Next Article in Journal
Mechanical Circulatory Support Devices in Patients with High-Risk Pulmonary Embolism
Previous Article in Journal
Comparison of Two Surgical Approaches for Coronary Artery Bypass of Left Anterior Descending Artery
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 13
Issue 11
10.3390/jcm13113159
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unsupervised Cluster Analysis in Patients with Cardiorenal Syndromes: Identifying Vascular Aspects

by
Jean-Baptiste de Freminville
1,2,*,
Jean-Michel Halimi
3,4,5,
Valentin Maisons
3,6,
Guillaume Goudot
2,7,
Arnaud Bisson
4,8,
Denis Angoulvant
4,8 and
Laurent Fauchier
4,8
1
Service de Cardiologie-Médecine Vasculaire, Hôpital Trousseau, Centre Hospitalier Regional Universitaire de Tours, 37044 Tours Cedex 9, France
2
Service de Medecine Vasculaire, Hopital Europeen Georges Pompidou, Assistance Publique Hôpitaux de Paris, Université Paris Cité, 75015 Paris, France
3
Néphrologie-Immunologie Clinique, Hôpital Bretonneau, Centre Hospitalier Regional Universitaire de Tours, 37000 Tours, France
4
Faculté de Medecine, UMR Inserm University of Tours 1327 ISCHEMIA “Membrane Signalling and Inflammation in Reperfusion Injuries”, 37044 Tours, France
5
F-CRIN INI-CRCT, 10, Boulevard Tonnellé, 37032 Tours, France
6
INSERM U1246 SPHERE, Universities of Nantes and Tours, 37044 Tours, France
7
INSERM U970 PARCC, Université Paris Cité, 75015 Paris, France
8
Service de Cardiologie, Centre Hospitalier Universitaire Trousseau et Faculté de Médecine, 37044 Tours, France
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(11), 3159; https://doi.org/10.3390/jcm13113159
Submission received: 11 April 2024 / Revised: 26 May 2024 / Accepted: 27 May 2024 / Published: 28 May 2024
(This article belongs to the Section Vascular Medicine)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Cardiorenal syndrome (CRS) is a disorder of the heart and kidneys, with one type of organ dysfunction affecting the other. The pathophysiology is complex, and its actual description has been questioned. We used clustering analysis to identify clinically relevant phenogroups among patients with CRS. Methods: Data for patients admitted from 1 January 2012 to 31 December 2012 were collected from the French national medico-administrative database. Patients with a diagnosis of heart failure and chronic kidney disease and at least 5 years of follow-up were included. Results: In total, 13,665 patients were included and four clusters were identified. Cluster 1 could be described as the vascular–diabetes cluster. It comprised 1930 patients (14.1%), among which 60% had diabetes, 94% had coronary artery disease (CAD), and 80% had peripheral artery disease (PAD). Cluster 2 could be described as the vascular cluster. It comprised 2487 patients (18.2%), among which 33% had diabetes, 85% had CAD, and 78% had PAD. Cluster 3 could be described as the metabolic cluster. It comprised 2163 patients (15.8%), among which 87% had diabetes, 67% dyslipidemia, and 62% obesity. Cluster 4 comprised 7085 patients (51.8%) and could be described as the low-vascular cluster. The vascular cluster was the only one associated with a higher risk of cardiovascular death (HR: 1.48 [1.32–1.66]). The metabolic cluster was associated with a higher risk of kidney replacement therapy (HR: 1.33 [1.17–1.51]). Conclusions: Our study supports a new classification of CRS based on the vascular aspect of pathophysiology differentiating microvascular or macrovascular lesions. These results could have an impact on patients’ medical treatment.

Graphical Abstract

1. Introduction

Cardiorenal syndrome (CRS) is defined as a disorder of the heart and kidneys, with one type of organ dysfunction affecting the other [1].
The most used classification of CRS distinguishes five types [2,3,4] and is based on the chronology of cardiac and renal events. However, this classification presents several issues. First, we recently found, in a large nationwide cohort study, that the chronology between kidney and heart events was not associated with different prognoses. Moreover, there was no synergism between the kidney and heart, which supports the fact that CRS is the consequence of a shared pathophysiology among these two organs, rather than one organ’s dysfunction caused by the other [5]. These pathological pathways have been studied and described and include microvascular and endothelial dysfunction, the activation of the sympathetic and renin–angiotensin–aldosterone (RAA) systems, alterations in nitric oxide bioavailability, or inflammation [6,7], and a consistent hypothesis is that the major common consequence is fibrosis [8]. The role of microvascular dysfunction in the development of HF and CKD is well documented, although the mechanism differs between HF with preserved ejection fraction (HFpEF) and that with reduced ejection fraction (HFrEF) [9,10,11,12]. In brief, in the first case, cardiac remodeling involves inflammation and endothelial dysfunction, whereas, in HFrEF, remodeling is predominantly the consequence of cardiomyocyte death. Regarding the kidneys, among the potential mechanisms, a central venous pressure increase and a reduction in renal blood flow lead to decreased nitric oxide (NO) production and a vicious circle involving sympathetic and RAA system activation, finally leading to fibrosis [8,9,13] (Figure 1).
Moreover, the acute types (1 and 3) and type 5 refer to specific situations with known systemic consequences and should be distinguished. This view was defended by Zoccali et al. in a recent position paper [14]. Another view is cardiovascular–kidney–metabolic syndrome, an entity recently recognized by the Amercian Heart Association, expanding the classical cardiorenal syndrome and advocating for a more holistic view [15]. While type 2 and type 4 (chronic CRS) should be redefined outside any chronological considerations, CRS may refer, in acute situations, to functional acute kidney injury (AKI) in conditions of decompensated heart failure, improving after the treatment of congestion. This definition of CRS, which would refer to some of the type 1 cases, is, to our knowledge, the most largely applied in clinical practice and in the literature [16,17,18]. In this study, we aimed to explore the fact that, rather than the clinical presentation, the pathophysiological mechanism involved could be used to better classify these patients and lead to optimized treatment [8].
Cluster analysis is an unsupervised machine learning method that categorizes complex entities without investigators’ supervision by segregating samples into homogenous groups [19,20,21]. Unsupervised analysis can have great value, as it allows one to explore data without a priori knowledge.
In this study case, we hypothesized that unsupervised clustering would corroborate the pathophysiological classification of CRS by identifying clinically relevant phenogroups related to known pathophysiological pathways.

