European Journal of Cardiovascular Medicine

The global estimated prevalence of CKD is 13.4% (11.7-15.1%), and patients with end-stage kidney disease (ESKD) needing renal replacement therapy OR dialysis is estimated between 4.902 and 7.083 million [1]. The kidneys are the frequent purpose of systemic immune and autoimmune diseases, containing systemic autoimmunity and vasculitis, immune complex- oriented serum illness, and supplement disorders [2]. The human microbiome is accountable for consolidating with the development and inflection of immune reactions. Therefore, there might be biological connectivity of spittle microbiome and immunological outlines in CKD victims [3]. Anaemia is a common problem of chronic kidney disease (CKD) and is associated with poor outcomes. Erythropoietin (EPO) deficiency is the main cause, but true or relative iron deficiency and infection often live together. CKD is an important risk element for excitation, cardiovascular disease (CVD), cardiovascular mortality, and disabled quality of life [4]. It is wanted to diagnose and categorize CKD and helpful to determine prognosis. It is used to determine when to begin individual medication, when to refer clients for specialist nephrology concerns, or when to begin kidney function replacement cure [5]. Although current diagnostic technology is extremely expanded, the electrocardiogram (ECG) is still a simple, non-invasive, and helpful diagnostic device for diagnosing the majority-asymptomatic gradual nature of CVD morbidity and dying in late-stage CKD victims [6]. ECG can discover heart rhythm disorders, cardiac conductivity abnormalities, chamber abnormalities, and signs of myocardial ischemia. Abnormalities of ECG that occur in late-stage CKD patients can independently estimate future CVD and risk of sudden death [7]. Increased quart (QT) interval and dispersal on an electrocardiogram (ECG) is associated with Left Ventricular Hypertrophy (LVH), the development of arrhythmias, and sudden cardiac death in other patient groups [8]

To detect CKD earlier, a computational approach for CKD screening using routinely acquired electrocardiograms (ECGs), a cheap, rapid, non-invasive, and commonly obtained test of the heart’s electrical activity [9]. The basic principles of electrocardiogram (ECG) interpretation in children are identical to those in adults, paediatric ECGs are more challenging to read as compared to adult ECGs [10]. The standard 12-lead ECG is an easily accessible and inexpensive bedside test. Since the advent of the electrocardiogram (ECG) over a century ago, its utility as a cost-effective and accessible diagnostic tool. Through the application of rules and recognition of certain patterns, expert readers of ECGs can infer a wide variety of cardiac diagnoses. For these reasons, the ECG has become a standard of care in the investigation of patients with suspected cardiac abnormalities related to CKD [11]. Moreover, the implementation of advanced software in most modern electrocardiographic machines means that vector cardiography indices can be derived with accuracy from standard 12-lead ECGs [12]. The ECG abnormalities were identified using the Minnesota electrocardiography coding system. They were classified into major and minor ECG abnormalities by Circular Hollow Section (CHS) personnel. It needs to be re-evaluated by easily available diagnostic procedures to prevent mortality and morbidity [13]. Patients with CKD have a disproportionate of Cyclic Voltammetry (CV) risk factors, such as diabetes and hypertension, as well as subclinical cardiovascular changes such as left ventricular hypertrophy, myocardial fibrosis, and diastolic dysfunction. It is not fully clear at which stage CKD patients start to develop manifest cardiovascular changes [14]. In addition to myocardial remodeling, CKD is associated with a variety of electrolyte abnormalities that also cause widespread ECG abnormalities (e.g., decreased T-wave amplitudes in hypokalaemia, large-amplitude T-waves, and prolonged Quantum Resonance Spectrometer (QRS) duration in hypercalcemia, and QT Interval Corrected Using Bazett’s Formula (QTcB) prolongation in hypocalcemia). The advancement of technology has enabled the utilization of bedside ultrasonography for identifying the underlying causes of congestion, not only in an outpatient department (OPD) setting but also in emergencies [15]. The objective of this study is to predict cardiac morbidity and mortality in different stages of CKD and identify preventive strategies. The remaining sections are arranged as follows: The literature review was described in Section 2, the study problem identification and motivation were described in Section 3, the proposed technique was described in Section 4, the results were discussed in Section 5, and the paper's conclusion was described in Section 6.

