Kidney Res Clin Pract > Volume 43(4); 2024 > Article
Thanapongsatorn, Tanomchartchai, and Assavahanrit: Long-term outcomes of acute kidney injury in acute decompensated heart failure: identifying true cardiorenal syndrome and unveiling prognostic significance



Cardiorenal syndrome (CRS) type 1 defined as acute kidney injury (AKI) in acute decompensated heart failure (ADHF), is complicated due to diverse definitions. Recently, a more precise CRS type 1 definition was proposed, mandating concurrent AKI and signs of unimproved heart failure (HF). Our study explores the incidence, predictors, and long-term outcomes of AKI in ADHF under this new definition.


A prospective observation study of ADHF patients categorized into the CRS type 1, pseudo-CRS, and non-AKI groups, followed for 12 months. CRS type 1 involved AKI with clinical congestion, while pseudo-CRS included AKI with clinical decongestion (clinical congestion score <2). The primary outcome was a 1-year composite of mortality or HF rehospitalization.


Among 250 consecutive ADHF patients, 46.0% developed CRS type 1; chronic kidney disease (CKD) and blood urea nitrogen were significant risk factors (odds ratios, 1.37; p = 0.002 and OR, 1.05; p < 0.001, respectively). The CRS type 1 group exhibited shorter times to AKI development and peak serum creatinine than the pseudo-CRS group (1 day vs. 4 days and 2 days vs. 4 days, respectively). At 12 months, composite outcomes of mortality or HF rehospitalization and CKD progression were significantly higher in the CRS type 1 group than in the pseudo-CRS and non-AKI groups (63.5% vs. 31.7% vs. 36.1%, p < 0.001; 28.1% vs. 16.2% vs. 11.4%, p = 0.024, respectively).


Distinguishing between CRS type 1 and pseudo-CRS is vital, highlighting significant disparities in short-term and long-term outcomes. Notably, pseudo-CRS exhibits comparable long-term cardiovascular and renal outcomes to those without AKI.


Cardiorenal syndrome (CRS) is a complex medical condition characterized by the bidirectional exacerbation of acute or chronic heart and kidney dysfunction. The consensus definition, introduced by the Acute Dialysis Quality Initiative (ADQI) group, seeks to standardize the classification of disorders where cardiac and renal diseases coexist [1]. CRS encompasses five distinct types, dependent on the sequence and chronicity of organ dysfunction [2,3]. CRS type 1 occurs when an acute cardiac event, such as acute heart failure (AHF), precipitates acute kidney injury (AKI). CRS type 2 manifests when chronic heart failure contributes to the development of chronic kidney disease (CKD). CRS type 3 emerges when AKI precedes heart dysfunction. CRS type 4 arises when CKD precedes chronic heart dysfunction, and CRS type 5 occurs when systemic conditions concurrently affect both the heart and kidneys, leading to dysfunction.
According to the ADQI definition, AKI in acute decompensated heart failure (ADHF) is defined as CRS type 1. However, the definition of CRS type 1 varies across the studies, resulting in discrepancies in reported incidences, identified risk factors, and observed outcomes [4]. In earlier research, the term “worsening renal failure (WRF)” was commonly utilized to describe this condition, typically characterized by an increase in serum creatinine (sCr) compared to the first day of admission [5,6]. This definition also had high variation across the studies, in terms of both the marker of renal function (sCr, cystatin C, or estimated glomerular filtration rate [eGFR]) and the magnitude of change that is considered significant [7]. Moreover, the conventional WRF definition may not capture patients who develop AKI on admission [8].
Recent studies have sought to enhance precision by integrating established AKI criteria, such as RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease), Acute Kidney Injury Network (AKIN), and Kidney Disease: Improving Global Outcomes (KDIGO) [9,10], providing a more standardized approach. Nevertheless, it is important to recognize that while these criteria are valuable for identifying AKI and characterizing the extent of renal function impairment, they do not inherently propose a pathophysiologically coherent entity. One of the crucial pathophysiological aspects of AKI in AHF is the presence of renal congestion. After fluid removal, the reversal of renal function is often observed [11,12]. Nonetheless, excessive fluid removal can also lead to AKI due to intravascular volume depletion or ischemic AKI. Some studies suggested that as long as decongestion occurs, AKI in AHF can positively impact survival [13,14]. Given these considerations, the European Society of Cardiology (ESC) [15] has proposed a comprehensive definition for CRS type 1, requiring both the concurrent presence of AKI by KDIGO criteria and signs of unimproved heart failure within a 7-day timeframe.
Since no studies adhered to this definition, past information regarding its incidence, risk factors, and outcomes may have been imprecise. We, therefore, conducted a prospective observational study to identify CRS type 1 and assess its long-term prognosis through a combined evaluation of the clinical response and changes in sCr during an AHF episode.


Study design and participants

A prospective observational study enrolled 250 patients admitted to the Central Chest Institute of Thailand (CCIT), a tertiary medical center specializing in cardiology, pulmonology, and cardiac surgery, with a diagnosis of ADHF between March 1 and October 31, 2022. The diagnostic criteria for ADHF followed the 2021 ESC Guidelines for the diagnosis and management of acute and chronic heart failure [16]. ADHF was defined as the rapid or gradual onset of symptoms and/or clinical signs of heart failure necessitating urgent medical attention. Diagnosis required the presence of either an abnormal electrocardiogram or radiological evidence of pulmonary edema on the chest X-ray, along with an N-terminal pro-B-type natriuretic peptide (Nt-proBNP) level equal to or exceeding 2,000 pg/mL.
Patients were excluded from the study if they met specific exclusion criteria, which included a hospital admission duration of less than 48 hours, a diagnosis of acute myocardial infarction, evidence of pulmonary thromboembolism, cardiac tamponade, heart failure following cardiac surgery, multiorgan failure, sepsis or septic shock, or the receipt of contrast-enhancing imaging studies during hospitalization. Additionally, patients with preexisting severe renal dysfunction, including end-stage renal disease (ESRD) requiring dialysis and those with an eGFR of less than 15 mL/min/1.73 m2, were excluded. The study also excluded patients with malignancy and those who had potential contributing causes for AKI that may predispose to the development of CRS type 3 including acute glomerulonephritis, postobstructive uropathy, rhabdomyolysis or acute pyelonephritis, and patients who had received nephrotoxic agents, including herbal supplements and nonsteroidal anti-inflammatory drugs, within the 3 months prior to admission. Patients who had previously participated in the study were also excluded to prevent data duplication and to ensure that each participant contributed unique information.

