|Year : 2020 | Volume
| Issue : 2 | Page : 162-168
A prospective single center study to assess the incidence and risk factors associated with cardiorenal syndrome with respect to its subtypes
Medikonda Parameswara Reddy, Nagamalesh Udigala Madappa, Anupama Hegde, VS Prakash
Department of Cardiology, MS Ramaiah Medical College and Hospital, Rajiv Gandhi University of Health Sciences, Bengaluru, Karnataka, India
|Date of Submission||25-May-2020|
|Date of Decision||16-Jul-2020|
|Date of Acceptance||16-Jul-2020|
|Date of Web Publication||27-Aug-2020|
Dr. Nagamalesh Udigala Madappa
Department of Cardiology, MS Ramaiah Medical College and Hospital, Rajiv Gandhi Institute of Health Sciences, Bengaluru - 560 054, Karnataka
Source of Support: None, Conflict of Interest: None
Background: Cardiorenal syndrome (CRS) is an evolving complex clinical condition of cardiac–renal dysfunction, for which incidence and risk factors remain to be fully assessed in the Indian subcontinent. The study determined the incidence of and risk factors for CRS and its impact on in-hospital mortality and readmission in a tertiary care hospital. Materials and Methods: This single-center prospective observational study included 158 patients with CRS. Sociodemographic, laboratory, and echocardiography parameters were recorded. Heart failure and acute–chronic kidney injuries were diagnosed, and the patients were accordingly classified. Data were statistically analyzed using software R version 3.6.3 and Microsoft Excel. Results: The study included 106 (67.1%) and 52 (32.9%) males and females, respectively, with a mean age of 62.87 ± 13.99 years. Eighty-five (53.8%) patients suffered from CRS Type 1. Dyspnea (n = 149) was the most common complaint. Diabetes mellitus (DM), rheumatic heart disease, chronic obstructive pulmonary disease, and chronic kidney disease were the common risk factors. Conclusion: In South India, CRS is associated with increasing age; hypertension; DM; relevant cardiovascular and kidney diseases; abnormal levels of blood urea nitrogen, creatinine, potassium, and albumin; and low estimated glomerular filtration rate, leading to poor patient outcomes. CRS Type 2 results in relatively less stability, high readmissions with heart failure, and higher mortality in patients. Given the diverse cultural background of India, the study proposes that although CRS is clinically diagnosed, it remains poorly characterized in India, mainly in South India. The present study found that COPD affects CRS, which is a rare finding.
Keywords: Acute kidney injury, cardiology, cardiorenal syndrome, chronic kidney failure, mortality, nephrology
|How to cite this article:|
Reddy MP, Madappa NU, Hegde A, Prakash V S. A prospective single center study to assess the incidence and risk factors associated with cardiorenal syndrome with respect to its subtypes. J Pract Cardiovasc Sci 2020;6:162-8
|How to cite this URL:|
Reddy MP, Madappa NU, Hegde A, Prakash V S. A prospective single center study to assess the incidence and risk factors associated with cardiorenal syndrome with respect to its subtypes. J Pract Cardiovasc Sci [serial online] 2020 [cited 2020 Sep 30];6:162-8. Available from: http://www.j-pcs.org/text.asp?2020/6/2/162/293595
| Introduction|| |
Cardiorenal syndrome (CRS) refers to conditions where acute or chronic dysfunction of the heart or kidneys leads to the dysfunction of other. A common comorbid condition reported in patients with chronic heart failure and acute decompensated heart failure is renal insufficiency., The prevalence of heart and kidney diseases is increasing annually (272/100,000 population), and the complexities of this interactive relationship continue to emerge.,
CRS lacked a universally accepted definition for long, and numerous related key questions yet remain unanswered. To understand the overall burden imposed by CRS, its incidence and outcomes should be determined. Coexistence of cardiac and renal diseases greatly increases morbidity, mortality, and cost of care. Clinical guidelines have classically treated cardiac and renal failure separately, but the characteristics of CRS should be elucidated more comprehensively to enhance the integrative clinical management of the syndrome.
To emphasize the bidirectional nature of heart–kidney interactions, the Acute Dialysis Quality Initiative has defined and classified CRS into the following five subtypes: (1) acute worsening of heart functions leading to acute kidney injury (AKI) (CRS Type 1), (2) chronic abnormalities in heart functions leading to progressive chronic kidney disease (CKD) (CRS Type 2), (3) acute worsening of kidney functions leading to acute heart dysfunction (CRS Type 3), (4) CKD leading to chronic heart disease (CRS Type 4), and (5) cardiac and renal dysfunction due to acute or chronic systemic disorders (CRS Type 5).
