical and laboratory-based prediction model derived from the multivariable logistic analysis was further transformed into a simplified point system for ease of clinical use.37 The key idea of the simplified point system is to round off the regression coefficients. The first step was to identify a continuous predictor with a wide range of values as the reference variable (i.e., BNP) and then categorize this variable into several clinically meaningful categories and obtain reference values (usually the middle value) for each category of the variable. Predictors other than the reference variable were also categorized accordingly. Finally, the reference value (usually the middle value) of each category of the predictor was calculated according to the value of its regression coefficient relative to that of the reference variable

point system. The risks of all-cause death, MAKE, and HFH across the ordinal risk subgroups were investigated using the aforementioned survival analyses, in which the ordinal risk subgroup was treated as a continuous covariate. All analyses were conducted using R version 4.0.1 (R Development Core Team).