Study measurement

Data during admission were collected from the electronic medical record (EMR) and paper records. The following variables during admission were collected: demographic data, comorbidities, current medication before admission, clinical parameters, and laboratory parameters. Evaluations of the left ventricular ejection fraction (LVEF) were conducted using transthoracic echocardiography during the index hospitalization or within 6 months after admission. Signs and symptoms of congestion, assessed by a modified clinical congestion score (CCS), were examined daily during the initial 7 days after admission or until discharge, whichever occurred first, by the treating physician. The CCS was calculated by summing the individual scores for orthopnea, jugular venous distension, and leg edema. The scores of <2 indicated mild and ≥3 indicated significant edema [17,18]. SCr levels were also measured daily during the initial 7 days after admission or until discharge, whichever occurred first. AKI was defined and staged in accordance with the KDIGO classification [10]. For the classification of AKI, only sCr was taken into account, since urine output values were difficult to collect. Baseline creatinine levels were determined using the most recent and lowest creatinine value within a range of 7 to 180 days before admission, as documented in the EMR. In instances where baseline creatinine data were unavailable, the lowest sCr level during the admission period was utilized [19]. The eGFR were determined by using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula with sCr. Additionally, data pertaining to in-hospital complications such as mortality, respiratory failure, cardiogenic shock, stroke, ventricular arrhythmia, and renal replacement therapy were collected. Long-term outcomes were scrutinized, encompassing mortality rates, heart failure rehospitalization incidences, incidence of CKD progression (defined as a change in CKD staging based on eGFR criteria), incidence of ESRD, sCr levels at 3 and 12 months, or the nearest values within a 3-month range sourced from EMR or phone calls.
We categorized the patients into three distinct groups: CRS type 1, pseudo-CRS, and no AKI. CRS type 1 was defined by a combination of criteria that included the following: 1) the presence of AKI, as defined by KDIGO criteria, characterized by sCr increase of ≥0.3 mg/dL within 48 hours or 1.5–1.9 times increase from the baseline sCr within 1–7 days during the hospitalization period. Additionally, 2) non-resolving or deteriorating heart failure status, defined as the presence of a CCS of ≥3 or continued radiological evidence of pulmonary congestion (e.g., Kerley B lines, pleural effusion), potentially accompanied by an increase in NT-proBNP levels at two key time points—admission day 1 (admission date) and the day of AKI diagnosis. Lastly, 3) the diagnostic process mandated the exclusion of alternative plausible explanations for AKI, such as volume depletion or exposure to nephrotoxic agents. Pseudo-CRS was defined as 1) the presence of AKI, as defined by KDIGO criteria, and 2) the requirement for confirmation, which involved the presence of signs/symptoms of decongestion (CCS <2) or signs/symptoms of hemoconcentration (defined as an increase in hemoglobin during hospital stay) [20]. The non-AKI group was defined as patients not meeting the criteria for AKI by KDIGO standards. The diagnostic process involved a comprehensive review by both a cardiologist and a nephrologist. Importantly, there were no limitations imposed regarding the treatment of AHF, and the treatment strategy was determined at the discretion of each treating physician.


The primary outcome was a composite outcome encompassing mortality and rehospitalization for heart failure within a 1-year follow-up period. We differentiate between patients with AKI in heart failure with congestion (CRS type 1), patients with AKI in heart failure with decongestion (pseudo-CRS), and patients without AKI (non-AKI).
The secondary outcomes were the incidence and risk factors of CRS type 1, in-hospital complications, and long-term outcomes including mortality, rehospitalization from heart failure, incidence of CKD, incidence of ESRD, and sCr levels at 3 and 12 months. The last follow-up date for survival status was October 30, 2023.

Sample size calculation and statistical analysis

According to a prospective study by Roy et al. [21], the incidence of composite endpoints of heart failure-related readmission, renal replacement therapy, and all-cause mortality at 1 year among ADHF using KDIGO criteria was 67.5% in the AKI group compared to 31.0% in the non-AKI group. To achieve a statistical power of 90% with an alpha level of 0.05 and accounting for a potential dropout rate of 20%, the calculated sample size for each of the CRS and non-AKI groups was approximately 46 patients. Given the lack of specific data for the pseudo-CRS group, we increased the overall sample size to 250 patients to ensure an adequate representation and allow for a meaningful comparison between these groups.
Categorical data were reported in percentage and frequency and analyzed using the chi-square and Fisher exact tests, as appropriate. Continuous data were reported as a mean accompanied by standard deviations or median accompanied by interquartile range (IQR), which represents the 25th to 75th percentiles of the distribution of the data and assessed via analysis of variance or the Kruskal-Wallis test, as appropriate. Risk factors for CRS type 1 were identified through univariate and multivariate logistic regression. All variables with a p-value of <0.05 in the univariate analysis, including hypertension, CKD, receiving furosemide, blood urea nitrogen (BUN), and sCr, were included in the multivariate models.
The primary outcome, a composite of all-cause mortality and rehospitalization from heart failure at 12 months, was analyzed with the Kaplan-Meier survival curves, and the log-rank test was used to calculate the statistical significance of the differences. For the association between variables and the composite of mortality and rehospitalization from heart failure at 12 months, multivariate Cox proportional hazard models were used to evaluate the estimated hazard ratios (HRs) and 95% confidence intervals (CIs). All variables that presented a p-value of <0.05 in the univariate analysis were included in the multivariate mode. Statistical significance was set at a p-value < 0.05, and all analyses were performed using STATA version 17.0 (StataCorp LLC).