Although the incidence of CRS is increasing, its pathophysiology and subsequent effective treatment measures are not fully elucidated, mainly in the Indian subcontinent., To our knowledge, this is the first study to investigate the epidemiology and clinical profile of CRS in South India. The study aimed to determine the incidence of and risk factors for CRS and its prognostic impact on in-hospital mortality and readmission in a tertiary care hospital.
| Materials and Methods|| |
The study was conducted in accordance with the Declaration of Helsinki and STROBE checklist. The study was approved by an institutional ethics committee (Ref no. SS-1/EC/09/2017), and written informed consent was obtained from all the patients.
Selection and description of participants
This was a single-center, prospective, observational study conducted on 158 patients with CRS in the departments of cardiology and nephrology at a tertiary care hospital from December 2016 to August 2018. Patients ≥20 years old were included in the study. Those <18 years old and/or presenting with sepsis were excluded from the study. Moreover, pregnant and lactating women were also excluded from the study. The demographic parameters, such as body mass index, risk factors, and comorbid conditions were recorded. Various laboratory parameters, such as complete blood count, blood urea nitrogen (BUN) (mg/dl), creatinine (mg/dl), and estimated glomerular filtration rate (eGFR) (ml/min/1.73 m2), were recorded. Moreover, echocardiography parameters (left ventricular ejection fraction and left ventricular diastolic dysfunction) at the time of admission, time of discharge, and 30-day- and 180-day-follow-up of all the patients were recorded.
Heart failure was diagnosed and classified according to the European Society of Cardiology criteria based on ejection fraction (EF) (heart failure with preserved ejection fraction [HFpEF]: EF >50%; heart failure with mid-range EF: EF 40%–49%; heart failure with reduced EF: EF <40%); and the New York Heart Association (NYHA) functional classification (Class I–IV) based on the severity of symptoms. Acute and chronic kidney injuries were diagnosed and classified according to the Kidney Disease Improving Global Outcomes guidelines. The patients were followed up after 30 and 180 days.
Data were compiled and analyzed using statistical software R version 3.6.3 (R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) and Microsoft Excel (Microsoft Corporation. Microsoft Excel [Internet] 2018. [Available from: https://office.microsoft.com/exce]). Descriptive and exploratory data analysis was used to study different continuous and categorical variables. Frequency and percentages were used to represent different categorical variables. Mean and standard deviation were used to represent different continuous variables. Friedman's analysis of variance was used to find the significant difference between different time points. Mann–Whitney U-test and independent sample t-test were used to find the significant difference between the different groups., With 95% confidence interval and 90% power, the sample size was calculated to be 138. P < 0.05 was considered statistically significant.
| Results|| |
This study included 106 (67.1%) males and 52 (32.9%) females. The male:female ratio was 2.04:1. Of all the patients, 69 (43.7%) were 60–80 years old, 65 (41.1%) were 40–60 years old, 16 (10.1%) were ≥80 years old, and eight (5.1%) were 20–40 years old. The mean age of the patients was 62.87 ± 13.99 years.
Diuretics (n = 145), beta-blockers (n = 126), angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (n = 39), aldosterone antagonists (n = 44), angiotensin receptor-neprilysin inhibitors (n = 25), calcium channel blockers (n = 12), implantable cardioverter-defibrillators (n = 8), and cardiac resynchronization therapy (n = 6) were the treatments provided. The mean duration of hospital stay for all patients with CRS was 5.33 ± 2.48 days. The mean duration of hospital stay for patients with CRS Type 2 was 6.70 ± 3.25 days; those with CRS Type 4, 6 ± 2.35 days; those with CRS Type 1, 4.70 ± 1.83 days; and those with CRS Type 3, 3.29 ± 0.49 days. [Figure 1] shows the number of patients with each of the four subtypes of CRS.
|Figure 1: Distribution of the study participants based on CRS subtypes. The diagram denotes the classification of patients based on the CRS subtype. Blue, orange, yellow, and gray areas represent classification of patients with CRS Types 1, 2, 3, and 4, respectively (number of patients [percentage]). CRS: Cardiorenal syndrome.|
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Age- and sex-wise distribution of all the study participants with respect to the CRS subtypes is shown in [Table 1].
|Table 1: Age- and sex-wise distribution of patients with respect to cardiorenal syndrome subtype|
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Classification of patients according to laboratory and echocardiography parameters in terms of CRS subtype is depicted in [Table 2].
|Table 2: Distribution of patients based on laboratory and echocardiography parameters with respect to cardiorenal syndrome subtype|
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[Table 3] represents classification of patients according to risk factors leading to CRS.