Literature Survey

Electrocardiograms (ECGs) are inexpensive, non-invasive, widely available, and rapid diagnostic tests frequently obtained during routine visits, before exercise, during preoperative evaluation, and for patients at increased risk of cardiovascular disease. Holmstrom et al [16] examined the performance of a deep learning model to detect CKD using ECG waveforms and to understand the key features of relevance for our deep learning model to be able to detect CKD, we performed two sets of experiments to evaluate the ECG parameters that are important for identifying CKD. The results suggest that deep learning-based ECG analysis may provide additional value in detecting various CKD stages, especially in younger patients. The clinical significance of this study lies in the potential enhancement of screening methods for the early detection of CKD, which is crucial to enable early treatment and prevent disease progression. Shahri et al [17] described electrocardiographic (ECG) and Echocardiographic (Echo) findings in dialysis patients. Also, to define the correlation between Echo findings and dialysis modality. The abnormal findings in ECG are common among pediatric dialysis patients. Additionally, Echo's assessment noted that systolic dysfunction was common. Significantly lower LV volume in diastole and systole in Peritoneal Dialysis (PD) compared to hemodialysis (HD) cases may suggest that PD patients encounter less pressure and volume loads. These findings can be considered a positive point of PD modality.

Daojun et al [18] proposed different machine learning approaches to build practical prediction models to achieve non-hemorrhagic immediate prediction of serum potassium concentration by ECG, thus providing early warning of death due to sudden adverse consequences of hyperkalemia. The finding of this study is the performance of the different models is related to the severity of hyperkalemia, and these models all performed better in predicting mild hyperkalemia with concentrations greater than 5mmol/L, at this time Extreme Gradient Boosting (XGB), AdaBoost, Seminal Vesicle Mesenchyme (SVM) and Chaotic Neural Network (CNN) performed significantly better than Lateral Rectus (LR). XGB had a higher Analytical Ultracentrifugation (AUC) in mild hyperkalemia, but SVM performed better in predicting more severe hyperkalemia. Yehia et al [19] suggested comparing specific ECG changes as markers of arrhythmias in patients with CKD and patients with end-stage renal disease (ESRD); all without clinically manifest heart disease, with normal control subjects. The findings provide that patients with stage 3-5 CKD and those with ESRD on regular hemodialysis exhibit significant ECG changes that are considered substrates for ventricular as well as supraventricular arrhythmias. Those changes were more evident in patients on hemodialysis. Samaria et al [20] suggested studying the prevalence of dyslipidemia, and its correlation with kidney function in patients on maintenance hemodialysis identifying the lipid pattern in patients on maintenance hemodialysis, and studying the ECG changes in patients on maintenance hemodialysis. The findings of this study show a difference in lipid profile between cases and controls and the study also shows a significant rise in triglyceride levels and a fall in High-Density Lipoprotein (HDL) levels among patients on maintenance hemodialysis. Kaushal et al [21] proposed to study the electrocardiographic and echocardiographic changes in patients with chronic kidney disease on maintenance hemodialysis. This study shows that the most common ECG changes associated with CKD were LVH followed by conduction defect and then ischemic changes. Srinivas et al [22] proposed to estimate various lipid profile abnormalities in Chronic Kidney Disease patients and to identify the predominant lipid pattern in chronic kidney disease patients. The findings show that High-Density Lipoprotein-Cell surface (HDL-C) levels are lower and triglycerides, total cholesterol, and Low-Density Lipoprotein Cell surface (LDL-C) levels were higher in the study group compared to controls and all are statistically significant also predominant lipid abnormality was reduced HDLC levels. Lou et al [23] suggested an extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. This study shows that 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. Ramezankhani et al [24] investigated the association between the Estimated Glomerular Filtration Rate (eGFR) slope and CVD among individuals with and without diabetes. The study provides valuable insights into the association between eGFR slope and CVD in individuals with and without diabetes also found that a steeper decline in the eGFR slope was significantly associated with a higher risk of CVD events in individuals with diabetes, even after adjusting for baseline eGFR. Ghafouri et al [25] suggested that through the detailed history-taking and cascade screening of families with BrS patients, clinicians should find asymptomatic cases and take timely actions to avoid malignant arrhythmic events and Sudden Cardiac Death (SCD). The findings validate the prognostic value of scoring systems for selecting therapeutic options in Brugada Syndrome (BrS) patients, especially asymptomatic patients.