Ethics approval and consent to participate

The study was conducted in accordance with Good Clinical Practice guidelines and the principles of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the CCIT (COA No. 031/2565). Written informed consent was obtained from all patients or their representatives prior to their participation in the study.


Patient characteristics

Between March 1 and October 31, 2022, a total of 250 consecutive patients meeting the study criteria were enrolled. Table 1 illustrates the baseline characteristics. The patient cohort consisted of 56.4% males, with a median age of 68.1 ± 15.9 years. A significant past medical history revealed that 75% had a history of hypertension, while a history of diabetes mellitus, dyslipidemia, prior myocardial infarction, atrial fibrillation, and previous heart failure hospitalization was also prevalent. The mean LVEF was 44.4% ± 19.7%, of which 47.6% had LVEF <40%. The current utilization rates of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers (ARB), ARB/neprilysin inhibitor, beta-blockers, spironolactone, and sodium-glucose cotransporter-2 inhibitor before admission were 35.6%, 4.8%, 57.2%, 26.8%, and 7.2%, respectively. The mean sCr at the baseline and at the admission were 1.18 ± 0.48 mg/dL and 1.46 ± 1.64 mg/dL, respectively. There was a significant difference in the median intravenous furosemide dosage administered during the first 72 hours of hospitalization across the groups. Specifically, the CRS group received a median dosage of 220 mg (IQR, 120–400 mg), the pseudo-CRS group received 220 mg (IQR, 120–320 mg), and the non-AKI group received 120 mg (IQR, 120–200 mg), respectively (p < 0.001).

Incidence, differentiation, and associated clinical features of cardiorenal syndrome type 1

In this cohort of 250 patients, AKI occurred in 156 patients (62.4%) during AHF. Among these, 115 patients (46.0%) were classified as CRS type 1, while 41 patients (16.5%) were categorized as pseudo-CRS. The CRS type 1 group exhibited significantly higher rates of underlying CKD, prior use of furosemide in current medication, BUN levels at admission, sCr levels at admission, and lower systolic blood pressure and bicarbonate levels compared to the pseudo-CRS and non-AKI groups. Notably, NT-proBNP levels did not differ between the groups (Table 1).
During admission, we observed a significant difference in sCr levels among the CRS type 1, pseudo-CRS, and non-AKI groups. Specifically, patients with CRS type 1 had significantly higher admission sCr, and maximum sCr compared to those in the pseudo-CRS and non-AKI groups. However, sCr at discharge did not differ between the groups. The median time from admission to the development of AKI in the CRS group was 1 day compared to 4 days in the pseudo-CRS group. Similarly, the median time from admission to the peak sCr level was 2 days compared to 4 days. Patients with CRS type 1 also exhibited a higher rate of AKI severity, with 14.8% in stage 3, 14.8% in stage 2, and 70.3% in stage 1. This is in contrast to the pseudo-CRS group, where there was a noticeable absence of AKIN stage 3, predominantly presenting with stage 1 (92.7%) and a smaller proportion in stage 2 (7.3%). Notably, no patients in the pseudo-CRS group required renal replacement therapy (Table 2).
In the univariate analysis, independent predictors of CRS type 1 at admission included comorbid hypertension (OR, 2.07; 95% CI, 1.03–3.43; p = 0.035), CKD (OR, 1.61; 95% CI, 1.35–1.92; p < 0.001), prior furosemide use in current medication (OR, 1.82; 95% CI, 1.10–3.02; p = 0.02), BUN levels (OR, 1.06; 95% CI, 1.03–1.08; p < 0.001), and sCr levels at admission (OR, 1.62; 95% CI, 1.10–2.40; p = 0.003). However, in the multivariate model, only CKD and BUN levels remained independently associated with CRS type 1 (OR, 1.37; 95% CI, 1.13–1.68; p = 0.002 and OR, 1.05; 95% CI, 1.02–1.07; p < 0.001) (Table 3).

In-hospital outcomes

In-hospital mortality rates were notably higher in patients with CRS type 1 at 10.4%, compared to 4.8% in pseudo-CRS and 4.3% in non-AKI. Patients with CRS type 1 also exhibited significantly elevated rates of respiratory failure (22.6% vs. 17.1% vs. 8.5%, p = 0.02), cardiogenic shock (24.4% vs. 17.1% vs. 7.5%, p = 0.005), and renal replacement therapy (8.7% vs. 2.4% vs. 0%, p = 0.008), respectively (Table 2).