[Table 3] exhibits a significant association between CRS and risk factors, such as diabetes mellitus (DM) (P = 0.030), rheumatic heart disease (RHD) (P = 0.047), chronic obstructive pulmonary disease (COPD) (P = 0.016), and CKD (P = 0.000) with CRS.
As shown in [Figure 2], majority of the patients belonged to NYHA Class 3 among all the CRS subtypes, i.e., 49 patients among CRS Type 1 (n = 85), 20 patients among CRS Type 2 (n = 39), and 13 patients among CRS Type 4 (n = 26). CRS Type 3 (n = 8) subtype included patients belonging to NYHA Class 2.
|Figure 2: NYHA classification of the study participants with respect to CRS subtypes. X and Y axes represent CRS subtype and number of patients, respectively. Blue, orange, and gray columns represent NYHA Class 2, 3, and 4, respectively. Note: Five of 158 patients were not classified as they had fatal outcome. CRS: Cardiorenal syndrome, NYHA: New York Heart Association.|
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This study diagnosed patients with AKI (n = 93) based on Kidney Disease Improving Global Outcomes guidelines and classified them into subclasses AKI-1, AKI-2, and AKI-3 [Figure 3]. Seventy-one (76.3%) patients with CRS were diagnosed to have AKI Class 1. Patients with CRS Type 2 (n = 39) and CRS Type 4 (n = 26) were diagnosed with CKD.
|Figure 3: Classification of the study participants based on AKI. X axis represents AKI subclass, whereas Y axis represents the number of patients. Blue and orange columns represent CRS Type 1 and 2, respectively. AKI: Acute kidney injury, CRS: Cardiorenal syndrome. Note: Patients under CRS 2 and CRS 4 were not classified under AKI.|
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The outcomes of all patients with CRS at the time of discharge and 30 and 180 days of follow-up are presented in [Table 4].
|Table 4: Classification based on outcomes at discharge and at 30 days and 180 days of follow-up, for all cardiorenal syndrome subtypes|
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As depicted in [Table 5], an association between clinical laboratory parameters and outcomes of the patients was established.
|Table 5: Association of laboratory parameters with the outcome of patients with cardiorenal syndrome|
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Favorable and nonfavorable outcomes exhibited an association with respect to BUN (P < 0.001), creatinine (P < 0.001), potassium (P < 0.001), and albumin (P < 0.001) levels at the time of admission. Creatinine levels (P < 0.001) had a statistically significant difference between both groups at the time of discharge and at 180 days follow-up, whereas eGFR (P < 0.001) had a statistically significant difference between both groups at 180-day follow-up.
| Discussion|| |
CRS represents an interaction between the heart and kidneys in terms of acute and chronic disease settings. Studies investigating the epidemiology and clinical profile of CRS in India are limited. The present study was undertaken to determine the same. The combined clinical consequences of cardiology- and nephrology-related dysfunctions must be evaluated to understand the potential preventive strategies. The current study included 106 males and 52 females, with a mean age of 62.87 ± 13.99 years, whereas in the study conducted by Suresh et al., the mean age of the study participants was 43.50 ± 14.53 years. Dyspnea (n = 149), pedal edema (n = 39), and palpitations (n = 39) were the most commonly reported complaints in this study. The mean duration of hospital stay was 5.33 ± 2.48 days. Concurrent with the literature, the study reported that CRS is prevalent in adults with increasing age, hypertension (HTN), DM, and cardiovascular and kidney diseases. To our knowledge, there are no studies published which associate the duration of hospital stay with the outcome in patients with CRS.
This study found a statistically significant association of DM (P = 0.030), RHD (P = 0.047), COPD (P = 0.016), and CKD (P = 0.000) with CRS. DM is becoming increasingly more prevalent and a well-known risk factor for cardiovascular disease and CKD. In this study, 86 (54.4%) patients had DM, i.e., >50% patients had cardiovascular disease and diabetes concomitantly. A strong association between diabetes and CRS Type 2 has been reported. Patients with both cardiovascular disease and diabetes often have double the chances of acquiring CKD than those individuals who had cardiovascular disease alone. Jishanth found that heart failure, smoking, DM, and left ventricular dysfunction increase the risk of CRS. The present study found that COPD affects CRS, which is a rare finding and different from other studies as to our knowledge, there are no previous studies which associate COPD with CRS. The pathophysiological mechanisms underlying the development of CRS in CKD include neurohumoral, hemodynamic, and CKD-related mechanisms, and, thus, reportedly, CKD leads to CRS.