1. Research Problem Definition and Motivation

Chronic kidney disease (CKD) is a prevalent medical condition associated with various cardiovascular complications. Among these complications, changes in electrocardiographic (ECG) patterns have been recognized as significant indicators of cardiac abnormalities in CKD patients. However, there is a need for a comprehensive understanding of the nature, prevalence, and clinical implications of these ECG changes in the context of CKD. To explore the clinical significance of ECG changes in CKD patients, including their impact on patient outcomes. Understanding the ECG changes in CKD patients is essential for early diagnosis, risk stratification, and improved patient management. This knowledge can aid in the development of targeted interventions to prevent and manage cardiac complications in this high-risk population. Additionally, it may provide insights into the pathophysiological mechanisms underlying these ECG changes. This study will adopt a descriptive cross-sectional design, collecting ECG data from a representative sample of CKD patients. ECG changes in this population, the study will contribute valuable information to the medical community, assisting in the development of better clinical strategies for CKD patients.

CKD and cardiovascular disease are intricately linked, and ECG changes may serve as a bridge between these two conditions. Investigating ECG changes in CKD patients is motivated by the need to better comprehend the mechanisms underlying this relationship. ECG is a widely accessible and non-invasive diagnostic tool. Identifying ECG changes in CKD patients can aid in the early detection of cardiac abnormalities, providing an opportunity for timely intervention to prevent adverse cardiovascular events. Certain ECG changes may be associated with increased cardiovascular risk in CKD patients. By characterizing these changes, the study can contribute to risk stratification and more targeted treatment strategies. CKD patients often have complex medical needs, and optimizing their care requires a thorough understanding of all associated health issues, including cardiac complications. Knowledge of ECG changes can lead to improved patient management and outcomes. the motivation for this study stems from the pressing need to unravel the cardiovascular implications of CKD, especially concerning ECG changes. The research attempts to clarify this issue to contribute to improved diagnosis, risk stratification, and care for CKD patients, ultimately reducing the burden of cardiovascular disease in this vulnerable population.

Objective of Study

• To predict the cardiological morbidity and mortality from Stages of CKD and necessary preventive and curative strategies to improve the outcomes.
• To study the correlation among Renal Parameters and ECG findings in different stages of CKD and factors influencing their association.

The methodology and processes that will be employed to gather, examine, and decipher information about changes in electrocardiograms in individuals suffering from chronic renal disease. Patients with chronic renal disease are among the target population. Whether the sample is convenience, stratified, or random, it needs to be specified precisely. Patients with chronic renal disease are part of the target population. Consequently, the research proposed a descriptive cross-sectional study to address an essential aspect of medical investigation. This study unfolded at the Department of Physiology, SCBMCH, in Cuttack, Odisha, during the timeframe from December 2019 to October 2021. It aimed to delve into electrocardiographic alterations in patients with chronic kidney disease, presenting a descriptive cross-sectional analysis to contribute valuable insights to the medical field.

illustrates the subsequent step involves the practical collection of samples from the identified CKD patient population. Take a participant's 12-lead ECG as a normal procedure. Gather clinical data from participants, such as the stage of their renal illness, other conditions, drugs, and pertinent medical background. Gathering and evaluating information on ECG alterations in patients with chronic renal disease. It highlights how crucial it is to take ethical issues, data quality, and the findings in clinical applicability. Calculating the sample size is an important factor that affects the accuracy and statistical power of the study. The sample size is calculated using Cochran's formula, among other techniques, while accounting for the required level of statistical confidence and the size of the population. To ensure representative and unbiased sampling, a random sampling technique is employed, granting each member of the population an equal opportunity to be included. Data collection is carried out, including the acquisition of pertinent patient data and electrocardiographic data, which constitute the basis for the study's analyses.