Long-term outcomes

The composite outcomes of mortality and rehospitalization from heart failure at 1 year were significantly higher in the CRS group compared with the pseudo-CRS and non-AKI groups, at 63.5%, 31.7%, and 36.1%, respectively (p < 0.001). When considering individual endpoints, the CRS type 1 group demonstrated a markedly elevated 1-year mortality rate of 25.2%, in contrast to the pseudo-CRS (9.8%) and non-AKI groups (12.8%), indicating a statistically significant difference (p = 0.02). Additionally, rehospitalization from heart failure was more prevalent in the CRS type 1 group (43.5%) compared to the pseudo-CRS (24.4%) and non-AKI groups (25.8%) (p = 0.01) (Table 4). Kaplan-Meier curves were used to evaluate estimates of 1-year mortality and rehospitalization from heart failure according to AKI and CRS status. The significance was notably lower in the CRS type 1 group, with p < 0.001 (Fig. 1).
The multivariate Cox proportional hazards analysis for the composite outcomes of mortality and rehospitalization from heart failure at 12 months also showed that CRS type 1 was found to be independently associated with the primary outcomes (adjusted HR, 1.70; 95% CI, 1.07–2.70; p = 0.04). Additionally, other variables independently associated with the outcomes included serum sodium levels at admission (adjusted HR, 0.96; 95% CI, 0.93–0.99; p = 0.02) and respiratory failure during admission (adjusted HR, 1.69; 95% CI, 1.05–2.71; p = 0.03) (Table 5).
At 12 months after an ADHF episode, patients with a history of CRS type 1 exhibited a significantly higher rate of CKD progression compared to those with pseudo-CRS and non-AKI, at 28.1%, 16.2%, and 25.8%, respectively (p = 0.02). Notably, four patients developed ESRD at 12 months, with three in the CRS group and one in the pseudo-CRS group. SCr at 3 and 12 months did not differ significantly between the groups, with only seven patients lacking sCr measurements at 12 months (Table 5).


Our study, a prospective evaluation of CRS type 1 among well-characterized ADHF patients, aims to provide a comprehensive understanding of its incidence and long-term implications. We revealed key findings: 1) A high prevalence of AKI in AHF cases, at approximately 62.4%; 2) The distinct association of CRS type 1 with increased occurrences of severe complications such as respiratory failure, cardiogenic shock, and prolonged hospital stays; 3) Identifying AKI in AHF between CRS type 1 and pseudo-CRS is very important due to substantial differences in prognosis and long-term outcomes; 4) The sCr pattern during AHF admission significantly differentiates CRS type 1 from pseudo-CRS, showing higher AKI staging, a shorter timeline from admission to AKI development, and peak sCr levels, indicating alignment with CRS type 1.
The reported incidence of CRS type 1 has varied widely due to differing definitions and the nature of various studies. Previously, the term “worsening renal function or WRF” was common in AHF publications. However, in recent years, there has been a shift toward utilizing KDIGO criteria for accurate diagnosis and the assessment of AKI’s severity and progression. Our study identified an incidence of AKI in AHF at 62.4%, which aligns with recent research, such as the systematic review and meta-analysis by Vandenberghe et al. [22]. Their study analysis of 64 studies involved 509,766 patients, showing an AKI incidence in AHF using KDIGO criteria at 47.4% (ranging from 39.1% to 63.3%). Their findings also highlighted a higher 28-day mortality rate and prolonged hospital stay associated with AKI in AHF.
While it’s crucial to note that not all AKI or acute decline in GFR in AHF are universally negative. A post-hoc analysis of the EVEREST trial revealed that acute declines in eGFR were not necessarily linked to a higher risk of adverse outcomes, provided there was clear evidence of decongestion. This was evidenced by changes in biomarkers like BNP, NT-proBNP, and weight, as well as hemoconcentration measures such as hematocrit [23]. Similarly, a post-hoc analysis of PROTECT data showed that patients with AKI in AHF experienced adverse outcomes only if they had lingering congestion at the time of renal function assessment [18]. Our findings in the pseudo-CRS group also demonstrated that AKI concurrent with decongestion did not elevate the risk of in-hospital adverse events.
Since the term “pseudo-CRS” is not widely used. However, to the best of our knowledge, there has been no validated terminology to describe the phenomenon of acute declines in eGFR in ADHF occurring concomitantly with decongestion or hemoconcentration, as we now realize that this condition is part of an appropriate response to therapy and not necessarily linked to a higher risk of adverse outcomes.
Martin et al. [24] introduced the term “pseudo-worsening renal function or pseudo-WRF” for AHDF patients with WRF with hemoconcentration, which had a better prognosis than true-WRF and similar prognosis of patients without WRF. Another study by Griffin et al. [20] also defined this phenomenon as “hemoconcentration and worsening creatinine.” Recently, the ESC [15] suggested distinguishing between true AKI in ADHF caused by venous congestion (which is termed as ‘cardiorenal syndrome’ or ‘CRS type 1’), and “pseudo-AKI” caused by decongestion (when only a functional decrease in eGFR is observed). Therefore, our definition of “pseudo-CRS” aligns with this concept and could be less confusing than the term “pseudo-WRF,” which is not uniform and has variation in terminology itself.
