# Clinical impact of congestion in patients admitted for cardiogenic shock

Miloud Cherbi, François Roubille, Eric Bonnefoy, Paul Gautier, Etienne Puymirat, Guillaume Baudry, Bruno Levy, Pascal Lim, Laurent Bonello, Hamid Merdji, et al.

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## To cite this version:

Miloud Cherbi, François Roubille, Eric Bonnefoy, Paul Gautier, Etienne Puymirat, et al. Clinical impact of congestion in patients admitted for cardiogenic shock. Archives of cardiovascular diseases, In press, 10.1016/j.acvd.2025.08.006. hal-05268478

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Clinical Research

# Clinical impact of congestion in patients admitted for cardiogenic shock

Miloud Cherbi<sup>a,b,*</sup>, François Roubille<sup>c</sup>, Eric Bonnefoy<sup>d</sup>, Paul Gautier<sup>a,b</sup>, Etienne Puymirat<sup>e</sup>, Guillaume Baudry<sup>f,g</sup>, Bruno Levy<sup>h</sup>, Pascal Lim<sup>i</sup>, Laurent Bonello<sup>j</sup>, Hamid Merdji<sup>k</sup>, Meyer Elbaz<sup>a,b</sup>, Clément Delmas<sup>a,b</sup>

ARTICLE INFO

Article history:

Received 14 May 2025

Received in revised form 5 August 2025

Accepted 11 August 2025

Available online xxx

Keywords:

Cardiogenic shock

Congestion

Prognosis

Death

ABSTRACT

**Background:** Recent guidelines have proposed dichotomizing acute heart failure and cardiogenic shock phenotypes based on signs/symptoms of hypoperfusion/congestion.

**Aim:** We aimed to assess the prognostic significance of congestion and its early evolution during the first 24 hours in a nationwide cardiogenic shock cohort.

**Methods:** FRENSHOCK was a prospective registry including 772 patients with cardiogenic shock from 49 centres. Patients were classified as cold and wet or cold and dry according to congestive signs. Death at 30 days was analysed according to baseline phenotype.

**Results:** Among 593 patients with cardiogenic shock included, 70.7% were male; the median age was 67.0 (58.0–77.0) years, and 521 patients (87.9%) presented with congestion. Congestive patients had higher rates of previous cardiac disease (60.5% vs. 43.1%; *P* < 0.01) and chronic kidney disease (24.2% vs. 12.5%; *P* = 0.04). No differences were found regarding Society for Cardiovascular Angiography and Interventions class distribution and lactate concentrations. Congestion was associated with an increased 30-day all-cause death rate (hazard ratio: 1.99, 95% confidence interval: 1.05–3.78; *P* = 0.04), particularly among patients with persistent congestion beyond 24 hours (hazard ratio: 2.29, 95% confidence interval: 1.20–4.36; *P* = 0.01). Conversely, patients with resolved congestion at 24 hours had similar outcomes to non-congestive patients (hazard ratio: 0.76, confidence interval: 0.31–1.88; *P* = 0.56). The negative impact of congestion was confirmed in multivariable Cox regression analysis.

**Conclusions:** Congestion and its persistence beyond 24 hours of management are frequent in patients with cardiogenic shock, and are significantly associated with an increased 30-day all-cause death rate, which may reflect either a direct harmful effect of congestion or difficulties in achieving decongestion in sicker patients. Further studies are warranted to clarify optimal decongestion strategies in patients with cardiogenic shock.

© 2025 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

1. Abbreviations

aMCS acute mechanical circulatory support

CI confidence interval

CS cardiogenic shock

<footnote><sup>a</sup> Intensive Cardiac Care Unit, CHU de Toulouse, 31059 Toulouse, France</footnote>

<footnote><sup>b</sup> Université Paul-Sabatier – Toulouse III, 31400 Toulouse, France</footnote>

<footnote><sup>c</sup> PhyMedExp, Cardiology Department, CHU de Montpellier, Université de Montpellier, Inserm, CNRS, INI-CRT, 34295 Montpellier, France</footnote>

<footnote><sup>d</sup> Intensive Cardiac Care Unit, Hospice Civils de Lyon, 69437 Lyon, France</footnote>

<footnote><sup>e</sup> Department of Cardiology, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France</footnote>

<footnote><sup>f</sup> Centre d'Investigations Cliniques Plurithématique, Inserm 1433, Institut Lorrain du Cœur et des Vaisseaux, CHRU de Nancy, Université de Lorraine, 54500 Vandœuvre-lès-Nancy, France</footnote>

<footnote><sup>g</sup> Cardiovascular and Renal Clinical Trialists (INI-CRCT), F-CRIN Network, 54500 Vandœuvre-lès-Nancy, France</footnote>

<footnote><sup>h</sup> Réanimation Médicale Brabois, CHRU Nancy, 54511 Vandœuvre-lès-Nancy, France</footnote>

<footnote><sup>i</sup> Department of Cardiology, Hôpitaux Universitaires Henri-Mondor, AP-HP, 94000 Créteil, France</footnote>

<footnote><sup>j</sup> Intensive Care Unit, Hôpital Universitaire Nord, 13015 Marseille, France</footnote>

<footnote><sup>k</sup> Medical Intensive Care Unit, CHU de Strasbourg, 67000 Strasbourg, France</footnote>

<footnote>* Corresponding author at: Intensive Cardiac Care Unit, Rangueil University Hospital, 1, avenue du Professeur-Jean-Poulhes, 31059 Toulouse cedex, France.
E-mail address: cherbi.miloud@gmail.com (M. Cherbi).</footnote>
M. Cherbi, F. Roubille, E. Bonnefoy et al.