2. Materials and Methods

2.1. Study Design

This longitudinal cohort study utilized the national hospitalization database, which covers hospital care for the entire French population. Data for patients admitted from 1 January 2012 to 31 December 2012 were collected from the national medico-administrative PMSI database (“Programme de Médicalisation des Systèmes d’Information”), a medicalized information system program inspired by the US Medicare system. This program, implemented in 2004, records hospital medical activity in a database, ensuring anonymity, and encompasses over 98% of the French population (approximately 67 million people) from birth (or immigration) to death (or emigration).
The hospitalization details are encoded in a standardized dataset, including age, sex, hospital stay duration, admission and discharge dates, mode of discharge, pathologies, and procedures. The medical information collected routinely comprises principal and secondary diagnoses based on the International Classification of Diseases, Tenth Revision (ICD-10). All medical procedures are also recorded using the national nomenclature, Classification Commune des Actes Medicaux (CCAM). The reliability of the PMSI data has been previously assessed [22], and this database has been effectively used in previous studies related to cardiovascular or cerebrovascular conditions [5,23].
The study was conducted retrospectively, and, as the patients were not involved in its conduct, there was no impact on their care. Ethical approval was not required, as all data were anonymized. The French Data Protection Authority granted access to the PMSI data. The procedures for data collection and management were approved by the Commission Nationale de l’Informatique et des Libertés (CNIL), the independent national ethical committee protecting human rights in France, which ensures that all information is kept confidential and anonymous, in compliance with the Declaration of Helsinki (MR-005 registration number 0415141119).

2.2. Patient Selection

This study was based on patients aged 18 years and over, admitted to a French hospital between 1 January 2012 and 31 December 2012, with a diagnosis of HF and CKD. The analysis focused on patients with at least 5 years of follow-up or who died during follow-up. The patients’ medical history in the 2 years before their hospitalization was collected. Patients with CRS were identified as patients with a diagnosis of both HF and CKD. For the cluster analysis, a random sample of 50% of these patients was finally included in this study.

2.3. Collected Data

Patient information (demographics, comorbidities, medical history, and events during hospitalization or follow-up) was described using data collected in the hospital records. For each hospital stay, combined diagnoses at discharge were obtained. Each variable was identified using ICD-10 codes (Appendix A Table A1: ICD-10 codes).

2.4. Outcomes

We aimed to evaluate the incidence of all causes of death, cardiovascular death, rehospitalization for HF, myocardial infarction, ischemic stroke, and KRT (defined by dialysis or renal transplantation) (Appendix A Table A1: ICD-10 codes).
The endpoints were evaluated with follow-up starting from the first hospitalization until the date of each specific outcome or the date of last news in the absence of the outcome. Information on outcomes during follow-up was obtained by analyzing the PMSI codes for each patient. We focused on the components of Major Adverse Renal and Cardiac Events (MARCE) [24,25], namely all-cause death, cardiovascular death, hospitalization for heart failure, myocardial infarction, stroke, and renal replacement therapy, with the exception of acute kidney injury, which were identified by using their respective ICD-10 or procedure codes. Cardiovascular death was determined based on the primary diagnosis during hospitalization resulting in death (ICD-10 codes: cardiovascular death—I00-I99). Rehospitalization was considered to be attributable to heart failure when heart failure was recorded as the main diagnosis.

2.5. Cluster Analysis

Unsupervised cluster analysis using the hierarchical clustering method was used to identify homogenous phenotypic subgroups of patients with CRS, without prior knowledge of the outcomes. All baseline clinical variables were used for the clustering process (Table 1). Agglomerative hierarchical clustering was performed, using Ward’s method, and the squared Euclidian distance was used for the variables of interest. Agglomerative hierarchical clustering is based on a ‘bottom-to-top’ approach where the clustering begins with a single patient, who is then grouped with another based on similarities regarding the specified clinical variables. The dendrogram (Figure 2) displays the clustering process with the vertical lines representing the various clusters and the distance between the clusters equating to the sum of the squared differences within all clusters. Small values of the distance indicate that the merged clusters are similar, and large values indicate the combination of 2 dissimilar (heterogeneous) clusters. The determination of the number of clusters was not prespecified. Two-, three-, and four-cluster models were examined. The four-cluster model formed much clearer patterns than the three-cluster model and was therefore used in this study.

2.6. Statistical Analysis

Qualitative variables were described as frequencies and percentages and quantitative variables as means (SDs). Comparisons were made using χ2 tests for categorical variables and the Student t test or nonparametric Kruskal–Wallis test, as appropriate, for continuous variables.
The 5-year yearly incidence of all-cause death, cardiovascular death, rehospitalization for HF, myocardial infarction, ischemic stroke, and KRT was calculated. Unadjusted and multivariable-adjusted Cox analyses were used to estimate the associations between clusters and clinical outcomes, and the results were expressed as hazard ratios (HR) and 95% confidence intervals (95% CI). Incidence rates (IR) were also reported in each cluster. Parameters associated with the risk of death, cardiovascular death, myocardial infarction, new episodes of HF, ischemic stroke, and KRT were used as covariates in the multivariable models.

3. Results

3.1. Baseline Characteristics of Patients

A total of 13,665 patients were included in this study, among whom 57% were men and 77% were older than 75 years. Most patients had hypertension (82%), 44% had diabetes, 34% had dyslipidemia, 24% had obesity, and 8% were active smokers. Peripheral artery disease (PAD) was present in 39% of the patients.
Regarding associated cardiac conditions, the majority (56%) had coronary artery disease, 23% had dilated cardiomyopathy, 53% had atrial fibrillation, and 25% had valvular heart disease.
The characteristics of all patients are detailed in Table 1 and Figure 3.