Distinguishing between CRS type 1 and pseudo-CRS during patient admission remains a considerable challenge. The presence of AKI in AHF creates a clinical conundrum, often leading physicians to consider halting decongestion early for fear of compromising renal function, inadvertently causing ineffective decongestion and potential adverse outcomes. Our study demonstrated an easy and simple method for identifying CRS type 1 from pseudo-CRS by assessing clinical response and sCr levels. We found that the severity of AKI and the creatinine profile could effectively demarcate these two conditions. Specifically, CRS type 1 frequently displayed AKI upon admission, reaching its peak sCr levels on day 2, while the pseudo-CRS group showed signs of AKI and peak sCr levels on day 4. Moreover, CRS type 1 typically demonstrated more severe AKI than the pseudo-CRS, with the latter rarely exhibiting AKI at AKIN stage 3. This can be understood as the mechanism behind pseudo-CRS arising from aggressive diuresis and excessive fluid removal, resulting in a delayed onset of AKI (Table 6).
Multivariable logistic regression analyses revealed that the presence of CKD and higher BUN levels at admission were independent predictors for CRS type 1. Consistent with many studies, CKD has been consistently associated with a higher likelihood of AKI, particularly within the CRS type 1 context. Notably, Hu et al. [25] emphasized CKD as a significant predictor of WRF. Serum BUN, a well-known biomarker for AKI diagnosis, is influenced by nonrenal factors independent of kidney function, reflecting various aspects of the catabolic state, production, and renal tubular handling [26]. Therefore, a higher BUN level at admission may indicate a patient’s elevated catabolic state and serve as a predictive factor for CRS type 1. These results highlight the importance of not only monitoring creatinine levels but also paying attention to simple laboratory parameters such as serum BUN in patients with AHF.
Despite numerous pieces of evidence indicating that AKI with decongestion leads to better in-hospital prognosis, some clinicians remain concerned about the long-term outcomes of AKI resulting from excessive dehydration due to aggressive diuresis. Our findings illustrate that as long as decongestion occurs, patients have a lower rate of long-term mortality and rehospitalization due to heart failure. Interestingly, the rate of CKD progression was significantly higher in the CRS group, but not in the pseudo-CRS and non-AKI groups. A recent post-hoc analysis from the EVEREST study further validates our findings, emphasizing that decongestion in patients with AHF does not increase the risk of adverse kidney outcomes in patients with heart failure [27].
The observed favorable outcomes in both the pseudo-CRS group and the non-AKI group can be attributed to the critical role of decongestion. Numerous studies have highlighted that renal venous congestion, rather than a decrease in cardiac output, constitutes a major pathophysiological factor in acute CRS and is associated with worsened outcomes during AHF [5,28]. Therefore, the maintenance of decongestion emerges as a crucial aspect for optimizing cardiorenal health among heart failure patients, contributing to the amelioration of venous congestion and cardiac refilling pressure. The reduction of renal congestion not only leads to a decrease in intra-renal venous pressure, mitigating the risk of renal venous congestion-related damage but also positively influences systemic hemodynamics. This impact includes a reduction in the activation of neurohormonal pathways known to be detrimental to renal function [29].
Our study had several strengths. Firstly, we provided valuable insights into the relationship of AKI in AHF patients with differences in prognosis by decongestion, an area with limited existing research. Secondly, the prospective design allows for the collection of detailed and real-time clinical data, contributing to the accuracy and reliability of our findings. Thirdly, the comprehensive assessment of AKI, including its severity and pattern during admission, provides a nuanced understanding of the cardiorenal dynamics in AHF. Moreover, the clear differentiation between CRS type 1 and pseudo-CRS based on clinical and laboratory parameters adds depth to our study, addressing the need for precise classification in this complex syndrome.
We acknowledge several limitations in our study. Firstly, its single-center design and a limited number of enrolled patients might impact the generalizability of our findings. A larger, multicenter sample would enhance the robustness of our conclusions. Secondly, the determination of congestion scores relied on a simple method, potentially benefiting from more sophisticated quantitative congestion assessment approaches. Thirdly, the lack of assessment of treatments at discharge hinders the evaluation of pharmacological effects on prognosis in our study design. Fourthly, our study assesses NT-proBNP levels at the time of admission, and these levels alone may not predict renal outcomes in AHF patients. The ESC guideline recommends measuring NT-proBNP both at admission for disease diagnosis and predischarge for the evaluation of disease prognosis [16,30]. Therefore, reassessing NT-proBNP levels before discharge becomes crucial to provide valuable insights into patient prognosis. Additionally, our study had a lower rate of sodium-glucose cotransporter-2 inhibitor (SGLT2i) use. Given the established benefits of SGLT2i in reducing the risk of mortality and rehospitalization in heart failure, both with reduced LVEF and preserved LVEF [3133], the lower usage in our study may be influenced by financial constraints and access to healthcare limitations, limiting our ability to observe the potential benefits of SGLT2i in this study.
In conclusion, AKI during ADHF was prevalent. Differentiating between CRS type 1 and pseudo-CRS is crucial due to marked disparities in both short-term and long-term outcomes. Notably, pseudo-CRS demonstrates comparable long-term cardiovascular and renal outcomes to those without AKI, offering valuable insights for clinicians managing AKI in ADHF with aggressive fluid removal.