Archives of Cardiovascular Disease xxx (xxxx) xxx-xxx

HF heart failure

HR hazard ratio

ICD implantable cardioverter-defibrillator

LVEF left ventricular ejection fraction

NT-proBNP N-terminal prohormone of brain natriuretic peptide

RRT renal replacement therapy

SCAI Society for Cardiovascular Angiography and Interventions

SGLT2i sodium-glucose cotransporter-2 inhibitor

## 2. Background

Cardiogenic shock (CS) is a life-threatening haemodynamic condition caused by primary cardiac dysfunction, resulting in inadequate cardiac output, which can lead to multiorgan failure and death [1]. Despite significant improvements in both pharmacological and device-based therapies, the CS death rate remains unacceptably high, approaching 25-30% at 1 month [2]. One proposed explanation for this ongoing issue is the protean nature of CS, encompassing a wide variety of aetiologies, now increasingly dominated by non-ischaemic causes [2], resulting in significant heterogeneity among patients included in various studies. Furthermore, there are still gaps in the risk stratification of patients with CS, and the evidence base for the available therapies remains generally weak, except for urgent revascularization for CS caused by acute myocardial infarction [3]. To better stratify patients with acute heart failure (HF), international guidelines [1,4,5] have emphasized a better characterization of the initial phenotype, and have proposed a simple classification based on the bedside physical evaluation of clinical symptoms and signs, considering: (1) the presence of hypoperfusion signs ("cold" vs. "warm", according to hypoperfusion or not); and (2) the presence of congestion signs ("wet" vs. "dry", according to the presence or absence of congestion).

Whereas the high frequency and negative impact of congestion have been well established in acute HF, being associated with increased rates of morbidity and death [6], its impact in the context of CS remains a matter of debate [7,8]. Congestion is likely to have a negative influence on outcomes in patients with CS, as it is known to contribute to reduced cardiac output - by causing subendocardial ischaemia, left ventricular remodelling and impaired cardiac venous drainage from coronary veins - and to renal dysfunction, through decreased renal perfusion and increased renal venous congestion, leading to a vicious circle [9]. Similarly, the optimal decongestion strategy in CS, which may involve diuretics, inodilators or renal replacement therapy (RRT), remains under debate [1,4-6].

The aim of this study was to evaluate the prognostic impact of congestion and its early evolution within the first 24 hours in a broad, unselected population of patients with CS, focusing on their association with all-cause death in a nationwide registry.

## 3. Methods

### 3.1. Patient population

As reported previously [2,10,11], FRENSHOCK was a prospective observational multicentre survey, conducted between April and October 2016, including 772 patients with CS in intensive care/intensive cardiac care units in France, recruited from various types of institutions (primary to tertiary centres, universities and non-university, public and private hospitals).

All adult patients with CS were included prospectively in this registry if they met at least one criterion from each of the following three components: (1) low cardiac output (low systolic blood pressure < 90 mmHg and/or the need for maintenance with vasopressors/inodilators and/or a low cardiac index < 2.2 L/min/m²); (2) left and/or right heart filling pressure elevation, defined by

clinical signs, radiology, blood tests, echocardiography or signs of invasive haemodynamic overload; and (3) signs of organ malperfusion, which could be clinical (oliguria, confusion, pale and/or cold extremities, mottled skin) and/or biological (lactate > 2 mmol/L, metabolic acidosis, renal failure, liver insufficiency).

Each patient had up to three identified triggers for CS that were determined by the local investigator, classified as ischaemic (type 1 or type 2 acute myocardial infarction following European guidelines), ventricular and supraventricular arrhythmia, conduction disorder, infectious disease, non-compliance or iatrogenesis. In accordance with international HF guidelines [1], congestion was consistently defined across all centres as the presence of signs or symptoms of left-sided HF (dyspnoea, rales and crepitations) and/or right-sided HF (ascites, hepatojugular reflux, hepatomegaly, lower limb oedema, pleural effusion) observed at admission and/or at 24 hours of care. According to guidelines and expert consensus [1,4,5], congestive patients with CS were classified as "cold and wet", whereas non-congestive patients with CS were classified as "cold and dry". After 24 hours of care, patients with congestive CS were subdivided according to their congestion status, defined as: persistence of congestion signs or symptoms when these signs were present both at baseline and after 24 hours of management; regression of congestion signs or symptoms when they were present at baseline, but resolved after 24 hours; and onset of congestion signs or symptoms when these signs were absent at baseline, but appeared during the first 24 hours of management. Patients classified as congestive had right-sided and/or left-sided signs, and after 24 hours of care, congestion was considered regardless of its sidedness.

### 3.2. Data collection

The data collection protocol has been published previously elsewhere [2,10,11]. Briefly, baseline characteristics, including demographics (age, sex, body mass index), risk factors (hypertension, diabetes, current smoking) and medical history (cardiomyopathy, implantable cardioverter-defibrillator [ICD], peripheral artery disease, chronic kidney disease) were collected. Data on baseline echocardiography (left ventricular ejection fraction [LVEF] and tricuspid annular plane systolic excursion [TAPSE]), the use of medications (inodilators, vasopressors) and organ replacement therapies - such as mechanical ventilation (invasive or non-invasive), acute mechanical circulatory support (aMCS, including intra-aortic balloon pump, extracorporeal membrane oxygenation or Impella® [Abiomed, Danvers, MA, USA]) - as well as RRT, were also collected. Follow-up was conducted at 30 days by the local investigator and at 1 year by dedicated research technicians from the French Society of Cardiology. Unfortunately, the Society for Cardiovascular Angiography and Interventions (SCAI) SHOCK Stage Classification [12] was not yet available at the time of our study, which is why these data could not be collected prospectively. However, by relying on the method previously described by Thayer et al. [13], we were able to retrospectively determine the maximum SCAI stage reached during hospitalization based on the total use of vasopressors, inotropes and aMCS devices. Briefly, stage A represents patients at risk of CS (not applicable to our population); stage B encompasses patients with early symptoms without hypoperfusion and thus no pharmacological or mechanical support; stage C includes patients with hypoperfusion requiring initial intervention with either one drug or one aMCS device; stage D refers to patients whose condition worsens despite initial intervention, necessitating additional drugs or aMCS treatment; and stage E identifies patients who deteriorate further and require maximal support, defined as needing at least two aMCS devices and two drugs during hospitalization.
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Study flow chart. CI: confidence interval; CS: cardiogenic shock.</figcaption></figure>

### 3.3. Endpoints

The primary endpoint was 30-day all-cause death. Secondary endpoints were 1-year all-cause death and the rate of heart transplantation or ventricular assist device implantation.