Out of 158 participants, 124 (78.5%) patients had systolic blood pressure >90 mmHg and 117 (74.1%) patients had systemic HTN. Elevated blood pressure not only causes direct cardiac and renal injury but also leads to elevated sympathetic neurohumoral activation and reflects an increased incidence of renal dysfunction in patients with decompensated chronic heart failure.
While treating AKI, cardiac injury should be addressed simultaneously and vice versa. Though limited data are available from clinical trials suggesting its beneficial role, diuretics have long been considered an initial and essential part in treating patients with CRS. Diuretics and beta-blockers are the commonly used drugs for treating complications, such as volume overload and cardiopulmonary and venous congestion that are noticed in the course of CRS. Despite the fact that treatment with diuretics is the mainstay in lowering volume overload, diuretic resistance, especially in the advanced stages of CRS, occurs frequently. Therefore, in advanced stages of CKD, aggressive therapy, such as combinations of loop and thiazide diuretics or continuous infusions of loop diuretics, comes into focus to achieve appropriate diuresis that maximizes decongestion and minimizes worsening of renal function. Thus, an early diagnosis of CRS may prevent diuretic resistance in treating volume overload.
Regarding the stability of patients during the follow-up period, the percentage of readmission with HF was high for patients with CRS Type 2 both at 30 days (19.4%) and 180 days of follow-up (22.9%). In-hospital mortality was high in patients with CRS Type 4 (15.4%). During follow-up, comparatively higher mortality rate was noticed among patients with CRS Type 2 at 30-day follow-up (2.8%) and 180-day follow-up (17.1%) than the other subtypes. The study conducted by Antit et al.reported that heart failure and mortality were significantly high among patients with CRS Type 2.
In this study, BUN (P < 0.001), creatinine (P < 0.001), potassium (P < 0.001), albumin (P < 0.001), and eGFR (P < 0.001) were associated with the outcomes in patients with CRS, strongly indicating that the three parameters are useful in assessing and prognosticating patients with CRS. Similarly, the study conducted by Palazzuoli et al. reported that BUN/creatinine ratio was elevated in patients with CRS. BUN/creatinine ratio is currently considered a useful diagnostic tool for identifying patients with adverse outcome and reduced glomerular filtration rate. Decreased eGFR could be a potent predictor for cardiovascular-related complications and mortality. In the study conducted by Horwich et al., hypoalbuminemia was associated with poor outcome in patients with chronic systolic heart failure and in patients with HFpEF., Liu et al. found that hypoalbuminemia is an independent and significant predictor for AKI and that AKI induced mortality in a clinical study of patients in intensive care, surgical, and other hospital settings.
The present study has certain limitations. The sample size included in the study was comparatively less. Future studies with larger sample size and more comprehensive and longer follow-up are required to validate the findings. In addition, the study was conducted in a tertiary care hospital in a city. Thus, the findings cannot be extrapolated to the general population, including rural citizens. Nevertheless, the present study provides a basis for future studies to study the subtypes of CRS and their individual pathophysiology.
| Conclusion|| |
Considering the cultural variations of India, the study suggests that although CRS is clinically diagnosed, it remains poorly characterized in India, mainly South India. As evaluated in the present study, in South India, CRS is associated with increasing age; HTN; DM; relevant cardiovascular and kidney diseases; abnormal levels of BUN, creatinine, potassium, and albumin; and eGFR, leading to poor patient outcomes. Moreover, the present study found that COPD affects CRS, which is a novel finding. DM, RHD, COPD, and CKD are the significant risk factors for CRS. Among all the subtypes, CRS Type 2 is associated with comparatively lesser stability, higher readmissions with heart failure, and higher mortality in patients. More studies are required to assess the clinical importance of various findings in patients with CRS in the Indian subcontinent. Early diagnosis and treatment of CRS are necessary to combat the subsequent complications and achieve favorable prognosis and better patient outcome.
The study was approved by the institutional ethics committee of MS Ramaiah Medical College (Ref no. SS 1/EC/09/2017.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Rangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, et al
. Cardiorenal syndrome: Classification, pathophysiology, diagnosis, and treatment strategies: A scientific statement from the American Heart Association. Circulation 2019;139:e840-78.
House AA, Wanner C, Sarnak MJ, Piña IL, McIntyre CW, Komenda P, et al
. Heart failure in chronic kidney disease: Conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2019;95:1304-17.
Sin P, Sanjanwala R, Zieroth S. Managing common co-morbidities in heart failure. Can J Gen Int Med 2020;15:14-21.
Udani SM, Koyner JL. The effects of heart failure on renal function. Cardiol Clin 2010;28:453-65.