Conflicts of interest

All authors have no conflicts of interest to declare.

Data sharing statement

The data presented in this study are available from the corresponding author upon reasonable request.

Authors’ contributions

Conceptualization, Methodology: All authors

Data curation, Formal analysis, Investigation: PT, AT

Writing–original draft: PT, AT

Writing–review & editing: All authors

All authors read and approved the final manuscript.


We thank all the investigators, members of the Nephrology Unit at the Central Chest Institute of Thailand, and all patients for participating in this study.

Figure 1.

The Kaplan-Meier curves.

It shows the rate of a composite of death and rehospitalization from heart failure at 12-month follow-up according to acute kidney injury (AKI) and cardiorenal syndrome (CRS) status.
Table 1.
Baseline characteristics of the study population
Characteristic Total CRS type 1 group Pseudo-CRS group Non-AKI group p-value
No. of patients 250 115 41 94
Age (yr) 68.1 ± 15.9 68.4 ± 15.2 65.2 ± 18.7 69.0 ± 15.3 0.84
Male sex 141 (56.4) 68 (59.1) 20 (48.8) 53 (56.4) 0.52
Body mass index (kg/m2) 24.5 ± 6.1 24.4 ± 5.5 26.1 ± 6.2 24.0 ± 6.7 0.18
 Diabetes mellitus 100 (40.0) 51 (44.4) 20 (48.8) 29 (30.8) 0.06
 Hypertension 189 (75.6) 94 (81.7) 34 (82.9) 61 (64.9) 0.01
 Dyslipidemia 181 (72.4) 86 (74.8) 34 (82.9) 61 (64.9) 0.07
 Coronary artery disease 122 (48.8) 61 (53.0) 18 (78.3) 43 (45.7) 0.46
 Atrial fibrillation 124 (49.6) 58 (50.4) 23 (56.1) 43 (45.7) 0.53
 Chronic kidney disease 93 (37.2) 63 (54.8) 11 (26.8) 18 (19.2) <0.001*
 Cerebrovascular disease 28 (11.2) 15 (13.0) 2 (4.9) 11 (11.7) 0.36
Current medication
 ACEi/ARB 89 (35.6) 42 (36.5) 14 (34.2) 33 (35.1) 0.96
 ARNI 12 (4.8) 5 (4.4) 2 (4.9) 5 (5.3) 0.95
 Beta-blocker 143 (57.2) 68 (59.1) 23 (56.1) 52 (55.3) 0.85
 Spironolactone 67 (26.8) 32 (27.8) 8 (19.5) 27 (28.7) 0.51
 SGLT2i 18 (7.2) 8 (7.0) 5 (15.2) 5 (5.3) 0.36
 Furosemide 134 (53.6) 71 (61.7) 16 (39.0) 47 (50.0) 0.03*
 Antiplatelets 111 (44.4) 56 (48.7) 20 (48.8) 33 (35.1) 0.11
 Anticoagulants 93 (37.2) 44 (38.3) 15 (36.6) 34 (36.2) 0.95
Vital signs and status
 SBP (mmHg) 128.9 ± 27.6 126.7 ± 25.8 138.7 ± 32.8 127.3 ± 26.7 0.045*
 EF (%) 44.4 ± 19.7 43.3 ± 20.7 45.6 ± 17.7 45.2 ± 19.5 0.73
 EF ≤40% 119 (47.6) 62 (53.9) 16 (39.0) 41 (43.6) 0.16
 NYHA IV 72 (28.8) 43 (37.4) 10 (24.4) 19 (20.2) 0.59
 Prior heart failure in 6 mo 120 (48.0) 64 (55.7) 17 (41.5) 39 (41.5) 0.08
Laboratory at admission
 Hemoglobin (g/dL) 11.8 ± 2.7 11.5 ± 2.4 12.2 ± 2.5 12.1 ± 3.2 0.16
 Hematocrit (%) 35.2 ± 7.3 34.3 ± 7.0 37.1 ± 7.1 35.6 ± 7.7 0.09
 BUN (mg/dL) 27.7 ± 17.0 34.4 ± 17.4 19.8 ± 6.9 22.4 ± 15.8 <0.001*
 Creatinine (mg/dL) 1.46 ± 1.64 1.71 ± 0.93 1.08 ± 0.33 1.09 ± 0.56 <0.001*
 eGFR (mL/min/1.73 m2) 58.9 ± 25.8 46.6 ± 24.6 64.9 ± 21.6 71.4 ± 21.9 <0.001*
 Sodium (mEq/L) 137.5 ± 5.2 136.9 ± 5.4 138.2 ± 5.1 138.1 ± 4.8 0.18
 Potassium (mEq/L) 4.0 ± 0.6 4.0 ± 0.6 3.9 ± 0.6 3.9 ± 0.5 0.10
 Bicarbonate (mEq/L) 24.9 ± 4.9 24.2 ± 5.3 26.6 ± 4.5 25.0 ± 4.5 0.02*
 NT-proBNP (pg/nL) 6,253 (3,026–12,706) 5,937 (2,927–11,839) 5,937 (2,456–19,219) 6,571 (3,328–13,579) 0.10
Intravenous diuretic dosing in 72 hr (mg) 160 (120–300) 220 (120–400) 220 (120–320) 120 (120–200) <0.001*

Data are expressed as number only, mean ± standard deviation, number (%), or median (interquartile range).

ACEi, angiotensin-converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin receptor blockers; ARNI, angiotensin receptor blocker/neprilysin inhibitor; BUN, blood urea nitrogen; CRS, cardiorenal syndrome; EF, ejection fraction; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; SBP, systolic blood pressure; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

* p < 0.05, statistically significant.

Table 2.
Association between clinical characteristics at admission and CRS type 1
Characteristic Univariate analysis
Multivariate analysis
Odds ratio (95% CI) p-value Odds ratio (95% CI) p-value
Clinical status
 Age 1.00 (0.99–1.01) 0.80
 Body mass index 0.81 (0.96–1.03) 0.80
 Systolic blood pressure 0.99 (0.99–1.01) 0.25
 EF ≤40% 1.60 (0.97–2.97) 0.07
 Prior heart failure in 6 mo 0.81 (0.48–1.33) 0.42
 Diabetes mellitus 1.40 (0.84–2.32) 0.19
 Hypertension 2.07 (1.03–3.43) 0.04* 1.11 (0.56–2.19) 0.76
 Coronary artery disease 1.24 (0.83–2.26) 0.22
 Chronic kidney disease 1.61 (1.35–1.92) <0.001* 1.37 (1.13–1.68) 0.002*
Current medication
 RAASi 1.09 (0.71–1.66) 0.69
 SGLT2i 0.93 (0.35–2.43) 0.88
 Spironolactone 1.09 (0.62–1.91) 0.76
 Furosemide 1.82 (1.10–3.02) 0.02* 1.23 (0.07–2.17) 0.48
 Statin 1.51 (0.90–2.51) 0.12
Laboratory at admission
 Hemoglobin 0.91 (0.83–1.00) 0.06
 Hematocrit 0.97 (0.93–1.01) 0.06
 Blood urea nitrogen 1.06 (1.03–1.08) <0.001* 1.05 (1.02–1.07) <0.001*
 Creatinine 1.62 (1.10–2.40) 0.003* 0.92 (0.77–1.11) 0.38
 Sodium 0.95 (0.91–1.01) 0.07
 NT-proBNP 0.99 (0.99–1.01) 0.66

CI, confidence interval; CRS, cardiorenal syndrome; EF, ejection fraction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; RAASi, renin-angiotensin-aldosterone system inhibitors; SGLT2i, sodium-glucose cotransporter-2 inhibitor.

* p < 0.05, statistically significant.