### 3.4. Ethics

The study was conducted in accordance with the Declaration of Helsinki and French law. Written consent was obtained for all patients. Recorded data and their storage were approved by the French Health Research Data Processing Advisory Committee (No. 15.897) and the French Data Protection Agency (No. DR-2016-109).

### 3.5. Statistical analysis

Continuous variables are reported as medians (interquartile ranges) when appropriate. Categorical variables are described as frequencies and percentages. The Shapiro-Wilk test was used to analyse the normal distributions of continuous variables. Accordingly, comparisons were made using the Mann-Whitney non-parametric test for continuous variables and the $\chi^2$ test or Fisher's exact test for categorical variables. Kaplan-Meier time-to-event curves for 30-day and 1-year all-cause deaths were used to compare death rates across groups. Differences between groups were assessed using the log-rank test, and hazard ratios (HRs) and their confidence intervals (CIs) were computed through Cox regression. A subgroup analysis was conducted within the congestive CS group, comparing patients presenting with left-sided, right-sided or bilateral HF signs/symptoms. Additionally, an exploratory analysis of predictors of all-cause death at 30 days was performed using a multivariable Cox regression model. According to the Prognosis Research Strategy (PROGRESS) guidelines [14], which advocate for a clinically hypothesis-driven approach for a priori selection of model variables, potential predictors were selected based on clinical and pathophysiological relevance, and included congestion phenotypes, baseline characteristics (age, sex, body

mass index, cardiovascular risk factors), co-morbidities (peripheral artery disease, chronic kidney disease, chronic obstructive pulmonary disease, active cancer, stroke), cardiac history (including ICD, previous percutaneous coronary intervention, sinus rhythm), cardiogenic CS triggers, cardiac arrest, LVEF, baseline concentrations of lactate and natriuretic peptides (N-terminal prohormone of brain natriuretic peptide [NT-proBNP]/brain natriuretic peptide), use of dobutamine, norepinephrine and loop diuretics and support with Impella®, intra-aortic balloon pump, ECLS and RRT. The variance inflation factor was used to ensure the absence of multicollinearity among variables. To assess potential selection bias, we compared included and excluded patients on baseline characteristics and adjusted death outcomes.

All tests were two-tailed. The value of $P \le 0.05$ was accepted as statistically significant. Analyses were performed using R software, version 4.3.2 (2023) (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).

## 4. Results

### 4.1. Baseline patient characteristics and in-hospital management

Overall, a total of 593 patients with CS were included, of whom 521 (87.9%) were congestive ("cold and wet") and 72 (12.1%) were not ("cold and dry"), as shown in Fig. 1. In the entire cohort, the median age was 67.0 (58.0–77.0) years, and 419 patients (70.7%) were men. A total of 135 (22.8%) patients presented with chronic kidney disease, 346 (58.3%) had a history of cardiomyopathy and 97 (16.4%) had an implanted ICD (Table 1). Ischaemic CS was the most reported trigger (n = 236, 39.8%), yet it did not constitute the majority. Among the patients, 56 (9.4%) had an initial cardiac arrest. 214 (36.1%) required invasive ventilation, the median LVEF was 25.0% (15.0–35.0%) and the median lactate concentration was 3.0 (2.0–4.0) mmol/L (Tables 2 and 3).