1.1          Study Period and Design

During the period spanning from December 2019 to October 2021, a dedicated and methodical research effort was undertaken to gain deeper insights into a specific subject of interest. The significant chapter of academic exploration and personal growth unfolded. This timeframe was marked by a series of intellectual endeavors and experiences that left an indelible impact on my journey of learning and development. In the field of medicine, it is critical to comprehend the complex relationships between chronic renal illness and its effects on heart health. To fully understand this complicated relationship, the cross-sectional study design, which is renowned for its capacity to present a picture of a particular population at a single point in time, is essential.

Study Population

The study population comprises individuals in all stages of Chronic Kidney Disease (CKD) with End-Stage Renal Disease (ESRD) who have sought treatment at the Outpatient Department of Nephrology at SCBMCH. These patients meet specific criteria, including experiencing azotemia for a duration exceeding three months, exhibiting signs and symptoms of uremia, demonstrating reduced kidney size bilaterally as confirmed by ultrasound (USG), presenting broadcasts in their urinary sediment, and displaying symptoms or signs indicative of renal osteodystrophy. The confirmation of these clinical criteria and investigative findings is conducted by the Department of Nephrology, ultimately diagnosing the patients as suffering from chronic kidney disease (CKD).

Inclusion and Exclusion Criteria: Inclusion criteria encompassed the random selection of cases of Chronic Kidney Disease (CKD) from the nephrology Outpatient Department (OPD), patients diagnosed with chronic kidney disease undergoing hemodialysis, and individuals aged 18 years or older. Conversely, exclusion criteria were applied, excluding patients with pre- existing cardiological conditions, such as ischemic heart disease, congenital heart disease, valvular heart disease, and cardiomyopathies, as well as individuals below the age of 18 years. These criteria were methodically designed to ensure the study's focus on electrocardiographic changes in CKD patients without the influence of prior heart-related ailments or age restrictions.

Biosocial Parameters of Study Participants

The study of human behavior and health is a multifaceted field that encompasses a diverse array of factors, including biological, social, and psychological dimensions. One crucial aspect of this research is of biosocial parameters of study participants.

Table 1: Biosocial Characteristics of Study Participants

Table 1, provides data on the biosocial characteristics of the study participants, with a sample size of 139 individuals. These characteristics provide valuable insights into the demographics of the study population. The majority of the participants were male, accounting for 82.7% of the sample, while females comprised 17.3%. A relatively small percentage of participants had higher education, with only 0.7% having postgraduate qualifications. Socioeconomic status is a key determinant of health outcomes and lifestyle choices. The data reveals a range of socioeconomic classes, with a substantial portion falling into the middle- class category. The data shows a broad age distribution, with a relatively small percentage of participants in the 18-29 and 30-39 age groups. The largest proportion falls within the 50-59 and more than 60 years age groups. This distribution is important for understanding how age- related factors may affect behavior and health outcomes.

Renal Morbidity Profile of Study Participants

Table 2 presents the distribution of the study participants among various stages of Chronic Kidney Disease (CKD). In essence, it outlines how many individuals from the research are categorized into different stages of CKD.

Table 2: Distribution of Study Participants among Different Stages of Chronic Kidney Disease

Table 2 presents a comprehensive breakdown of the study participants, consisting of 139 individuals, across distinct stages of Chronic Kidney Disease (CKD) based on their Glomerular Filtration Rate (GFR). The stages include Stage 1, where GFR is normal or high (not specified in the table), Stage 2 characterized as Mild CKD (GFR = 60-89 mL/min) with 31 participants

(22.3%), Stage 3 marked as Moderate CKD (GFR = 30-59 mL/min) featuring 33 participants (23.7%), Stage 4 denoting Severe CKD (GFR = 15-29 mL/min) with 31 participants (22.3%), and finally, Stage 5, the End Stage CKD (GFR <15 mL/min), which is the largest group, encompassing 44 participants (31.7%). This tabulated data serves to classify participants according to the severity of their CKD.