3.2. Cluster Analysis

Based on the hierarchical cluster analysis, four different clusters were identified (central illustration).
The patients’ characteristics per cluster are shown in Table 1 and Figure 4.

3.2.1. Cluster 1

Cluster 1 comprised 1930 patients (14.1% of the population). It could be described as the vascular–diabetes cluster. The mean age was the second lowest (76 years old) and 60% of the patients were over 75 years old. However, 91% of the patients had hypertension, 60% had diabetes, 94% had coronary artery disease, and 80% had PAD. Atrial fibrillation was less frequent than in the other clusters (38%).

3.2.2. Cluster 2

Cluster 2 comprised 2487 patients (18.2% of the population). It could be described as the vascular cluster. The mean age was the highest (82 years old) and 85% of the patients were over 75 years old; 66% of the patients had atrial fibrillation and 38% of the patients had valvular heart disease. Only 33% of the patients had diabetes, but 78% had PAD. Anemia was also the most frequent in this group (47.3%).

3.2.3. Cluster 3

Cluster 3 comprised 2163 patients (15.8% of the population). It could be described as the metabolic cluster. The mean age was 74 years old and only 50% of the patients were over 75 years old. Diabetes was present in almost 87% of the patients, dyslipidemia in 67%, and obesity in 62%, whereas only 46% had PAD.

3.2.4. Cluster 4

Cluster 4 comprised 7085 patients (51.8% of the population). It could be described as the low-vascular cluster. There were more women than in the other clusters (51%). The mean age was high (82 years old) and 86% of the patients were over 75 years old. These patients had fewer vascular comorbidities than in the other clusters: 31% of the patients had diabetes, 32% had coronary artery disease, 11% had PAD, 56% had atrial fibrillation, 17% had dyslipidemia, and 16% were obese.

3.3. Clinical Outcomes

3.3.1. Univariate Analysis

The results of the survival analysis are shown in Table 2. In the univariate analysis, the metabolic cluster (cluster 3) was associated with a lower risk of all-cause death (HR: 0.89 [0.82–0.95]), but not cardiovascular death (HR: 0.92 [0.82–1.05]), than in the vascular–diabetes cluster (cluster 1), and with a higher risk of dialysis or renal transplantation (HR: 1.31 [1.16–1.49]). The vascular cluster (cluster 2) and the low-vascular cluster (cluster 4) were associated with a higher risk of all-cause death (HR: 1.73 [1.62–1.85] and HR: 1.49 [1.40–1.58], respectively, reference group cluster 1), a higher risk of cardiovascular death (HR: 1.90 [1.69–2.12] and HR: 1.37 [1.24–1.52], respectively), a higher risk of myocardial infarction (HR: 1.58 [1.46–1.70] and HR: 1.28 [1.20–1.36], respectively), and a lower risk of dialysis or renal transplantation (HR: 0.79 [0.68–0.92] and HR: 0.80 [0.71–0.90], respectively).
The risk of myocardial infarction or ischemic stroke was not different between the vascular–diabetes cluster and the other clusters.

3.3.2. Multivariate Analysis

The results of the survival analysis are shown in Table 2. The analyses were adjusted for age and sex.
In the multivariable analyses, the young–metabolic cluster was not associated with a lower risk of death (HR: 1.01 [0.94–1.09]) or cardiovascular death (HR: 1.04 [0.92–1.18]) than the young–vascular–diabetes cluster, but it was still associated with a higher risk of KRT (HR: 1.33 [1.17–1.51]). The vascular cluster and the low-vascular cluster were still associated with a higher risk of death (HR: 1.40 [1.31–1.50] and HR: 1.20 [1.13–1.28], respectively), but only the vascular cluster was associated with a higher risk of cardiovascular death (HR: 1.48 [1.32–1.66]). The risk of dialysis or renal transplantation was not lower in the vascular cluster or the low-vascular cluster than in the vascular–diabetes cluster.
The risk of myocardial infarction or ischemic stroke was still not different between the vascular cluster and the other clusters.
The risk of rehospitalization for heart failure was higher in the vascular cluster, the young–metabolic cluster, and the low-vascular cluster than in the vascular–diabetes cluster.