Congestive patients exhibited higher rates of chronic kidney disease (24.2% vs. 12.5%; $P = 0.04$) and history of cardiac disease (60.5% vs. 43.1%; $P < 0.01$), with a notable prevalence of dilated cardiomy-
<table><caption>**Table 1**
Baseline characteristics and medical history by congestion status.</caption><thead><tr><th></th><th>Overall population (n=593)</th><th>Congestive (n=521)</th><th>Non-congestive (n=72)</th><th>P</th></tr></thead><tbody><tr><td>Age (years)</td><td>67.0 (58.0–77.0)</td><td>67.0 (59.0–77.0)</td><td>67.5 (57.5–76.0)</td><td>0.76</td></tr><tr><td>Male sex</td><td>419 (70.7)</td><td>372 (71.4)</td><td>47 (65.3)</td><td>0.35</td></tr><tr><td>Body mass index (kg/m²)</td><td>24.7 (22.4–28.2) (n=587)</td><td>24.7 (22.3–28.3) (n=518)</td><td>24.7 (22.6–26.6) (n=69)</td><td>0.94</td></tr><tr><td>Risk factors</td><td></td><td></td><td></td><td></td></tr><tr><td>Diabetes mellitus</td><td>166 (28.0) (n=592)</td><td>145 (27.9) (n=520)</td><td>21 (29.2)</td><td>0.93</td></tr><tr><td>Hypertension</td><td>289 (48.7)</td><td>253 (48.6)</td><td>36 (50.0)</td><td>0.92</td></tr><tr><td>Dyslipidaemia</td><td>217 (36.6)</td><td>188 (36.1)</td><td>29 (40.3)</td><td>0.57</td></tr><tr><td>Current smoker</td><td>157 (27.5) (n=570)</td><td>137 (27.1) (n=506)</td><td>20 (31.3) (n=64)</td><td>0.58</td></tr><tr><td>Medical history</td><td></td><td></td><td></td><td></td></tr><tr><td>Peripheral artery disease</td><td>75 (12.6)</td><td>67 (12.9)</td><td>8 (11.1)</td><td>0.82</td></tr><tr><td>Chronic kidney disease</td><td>135 (22.8)</td><td>126 (24.2)</td><td>9 (12.5)</td><td>0.04</td></tr><tr><td>COPD</td><td>39 (6.6)</td><td>37 (7.1)</td><td>2 (2.8)</td><td>0.21</td></tr><tr><td>ICD</td><td>97 (16.4)</td><td>94 (18.0)</td><td>3 (4.2)</td><td>&lt;0.01</td></tr><tr><td>Active cancer</td><td>42 (7.1)</td><td>39 (7.5)</td><td>3 (4.2)</td><td>0.43</td></tr><tr><td>Stroke</td><td>48 (8.1)</td><td>47 (9.0)</td><td>1 (1.4)</td><td>0.046</td></tr><tr><td>Previous PCI</td><td>136 (22.9)</td><td>119 (22.8)</td><td>17 (23.6)</td><td>1.00</td></tr><tr><td>History of cardiac disease</td><td></td><td></td><td></td><td></td></tr><tr><td>All causes</td><td>346 (58.3)</td><td>315 (60.5)</td><td>31 (43.1)</td><td>&lt;0.01</td></tr><tr><td>Ischaemic</td><td>187 (31.5)</td><td>167 (32.1)</td><td>20 (27.8)</td><td>0.55</td></tr><tr><td>Hypertrophic</td><td>10 (1.7)</td><td>10 (1.9)</td><td>0 (0.0)</td><td>0.62</td></tr><tr><td>Toxic</td><td>24 (4.0)</td><td>22 (4.2)</td><td>2 (2.8)</td><td>0.76</td></tr><tr><td>Dilated</td><td>61 (10.3)</td><td>59 (11.3)</td><td>2 (2.8)</td><td>0.04</td></tr><tr><td>Valvular</td><td>56 (9.4)</td><td>51 (9.8)</td><td>5 (6.9)</td><td>0.58</td></tr><tr><td>Hypertensive</td><td>20 (3.4)</td><td>17 (3.3)</td><td>3 (4.2)</td><td>0.72</td></tr><tr><td>Previous medications</td><td></td><td></td><td></td><td></td></tr><tr><td>ACE inhibitor</td><td>225 (37.9)</td><td>205 (39.3)</td><td>20 (27.8)</td><td>0.08</td></tr><tr><td>Sacubitril/valsartan</td><td>15 (2.6) (n=567)</td><td>14 (2.8) (n=499)</td><td>1 (1.5) (n=68)</td><td>1.00</td></tr><tr><td>Beta-blocker</td><td>253 (42.7)</td><td>223 (42.8)</td><td>30 (41.7)</td><td>0.96</td></tr><tr><td>Loop diuretic</td><td>306 (51.6)</td><td>286 (54.9)</td><td>20 (27.8)</td><td>&lt;0.01</td></tr><tr><td>Aldosterone antagonist</td><td>94 (15.9)</td><td>88 (16.9)</td><td>6 (8.3)</td><td>0.09</td></tr><tr><td>SCAI stage</td><td></td><td></td><td></td><td>0.29</td></tr><tr><td>B</td><td>51 (8.6)</td><td>41 (7.9)</td><td>10 (13.9)</td><td></td></tr><tr><td>C</td><td>222 (37.4)</td><td>198 (38.0)</td><td>24 (33.3)</td><td></td></tr><tr><td>D</td><td>302 (50.9)</td><td>267 (51.2)</td><td>35 (48.6)</td><td></td></tr><tr><td>E</td><td>18 (3.0)</td><td>15 (2.9)</td><td>3 (4.2)</td><td></td></tr><tr><td>CS triggers</td><td></td><td></td><td></td><td></td></tr><tr><td>Ischaemic</td><td>236 (39.8)</td><td>202 (38.8)</td><td>34 (47.2)</td><td>0.21</td></tr><tr><td>Supraventricular tachycardia</td><td>83 (14.0)</td><td>74 (14.2)</td><td>9 (12.5)</td><td>0.83</td></tr><tr><td>Infectious disease</td><td>71 (12.0)</td><td>69 (13.2)</td><td>2 (2.8)</td><td>0.02</td></tr><tr><td>Ventricular arrhythmia</td><td>69 (11.6)</td><td>57 (10.9)</td><td>12 (16.7)</td><td>0.22</td></tr><tr><td>Iatrogenesis</td><td>39 (6.6)</td><td>34 (6.5)</td><td>5 (6.9)</td><td>0.80</td></tr><tr><td>Non-observance</td><td>23 (3.9)</td><td>22 (4.2)</td><td>1 (1.4)</td><td>0.34</td></tr><tr><td>Mechanical complications</td><td>18 (3.0)</td><td>16 (3.1)</td><td>2 (2.8)</td><td>1.00</td></tr><tr><td>Conduction disorder</td><td>14 (2.4)</td><td>10 (1.9)</td><td>4 (5.6)</td><td>0.08</td></tr></tbody></table>

Data are expressed as median (interquartile range) or number (%). ACE: angiotensin-converting enzyme; COPD: chronic obstructive pulmonary disease; CS: cardiogenic shock; ICD: implantable cardioverter-defibrillator; PCI: percutaneous coronary intervention; SCAI: Society for Cardiovascular Angiography and Interventions.

opathy (11.3% vs. 2.8% P=0.04); they were also more frequently treated with loop diuretics at baseline (54.9% vs. 27.8%; P<0.01). However, there were no significant differences between congestive and non-congestive CS in terms of SCAI class distribution or CS triggers, except for a higher prevalence of infectious diseases in congestive patients (13.2% vs. 2.8%; P=0.02) (Table 1).

