Patterns and outcomes of cardiovascular complications among adult patients with chronic kidney diseases attending Benjamin Mkapa Hospital in Dodoma, Tanzania A protocol of a prospective longitudinal study

Introduction Chronic Kidney Disease (CKD) affects more than 800 million people globally, the high cardiovascular mortality and morbidity related to CKD result from unique CKD-related risk factors such as anemia and mineral metabolism disturbances and underlying CKD risk factors such as hypertension, diabetes mellitus, and dyslipidemia. The presence of CKD-related structural and functional alterations in the heart and blood vessels increases the risk of hypertension, left ventricular hypertrophy, and heart failure. In advanced CKD stages, the increased risk of hospitalization, heart failure worsening, stroke and death are commonly seen due to increased risk of ischemic heart disease, arrhythmias, and pericarditis. Methodology The study will follow a prospective longitudinal design recruiting patients 18 and above years with CKD stage 3 confirmed by estimated glomerular filtration rate (eGFR) < 60ml/min/1.73m2 for the past 3 months, or proteinuria of greater than 200mg/g attending Benjamin Mkapa nephrology and cardiology clinics. Cardiovascular complications will be identified following screening .as per operational definition and a comprehensive baseline assessment. A monthly assessment of cardiovascular complications will be done for a period of 6 months through clinical examinations, imaging and laboratory tests. Cornell and Sokolow-Lyon’s ECG criteria will detect LVH, but ECHO will rule it out. The Framingham criteria will be used to diagnose heart failure. A 12-lead ECG will detect arrhythmias, and an ECG plus cardiac markers will be used to diagnose ischemic heart disease. ECG and ECHO findings of pericardial sac thickening or fluid presence will indicate pericarditis. Continuous and discrete variables will be summarized as mean ±standard deviation (SD), medians, and interquartile ranges, while categorical data will be summarized as frequency and proportion. A binomial logistic regression model will be used to assess the association between independent variables and CVD outcomes