4. Discussion

Our study presents a four-cluster distribution of patients with coupled cardiac and renal involvement, defining CRS. These four clusters correspond to the distribution of patients according to their comorbidities and prognosis, which does not follow the usual CRS classification and thus questions the practical usefulness of this classification and suggests a vascular aspect of pathophysiology in CRS. The objective of this study was to propose a pathophysiological classification of CRS by identifying clinically relevant phenogroups related to known pathophysiological pathways. Its main finding is the identification of four data-driven clusters, which give insights into the different clinical phenotypes of cardiorenal syndromes and can be linked to a known pathophysiology.
The first cluster (14.1% of the population) identified patients with severe vascular damage and frequent diabetes. The second cluster (18.2% of the population) identified patients with severe vascular damage but mostly without diabetes. The third cluster (15.8% of the population) identified patients with metabolic syndrome, diabetes, obesity, dyslipidemia, and fewer macrovascular complications. Finally, the fourth cluster (51.8% of the population) identified patients with fewer comorbidities than in the other clusters, apart from a high rate of atrial fibrillation. This illustrates the variety of clinical phenotypes in cardiorenal syndromes, where the vascular and metabolic history play an important role. Indeed, most comorbidities associated with microvascular dysfunction include diabetes, obesity, dyslipidemia, and hypertension [9,12]. However, these are also risk factors for atherosclerosis and macrovascular lesions. As we have already shown in a previous study, diabetes is a major risk factor for CRS [23]. However, diabetes with (cluster 1) or without (cluster 3) severe macrovascular damage may lead to CRS by different pathophysiological pathways: primary ischemic lesions in the first case and primary microvascular dysfunction in the second case. Moreover, most of our patients (cluster 4) had low vascular damage and few risk factors. We therefore hypothesize that, in these patients, the primary mechanism is microvascular dysfunction, associated with HFpEF, and heart and kidney fibrosis. This is of importance as it may help physicians to better select interventions aimed at preventing clinical events, such as lipid-lowering therapies, anti-hypertensive therapies, or inflammation-modulating therapies.
Of note, anemia is frequent in our population, which is not surprising, and its association with CKD, HF, and CRS is known. The triad of HF, CKD, and anemia is sometimes referred as cardiorenal anemia syndrome [26]. It probably involves hypoxia, nitric oxide secretion, and vasodilation and lowers the blood pressure, which impairs kidney function and can result in sympathetic RAAS activation and renal vasoconstriction and finally fibrosis [27,28,29].
Regarding the impact on the prognoses of these patients, we focused on the components of MARCE, namely death, cardiovascular death, hospitalization for heart failure, myocardial infarction, stroke, and renal replacement therapy, with the exception of acute kidney injury [25]. The risks of death and cardiovascular death seemed to be lower in the vascular–diabetes cluster, even after adjustment for age and sex. On the other hand, the risk of dialysis or renal transplantation was the highest in the metabolic cluster, which was consistent with the high proportion of patients with diabetes and the known impact of cardiovascular risk factors in kidney function decline [1]. The age and sex ratios were very different within the clusters, which may have had a significant impact on the outcomes, even after statistical adjustment. The frailty index was comparable in clusters 2 to 4 and slightly lower in cluster 1, which could explain the lower risk of death in the vascular–diabetes cluster. As expected, macrovascular damage seemed to be associated with a higher risk of death, cardiovascular death, or heart failure (cluster 2). The high proportion of anemia in this cluster may also be related to this poor prognosis.
Environmental or genetic factors could also be involved [30]. Geographical data and the possibility of pollution exposition could shed some light on this question, but, unfortunately, these data were not accessible. The description of CRS based on pathophysiology can greatly impact patients’ care, as individualized medical treatments can be proposed according to the patients’ phenotypes [31].
The strengths of this study are its size and the absence of selection bias because of the exhaustive extraction of all ICD-10 codes from French patients hospitalized in 2012. Limitations include the lack of biological parameters, imaging parameters, and information on drug treatments. Indeed, the elevation of B-type natriuretic peptide, serum creatinine, or more specific ones such as cystatin C is associated with the diagnosis and also prognosis of CRS [4,32,33]. Echocardiography may carry prognostic value in patients with CRS. In a cohort of 30,681 patients, including 2512 patients with CRS, an increasing PA pressure and higher RV diameter were independently associated with a higher incidence of CRS [34]. Regarding kidney imaging, renal ultrasonography, namely intrarenal venous flow, is associated with the prognosis of patients with CRS [35]. Another limitation is that only in-hospital events and codes were included in this analysis. However, it is plausible that all patients with such events are managed in hospitals.

5. Conclusions

In conclusion, our unsupervised analysis identified statistically driven groups of patients with different phenotypes but similar prognoses, which supports the classification of CRS based on the vascular aspect of pathophysiology, rather than the chronology of heart and kidney failure. This approach could help to individualize medical treatment.

Author Contributions

Conceptualization, L.F. and J.-B.d.F.; methodology, L.F.; software, L.F.; validation, all authors; writing—original draft preparation, J.-B.d.F.; writing—review and editing, J.-B.d.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data can be shared on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. ICD10 codes.
Table A1. ICD10 codes.
Comorbidity or Medical HistoryCodes
AF management careI48
Strokes
  Ischaemic strokeI63, I66, I67
  Stroke, unspecifiedI64
  Haemorrhagic strokeI60–I62, I69
Ischaemic heart diseaseI20–I25
Heart failureI50, I110, I130, I132, I131, I139
Valvular diseaseI05–I091, I33–I39, Q22, Q23
HypertensionI10–I15
Diabetes mellitusE10–E14
Vascular diseases
  Myocardial infarctionI21, I252
  Peripheral arterial diseaseI70–I73
  OcclusionsI65, I77
ObesityE65–E66
Chronic kidney diseaseN17–N19 (+N28) codes for renal insufficiency, dialysis (Z49, Z992), E102, I12, I13 (transplantation (Z940, T861) was excluded)
Liver diseaseK70–K77, procedures for liver transplantation or resection
DyslipidaemiaE78
AnaemiaD50–D64
Lung diseaseJ40–J70, J961
  Including emphysema and chronic obstructive pulmonary diseaseJ43, J44
Alcohol-related diagnosesE244, F10, G312, G621, G721, I426, K292, K70, K860, O354, P043, Q860, T51, Y90, Y91, Z502, Z714
Cancer within preceding 5 yearsEntire C-series
Table A2. Incident outcomes according to the type of cardiorenal syndrome cluster.
Table A2. Incident outcomes according to the type of cardiorenal syndrome cluster.
Cluster 1Cluster 2Cluster 3Cluster 4
All-cause death1362 (20.2)2158 (39.5)1468 (17.6)5811 (32.5)
Cardiovascular death476 (7.1)837 (15.3)534 (6.4)1888 (10.6)
Rehospitalization for heart failure1222 (29.7)1624 (55.4)1569 (34.5)4328 (42.2)
Myocardial infarction132 (2.0)100 (1.9)187 (2.3)302 (1.7)
Ischemic stroke110 (1.7)118 (2.2)137 (1.7)343 (2.0)
Dialysis or renal transplantation397 (6.7)282 (5.5)616 (8.8)886 (5.5)