Lourenco C, Teixeira R, António N, Monteiro S, Baptista R, Jorge E, et al
. Impact of renal function on mortality and incidence of major adverse cardiovascular events following acute coronary syndromes. Rev Port Cardiol 2010;29:1331-52.
Gnanaraj J, Radhakrishnan J. Cardio-renal syndrome. F1000Res 2016;5:2123.
Lainscak M, Spoletini I, Coats A. Definition and classification of heart failure. Int Cardiovasc Forum J 2017;10:3-7.
Singh K, Waikar SS, Samal L. Evaluating the feasibility of the KDIGO CKD referral recommendations. BMC Nephrol 2017;18:223.
Scheff SW, editors. Nonparametric statistics. In: Fundamental Statistical Principles for the Neurobiologist. London: Academic Press; 2016. p. 157-82.
Kim HY. Statistical notes for clinical researchers: Nonparametric statistical methods: 1. Nonparametric methods for comparing two groups. Restor Dent Endod 2014;39:235-9.
Kim TK. T
test as a parametric statistic. Korean J Anesthesiol 2015;68:540-6.
Clementi A, Virzì GM, Battaglia GG, Ronco C. Neurohormonal, endocrine, and immune dysregulation and inflammation in cardiorenal syndrome. Cardiorenal Med 2019;9:265-73.
Bhattacharya PK, Roy A, Jamil M, Barman B, Murti SV, Marak PR. Clinical profile and determinants of short-term outcome of acute kidney injury: A hospital-based prospective study from Northeastern India. J Lab Physicians 2019;11:5-10.
] [Full text]
Suresh H, Arun BS, Moger V, Swamy M. Cardiorenal syndrome type 4. A study of cardiovascular diseases in chronic kidney disease. Indian Heart J 2017;69:11-6.
Siriwardhana C, Lim E, Davis J, Chen JJ. Progression of diabetes, ischemic heart disease, and chronic kidney disease in a three chronic conditions multistate model. BMC Public Health 2018;18:752.
Karnib HH, Ziyadeh FN. The cardiorenal syndrome in diabetes mellitus. Diabetes Res Clin Pract 2010;89:201-8.
Tsuruya K, Eriguchi M. Cardiorenal syndrome in chronic kidney disease. Curr Opin Nephrol Hypertens 2015;24:154-62.
Banerjee S, Panas R. Diabetes and cardiorenal syndrome: Understanding the “Triple Threat.” Hellenic J Cardiol 2017;58:342-7.
Di Lullo L, Reeves PB, Bellasi A, Ronco C. Cardiorenal syndrome in acute kidney injury. Seminars Nephrol 2019;39:31-40.
Pokhrel N, Maharjan N, Dhakal B, Arora RR. Cardiorenal syndrome: A literature review. Exp Clin Cardiol 2008;13:165-70.
Gigante A, Liberatori M, Gasperini ML, Sardo L, Di Mario F, Dorelli B, et al
. Prevalence and clinical features of patients with the cardiorenal syndrome admitted to an internal medicine ward. Cardiorenal Med 2014;4:88-94.
Antit S, Ben Kaab B, Slama I, Chenik S, Menjour MB, Boussabeh I, et al
. Cardio-renal syndrome type 2: Therapeutic and prognostic impact. Arch Cardiovasc Dis Suppl 2018;10:202.
Palazzuoli A, Ruocco G, Pellegrini M, Martini S, Del Castillo G, Beltrami M, et al
. Patients with cardiorenal syndrome revealed increased neurohormonal activity, tubular and myocardial damage compared to heart failure patients with preserved renal function. Cardiorenal Med 2014;4:257-68.
Anyanwagu U, Donnelly R, Idris I. Individual and combined relationship between reduced eGFR and/or increased urinary albumin excretion rate with mortality risk among insulin-treated patients with type 2 diabetes in routine practice. Kidney Dis (Basel) 2019;5:91-9.
Horwich TB, Kalantar-Zadeh K, MacLellan RW, Fonarow GC. Albumin levels predict survival in patients with systolic heart failure. Am Heart J 2008;155:883-9.
Mehrotra S, Sharma TM, Bahl A. Impact of comorbidities in heart failure–prevalence, effect on functional status, and outcome in Indian population: A single-center experience. J Clin Prev Cardiol 2019;8:166.
Liu M, Chan CP, Yan BP, Zhang Q, Lam YY, Li RJ, et al
. Albumin levels predict survival in patients with heart failure and preserved ejection fraction. Eur J Heart Fail 2012;14:39-44.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]