Table 3: Dialysis Status of Study Subjects

  Table 3 outlines the Dialysis Status of the study subjects, comprising a total of 139 individuals. It categorizes participants into two groups based on their dialysis status. The "Yes" category includes 44 participants, accounting for 31.6% of the total, who have undergone dialysis. In contrast, the "No" category encompasses 95 participants, constituting 68.4% of the total, who have not undergone dialysis.

2.1.1 CKD Confirmation Tests

The confirmation of Chronic Kidney Disease (CKD) in the study participants involved a comprehensive series of investigations. These examinations encompassed various aspects of health and included urine complete examination, which evaluated parameters such as pH, specific gravity, protein, sugar, and microscopy. Additionally, blood tests were conducted to assess HB% (hemoglobin levels), FBS/PPBS (fasting and post-prandial blood sugar levels), blood urea, serum creatinine, serum electrolytes, serum calcium, phosphorus, and lipid profile. Radiological evaluations were also part of the diagnostic process, involving chest and abdominal X-rays, which included the Kidney, Ureters, and Bladder (KUB) region. Moreover, abdominal and pelvic ultrasounds were performed to further enhance the diagnostic accuracy. These thorough investigations provided a comprehensive view of the participant's health status, ensuring that the presence and extent of CKD were reliably confirmed.

2.2 Statistical Analysis

The comprehensive statistical analysis is designed to unravel the nuances of CKD across multiple facets. By dissecting the distribution of study participants, dialysis utilization, and biochemical and ECG parameters, provide a deeper understanding of the disease's clinical and epidemiological dimensions using Microsoft Excel.

To report the findings of our descriptive cross-sectional study on the Electrocardiographic changes in Chronic Kidney Disease (CKD) patients. Data analysis and presentation were performed using Microsoft Office Student Edition 2019, with Microsoft Excel utilized for organizing and displaying data. Various tables and spreadsheets were created to present information on demographic characteristics, ECG measurements, biochemical markers, and other pertinent data.

Table 4: Distribution of Serum Urea and Creatinine Levels among Different Stages of CKD

Table 4 there are no participants in stage 1 with normal or high GFR (GFR > 90 mL/min), hence no precise serum urea or creatinine levels are given. The mean serum urea level in stage 2 mild CKD (GFR = 60-89 mL/min) is 67.7 mg/dL on average, with a standard deviation (SD) of 26.4. With an SD of 1.6, the mean serum creatinine level is 3.57 mol/L. “Stage 3 Moderate CKD” The mean blood urea level is 69.3 mg/dL with an SD of 20.1 (GFR = 30-59 mL/min). With an SD of 1.3, the mean serum creatinine level is 4.1 mol/L. “Stage 4 Severe CKD (GFR= 15–29 mL/min)” The mean blood urea level in this stage is 85.4 mg/dL with a standard deviation of 18.6 serum creatinine average.

• Distribution of R-S Interval in Lead 1 and Lead aVF In Different Stages of CKD The distribution of R-S intervals in Lead I and Lead aVF can be important when assessing cardiac health, particularly in the context of Chronic Kidney Disease (CKD). The time gap between an electrocardiogram's (ECG) R-wave and S-wave tells us something about the electrical activity and conduction of the This interval is known as the R-S interval. Because CKD affects the cardiovascular system, changes in the R-S interval might occur along with other cardiac parameters.

Table 5: Distribution of R-S Interval in Lead 1 In Different Stages of CKD

Table 5 provides information about the distribution of R-S Interval in Lead 1 in different stages of Chronic Kidney Disease (CKD). The R-S Interval in Lead 1, and has two categories: "Positive" and "Negative." “Stages of CKD” and indicate the different stages of CKD being compared: Stage 2, Stage 3, Stage 4, and Stage 5. The Chi-Square test is used to determine if there is an association or dependency between the two categorical variables such as R-S Interval and CKD stage. The p-value in this column represents the significance level associated with the Chi-Square test is .74. Statistically significant association between the R-S Interval and CKD stage. Based on the provided information, it appears that the Chi-Square test was used to assess the relationship between R-S Interval in Lead 1 and CKD stage.