References

  1. Shlipak, M.G.; Katz, R.; Kestenbaum, B.; Fried, L.F.; Siscovick, D.; Sarnak, M.J. Clinical and Subclinical Cardiovascular Disease and Kidney Function Decline in the Elderly. Atherosclerosis 2009, 204, 298–303. [Google Scholar] [CrossRef] [PubMed]
  2. Ronco, C.; Haapio, M.; House, A.A.; Anavekar, N.; Bellomo, R. Cardiorenal Syndrome. J. Am. Coll. Cardiol. 2008, 52, 1527–1539. [Google Scholar] [CrossRef] [PubMed]
  3. Ronco, C.; McCullough, P.; Anker, S.D.; Anand, I.; Aspromonte, N.; Bagshaw, S.M.; Bellomo, R.; Berl, T.; Bobek, I.; Cruz, D.N.; et al. Cardio-Renal Syndromes: Report from the Consensus Conference of the Acute Dialysis Quality Initiative. Eur. Heart J. 2010, 31, 703–711. [Google Scholar] [CrossRef] [PubMed]
  4. Rangaswami, J.; Bhalla, V.; de Boer, I.H.; Staruschenko, A.; Sharp, J.A.; Singh, R.R.; Lo, K.B.; Tuttle, K.; Vaduganathan, M.; Ventura, H.; et al. Cardiorenal Protection with the Newer Antidiabetic Agents in Patients with Diabetes and Chronic Kidney Disease: A Scientific Statement from the American Heart Association. Circulation 2020, 142, e265–e286. [Google Scholar] [CrossRef] [PubMed]
  5. Halimi, J.-M.; de Fréminville, J.-B.; Gatault, P.; Bisson, A.; Gueguen, J.; Goin, N.; Sautenet, B.; Maisons, V.; Herbert, J.; Angoulvant, D.; et al. Long-Term Impact of Cardiorenal Syndromes on Major Outcomes Based on Their Chronology: A Comprehensive French Nationwide Cohort Study. Nephrol. Dial. Transplant. 2022, 37, 2386–2397. [Google Scholar] [CrossRef]
  6. Zoccali, C.; Mallamaci, F. The Chronology of the Clinical Cardiorenal Links and Health Outcomes: Problematic Issues of the Cardiorenal Syndrome Construct. Nephrol. Dial. Transplant. 2022, 37, 2300–2302. [Google Scholar] [CrossRef] [PubMed]
  7. Zoccali, C.; Goldsmith, D.; Agarwal, R.; Blankestijn, P.J.; Fliser, D.; Wiecek, A.; Suleymanlar, G.; Ortiz, A.; Massy, Z.; Covic, A.; et al. The Complexity of the Cardio-Renal Link: Taxonomy, Syndromes, and Diseases. Kidney Int. Suppl. 2011, 1, 2–5. [Google Scholar] [CrossRef]
  8. Zannad, F.; Rossignol, P. Cardiorenal Syndrome Revisited. Circulation 2018, 138, 929–944. [Google Scholar] [CrossRef]
  9. Ter Maaten, J.M.; Damman, K.; Verhaar, M.C.; Paulus, W.J.; Duncker, D.J.; Cheng, C.; van Heerebeek, L.; Hillege, H.L.; Lam, C.S.P.; Navis, G.; et al. Connecting Heart Failure with Preserved Ejection Fraction and Renal Dysfunction: The Role of Endothelial Dysfunction and Inflammation. Eur. J. Heart Fail. 2016, 18, 588–598. [Google Scholar] [CrossRef]
  10. Paulus, W.J.; Tschöpe, C. A Novel Paradigm for Heart Failure with Preserved Ejection Fraction: Comorbidities Drive Myocardial Dysfunction and Remodeling through Coronary Microvascular Endothelial Inflammation. J. Am. Coll. Cardiol. 2013, 62, 263–271. [Google Scholar] [CrossRef]
  11. Travers, J.G.; Kamal, F.A.; Robbins, J.; Yutzey, K.E.; Blaxall, B.C. Cardiac Fibrosis. Circ. Res. 2016, 118, 1021–1040. [Google Scholar] [CrossRef]
  12. Sorop, O.; Heinonen, I.; van Kranenburg, M.; van de Wouw, J.; de Beer, V.J.; Nguyen, I.T.N.; Octavia, Y.; van Duin, R.W.B.; Stam, K.; van Geuns, R.-J.; et al. Multiple Common Comorbidities Produce Left Ventricular Diastolic Dysfunction Associated with Coronary Microvascular Dysfunction, Oxidative Stress, and Myocardial Stiffening. Cardiovasc. Res. 2018, 114, 954–964. [Google Scholar] [CrossRef]
  13. Martin, F.L.; McKie, P.M.; Cataliotti, A.; Sangaralingham, S.J.; Korinek, J.; Huntley, B.K.; Oehler, E.A.; Harders, G.E.; Ichiki, T.; Mangiafico, S.; et al. Experimental Mild Renal Insufficiency Mediates Early Cardiac Apoptosis, Fibrosis, and Diastolic Dysfunction: A Kidney-Heart Connection. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2012, 302, R292–R299. [Google Scholar] [CrossRef]
  14. Zoccali, C.; Mallamaci, F.; Halimi, M.; Rossignol, P.; Sarafidis, P.; De Caterina, R.; Giugliano, R.; Zannad, F. From Cardio Renal Syndrome to Chronic Cardiovascular and Kidney Disorder: A Conceptual Transition. Clin. J. Am. Soc. Nephrol. 2023. [Google Scholar] [CrossRef] [PubMed]