Right heart catheterization or Swan-Ganz catheter measurements were performed in 92 patients (17.7%) from the congestion group and seven patients (9.7%) from the non-congestion group (P=0.13). Additionally, congestive patients exhibited higher median concentrations of creatinine (135.0 vs. 99.0 µmol/L; P<0.01) and NT-proBNP (11,554.0 vs. 3894.0 pg/mL; P=0.01), a lower median prothrombin time (57.0% vs. 73.0%; P<0.01), a lower LVEF (22.0% vs. 30.0% P<0.01) and a smaller TAPSE (12.0 vs. 16.0 mm; P=0.02) (Table 2).

There was no difference between groups regarding the use of inodilators/vasopressors, non-invasive or invasive respiratory support or any type of aMCS. However, congestive patients with CS were more frequently treated with loop diuretics (71.7% vs. 38.9%; P<0.01), with no significant difference in the use of RRT (16.1% vs. 11.1%; P=0.35) (Table 3).

## 4.2. Association between CS phenotypes and all-cause death

After 30 days of follow-up, 143 patients had died, including 133 in the congestive group (25.5%) and 10 in the non-congestive group (13.9%). Congestion was therefore associated with a significant increase in the all-cause death rate (25.5% vs. 13.9%; HR: 1.99, 95% CI: 1.05–3.78; P=0.04) (Fig. 2A). Moreover, patients with persistent congestion after 24 hours of management demonstrated an even greater increase in the 30-day all-cause death rate, with an HR of 2.29 (95% CI: 1.20–4.36; P=0.01) (Fig. 2B). Conversely, patients who exhibited regression of congestion at 24 hours showed no significant difference in the 30-day all-cause death rate compared with non-congestive patients (HR: 0.76, 95% CI: 0.31–1.88; P=0.56). No differences were found between congestive patients with CS presenting with left-sided, right-sided or bilateral HF signs/symptoms (Fig. A.1).

After 1 year of follow-up, 266 patients had died, including 241 in the congestive group (46.3%) and 25 in the non-congestive group (34.7%). Congestion remained associated with a significant increase in the all-cause death rate (46.3% vs. 34.7%; HR: 1.52, 95% CI: 1.01–2.30; P=0.046), with an even stronger association
<table><caption>**Table 2**

Clinical presentation, laboratory findings and echocardiographic variables by congestion status.</caption><thead><tr><th></th><th>Overall population<br>(n=593)</th><th>Congestive<br>(n=521)</th><th>Non-congestive<br>(n=72)</th><th>P</th></tr></thead><tbody><tr><td>Clinical presentation at admission</td><td></td><td></td><td></td><td></td></tr><tr><td>SBP (mmHg)</td><td>99.0 (85.0–115.0)<br>(n=592)</td><td>99.0 (85.0–115.0)<br>(n=520)</td><td>96.0 (80.8–117.8)</td><td>0.46</td></tr><tr><td>DBP (mmHg)</td><td>61.0 (52.0–74.0)<br>(n=592)</td><td>61.0 (52.0–74.0)<br>(n=520)</td><td>61.5 (53.5–72.5)</td><td>0.91</td></tr><tr><td>MBP (mmHg)</td><td>74.0 (63.3–86.0)<br>(n=590)</td><td>75.0 (64.0–86.0)<br>(n=518)</td><td>73.0 (63.0–85.3)</td><td>0.80</td></tr><tr><td>Skin mottling</td><td>197 (37.3) (n=528)</td><td>179 (38.8) (n=461)</td><td>18 (26.9) (n=67)</td><td>0.08</td></tr><tr><td>Cardiac arrest</td><td>56 (9.4)</td><td>41 (7.9)</td><td>15 (20.8)</td><td>&lt;0.01</td></tr><tr><td>Blood tests at admission</td><td></td><td></td><td></td><td></td></tr><tr><td>Sodium (mmol/L)</td><td>135.0 (132.0–139.0)<br>(n=588)</td><td>135.0 (131.0–139.0)<br>(n=518)</td><td>136.5 (134.0–139.0)<br>(n=70)</td><td>0.09</td></tr><tr><td>Potassium (mmol/L)</td><td>4.0 (4.0–5.0) (n=494)</td><td>4.0 (4.0–5.0) (n=442)</td><td>4.0 (3.8–4.3) (n=52)</td><td>0.02</td></tr><tr><td>Creatinine (µmol/L)</td><td>131.5 (94.0–186.3)<br>(n=588)</td><td>135.0 (97.3–194.0)<br>(n=518)</td><td>99.0 (81.8–133.0)<br>(n=70)</td><td>&lt;0.01</td></tr><tr><td>Bilirubin (mg/L)</td><td>16.2 (9.1–29.0)<br>(n=419)</td><td>17.0 (9.3–29.0)<br>(n=379)</td><td>15.5 (8.7–20.1) (n=40)</td><td>0.13</td></tr><tr><td>Arterial blood lactate (mmol/L)</td><td>3.0 (2.0–4.0) (n=531)</td><td>3.0 (2.0–4.0) (n=465)</td><td>3.0 (2.0–4.8) (n=66)</td><td>0.17</td></tr><tr><td>ASAT (IU/L)</td><td>87.0 (39.0–291.0)<br>(n=425)</td><td>84.0 (38.5–290.5)<br>(n=391)</td><td>140.5 (63.5–428.8)<br>(n=34)</td><td>0.14</td></tr><tr><td>ALAT (IU/L)</td><td>58.0 (27.0–181.5)<br>(n=435)</td><td>58.0 (27.0–184.0)<br>(n=401)</td><td>59.0 (26.0–127.5)<br>(n=34)</td><td>0.80</td></tr><tr><td>PT (%)</td><td>60.0 (38.0–77.0)<br>(n=568)</td><td>57.0 (36.0–76.0)<br>(n=499)</td><td>73.0 (58.0–88.0)<br>(n=69)</td><td>&lt;0.01</td></tr><tr><td>NT-proBNP (pg/mL)</td><td>10,900.0<br>(4132.0–28,661.0)<br>(n=171)</td><td>11,554.0<br>(4474.0–30,433.0)<br>(n=158)</td><td>3894.0<br>(552.0–11,396.0)<br>(n=13)</td><td>0.01</td></tr><tr><td>BNP (pg/mL)</td><td>1187.0 (511.0–2778.0)<br>(n=218)</td><td>1334.0 (561.0–2848.0)<br>(n=195)</td><td>769.0 (435.0–1821.0)<br>(n=23)</td><td>0.08</td></tr><tr><td>Baseline echocardiography</td><td></td><td></td><td></td><td></td></tr><tr><td>LVEF (%)</td><td>25.0 (15.0–35.0)<br>(n=588)</td><td>22.0 (15.0–35.0)<br>(n=517)</td><td>30.0 (20.0–42.0)<br>(n=71)</td><td>&lt;0.01</td></tr><tr><td>TAPSE (mm)</td><td>13.0 (10.0–17.0)<br>(n=216)</td><td>12.0 (10.0–16.0)<br>(n=193)</td><td>16.0 (11.0–21.0)<br>(n=23)</td><td>0.02</td></tr><tr><td>Right heart catheterization or<br>Swan-Ganz catheter<br>measurements</td><td>99 (16.7)</td><td>92 (17.7)</td><td>7 (9.7)</td><td>0.13</td></tr></tbody></table>