Table 3.
Renal function and in-hospital outcomes during admission by AKI and CRS status
Variable CRS type 1 group (n = 115) Pseudo-CRS group (n = 41) Non-AKI group (n = 94) p-value
Creatinine levels during admission (mg/dL)
 Baseline 1.16 ± 0.36 1.12 ± 0.44 1.22 ± 0.61 0.43
 Admission date 1.71 ± 0.93 1.08 ± 0.33 1.09 ± 0.56 <0.001*
 Maximum 2.41 ± 1.69 1.56 ± 0.44 1.22 ± 0.55 <0.001*
 Discharge date 1.35 ± 0.61 1.48 ± 0.84 1.26 ± 0.63 0.21
Time to AKI diagnosis (day) 1 (1–2) 4 (3–5) - 0.001*
Time to peak creatinine (day) 2 (1–3) 4 (4–6) - 0.001*
AKI staging <0.001*
 Stage 1 81 (70.3) 38 (92.7) -
 Stage 2 17 (14.8) 3 (7.3) -
 Stage 3 17 (14.8) 0 (0) -
Renal replacement therapy 10 (8.7) 0 (0) 0 (0) 0.002*
In-hospital mortality 12 (10.4) 2 (4.8) 4 (4.3) 0.19
Respiratory failure 26 (22.6) 7 (17.1) 8 (8.5) 0.02*
Cardiogenic shock 28 (24.4) 7 (17.1) 7 (7.5) 0.005*
Ventricular arrhythmia 10 (8.7) 2 (4.9) 2 (2.1) 0.12
Length of stay (day) 11.0 (7–20) 8.0 (6–13) 6.5 (5–12) 0.001*

Data are expressed as mean ± standard deviation, median (interquartile range), or number (%).

AKI, acute kidney injury; CRS, cardiorenal syndrome.

* p < 0.05, statistically significant.

Table 4.
Long-term outcomes at 1-year by AKI and CRS status
Outcome CRS type 1 group (n = 115) Pseudo-CRS group (n = 41) Non-AKI group (n = 94) p-value
Composite outcomes of mortality and rehospitalization from HF 73 (63.5) 13 (31.7) 34 (36.1) <0.001*
 1-yr mortality 29 (25.2) 4 (9.8) 12 (12.8) 0.02*
 Rehospitalization from HF 50 (43.5) 10 (24.4) 24 (25.8) 0.01*
CKD progression at 1 yr (% among survivors with sCr available)a 23 (28.1) 6 (16.2) 9 (11.4) 0.02*
End-stage renal disease (% among survivors)b 3 (3.5) 1 (2.7) 0 (0) 0.38
 3 mo 1.47 ± 0.98 1.13 ± 0.47 1.06 ± 0.59 0.99
 1 yr 1.51 ± 1.13 1.26 ± 0.60 1.08 ± 0.68 >0.99

Data are expressed as number (%) or mean ± standard deviation.

AKI, acute kidney injury; CKD; chronic kidney disease; CRS, cardiorenal syndrome; HF, heart failure; sCr, serum creatinine.

a Defined as a change in CKD staging based on estimated glomerular filtration rate criteria.

b Among 198 patients with 1-year sCr measurement.

c Available in 202 patients at 3 months and 198 patients at 1 year.

* p < 0.05, statistically significant.

Table 5.
Predictors of composite outcomes of mortality or HF rehospitalization at 12 months by multivariate Cox regression model
Variable Hazard ratio (95% CI) p-value
CRS type 1 1.70 (1.07–2.70) 0.02*
Prior HF in 6 months 1.39 (0.96–2.0) 0.08
Chronic kidney disease 1.28 (0.85–1.93) 0.24
BUN levels at admission 1.00 (0.90–1.01) 0.70
Sodium levels at admission 0.96 (0.93–0.99) 0.02*
NT-proBNP levels at admission 1.00 (0.99–1.00) 0.13
Renal replacement therapy during admission 1.07 (0.46–2.47) 0.87
Respiratory failure during admission 1.69 (1.05–2.71) 0.03*

BUN, blood urea nitrogen; CI, confidence interval; CRS, cardiorenal syndrome; HF, heart failure; NT-proBNP, N-terminal pro-B-type natriuretic peptide.

* p < 0.05, statistically significant.

Table 6.
Differentiation between CRS type 1 and pseudo-CRS
Feature CRS type 1 Pseudo-CRS
Clinical status Non-resolving or deteriorating Improvement
Volume status Congestion Dehydration
Serum creatinine change Large changes Small changes
Time to AKI diagnosis Shorter duration Longer duration
Time to peak creatinine Shorter duration Longer duration
Severity of AKI Higher Lower (rarely AKIN 3)
Diuretic response Not appropriate diuretic efficiency Appropriate diuretic efficiency
Prognosis Poor Favorable

AKI, acute kidney injury; AKIN, Acute Kidney Injury Network; CRS, cardiorenal syndrome.