Table 6: Distribution of R-S Interval in Lead aVF in Different Stages of CKD

Table 6 presents the distribution of R-S Interval in Lead and for different stages of Chronic Kidney Disease (CKD). The R-S Interval in Lead and has two categories: "Positive" and "Negative." It is a categorical variable indicating the presence or absence of a positive R-S Interval. The Chi-Square test is used to assess the association or independence between two categorical variables, in this case, the R-S Interval and CKD stage at 1.08. The p-value in this column represents the significance level associated with the Chi-Square test at 0.78. It indicates whether there is a statistically significant association between the R-S Interval in Lead aVF and CKD stage.

1.2  Distribution of ST-T-Interval and T-Wave Height in Different Stages of CKD

The distribution of T-wave height and ST-T intervals in various phases of Chronic Kidney Disease (CKD) can reveal important information about the heart health of CKD patients. Because of the intricate relationship between kidney function and the cardiovascular system, cardiac irregularities are common in chronic kidney disease (CKD) and can be detected by changes in the ST-T interval and T-wave height on an electrocardiogram (ECG).

Table 7: Distribution of T-Wave Height in Different Stages of CKD

Table 7 displays the distribution of T-wave Height in millimeters (mm) for different stages of Chronic Kidney Disease (CKD). Interpret the table. The T-wave Height has two categories: "Tall" and "Normal." T-wave height is a continuous variable categorized into two groups based on some predefined criteria. The Chi-Square test is used to assess the association or independence between two categorical variables and the output is 1.35. The p-value in this column represents the significance level associated with the Chi-Square test AT 0.71.

Table 8: Distribution of ST-T-Interval in Different Stages of CKD

Table 8 provides information on the distribution of the ST-T interval in different stages of Chronic Kidney Disease (CKD). The p-value in this column represents the significance level associated with the Chi-Square test at 1.6. The provided χ² value is 1.6, and the p-value is 0.94. This suggests that there is no statistically significant association between the ST-T interval and the CKD stage. The p-value of 0.94 is relatively high and does not meet the typical threshold (often set at 0.05) for statistical significance.

• Comparison of Electrocardiogram Features among CKD Patients of Different Stages Chronic Kidney Disease (CKD) can have a significant impact on various cardiac parameters, including ECG The PR interval represents the time it takes for the electrical impulse to travel from the atria to the ventricles. In CKD due to electrolyte imbalances and conduction system abnormalities, which are common in CKD. Prolonged PR intervals can lead to heart block and arrhythmias. The QRS complex represents ventricular depolarization. While in early CKD stages, QRS duration is usually normal, in advanced CKD, there can be a widening of the QRS complex. This may be indicative of conduction system abnormalities and an increased risk of arrhythmias.

Table 9: Comparison Of PR Interval In Sec Among Different Stages Of CKD

A comparison of the PR interval in seconds (sec) for various stages of chronic kidney disease (CKD) is shown in Table 9. “CKD stage 2 (GFR = 60-89 mL/min)” With a standard deviation of 0.03 seconds, the mean PR interval is 0.17 seconds. The one-way ANOVA test yielded a p- value of 0.06. Moderate CKD “Stage 3 (GFR = 30-59 mL/min)” With a standard deviation of 0.04 seconds, the mean PR interval is 0.18 seconds. Stage 4 Severe CKD: The mean PR interval is 0.17 seconds, with a standard variation of 0.03 seconds (GFR = 15–29 mL/min). The mean PR interval in stage 5 end-stage CKD (GFR 15 mL/min) is 0.19 seconds, with a standard deviation of 0.04 seconds. A statistical indicator of the variations in PR intervals between the stages of CKD is the p-value from the one-way ANOVA test. The significance threshold is not particularly high, but a p-value of 0.06 implies that there may be some statistically significant differences in PR intervals among the phases of CKD.