Data are expressed as median (interquartile range) or number (%). ALAT: alanine aminotransferase; ASAT: aspartate aminotransferase; BNP: brain natriuretic peptide; DBP: diastolic blood pressure; LVEF: left ventricular ejection fraction; MBP: mean blood pressure; NT-proBNP: N-terminal prohormone of brain natriuretic peptide; PT: prothrombin time; SBP: systolic blood pressure; TAPSE: tricuspid annular plane systolic excursion.

<table><caption>**Table 3**

In-hospital treatments and mechanical support by congestion status.</caption><thead><tr><th></th><th>Overall<br>population<br>(n=593)</th><th>Congestive<br>(n=521)</th><th>Non-congestive<br>(n=72)</th><th>P</th></tr></thead><tbody><tr><td>Medications used</td><td></td><td></td><td></td><td></td></tr><tr><td>Dobutamine</td><td>480 (80.9)</td><td>427 (82.0)</td><td>53 (73.6)</td><td>0.13</td></tr><tr><td>Norepinephrine</td><td>303 (51.1)</td><td>270 (51.8)</td><td>33 (45.8)</td><td>0.41</td></tr><tr><td>Epinephrine</td><td>71 (12.0)</td><td>58 (11.1)</td><td>13 (18.1)</td><td>0.13</td></tr><tr><td>Levosimendan</td><td>48 (8.1)</td><td>46 (8.8)</td><td>2 (2.8)</td><td>0.13</td></tr><tr><td>Loop diuretics</td><td>401 (67.7) (n=592)</td><td>373 (71.7) (n=520)</td><td>28 (38.9)</td><td>&lt;0.01</td></tr><tr><td>Respiratory support</td><td></td><td></td><td></td><td></td></tr><tr><td>Non-invasive</td><td>159 (26.8)</td><td>138 (26.5)</td><td>21 (29.2)</td><td>0.73</td></tr><tr><td>Invasive</td><td>214 (36.1)</td><td>182 (34.9)</td><td>32 (44.4)</td><td>0.15</td></tr><tr><td>Acute MCS</td><td></td><td></td><td></td><td></td></tr><tr><td>IABP</td><td>39 (6.6)</td><td>36 (6.9)</td><td>3 (4.2)</td><td>0.61</td></tr><tr><td>Microaxial flow pump</td><td>22 (3.7)</td><td>18 (3.5)</td><td>4 (5.6)</td><td>0.33</td></tr><tr><td>ECLS</td><td>56 (9.4)</td><td>47 (9.0)</td><td>9 (12.5)</td><td>0.47</td></tr><tr><td>Renal replacement therapy</td><td>92 (15.5)</td><td>84 (16.1)</td><td>8 (11.1)</td><td>0.35</td></tr></tbody></table>

Data are expressed as number (%). ECLS: extracorporeal life support; IABP: intra-aortic balloon pump; MCS: mechanical circulatory support.

observed in patients with persistent congestion after 24 hours of management (HR: 1.69, 95% CI: 1.12–2.56; P=0.01) (Fig. 3A and B). Conversely, patients who exhibited regression of congestion at 24 hours showed no increase in the all-cause death rate (HR: 0.99, 95% CI: 0.58–1.68; P=0.96). Eventually, the congestive phenotype was also significantly associated with a higher rate of heart transplantation or ventricular assist device implantation at 1-year follow-up (10.9% vs. 1.4%; P=0.02). After adjustment for baseline

covariates, 30-day and 1-year death rates did not differ significantly between included and excluded patients (Table A.1).

### 4.3. Multivariable analysis

In a multivariable Cox regression model adjusted for baseline characteristics, LVEF, lactate, natriuretic peptides, CS triggers, use of dobutamine, norepinephrine, loop diuretics, aMCS modalities
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** A–B. Kaplan–Meier curves for 30-day all-cause death according to: (A) cardiogenic shock (CS) phenotype; and (B) its evolution at 24 hours (B). HR: hazard ratio.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** A–B. Kaplan–Meier curves for 1-year all-cause death according to: (A) cardiogenic shock (CS) phenotype; and (B) its evolution at 24 hours. HR: hazard ratio.</figcaption></figure>

and RRT, the phenotype with persistent congestion after 24 hours of care emerged as one of the few significant factors associated with 30-day all-cause death, with an HR of 2.28 (95% CI: 1.01–5.21, P = 0.047) (Table 4). There was no multicollinearity among variables included in the multivariable model, with all variance inflation factor values < 5.