1. Ronco C, McCullough P, Anker SD, et al. Cardio-renal syndromes: report from the consensus conference of the acute dialysis quality initiative. Eur Heart J 2010;31:703–711.
crossref pmid
2. Ronco C, Haapio M, House AA, Anavekar N, Bellomo R. Cardiorenal syndrome. J Am Coll Cardiol 2008;52:1527–1539.
crossref pmid
3. Ronco C, Bellasi A, Di Lullo L. Cardiorenal syndrome: an overview. Adv Chronic Kidney Dis 2018;25:382–390.
crossref pmid
4. Cruz DN. Cardiorenal syndrome in critical care: the acute cardiorenal and renocardiac syndromes. Adv Chronic Kidney Dis 2013;20:56–66.
crossref pmid
5. Mullens W, Abrahams Z, Francis GS, et al. Importance of venous congestion for worsening of renal function in advanced decompensated heart failure. J Am Coll Cardiol 2009;53:589–596.
crossref pmid pmc
6. Forman DE, Butler J, Wang Y, et al. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004;43:61–67.
crossref pmid
7. Damman K, Voors AA, Navis G, van Veldhuisen DJ, Hillege HL. The cardiorenal syndrome in heart failure. Prog Cardiovasc Dis 2011;54:144–153.
crossref pmid
8. Shirakabe A, Hata N, Kobayashi N, et al. Worsening renal function definition is insufficient for evaluating acute renal failure in acute heart failure. ESC Heart Fail 2018;5:322–331.
crossref pmid pmc pdf
9. Shirakabe A, Hata N, Kobayashi N, et al. Long-term prognostic impact after acute kidney injury in patients with acute heart failure. Int Heart J 2012;53:313–319.
crossref pmid
10. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl 2012;2:1–138.
11. Uthoff H, Breidthardt T, Klima T, et al. Central venous pressure and impaired renal function in patients with acute heart failure. Eur J Heart Fail 2011;13:432–439.
crossref pmid pdf
12. Núñez J, Miñana G, Santas E, Bertomeu-González V. Cardiorenal syndrome in acute heart failure: revisiting paradigms. Rev Esp Cardiol (Engl Ed) 2015;68:426–435.
crossref pmid
13. Testani JM, Chen J, McCauley BD, Kimmel SE, Shannon RP. Potential effects of aggressive decongestion during the treatment of decompensated heart failure on renal function and survival. Circulation 2010;122:265–272.
crossref pmid pmc
14. Greene SJ, Gheorghiade M, Vaduganathan M, et al. Haemoconcentration, renal function, and post-discharge outcomes among patients hospitalized for heart failure with reduced ejection fraction: insights from the EVEREST trial. Eur J Heart Fail 2013;15:1401–1411.
crossref pmid pmc pdf
15. Damman K, Tang WH, Testani JM, McMurray JJ. Terminology and definition of changes renal function in heart failure. Eur Heart J 2014;35:3413–3416.
crossref pmid pmc
16. McDonagh TA, Metra M, Adamo M, et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2021;42:3599–3726.
crossref pmid pdf
17. Ambrosy AP, Pang PS, Khan S, et al. Clinical course and predictive value of congestion during hospitalization in patients admitted for worsening signs and symptoms of heart failure with reduced ejection fraction: findings from the EVEREST trial. Eur Heart J 2013;34:835–843.
crossref pmid
18. Metra M, Cotter G, Senger S, et al. Prognostic significance of creatinine increases during an acute heart failure admission in patients with and without residual congestion: a post hoc analysis of the PROTECT data. Circ Heart Fail 2018;11:e004644.
19. Thomas ME, Blaine C, Dawnay A, et al. The definition of acute kidney injury and its use in practice. Kidney Int 2015;87:62–73.
crossref pmid
20. Griffin M, Rao VS, Fleming J, et al. Effect on survival of concurrent hemoconcentration and increase in creatinine during treatment of acute decompensated heart failure. Am J Cardiol 2019;124:1707–1711.
crossref pmid pmc
21. Roy AK, Mc Gorrian C, Treacy C, et al. A comparison of traditional and novel definitions (RIFLE, AKIN, and KDIGO) of acute kidney injury for the prediction of outcomes in acute decompensated heart failure. Cardiorenal Med 2013;3:26–37.
crossref pmid pmc pdf
22. Vandenberghe W, Gevaert S, Kellum JA, et al. Acute kidney injury in cardiorenal syndrome type 1 patients: a systematic review and meta-analysis. Cardiorenal Med 2016;6:116–128.
crossref pmid pdf
23. McCallum W, Tighiouart H, Testani JM, et al. Acute kidney function declines in the context of decongestion in acute decompensated heart failure. JACC Heart Fail 2020;8:537–547.
crossref pmid pmc
24. Martins JL, Santos L, Faustino A, Viana J, Santos J. Worsening or ‘pseudo-worsening’ renal function? The prognostic value of hemoconcentration in patients admitted with acute heart failure. Rev Port Cardiol (Engl Ed) 2018;37:595–602.
crossref pmid
25. Hu W, He W, Liu W, et al. Risk factors and prognosis of cardiorenal syndrome type 1 in elderly Chinese patients: a retrospective observational cohort study. Kidney Blood Press Res 2016;41:672–679.
crossref pmid pdf
26. Edelstein CL. Biomarkers of acute kidney injury. Adv Chronic Kidney Dis 2008;15:222–234.
crossref pmid pmc
27. McCallum W, Tighiouart H, Testani JM, et al. Rates of reversal of volume overload in hospitalized acute heart failure: association with long-term kidney function. Am J Kidney Dis 2022;80:65–78.
crossref pmid
28. Burnett JC, Knox FG. Renal interstitial pressure and sodium excretion during renal vein constriction. Am J Physiol 1980;238:F279–F282.
crossref pmid
29. Cooper LB, Mentz RJ, Stevens SR, et al. Hemodynamic predictors of heart failure morbidity and mortality: fluid or flow? J Card Fail 2016;22:182–189.
crossref pmid
30. Lam CS, Li YH, Bayes-Genis A, et al. The role of N-terminal pro-B-type natriuretic peptide in prognostic evaluation of heart failure. J Chin Med Assoc 2019;82:447–451.
crossref pmid
31. Butler J, Packer M, Filippatos G, et al. Effect of empagliflozin in patients with heart failure across the spectrum of left ventricular ejection fraction. Eur Heart J 2022;43:416–426.
crossref pmid pdf
32. McMurray JJ, Solomon SD, Inzucchi SE, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med 2019;381:1995–2008.
33. Solomon SD, McMurray JJ, Claggett B, et al. Dapagliflozin in heart failure with mildly reduced or preserved ejection fraction. N Engl J Med 2022;387:1089–1098.
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