Table 10: Comparison Of QRS Duration In Sec Among Different Stages Of CKD

                            # One-way ANOVA test applied

Table 10 compares the length of the QRS in seconds (sec) at various stages of chronic kidney disease (CKD). The table shows the one-way ANOVA test's f-value, p-value, and mean values with standard deviations. “Stage 2 Mild CKD” The mean QRS length is 0.10 seconds, with a standard deviation of 0.02 seconds (GFR = 60-89 mL/min). The one-way ANOVA test yielded a p-value of 0.73. The mean QRS duration in stage 3 moderate CKD (GFR = 30-59 mL/min) is 0.09 seconds, with a standard deviation of 0.02 seconds. “Stage 4 Severe CKD” The mean QRS length is 0.10 seconds, with a standard deviation of 0.02 seconds (GFR = 15–29 mL/min). The mean QRS duration in stage 5 end-stage CKD (GFR 15 mL/min) is 0.09 seconds, with a standard deviation of 0.02 seconds. The one-way ANOVA test's p-value of 0.73 shows that there are no statistically significant differences in the length of the QRS among the various stages of CKD. This shows that even as kidney function falls across the various phases of CKD, QRS length does not change appreciably.

Table 11: Comparison of Qtc Duration in Sec among Different Stages of CKD

                            # One-way ANOVA test applied

In Table 11, the duration of the QTc interval (QTc interval adjusted for heart rate) is contrasted for each stage of chronic kidney disease (CKD). CKD stages: This column lists the various CKD stages that are being contrasted. QTc duration in sec (mean SD). The mean (average) QTc duration in seconds and the standard deviation (SD) are shown for each stage of CKD in this column. –Value. The one-way ANOVA (Analysis of Variance) test that was performed on these data is represented by the "F-value" in statistics. It is used to determine whether there are statistically significant variations in the QTc length means throughout the various CKD stages. p-Value. The "P-value" represents the degree of significance for the ANOVA test. It reveals the likelihood that the observed variations in QTc duration are the result of chance. The p-value in this instance is less than 0.01 (usually represented as "0.01"). This indicates that there are statistically significant differences in QTc duration throughout the phases of CKD. In conclusion, the table demonstrates that the QTc duration varies statistically significantly depending on the stage of CKD. The ANOVA test indicates that it is unlikely that these differences are the result of chance.

The results of pairwise comparisons of the P-Wave duration in seconds (sec) at various stages of chronic kidney disease (CKD) are shown in Figure 2. The analyses for each pair of CKD stages, together with the corresponding p-values, “CKD stage 2 versus stage 3” According to the p-value of 0.73, there is no statistically significant variation in the length of the P-Wave between these two stages. The p-value for the comparison between Stage 2 and Stage 4 of CKD is 0.67, showing that there is no statistically significant difference in the length of the P-Wave between these two stages (table 12). The p-value is less than 0.01, showing a statistically significant difference in the length of the P-Wave between Stages 2 and 5 of CKD. The p-value of 0.99 indicates that there is no statistically significant variation in the length of the P-Wave between Stages 3 and 4 of CKD.

Table 12: Inter Group Comparison of Serum P-Wave Duration in Sec

The p-value for the comparison between Stage 3 and Stage 5 of CKD is 0.72, showing that there is no statistically significant variation in the length of the P-Wave between these two stages. The p-value of 0.66 shows that there is no statistically significant variation in the length of the P-Wave between Stages 4 and 5 of CKD.

 shows the findings of pairwise comparisons of the PR interval in seconds (sec) among various stages of chronic kidney disease (CKD). These comparisons examine if the PR intervals between the various CKD stages differ statistically significantly. The p-value of 0.97 shows that there is no statistically significant difference in the PR interval between Stages 2 and 3 of CKD. The p-value of 0.92 indicates that there is no statistically significant difference in the PR interval between Stages 2 and 4 of CKD. The p-value for the comparison between stage 2 and stage 5 of CKD is 0.26, meaning that the PR interval is not statistically different in either case.

Table 13: Inter Group Comparison of Serum PR Interval in Seconds

The p-value of 0.71 indicates that there is no statistically significant difference in the PR interval between Stages 3 and 4 of CKD (table 13). There is no statistically significant difference in the PR interval between Stages 3 and 5 of CKD, according to the p-value of 0.49. The p-value of 0.06 indicates a slightly significant difference in the PR interval between Stages 4 and 5 of CKD. Overall, the results the no statistically significant changes in PR interval between the various stages of CKD, except for a slightly significant difference between Stage 4 and Stage 5.