## 5. Discussion

In this study, we demonstrate that baseline clinical congestive phenotype provides valuable prognostic insights for patients admitted with CS. Although data regarding the influence of congestion in CS are scarce, we report that: (1) clinical congestion is an extremely common phenotype, affecting nearly 90% of patients in our cohort of unselected patients with CS, encompassing various CS aetiologies; (2) congestion and its persistence after 24 hours of care are associated with a significant increase in the rate of all-cause death at 30 days, even after adjustment for potential confounding factors; (3) normalization of congestion signs at 24 hours brings the cold and wet phenotype to the same prognosis as cold and dry patients; and (4) this death risk associated with congestion is not adequately captured by conventional CS phenotyping indices, such as the SCAI classification.

Despite therapeutic improvements, the CS death rate remains unacceptably high. Congestion appears as a very frequent feature in acute HF, with a reported prevalence reaching up to 80–90% in

<caption>**Table 4**</caption>

Independent variables associated with 30-day death in multivariable analysis.

<table><thead><tr><th></th><th>30-day death</th><th></th></tr><tr><th></th><th>aHR (95% CI)<sup>a</sup></th><th>P</th></tr></thead><tbody><tr><td>CS phenotype</td><td></td><td></td></tr><tr><td>Non-congestive</td><td>Ref.</td><td>—</td></tr><tr><td>Regression of congestion at 24 hours</td><td>0.53 (0.18–1.53)</td><td>0.37</td></tr><tr><td>Onset of congestion at 24 hours</td><td>2.50 (0.74–8.54)</td><td>0.15</td></tr><tr><td>Persistence of congestion at 24 hours</td><td>2.28 (1.01–5.21)</td><td>0.047</td></tr><tr><td>Age (per year)</td><td>1.05 (1.03–1.08)</td><td>&lt;0.01</td></tr><tr><td>LVEF ≤ 30%</td><td>1.89 (1.13–3.14)</td><td>0.01</td></tr><tr><td>Infectious trigger</td><td>2.35 (1.35–4.09)</td><td>&lt;0.01</td></tr><tr><td>Use of norepinephrine</td><td>1.80 (1.15–2.84)</td><td>0.01</td></tr><tr><td>IABP</td><td>2.37 (1.17–4.80)</td><td>0.02</td></tr><tr><td>ECLS</td><td>2.14 (1.12–4.09)</td><td>0.02</td></tr><tr><td>Renal replacement therapy</td><td>1.71 (1.08–2.71)</td><td>0.02</td></tr></tbody></table>

aHR: adjusted hazard ratio; CI: confidence interval; CS: cardiogenic shock; ECLS: extracorporeal life support; IABP: intra-aortic balloon pump; ICD: implantable cardioverter-defibrillator; LVEF: left ventricular ejection fraction.

a Adjusted for baseline characteristics (age, sex, body mass index, cardiovascular risk factors), co-morbidities (peripheral artery disease, chronic kidney disease, chronic obstructive pulmonary disease, active cancer, stroke), cardiac history (history of cardiac disease, implantable cardioverter-defibrillator, previous percutaneous coronary intervention, sinus rhythm), CS triggers, cardiac arrest, LVEF, baseline concentrations of lactate and natriuretic peptides, use of dobutamine, norepinephrine and loop diuretics, support with Impella®, IABP, ECLS or renal replacement therapy.
large-scale international studies [15,16]. Importantly, congestion in the context of CS may differ from that observed in acute decompensated HF, both in terms of its pathophysiological mechanisms and its prognostic implications. In CS, congestion often coexists with systemic hypoperfusion, and may reflect more complex haemodynamic alterations, including elevated filling pressures despite low cardiac output, which likely modify treatment response and outcomes. Here, we demonstrate that the congestive CS phenotype (cold and wet patients), assessed at the bedside through the presence of left and/or right HF signs/symptoms, provides a simple and effective way to identify patients at risk, even after adjustment for concentrations of natriuretic peptides. Hence, it could represent a straightforward and efficient solution to stratify the 30-day risk of death in patients with CS, which is highly accessible in daily practice, including in non-tertiary centres, as it relies solely on bedside clinical assessment. Our data obtained in the context of CS align with those already established in chronic HF or acutely decompensated HF, where congestion has consistently been associated with a significant increase in deaths [17,18]. Several factors can explain this negative impact. First, elevated central venous pressure leads to renal congestion (also referred to as backward failure) [19]. Indeed, there is a strong, graded relationship between an increase in renal venous pressure and a reduction in urine output, with kidney blood flow being more severely affected by an increase in venous pressure than by a similar decrease in arterial pressure. Similarly, the hypoperfusion observed in CS leads to decreased kidney perfusion, hindering the elimination of fluid overload. This creates a vicious cycle through the upregulation of the sympathetic nervous system and the renin-angiotensin-aldosterone system, which in turn induces glomerular arteriolar vasoconstriction and reduces sodium excretion [20].