  15. Ndumele, C.E.; Rangaswami, J.; Chow, S.L.; Neeland, I.J.; Tuttle, K.R.; Khan, S.S.; Coresh, J.; Mathew, R.O.; Baker-Smith, C.M.; Carnethon, M.R.; et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory from the American Heart Association. Circulation 2023, 148, 1606–1635. [Google Scholar] [CrossRef] [PubMed]
  16. Bart, B.A.; Goldsmith, S.R.; Lee, K.L.; Givertz, M.M.; O’Connor, C.M.; Bull, D.A.; Redfield, M.M.; Deswal, A.; Rouleau, J.L.; LeWinter, M.M.; et al. Ultrafiltration in Decompensated Heart Failure with Cardiorenal Syndrome. N. Engl. J. Med. 2012, 367, 2296–2304. [Google Scholar] [CrossRef]
  17. Bertoli, S.V.; Musetti, C.; Ciurlino, D.; Basile, C.; Galli, E.; Gambaro, G.; Iadarola, G.; Guastoni, C.; Carlini, A.; Fasciolo, F.; et al. Peritoneal Ultrafiltration in Refractory Heart Failure: A Cohort Study. Perit. Dial. Int. 2014, 34, 64–70. [Google Scholar] [CrossRef] [PubMed]
  18. Jentzer, J.C.; Bihorac, A.; Brusca, S.B.; Del Rio-Pertuz, G.; Kashani, K.; Kazory, A.; Kellum, J.A.; Mao, M.; Moriyama, B.; Morrow, D.A.; et al. Contemporary Management of Severe Acute Kidney Injury and Refractory Cardiorenal Syndrome: JACC Council Perspectives. J. Am. Coll. Cardiol. 2020, 76, 1084–1101. [Google Scholar] [CrossRef]
  19. Meijs, C.; Brugts, J.J.; Lund, L.H.; Linssen, G.C.M.; Rocca, H.-P.B.-L.; Dahlström, U.; Vaartjes, I.; Koudstaal, S.; Asselbergs, F.W.; Savarese, G.; et al. Identifying Distinct Clinical Clusters in Heart Failure with Mildly Reduced Ejection Fraction. Int. J. Cardiol. 2023, 386, 83–90. [Google Scholar] [CrossRef]
  20. Shah, S.J.; Katz, D.H.; Selvaraj, S.; Burke, M.A.; Yancy, C.W.; Gheorghiade, M.; Bonow, R.O.; Huang, C.-C.; Deo, R.C. Phenomapping for Novel Classification of Heart Failure with Preserved Ejection Fraction. Circulation 2015, 131, 269–279. [Google Scholar] [CrossRef]
  21. Uijl, A.; Savarese, G.; Vaartjes, I.; Dahlström, U.; Brugts, J.J.; Linssen, G.C.M.; Van Empel, V.; Brunner-La Rocca, H.; Asselbergs, F.W.; Lund, L.H.; et al. Identification of Distinct Phenotypic Clusters in Heart Failure with Preserved Ejection Fraction. Eur. J. Heart Fail. 2021, 23, 973–982. [Google Scholar] [CrossRef] [PubMed]
  22. Chantry, A.A.; Deneux-Tharaux, C.; Cans, C.; Ego, A.; Quantin, C.; Bouvier-Colle, M.-H.; GRACE study group. Hospital Discharge Data Can Be Used for Monitoring Procedures and Intensive Care Related to Severe Maternal Morbidity. J. Clin. Epidemiol. 2011, 64, 1014–1022. [Google Scholar] [CrossRef]
  23. Maisons, V.; Halimi, J.-M.; Fauchier, G.; de Fréminville, J.-B.; Goin, N.; Gueguen, J.; Gatault, P.; Sautenet, B.; Angoulvant, D.; Herbert, J.; et al. Type 2 Diabetes and Cardiorenal Syndromes. A Nationwide French Hospital Cohort Study. Diabetes Metab. 2023, 49, 101441. [Google Scholar] [CrossRef] [PubMed]
  24. Zoccali, C.; Vanholder, R.; Massy, Z.A.; Ortiz, A.; Sarafidis, P.; Dekker, F.W.; Fliser, D.; Fouque, D.; Heine, G.H.; Jager, K.J.; et al. The Systemic Nature of CKD. Nat. Rev. Nephrol. 2017, 13, 344–358. [Google Scholar] [CrossRef]
  25. Ronco, C.; Ronco, F.; McCullough, P.A. A Call to Action to Develop Integrated Curricula in Cardiorenal Medicine. Blood Purif. 2017, 44, 251–259. [Google Scholar] [CrossRef] [PubMed]
  26. McCullough, P.A. Anemia of Cardiorenal Syndrome. Kidney Int. Suppl. 2021, 11, 35–45. [Google Scholar] [CrossRef] [PubMed]
  27. Charytan, D.M.; Fishbane, S.; Malyszko, J.; McCullough, P.A.; Goldsmith, D. Cardiorenal Syndrome and the Role of the Bone-Mineral Axis and Anemia. Am. J. Kidney Dis. 2015, 66, 196–205. [Google Scholar] [CrossRef] [PubMed]
  28. Comín-Colet, J.; Enjuanes, C.; González, G.; Torrens, A.; Cladellas, M.; Meroño, O.; Ribas, N.; Ruiz, S.; Gómez, M.; Verdú, J.M.; et al. Iron Deficiency Is a Key Determinant of Health-Related Quality of Life in Patients with Chronic Heart Failure Regardless of Anaemia Status. Eur. J. Heart Fail. 2013, 15, 1164–1172. [Google Scholar] [CrossRef]
  29. Hatamizadeh, P.; Fonarow, G.C.; Budoff, M.J.; Darabian, S.; Kovesdy, C.P.; Kalantar-Zadeh, K. Cardiorenal Syndrome: Pathophysiology and Potential Targets for Clinical Management. Nat. Rev. Nephrol. 2013, 9, 99–111. [Google Scholar] [CrossRef]