The results of pairwise comparisons of the QRS duration in seconds (sec) among various stages of chronic kidney disease (CKD) are shown in Figure 4. The p-value of 0.85 shows that there is no statistically significant variation in the length of the QRS between Stages 2 and 3 of CKD. The p-value of 0.90 indicates that there is no statistically significant difference in QRS duration between Stages 2 and 4 of CKD. The p-value for the comparison between Stage 2 and Stage 5 of CKD is 0.70, showing that there is no statistically significant difference in the length of the QRS between these two stages.

Table 14: Inter Group Comparison of QRS Duration In Seconds

Table 14 presents the results of an inter-group comparison of QRS duration in different stages of Chronic Kidney Disease (CKD). The pairwise comparisons and associated p-values indicate that there are no statistically significant

differences in QRS duration between any of the CKD stages. The p-values for all comparisons are higher than the conventional significance level of 0.05, suggesting that QRS duration does not vary significantly among the CKD stages examined

provides the results of Pairwise Comparisons of QTc duration among CKD stages and the associated p-values. Comparison between Stage 2 and Stage 3 of CKD shows that there is no statistically significant difference in QTc duration between these two stages, as the p- value (0.98) is not less than 0.05, which is a common threshold for statistical significance. Similarly, there is no statistically significant difference in QTc duration between Stage 2 and Stage 4 of CKD, as the p-value (0.99) is not less than 0.05. In contrast, the comparison between Stage 2 and Stage 5 of CKD shows a statistically significant difference in QTc duration, as the p-value is less than 0.01, indicating a highly significant difference. There is no statistically significant difference in QTc duration between Stage 3 and Stage 4 of CKD, as the p-value (0.99) is not less than 0.05.

Table 15: Intergroup Comparison of QTc Duration In Sec Among Different Stages Of CKD

The comparison between Stage 3 and Stage 5 of CKD indicates a statistically significant difference in QTc duration, with a p-value less than 0.01 (table 15). Finally, the comparison between Stage 4 and Stage 5 of CKD also shows a statistically significant difference in QTc duration, with a p-value less than 0.01.

This comprehensive study on electrocardiographic changes in CKD patients revealed significant insights into the prevalence and clinical profiles of CKD patients with ESRD. Despite the challenges posed by the COVID-19 pandemic, the study successfully identified a sample of 139 individuals meeting the stringent criteria for CKD. The research underscored the importance of monitoring and early diagnosis of CKD, especially in its advanced stages, to facilitate appropriate clinical management. The findings contribute to a better understanding of CKD and its cardiac implications, emphasizing the need for holistic patient care. The biosocial characteristics of the study participants reveal a diverse sample, with a significant majority of male participants (82.7%) and a range of educational backgrounds among the heads of the family. The majority of participants belonged to the Hindu religion (90%), and the age distribution varied. In terms of renal morbidity profiles, the distribution of study participants across different stages of CKD showed a substantial representation across Stages 2, 3, 4, and 5, with Stage 5 (End Stage CKD) having the highest percentage (31.7%). A notable proportion of participants (31.6%) had undergone dialysis, indicating the severity of CKD in the sample. Further analysis of medical parameters, including serum urea, serum creatinine, and serum potassium levels, revealed significant variations among CKD stages. For example, serum urea and creatinine levels increased with the progression of CKD stages, as expected. Similarly, serum potassium levels also showed a significant increase with CKD stage progression. In addition to these parameters, electrocardiogram features, such as P-wave heights, P-wave duration, and QRS duration, were compared among different CKD stages. The results indicated that while there were some variations, not all comparisons showed statistically significant differences. It highlights the importance of monitoring medical parameters and ECG features across different CKD stages for early diagnosis and appropriate management. However, further research is needed to explore the clinical implications of these findings. This study underscores the significance of early CKD diagnosis and management, considering the diverse characteristics and medical profiles of individuals with CKD in this study.

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