Consistently, achieving early decongestion brings patients initially presenting with congestion to the same prognosis as non-congestive patients. Therefore, this corroborates that decongestion could be one of the key goals in the management of patients with CS, and should be achieved as efficiently and swiftly as possible. Despite the high prevalence and negative impact of congestion on patient outcomes, there are no clear recommendations for optimal decongestion strategies, leading to wide heterogeneity in clinical practice regarding the use of diuretics, RRT and inodilators/vasopressors [1,4,5]. Until now, data have focused primarily on acute HF without CS. Developing effective treatment options is particularly challenging. For instance, the ADVOR trial [21] demonstrated that add-on therapy with acetazolamide in acute HF improved the likelihood of successful decongestion, but did not significantly impact the risk of death. Similarly, the CLOROTIC trial [22], which evaluated hydrochlorothiazide, showed improved decongestion outcomes, but no formal benefit in terms of death. In contrast, trials investigating sodium-glucose cotransporter-2 inhibitors (SGLT2is) have suggested a reduction in rates of death and rehospitalization [23]. Additionally, randomized studies comparing ultrafiltration with diuretic therapy in acute HF found that ultrafiltration effectively reduced fluid retention and body weight, but did not significantly improve the rates of death or rehospitalization [24]. However, these studies did not specifically address CS. To the best of our knowledge, only a few trials have addressed this issue, whether regarding the use of RRT (NCT06696235) or the optimization of medical treatment, such as the EMPASHOCK trial (NCT05879276), which is designed to assess the efficacy and safety of empagliflozin in patients with CS.

From an organizational standpoint, multiple strategies could be explored to optimize decongestion approaches. One potential option is the use of Swan-Ganz catheter monitoring, especially in the most severe subgroups of patients with hypotension or shock [25]; current indications [26] primarily apply to patients with CS supported by aMCS or those undergoing evaluation for

heart transplantation or ventricular assist device implantation. Unfortunately, very few patients in our study were monitored with a Swan-Ganz catheter, preventing us from drawing definitive conclusions. From another standpoint, given that a significant number of patients with acute HF exhibit an insufficient response to loop diuretics, European guidelines [1] recommend the early and repeated assessment of spot urinary sodium to guide diuretic therapy.

Finally, it is evident that improving the management of congestion also heavily relies on a preventive approach aimed at halting its progression. Indeed, it is well established that congestion often develops gradually over time before acute presentation [27]. Consequently, it can be assumed that its progressive onset could be effectively mitigated through telemonitoring programmes, for instance, by tracking weight fluctuations and adjusting diuretic dosages accordingly [28]. Finally, the fight against congestion is also hindered by therapeutic inertia among physicians. In acute settings, although congestion is a key treatment target, up to 50% of patients fail to lose body weight during hospitalization [29], partly related to the lack of formal recommendations regarding optimal decongestion strategies. In chronic settings, although recent guidelines [1,4,5] unequivocally emphasize the critical need to implement a four-drug regimen as soon as possible (SGLT2is, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor-neprilysin inhibitors and mineralocorticoid receptor antagonists), multiple studies have identified significant delays in their initiation, underscoring widespread therapeutic inertia [30]. Yet, there is no evidence from randomized controlled trials supporting the approach of waiting for complete decongestion before initiating the HF four pillars rather than starting them earlier [30]. For instance, in a subanalysis of the STRONG-HF trial, a comprehensive neurohormonal blockade was associated with a significantly lower dose of diuretics at day 90 [31]. Consistently, SGLT2i use [23] and up titration of angiotensin receptor-neprilysin inhibitors have been seen to be associated with better decongestion and less diuretic use.

## 5.1. Study limitations

The primary limitation of this study is its small sample size, which reduces statistical power, particularly for subgroup analyses. Approximately 23% of patients were excluded because of missing data; however, comparative analyses showed no significant difference in adjusted 30-day or 1-year death rates, suggesting limited selection bias. Multiple comparison adjustments were not applied to baseline descriptive analyses, given their exploratory nature aimed at characterizing phenotypic differences between patient groups, rather than testing prespecified confirmatory hypotheses [32,33]. In line with current recommendations, significant findings should be interpreted as hypothesis generating rather than definitive. An important limitation of our study is the potential for reverse causality. It is possible that patients with more severe CS are less able to achieve decongestion, meaning persistent congestion may be a marker of illness severity rather than a direct cause of increased deaths. This possibility should be considered when interpreting the association between congestion and outcomes. Additionally, we lack detailed data on echocardiographic indices or invasive assessments of congestion. Common decongestion metrics, such as repeated measurements of weight, urine output or natriuresis, were also unavailable. The absence of these quantitative variables may have affected the precision and accuracy of congestion classification, potentially limiting the identification of distinct congestion phenotypes and their prognostic implications. However, using a purely clinical evaluation offers the advantage of being a straightforward method to classify CS phenotypes, and is consistent with international guidelines. Further distinction between left-sided,
right-sided and bilateral congestion during the course of care would be informative, but was not feasible because of the small subgroup sizes, warranting future studies. We did not conduct a formal inter-rater variability analysis for the definition of congestion signs and symptoms. However, this potential bias was minimized by referring to internationally accepted HF guidelines, which were uniformly applied across all participating centres. A congestion score based on the number of clinical signs could not be calculated, as individual signs were not separately recorded in the dataset. Congestion was assessed as a binary variable based on the presence of left- and/or right-sided signs, limiting the granularity of the phenotypic characterization. Furthermore, we did not have sufficiently detailed data regarding the dosage or sequencing of diuretics and RRT to effectively compare their impact on death. Lastly, the management of congestion in daily practice remains a matter of debate

in these patients, leading to considerable heterogeneity between centres.

## 6. Conclusions

Congestion and its persistence beyond 24 hours of management are common in patients with CS, and are significantly associated with an increased 30-day all-cause death rate. Although these findings highlight the potential prognostic importance of congestion, it remains unclear whether persistent congestion directly contributes to worse outcomes or primarily reflects illness severity and challenges in decongestion. Further studies are needed to better understand the underlying mechanisms and to define optimal decongestion strategies in this high-risk population (Central Illustration).

<figure><img src="image_7.png" /><figcaption>**Central Illustration.** Prevalence and prognostic impact of congestion in cardiogenic shock.</figcaption></figure>
## Sources of funding

None.

## Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.acvd.2025.08.006.

## Disclosure of interest

The authors declare that they have no competing interest.

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