  30. Xu, Y.; Andersson, E.M.; Krage Carlsen, H.; Molnár, P.; Gustafsson, S.; Johannesson, S.; Oudin, A.; Engström, G.; Christensson, A.; Stockfelt, L. Associations between Long-Term Exposure to Low-Level Air Pollution and Risk of Chronic Kidney Disease-Findings from the Malmö Diet and Cancer Cohort. Environ. Int. 2022, 160, 107085. [Google Scholar] [CrossRef]
  31. Shah, S.J.; Kitzman, D.W.; Borlaug, B.A.; van Heerebeek, L.; Zile, M.R.; Kass, D.A.; Paulus, W.J. Phenotype-Specific Treatment of Heart Failure with Preserved Ejection Fraction: A Multiorgan Roadmap. Circulation 2016, 134, 73–90. [Google Scholar] [CrossRef] [PubMed]
  32. Manzano-Fernández, S.; Boronat-Garcia, M.; Albaladejo-Otón, M.D.; Pastor, P.; Garrido, I.P.; Pastor-Pérez, F.J.; Martínez-Hernández, P.; Valdés, M.; Pascual-Figal, D.A. Complementary Prognostic Value of Cystatin C, N-Terminal pro-B-Type Natriuretic Peptide and Cardiac Troponin T in Patients with Acute Heart Failure. Am. J. Cardiol. 2009, 103, 1753–1759. [Google Scholar] [CrossRef] [PubMed]
  33. Palazzuoli, A.; Ruocco, G.; Pellegrini, M.; Martini, S.; Del Castillo, G.; Beltrami, M.; Franci, B.; Lucani, B.; Nuti, R. Patients with Cardiorenal Syndrome Revealed Increased Neurohormonal Activity, Tubular and Myocardial Damage Compared to Heart Failure Patients with Preserved Renal Function. Cardiorenal Med. 2014, 4, 257–268. [Google Scholar] [CrossRef] [PubMed]
  34. Mavrakanas, T.A.; Khattak, A.; Singh, K.; Charytan, D.M. Epidemiology and Natural History of the Cardiorenal Syndromes in a Cohort with Echocardiography. Clin. J. Am. Soc. Nephrol. 2017, 12, 1624–1633. [Google Scholar] [CrossRef]
  35. Iida, N.; Seo, Y.; Sai, S.; Machino-Ohtsuka, T.; Yamamoto, M.; Ishizu, T.; Kawakami, Y.; Aonuma, K. Clinical Implications of Intrarenal Hemodynamic Evaluation by Doppler Ultrasonography in Heart Failure. JACC Heart Fail. 2016, 4, 674–682. [Google Scholar] [CrossRef]
Jcm 13 03159 g001
Figure 1. Cardiorenal syndrome connection.
Figure 1. Cardiorenal syndrome connection.
Jcm 13 03159 g001
Jcm 13 03159 g002
Figure 2. Dendrogram generated by hierarchical clustering process showing the CRS clusters. The dendrogram graph is a visual representation of the hierarchical clustering process. Vertical lines are clusters that are joined together, and the position of the line on the scale indicates the distance at which the clusters are joined. Clusters are identified by different colors.
Figure 2. Dendrogram generated by hierarchical clustering process showing the CRS clusters. The dendrogram graph is a visual representation of the hierarchical clustering process. Vertical lines are clusters that are joined together, and the position of the line on the scale indicates the distance at which the clusters are joined. Clusters are identified by different colors.
Jcm 13 03159 g002
Jcm 13 03159 g003
Figure 3. Flow chart.
Figure 3. Flow chart.
Jcm 13 03159 g003
Jcm 13 03159 g004
Figure 4. Heatmap of baseline characteristics of patients with CRS according to patient clusters.
Figure 4. Heatmap of baseline characteristics of patients with CRS according to patient clusters.
Jcm 13 03159 g004
Table 1. Baseline characteristics of patients with CRS according to patient clusters.
Table 1. Baseline characteristics of patients with CRS according to patient clusters.
Cluster 1Cluster 2Cluster 3Cluster 4Total
(n = 1930)(n = 2487)(n = 2163)(n = 7085)(n = 13,665)
Age (years), mean ± SD76.4 ± 9.981.7 ± 8.673.9 ± 9.982.4 ± 9.780.1 ± 10.2
Age ≥ 75 yrs, n (%)1168 (60.5)2111 (84.9)1071 (49.5)6109 (86.2)10,459 (76.5)
Sex (male), n (%)1490 (77.2)1643 (66.1)1199 (55.4)3472 (49.0)7804 (57.1)
Hypertension, n (%)1752 (90.8)2059 (82.8)2056 (95.1)5343 (75.4)11,210 (82.0)
Diabetes mellitus, n (%)1166 (60.4)825 (33.2)1876 (86.7)2199 (31.0)6066 (44.4)
Smoker, n (%)247 (12.8)280 (11.3)271 (12.5)275 (3.9)1073 (7.9)
Dyslipidemia, n (%)1094 (56.7)949 (38.2)1453 (67.2)1226 (17.3)4722 (34.6)
Obesity, n (%)455 (23.6)348 (14.0)1331 (61.5)1113 (15.7)3247 (23.8)
Alcohol consumption, n (%)108 (5.6)121 (4.9)170 (7.9)380 (5.4)779 (5.7)
Atrial fibrillation, n (%)737 (38.2)1633 (65.7)909 (42.0)3969 (56.0)7248 (53.0)
Previous pacemaker or ICD, n (%)309 (16.0)1003 (40.3)283 (13.1)1332 (18.8)2927 (21.4)
Cardiac valve disease, n (%)269 (13.9)938 (37.7)540 (25.0)1605 (22.7)3352 (24.5)
Dilated cardiomyopathy, n (%)241 (12.5)663 (26.7)707 (32.7)1474 (20.8)3085 (22.6)
Coronary artery disease, n (%)1809 (93.7)2131 (85.7)1462 (67.6)2254 (31.8)7656 (56.0)
Previous MI, n (%)683 (35.4)368 (14.8)99 (4.6)30 (0.4)1180 (8.6)
Previous PCI, n (%)744 (38.5)162 (6.5)177 (8.2)91 (1.3)1174 (8.6)
Previous CABG, n (%)62 (3.2)57 (2.3)77 (3.6)36 (0.5)232 (1.7)
Peripheral artery disease, n (%)1534 (79.5)1948 (78.3)1002 (46.3)787 (11.1)5271 (38.6)
Ischemic stroke, n (%)112 (5.8)122 (4.9)79 (3.7)217 (3.1)530 (3.9)
Intracranial bleeding, n (%)26 (1.3)35 (1.4)22 (1.0)118 (1.7)201 (1.5)
Lung disease, n (%)464 (24.0)999 (40.2)643 (29.7)1999 (28.2)4105 (30.0)
Liver disease, n (%)80 (4.1)168 (6.8)137 (6.3)481 (6.8)866 (6.3)
Thyroid diseases, n (%)163 (8.4)551 (22.2)357 (16.5)1118 (15.8)2189 (16.0)
Anemia, n (%)448 (23.2)1177 (47.3)785 (36.3)2318 (32.7)4728 (34.6)
Previous cancer, n (%)582 (30.2)468 (18.8)185 (8.6)1093 (15.4)2328 (17.0)
Poor nutrition, n (%)129 (6.7)526 (21.1)164 (7.6)1073 (15.1)1892 (13.8)
Cognitive impairment, n (%)119 (6.2)322 (12.9)130 (6.0)1143 (16.1)1714 (12.5)
Frailty index, mean ± SD13.8 ± 9.515.4 ± 9.915.0 ± 10.115.0 ± 9.814.9 ± 9.9
Frailty index > 13, n (%)879 (45.5)1302 (52.4)1089 (50.3)3570 (50.4)6840 (50.1)
Values are n (%) or mean ± SD. CABG = coronary artery bypass graft; CKD = chronic kidney disease; COPD = chronic obstructive pulmonary disease; ICD = implantable cardioverter defibrillator; MI = myocardial infarction; PCI = percutaneous coronary intervention; SD = standard deviation.
Table 2. Hazard ratios (95% CI) associated with cardiorenal syndrome clusters (vs. cluster 1) for incident outcomes.
Table 2. Hazard ratios (95% CI) associated with cardiorenal syndrome clusters (vs. cluster 1) for incident outcomes.
Unadjusted HR (95% CI)pAdjusted HR
(95% CI)		p
All-cause death
Cluster 2 (vs. Cluster 1)1.73 (1.62–1.85)<0.00011.40 (1.31–1.50)<0.0001
Cluster 3 (vs. Cluster 1)0.89 (0.82–0.95)0.0011.01 (0.94–1.09)0.75
Cluster 4 (vs Cluster 1)1.49 (1.40–1.58)<0.00011.20 (1.13–1.28)<0.0001
Cardiovascular death
Cluster 2 (vs. Cluster 1)1.90 (1.69–2.12)<0.00011.48 (1.32–1.66)<0.0001
Cluster 3 (vs. Cluster 1)0.92 (0.82–1.05)0.211.04 (0.92–1.18)0.53
Cluster 4 (vs. Cluster 1)1.37 (1.24–1.52)<0.00011.05 (0.94–1.16)0.39
Rehospitalization for HF
Cluster 2 (vs. Cluster 1)1.58 (1.46–1.70)<0.00011.43 (1.33–1.55)<0.0001
Cluster 3 (vs. Cluster 1)1.14 (1.06–1.23)0.0011.18 (1.10–1.27)<0.0001
Cluster 4 (vs. Cluster 1)1.28 (1.20–1.36)<0.00011.17 (1.09–1.25)<0.0001
Myocardial infarction
Cluster 2 (vs. Cluster 1)0.92 (0.71–1.20)0.550.89 (0.69–1.16)0.40
Cluster 3 (vs. Cluster 1)1.15 (0.92–1.44)0.221.24 (0.99–1.56)0.06
Cluster 4 (vs. Cluster 1)0.86 (0.70–1.05)0.140.86 (0.70–1.06)0.17
Ischemic stroke
Cluster 2 (vs. Cluster 1)1.31 (1.01–1.70)0.041.13 (0.87–1.47)0.38
Cluster 3 (vs. Cluster 1)1.00 (0.78–1.29)0.981.01 (0.79–1.31)0.91
Cluster 4 (vs. Cluster 1)1.16 (0.94–1.44)0.170.97 (0.78–1.22)0.82
Dialysis or renal transplantation
Cluster 2 (vs. Cluster 1)0.79 (0.68–0.92)0.0030.99 (0.85–1.16)0.93
Cluster 3 (vs. Cluster 1)1.31 (1.16–1.49)<0.00011.33 (1.17–1.51)<0.0001
Cluster 4 (vs. Cluster 1)0.80 (0.71–0.90)<0.00011.04 (0.92–1.17)0.52
HR = hazard ratio; Adjusted HR = age- and sex-adjusted.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

de Freminville, J.-B.; Halimi, J.-M.; Maisons, V.; Goudot, G.; Bisson, A.; Angoulvant, D.; Fauchier, L. Unsupervised Cluster Analysis in Patients with Cardiorenal Syndromes: Identifying Vascular Aspects. J. Clin. Med. 2024, 13, 3159. https://doi.org/10.3390/jcm13113159

AMA Style

de Freminville J-B, Halimi J-M, Maisons V, Goudot G, Bisson A, Angoulvant D, Fauchier L. Unsupervised Cluster Analysis in Patients with Cardiorenal Syndromes: Identifying Vascular Aspects. Journal of Clinical Medicine. 2024; 13(11):3159. https://doi.org/10.3390/jcm13113159

Chicago/Turabian Style

de Freminville, Jean-Baptiste, Jean-Michel Halimi, Valentin Maisons, Guillaume Goudot, Arnaud Bisson, Denis Angoulvant, and Laurent Fauchier. 2024. "Unsupervised Cluster Analysis in Patients with Cardiorenal Syndromes: Identifying Vascular Aspects" Journal of Clinical Medicine 13, no. 11: 3159. https://doi.org/10.3390/jcm13113159

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop