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Electroencephalographic Patterns in Patients with Cardiometabolic Diseases: A Pilot Observational Study (0)

 

1. Background

Cardiometabolic diseases, including type 2 diabetes mellitus, arterial hypertension, obesity, and metabolic syndrome, are major contributors to global morbidity and mortality. These disorders are increasingly recognized to affect the brain through chronic cerebral hypoperfusion, microvascular injury, inflammation, and impaired autoregulation. Such changes may produce subclinical functional brain abnormalities long before overt neurological events such as stroke or dementia occur. Electroencephalography (EEG) is a non‑invasive, bedside measure of brain electrical activity that can detect these functional changes and has been used to study cerebral consequences of metabolic and vascular disease (Abubakar & Gan, 2016; Rafnsson et al., 2007).

Multiple studies document EEG abnormalities in people with diabetes, even in the absence of overt neurological disease. The most commonly reported findings are diffuse slowing of background rhythm (increased theta/delta), reduced amplitude, and occasional focal slowing, particularly in association with poor glycemic control or microvascular complications. Quantitative EEG (qEEG) studies demonstrate altered power spectra and connectivity patterns in type 2 diabetes mellitus, which correlate with cognitive impairment and white matter abnormalities on MRI (Akbari & Rezaei, 2021; Vecchio et al., 2020). These changes are thought to reflect chronic microangiopathy and metabolic brain dysfunction.

EEG abnormalities in hypertensive patients have been recognized since early EEG research, with increased rates of background slowing and low‑voltage theta/delta activity compared with normotensive controls. These abnormalities are more pronounced in patients with coexisting cerebrovascular disease. Hypertension‑related impairment of cerebral autoregulation and progressive small vessel disease are plausible contributors to such EEG patterns. More recent work links blood pressure variability and reduced cerebrovascular reactivity to EEG signatures indicative of impaired perfusion (de Heus et al., 2018).

Emerging qEEG studies in metabolic syndrome and obesity report alterations in resting‑state power spectra and connectivity, including frontal slowing and changes in alpha rhythm. These EEG alterations may be related to insulin resistance, systemic inflammation, and comorbid sleep‑disordered breathing, all of which commonly coexist with obesity. Such findings have also been associated with cognitive complaints and increased dementia risk (Cassani et al., 2018; García‑Martínez et al., 2019).

Several mechanisms may translate cardiometabolic derangements into EEG changes:

- Cerebral small vessel disease produces chronic hypoperfusion and white matter injury, leading to reduced fast frequencies and increased slow wave activity (Akbari & Rezaei, 2021).
- Impaired cerebral autoregulation, driven by hypertension and blood pressure variability, may cause intermittent hypoperfusion detectable as background slowing (de Heus et al., 2018).
- Metabolic and inflammatory effects, such as hyperglycemia, insulin resistance, and cytokine activity, alter neuronal excitability and network synchrony (Abubakar & Gan, 2016).

Resting EEG slowing and altered spectral markers have been associated with cognitive impairment and may predict progression to dementia in older adults. In cardiometabolic populations, these EEG changes may therefore function as early functional biomarkers of brain involvement and future cerebrovascular or cognitive outcomes. Additionally, EEG is sensitive to acute ischemia and hypoperfusion in perioperative and vascular settings, reinforcing its utility as a marker of cerebral perfusion dynamics (Rafnsson et al., 2007; Vecchio et al., 2020).

Despite increasing evidence, several gaps remain. First, there is limited published EEG data from African and other low‑resource settings, where cardiometabolic disease prevalence is rising and risk factor patterns differ from high‑income countries (Abubakar & Gan, 2016). Second, few studies integrate EEG findings with contemporaneous cardiac (echocardiography), renal (urinalysis), and imaging data to map multi‑system interactions. This pilot study will provide feasibility data and preliminary correlations needed to design a powered, integrative study.

Thus, there is a compelling rationale to conduct a focused EEG pilot study in a cardiometabolic clinic population to generate preliminary prevalence estimates, explore associations with clinical / metabolic variables, and assess feasibility for a larger multi‑system investigation.

2. Study Objectives

Primary Objective:
- To characterize EEG patterns in patients with cardiometabolic disorders.

Secondary Objectives:
1. To determine the prevalence and types of EEG abnormalities (background slowing, amplitude reduction, focal slowing, epileptiform discharges) in this population.
2. To examine associations between EEG abnormalities and cardiometabolic parameters such as blood pressure, BMI, HbA1c, and lipid profile.
3. To assess the feasibility and critical methodological considerations for scaling to a multi‑system study that integrates EEG, echocardiography, renal and imaging assessments.

3. Methods

Study Design:
Observational, cross‑sectional pilot study.

Study Setting:
[Your clinic or hospital name], [City, Country].

Study Population:
- Inclusion criteria: Adults (≥18 years) with one or more of the following: type 2 diabetes mellitus, arterial hypertension, obesity, or metabolic syndrome.
- Exclusion criteria: Known epilepsy or seizure disorders; history of stroke or central nervous system infection; severe cognitive impairment interfering with consent or EEG interpretation; other primary neurological disorders.

Sample Size:
Approximately 20‑40 participants.

Data Collection Procedures:
- Demographics: Age, sex, BMI, duration of disease, medications
- Clinical / Metabolic Measures: BP, HbA1c, fasting glucose, lipid profile, renal function
- EEG Recording: Standard 10‑20 montage, 20‑30 minutes, eyes open/closed, variables: background rhythm, amplitude, focal slowing, epileptiform discharges
- Medications: Antihypertensives, antidiabetics, lipid‑lowering drugs, sedatives

Data Analysis:
- Descriptive statistics (means, SDs, frequencies)
- Prevalence of EEG abnormalities described
- Correlational analyses between EEG abnormalities and clinical/metabolic measures
- Comparative analyses if data allows (e.g., slowing vs no slowing)
- Software: SPSS/Stata/R

Ethical Considerations:
- Ethical approval from [Institutional Review Board / Ethics Committee]
- Written informed consent from participants
- Confidentiality ensured with coded IDs
- EEG is safe and non‑invasive

4. Expected Outcomes

- Estimate prevalence of EEG abnormalities in cardiometabolic patients
- Describe common types of EEG changes (background slowing, amplitude reduction)
- Preliminary associations with metabolic/cardiovascular parameters
- Feasibility assessment (recruitment rate, data quality, challenges)

5. Significance

This pilot study will provide foundational data on cerebral functional involvement in cardiometabolic disease in a local/regional population. The findings will help in risk stratification, early detection of brain involvement, and contribute to designing interventions that may prevent or delay neurological complications. These results will also underpin a larger, multi‑organ, longitudinal study integrating EEG, echocardiography, renal measures, and imaging.

6. Timeline

- Preparation and approvals: 2 weeks
- Recruitment: 4 weeks
- EEG & data collection: 4 weeks
- Data analysis: 2 weeks
- Report writing: 2-3 weeks

7. Budget Estimate

EEG consumables (electrodes, gels): $100‑200
Technician/research assistant: [insert]
Data management/statistics: $50‑100
Miscellaneous: $50‑100
Total: ~$200‑400 + personnel costs

8. References

Abubakar, S. A., & Gan, S. H. (2016). The impact of diabetes mellitus on cognitive impairment and dementia: An overview. Biomedicine & Pharmacotherapy, 83, 1007–1012. https://doi.org/10.1016/j.biopha.2016.07.070

Akbari, T., & Rezaei, O. (2021). Electroencephalographic changes in diabetes mellitus: A review. Neuropsychiatric Disease and Treatment, 17, 1797–1806. https://doi.org/10.2147/NDT.S311995

Cassani, R., Estévez, P. A., Martínez‑Montes, E., & Ossandón, T. (2018). Quantitative EEG analysis of the effects of metabolic syndrome on brain activity. Frontiers in Neuroscience, 12, 682. https://doi.org/10.3389/fnins.2018.00682

de Heus, R., Olde Rikkert, M. G. M., Tzourio, C., Leeuwis, A. E., Bouvy, W. H., Barten, R. P. M., … van Dijk, E. J. (2018). Cerebral blood flow and cerebrovascular reactivity are impaired in hypertension and relate to cognitive decline. Journal of Hypertension, 36(8), 1638–1646. https://doi.org/10.1097/HJH.0000000000001745

García‑Martínez, B., Soria, M. L., Rodríguez‑Fernández, J. M., & Serrano, J. (2019). EEG markers of metabolic syndrome and obesity: A review of current evidence. Clinical Neurophysiology, 130(8), 1451–1460. https://doi.org/10.1016/j.clinph.2019.05.006

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 Background

Cardio-Kidney-Metabolic (CKM) disease is an integrated health concept that recognizes the close relationship between cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes mellitus (T2DM), obesity, hypertension, and other metabolic disorders. These conditions frequently coexist and interact through shared biological pathways, resulting in accelerated disease progression and an increased risk of cardiovascular events, kidney failure, disability, and premature mortality. In 2023, the American Heart Association (AHA) introduced the CKM health framework to promote a comprehensive approach to the prevention, early detection, risk stratification, and management of these interconnected conditions (Ndumele et al., 2023).

The global burden of CKM disease continues to increase due to population ageing, urbanization, unhealthy diets, physical inactivity, tobacco use, and obesity. According to the World Health Organization (WHO), non-communicable diseases (NCDs) account for approximately 74% of all deaths worldwide, with cardiovascular diseases responsible for the largest proportion. Diabetes and chronic kidney disease also contribute substantially to morbidity, mortality, and healthcare expenditure, particularly in low- and middle-income countries (WHO, 2023).

In Kenya, the prevalence of hypertension, diabetes mellitus, obesity, and chronic kidney disease has increased over the past decade. This epidemiological transition has placed significant pressure on the healthcare system, which continues to face challenges related to early diagnosis, continuity of care, long-term patient monitoring, and coordinated multidisciplinary management. Many patients present with multiple coexisting conditions requiring comprehensive assessment, regular follow-up, and timely clinical interventions.

Effective management of CKM disease depends on the availability of complete, accurate, and standardized clinical information. Healthcare providers require access to longitudinal patient data, including demographic characteristics, medical history, physical examination findings, laboratory investigations, medications, imaging studies, and clinical outcomes. Such information supports clinical decision-making, monitoring of disease progression, evaluation of treatment effectiveness, and identification of patients at increased risk of adverse outcomes.

However, routine clinical documentation in many healthcare facilities remains fragmented. Patient information is often distributed across paper records, laboratory reports, imaging reports, pharmacy records, and independent electronic systems that do not communicate with one another. This fragmentation contributes to incomplete documentation, duplication of data, poor continuity of care, and difficulties in monitoring patients over time. It also limits the ability of healthcare facilities to conduct clinical audits, measure quality indicators, evaluate outcomes, and generate evidence for service improvement.

Clinical registries have become an important component of modern healthcare systems because they provide a structured mechanism for collecting standardized information on patients with specific diseases or health conditions. According to Gliklich, Leavy, and Dreyer (2020), patient registries are organized systems that use observational methods to collect uniform data for evaluating specified outcomes among defined populations. Registry data support routine clinical care, quality improvement, surveillance, health services research, and policy development.

Digital clinical registries provide several advantages over traditional paper-based systems. They improve data completeness, minimize transcription errors, facilitate real-time data entry, enhance accessibility of patient information, support automated reporting, and enable longitudinal follow-up. Digital registries can also improve monitoring of quality indicators, facilitate multidisciplinary care, and generate reliable data for clinical research and healthcare planning (WHO, 2021).

Although disease registries have been successfully implemented for cardiovascular disease, renal disease, diabetes, cancer, and stroke in many countries, there is currently no standardized Digital Cardio-Kidney-Metabolic Clinical Registry specifically designed for integrated CKM care in Coastal Kenya. Existing documentation systems generally focus on individual diseases rather than the broader CKM continuum and therefore do not adequately capture the complexity of patients living with multiple interconnected conditions.

The development of a Digital Cardio-Kidney-Metabolic Clinical Registry represents an opportunity to strengthen routine clinical documentation by providing a standardized platform for collecting comprehensive patient information across the CKM spectrum. Such a registry would support continuity of care, improve data quality, facilitate monitoring of patient outcomes, strengthen quality improvement initiatives, and provide a sustainable foundation for future registry-based clinical and public health research.

This study therefore proposes the development, pilot implementation, and evaluation of a Digital Cardio-Kidney-Metabolic Clinical Registry for routine care in Coastal Kenya. The registry will be designed to capture standardized clinical information, support routine patient management, and improve the quality and accessibility of health information. The pilot evaluation will assess the registry's feasibility, usability, data quality, completeness, and acceptability among healthcare workers during routine clinical practice. The findings will provide evidence to guide future scale-up of CKM registries within Kenya and contribute to strengthening digital health systems for the management of non-communicable diseases.

Problem Statement

Cardio-Kidney-Metabolic (CKM) disease is an emerging public health challenge characterized by the coexistence of cardiovascular disease, chronic kidney disease, diabetes mellitus, hypertension, obesity, and other metabolic disorders. These conditions require lifelong follow-up, multidisciplinary management, and continuous monitoring to reduce disease progression, complications, hospitalization, and premature mortality (Ndumele et al., 2023).

Despite the increasing burden of CKM disease in Kenya, routine clinical documentation remains fragmented and inconsistent. In many health facilities, patient information is recorded using paper-based records or multiple non-integrated electronic systems, resulting in incomplete documentation, duplication of information, poor continuity of care, and limited longitudinal follow-up. Consequently, healthcare providers may not have access to comprehensive patient information required for effective clinical decision-making, monitoring of disease progression, or evaluation of treatment outcomes.

The absence of a standardized Digital Cardio-Kidney-Metabolic Clinical Registry further limits the ability of healthcare facilities to monitor quality of care, measure clinical performance indicators, conduct routine clinical audits, and generate reliable evidence for planning and service improvement. In addition, the lack of standardized patient-level data constrains clinical research and reduces opportunities to evaluate the epidemiology, management, and outcomes of CKM disease in Kenya.

Although digital health technologies are increasingly being adopted to strengthen health information systems, there is limited evidence on the development and implementation of integrated digital clinical registries specifically designed for CKM care in Kenya. Establishing a standardized Digital Cardio-Kidney-Metabolic Clinical Registry may improve the quality of routine clinical documentation, facilitate longitudinal patient follow-up, support multidisciplinary care, and strengthen the use of clinical data for quality improvement and future research.

Justification of the Study

The burden of cardiovascular disease, diabetes mellitus, chronic kidney disease, hypertension, and obesity continues to increase in Kenya, creating a growing need for integrated systems that support long-term patient management. Effective management of CKM disease depends on accurate, complete, and timely clinical information that enables healthcare providers to monitor disease progression, evaluate treatment outcomes, and coordinate multidisciplinary care.

A Digital Cardio-Kidney-Metabolic Clinical Registry will provide a standardized platform for collecting, storing, and managing patient information throughout the continuum of care. By improving the completeness and quality of clinical documentation, the registry will support evidence-based clinical decision-making, facilitate continuity of care, and strengthen routine monitoring of patient outcomes.

The registry will also enhance quality improvement by enabling healthcare facilities to monitor key performance indicators, conduct clinical audits, identify gaps in service delivery, and evaluate adherence to clinical guidelines. Furthermore, it will establish a sustainable digital database that can support future observational studies, implementation research, and registry-based clinical investigations involving electrocardiography, echocardiography, laboratory monitoring, imaging, and long-term CKM outcomes.

At the health system level, the findings from this study will contribute to strengthening digital health implementation and health information systems for non-communicable diseases. The registry may serve as a scalable model for integrated CKM care in other counties and support national efforts to improve chronic disease surveillance, healthcare planning, and resource allocation. The study therefore has the potential to benefit patients, healthcare providers, health managers, researchers, and policymakers by improving the availability and use of high-quality clinical information for CKM care.

Research Question

Can a Digital Cardio-Kidney-Metabolic Clinical Registry be developed, pilot implemented, and successfully evaluated to support routine patient care in Coastal Kenya?


6.0 General Objective

To develop, pilot implement, and evaluate a Digital Cardio-Kidney-Metabolic Clinical Registry for routine care in Coastal Kenya.


7.0 Specific Objectives

  1. To identify the minimum dataset required for a Digital Cardio-Kidney-Metabolic Clinical Registry.
  2. To design and develop a Digital Cardio-Kidney-Metabolic Clinical Registry for routine patient care.
  3. To pilot implement the Digital Cardio-Kidney-Metabolic Clinical Registry in selected health facilities in Coastal Kenya.
  4. To evaluate the registry in terms of data completeness, data quality, usability, feasibility, acceptability, and implementation during routine clinical practice.

8.0 Literature Review

8.1 Cardio-Kidney-Metabolic Disease

Cardio-Kidney-Metabolic (CKM) disease is a recently introduced framework that recognizes the biological and clinical interaction between cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes mellitus (T2DM), obesity, hypertension, and related metabolic disorders. The American Heart Association (AHA) proposed the CKM framework to promote integrated prevention, early detection, risk assessment, and multidisciplinary management of these interconnected conditions (Ndumele et al., 2023).

The CKM framework recognizes that dysfunction in one organ system frequently accelerates disease progression in others. For example, diabetes mellitus increases the risk of chronic kidney disease and cardiovascular disease, while chronic kidney disease independently increases cardiovascular morbidity and mortality. Similarly, obesity contributes to hypertension, insulin resistance, dyslipidaemia, and chronic inflammation, creating a cycle of progressive organ damage.

The integrated nature of CKM disease requires coordinated clinical management supported by comprehensive patient information collected throughout the continuum of care.


8.2 Burden of Cardio-Kidney-Metabolic Disease

Non-communicable diseases are responsible for approximately three-quarters of global deaths, with cardiovascular diseases remaining the leading cause of mortality worldwide (WHO, 2023). The prevalence of hypertension, diabetes, obesity, and chronic kidney disease continues to increase in sub-Saharan Africa due to population growth, urbanization, dietary changes, physical inactivity, tobacco use, and ageing populations.

Kenya is experiencing a similar epidemiological transition. The prevalence of hypertension and diabetes has increased substantially over the last decade, while obesity and chronic kidney disease are becoming increasingly common. These conditions frequently coexist, resulting in complex patients who require long-term multidisciplinary management.

The growing burden of CKM disease has increased demand for healthcare services while highlighting the need for integrated patient management systems capable of supporting longitudinal follow-up.


8.3 Clinical Registries

Clinical registries are organized systems designed to collect standardized information on patients with specific diseases or health conditions for predetermined clinical, scientific, or policy purposes (Gliklich et al., 2020).

Unlike routine hospital records, registries collect predefined data elements using standardized definitions, thereby improving consistency and comparability of information across patients and healthcare facilities.

Disease registries have been widely used in cardiovascular disease, chronic kidney disease, diabetes, stroke, cancer, trauma, and congenital disorders. Registry data support:

  • Clinical care

  • Disease surveillance

  • Quality improvement

  • Clinical audit

  • Performance monitoring

  • Health services research

  • Guideline implementation

  • Policy formulation

Several international registries have demonstrated improvements in adherence to clinical guidelines, patient outcomes, and quality of care through continuous monitoring and feedback.


8.4 Digital Health and Clinical Registries

Digital health refers to the application of information and communication technologies to improve healthcare delivery, health information management, and health system performance (WHO, 2021).

Digital clinical registries represent one of the most important applications of digital health because they enable:

  • Standardized electronic data collection

  • Real-time data entry

  • Longitudinal patient tracking

  • Automated reporting

  • Clinical decision support

  • Monitoring of quality indicators

  • Secure storage of patient information

  • Data sharing across healthcare teams

Compared with paper-based systems, digital registries improve data completeness, reduce transcription errors, enhance accessibility of patient records, and facilitate continuous monitoring of patient outcomes.

The WHO Global Strategy on Digital Health emphasizes strengthening health information systems through digital technologies to improve healthcare quality, efficiency, and equity (WHO, 2021).


8.5 Cardio-Kidney-Metabolic Registries

Several disease registries exist for cardiovascular disease, diabetes mellitus, chronic kidney disease, and heart failure. However, these registries generally focus on individual diseases rather than the integrated CKM continuum.

Because CKM disease represents an interaction between multiple chronic conditions, separate disease registries may not adequately capture the complexity of patient care.

An integrated CKM Clinical Registry has the potential to collect comprehensive patient information within a single platform, allowing healthcare providers to monitor cardiovascular, renal, and metabolic health simultaneously.

Such a registry can support multidisciplinary care while facilitating monitoring of disease progression, treatment outcomes, and quality indicators.


8.6 Registry Evaluation

Developing a registry alone is insufficient; its performance must also be evaluated to determine whether it is suitable for routine clinical use.

Evaluation commonly includes assessment of:

  • Data completeness

  • Data accuracy

  • Usability

  • Feasibility

  • Acceptability

  • User satisfaction

  • Timeliness

  • Sustainability

Implementation science frameworks, including RE-AIM, provide structured approaches for evaluating healthcare innovations. RE-AIM examines five domains:

  • Reach

  • Effectiveness

  • Adoption

  • Implementation

  • Maintenance

These domains provide comprehensive information regarding the practical use and long-term sustainability of digital health interventions.


8.7 Knowledge Gap

Although numerous disease-specific registries have been developed internationally, there is limited evidence regarding integrated Digital Cardio-Kidney-Metabolic Clinical Registries in low- and middle-income countries.

In Kenya, most healthcare facilities continue to rely on fragmented documentation systems that do not adequately support integrated CKM care. Published literature describing the development, pilot implementation, and evaluation of CKM clinical registries is scarce.

Consequently, healthcare providers have limited access to standardized longitudinal data required for monitoring patient outcomes, conducting quality improvement initiatives, and supporting evidence-based decision-making.

Developing and evaluating a Digital Cardio-Kidney-Metabolic Clinical Registry in Coastal Kenya will address this gap by providing an integrated digital platform capable of improving clinical documentation, continuity of care, data quality, and future registry-based research.


8.8 Conceptual Framework

The study is based on the premise that standardized digital clinical documentation improves the quality of patient care and health information.

Inputs

  • Literature review

  • Stakeholder engagement

  • Clinical guidelines

  • Digital health expertise

  • Information technology infrastructure

Processes

  • Identification of the minimum CKM dataset

  • Registry design

  • Software development

  • Healthcare worker training

  • Pilot implementation

Outputs

  • Functional Digital CKM Clinical Registry

  • Standardized electronic clinical documentation

  • High-quality patient data

  • Registry reports and dashboards

Outcomes

  • Improved data completeness

  • Improved data quality

  • Better continuity of care

  • Enhanced multidisciplinary management

  • Improved monitoring of CKM patients

Long-Term Impact

  • Strengthened routine CKM care

  • Improved quality improvement programmes

  • Enhanced digital health systems

  • Sustainable registry-based research platform

  • Better patient outcomes

9.0 Methodology

9.1 Study Design

This study will employ a mixed-methods implementation study comprising three sequential phases: (i) development of a Digital Cardio-Kidney-Metabolic (CKM) Clinical Registry, (ii) pilot implementation of the registry in selected health facilities, and (iii) evaluation of the registry during routine clinical care. The study will combine quantitative and qualitative methods to evaluate the performance and implementation of the registry.


9.2 Study Area

The study will be conducted in selected health facilities providing Cardio-Kidney-Metabolic services in Coastal Kenya. These facilities offer care for patients with hypertension, diabetes mellitus, chronic kidney disease, obesity, dyslipidaemia, and cardiovascular diseases.


9.3 Study Population

The study population will comprise:

  • Adult patients (≥18 years) diagnosed with one or more Cardio-Kidney-Metabolic conditions and receiving care at participating facilities.

  • Healthcare professionals involved in CKM care, including physicians, clinical officers, nurses, nutritionists, pharmacists, laboratory personnel, and health records officers who will use the registry.


9.4 Eligibility Criteria

Inclusion Criteria

Patients

Participants will be eligible if they:

  • Are aged 18 years or older.

  • Have at least one confirmed Cardio-Kidney-Metabolic condition.

  • Attend one of the participating health facilities during the study period.

  • Provide written informed consent where required.

Healthcare Workers

Healthcare workers will be eligible if they:

  • Are directly involved in CKM patient management.

  • Have received training on the registry.

  • Consent to participate in the evaluation.


Exclusion Criteria

Patients who decline participation or have insufficient clinical information for registration will be excluded. Healthcare workers not directly involved in registry use will not participate in the evaluation.


9.5 Study Phases

Phase I: Development of the Registry

This phase will involve the design and development of the Digital CKM Clinical Registry.

Activities will include:

  • Comprehensive literature review.

  • Review of national and international CKM guidelines.

  • Review of existing cardiovascular, diabetes, and kidney disease registries.

  • Identification of the minimum CKM dataset.

  • Development of standardized data definitions.

  • Development of electronic data collection forms.

  • Database design.

  • User interface development.

  • Development of a registry user manual.

  • Development of standard operating procedures (SOPs).


Phase II: Pilot Implementation

The registry will be introduced into routine clinical practice in selected health facilities.

Activities will include:

  • Installation of the registry.

  • Training healthcare workers.

  • Registration of eligible CKM patients.

  • Routine electronic data entry.

  • Technical support and supervision.

  • Monitoring of registry utilization.


Phase III: Registry Evaluation

The pilot registry will be evaluated using the RE-AIM Implementation Framework.

The evaluation will assess:

Reach

  • Number of participating facilities.

  • Number of trained healthcare workers.

  • Number and proportion of eligible CKM patients enrolled.

Effectiveness

  • Completeness of clinical documentation.

  • Data quality.

  • Availability of longitudinal patient information.

  • Improvement in routine documentation.

Adoption

  • Proportion of clinicians routinely using the registry.

  • Frequency of registry use.

  • Uptake across different clinical departments.

Implementation

  • Ease of use.

  • Time required for patient registration.

  • Technical challenges encountered.

  • Fidelity to registry procedures.

Maintenance

  • Continued use of the registry during the pilot period.

  • Intention to continue using the registry after completion of the study.

  • Recommendations for scale-up.


9.6 Registry Variables

The registry will collect standardized information including:

Demographic Information

  • Patient identification number

  • Age

  • Sex

  • Residence

  • Occupation

Clinical Information

  • Diagnosis

  • Medical history

  • Smoking status

  • Alcohol use

  • Physical activity

  • Family history

Anthropometric Measurements

  • Height

  • Weight

  • Body Mass Index

  • Waist circumference

Vital Signs

  • Blood pressure

  • Heart rate

  • Respiratory rate

  • Oxygen saturation

Laboratory Data

  • Blood glucose

  • HbA1c

  • Serum creatinine

  • Estimated glomerular filtration rate (eGFR)

  • Lipid profile

  • Urine albumin

Investigations

  • Electrocardiography (ECG)

  • Echocardiography

  • Spirometry

  • Carotid duplex ultrasound

  • Other clinically indicated investigations

Treatment

  • Current medications

  • Lifestyle interventions

Outcomes

  • Follow-up visits

  • Hospital admissions

  • Cardiovascular events

  • Disease progression

  • Mortality (where applicable)


9.7 Data Collection

Data will be entered directly into the Digital CKM Clinical Registry during routine patient consultations using standardized electronic data collection forms.

Healthcare workers will receive training before implementation to ensure consistency in data entry.

Routine data quality checks will be conducted throughout the study period.


9.8 Data Quality Assurance

Data quality will be maintained through:

  • Standardized data definitions.

  • Mandatory electronic fields.

  • Validation rules within the registry.

  • User training.

  • Routine supervision.

  • Periodic data quality audits.

  • Double-checking selected patient records against source documents.


9.9 Data Analysis

Quantitative data will be analysed using SPSS version 29 (or equivalent statistical software).

Descriptive statistics will include:

  • Frequencies

  • Percentages

  • Means

  • Standard deviations

  • Medians

  • Interquartile ranges

Registry performance indicators such as completeness, adoption, and usability will be summarized using descriptive statistics.

Qualitative data from interviews or questionnaires will be analysed using thematic analysis to identify facilitators, barriers, and recommendations for improving registry implementation.


9.10 Ethical Considerations

Ethical approval will be obtained from the appropriate Institutional Research Ethics Committee before commencement of the study.

Administrative approval will also be obtained from participating health facilities.

Participants will provide informed consent where required. Patient confidentiality will be maintained through the use of unique registry identification numbers, password-protected databases, and restricted access to identifiable information.

The registry will comply with applicable national data protection requirements and institutional policies governing the collection, storage, and use of health information.


9.11 Dissemination of Findings

Study findings will be disseminated through:

  • Submission of a Master's dissertation.

  • Presentations at scientific conferences.

  • Publication in peer-reviewed journals.

  • Reports to participating health facilities.

  • Dissemination to relevant stakeholders within the Ministry of Health and other partners to inform future implementation and scale-up of CKM registries.

10.0 Expected Outcomes

The study is expected to achieve the following outcomes:

  1. Development of a standardized Digital Cardio-Kidney-Metabolic (CKM) Clinical Registry for routine patient care.

  2. Identification and documentation of a minimum dataset for CKM patient management in Kenya.

  3. Development of standardized electronic clinical documentation forms for CKM care.

  4. Successful pilot implementation of the registry in selected health facilities in Coastal Kenya.

  5. Improved completeness, consistency, and quality of routine CKM clinical documentation.

  6. Improved longitudinal follow-up of CKM patients through standardized electronic records.

  7. Evidence on the usability, feasibility, acceptability, and implementation of the registry among healthcare workers.

  8. Recommendations for scaling up the registry to other healthcare facilities in Kenya.


11.0 Significance of the Study

The proposed study is expected to contribute significantly to clinical practice, digital health, health systems strengthening, and future research.

11.1 Clinical Significance

The registry will improve the quality of routine clinical documentation by providing standardized electronic records for patients with Cardio-Kidney-Metabolic disease. Improved documentation will facilitate continuity of care, multidisciplinary management, monitoring of disease progression, and evidence-based clinical decision-making.

11.2 Digital Health Significance

The study will contribute to the implementation of digital health technologies by demonstrating the feasibility of a disease-specific electronic clinical registry in routine healthcare settings. It will provide practical evidence on the implementation of digital health solutions for chronic disease management in resource-limited settings.

11.3 Health System Significance

The registry will strengthen health information systems by improving data quality, standardization, and accessibility. Reliable patient-level information will support quality improvement programmes, clinical audits, service planning, and performance monitoring.

11.4 Research Significance

The registry will establish a sustainable database that can support future observational studies, implementation research, clinical audits, and registry-based studies on cardiovascular, kidney, and metabolic diseases.

11.5 Policy Significance

The findings will provide evidence that may inform national efforts to strengthen digital health systems and improve the management of non-communicable diseases. The registry may serve as a model for implementation in other counties and healthcare facilities.


12.0 Study Limitations

The study may encounter several limitations.

The pilot implementation will be conducted in selected health facilities in Coastal Kenya; therefore, the findings may not be immediately generalizable to all healthcare settings in Kenya.

Variations in digital literacy among healthcare workers may influence registry adoption and usability during the pilot phase.

Internet connectivity and availability of computer equipment may affect real-time data entry in some facilities.

Because the study focuses on pilot implementation, it may not assess long-term sustainability or long-term patient outcomes.

Measures such as user training, technical support, standardized operating procedures, and routine supervision will be implemented to minimize these limitations.


13.0 Work Plan

Activity Month 1 Month 2 Month 3 Month 4 Month 5 Month 6
Literature review        
Protocol development        
Ethical approval        
Registry design        
Registry development        
Training of healthcare workers          
Pilot implementation        
Data quality monitoring        
Registry evaluation          
Data analysis          
Report writing          
Dissemination          

14.0 Budget

Budget Item Estimated Cost (KES)
Literature review 20,000
Stakeholder consultation meetings 30,000
Registry design and software development 70,000
Training of healthcare workers 40,000
Data collection and monitoring 30,000
Internet and communication 10,000
Local transport 15,000
Data analysis 15,000
Printing and stationery 10,000
Report production and dissemination 10,000
Contingency 20,000
Total Estimated Budget 250,000

15.0 References

Gliklich, R. E., Leavy, M. B., & Dreyer, N. A. (2020). Registries for Evaluating Patient Outcomes: A User's Guide (4th ed.). Agency for Healthcare Research and Quality.

Institute of Medicine. (2012). Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. National Academies Press.

Ministry of Health Kenya. (2023). Kenya Digital Health Strategy 2023–2028. Nairobi, Kenya.

Ndumele, C. E., Rangaswami, J., Chow, S. L., et al. (2023). Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation, 148(20), e333–e355.

Proctor, E., Silmere, H., Raghavan, R., et al. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65–76.

World Health Organization. (2021). Global Strategy on Digital Health 2020–2025. Geneva: World Health Organization.

World Health Organization. (2023). Noncommunicable Diseases Progress Monitor 2023. Geneva: World Health Organization.


16.0 Appendices

Appendix I: Minimum Cardio-Kidney-Metabolic Dataset

Appendix II: Digital CKM Clinical Registry Data Dictionary

Appendix III: Patient Registration Form

Appendix IV: Healthcare Worker Questionnaire

Appendix V: Registry Usability Evaluation Questionnaire

Appendix VI: Standard Operating Procedures for Registry Use

Appendix VII: Participant Information Sheet and Informed Consent Form

Appendix VIII: Conceptual Framework

Appendix IX: Data Flow Diagram

Appendix X: Registry Screen Layouts 

 

APPENDIX VIII: CONCEPTUAL FRAMEWORK

Conceptual Framework

The conceptual framework illustrates how the development and implementation of a Digital Cardio-Kidney-Metabolic (CKM) Clinical Registry is expected to improve clinical documentation, support routine patient care, and strengthen health information systems. The framework is based on the premise that standardized electronic data collection improves data quality, facilitates longitudinal patient follow-up, and enhances evidence-based clinical decision-making.

 
                 HEALTH SYSTEM NEED

 Increasing burden of CKM disease
 Fragmented clinical documentation
 Lack of standardized patient data
 Poor continuity of care
 Limited quality improvement
 Limited research data
                     │
                     ▼
          DEVELOPMENT PHASE

 Literature review
 Stakeholder consultation
 Clinical guidelines
 Minimum CKM dataset
 Registry design
 Software development
                     │
                     ▼
      DIGITAL CKM CLINICAL REGISTRY
                     │
                     ▼
        PILOT IMPLEMENTATION

 Training healthcare workers
 Patient registration
 Electronic documentation
 Routine clinical use
 Data quality monitoring
                     │
                     ▼
        REGISTRY EVALUATION
 (RE-AIM Implementation Framework)

 Reach
 Effectiveness
 Adoption
 Implementation
 Maintenance
                     │
                     ▼
          EXPECTED OUTCOMES

 Improved documentation
 Improved data quality
 Better continuity of care
 Better multidisciplinary care
 Improved clinical decision-making
 Better quality improvement
 Registry-based research
                     │
                     ▼
          LONG-TERM IMPACT

 Stronger Digital Health Systems
 Improved CKM care
 Better patient outcomes
 National scale-up
 

Explanation

The study begins by identifying the need for an integrated CKM registry due to fragmented documentation and the increasing burden of CKM disease. The registry will be developed using evidence from the literature, stakeholder consultations, and clinical guidelines to define a standardized minimum dataset. After development, the registry will be pilot implemented in selected health facilities and evaluated using the RE-AIM implementation framework. Successful implementation is expected to improve data quality, continuity of care, and clinical decision-making while establishing a sustainable platform for quality improvement and future research.


APPENDIX IX: MINIMUM CARDIO-KIDNEY-METABOLIC DATASET

Section A: Patient Identification

Variable Type
Registry ID Auto-generated
Hospital Number Text
National ID Text
Date of Registration Date
Clinic Site Text

Section B: Demographics

Variable
Age
Sex
Residence
Education
Occupation
Marital Status

Section C: Lifestyle Factors

Variable
Smoking
Alcohol
Physical Activity
Diet
Family History

Section D: Clinical Diagnoses

Variable
Hypertension
Diabetes Mellitus
Chronic Kidney Disease
Obesity
Dyslipidaemia
Heart Failure
Coronary Artery Disease
Stroke
Peripheral Artery Disease

Section E: Anthropometry

Variable
Weight
Height
BMI
Waist Circumference

Section F: Vital Signs

Variable
Blood Pressure
Heart Rate
Respiratory Rate
Oxygen Saturation

Section G: Laboratory Tests

Variable
Fasting Blood Glucose
HbA1c
Serum Creatinine
eGFR
Urine Albumin
Total Cholesterol
LDL Cholesterol
HDL Cholesterol
Triglycerides

Section H: Cardiovascular Assessment

Variable
ECG Findings
Echocardiography Findings
Left Ventricular Ejection Fraction
Left Ventricular Hypertrophy
Left Atrial Size

Section I: Kidney Assessment

Variable
CKD Stage
Albuminuria Category
Renal Ultrasound Findings

Section J: Metabolic Assessment

Variable
Body Mass Index
Metabolic Syndrome
Obesity Class

Section K: Medications

Variable
Antihypertensives
Antidiabetic Drugs
Statins
Antiplatelets
SGLT2 Inhibitors
GLP-1 Receptor Agonists

Section L: Outcomes

Variable
Hospital Admission
Cardiovascular Event
Dialysis
Death
Follow-up Status

APPENDIX X: DATA FLOW DIAGRAM

 

 
Patient Registration
        │
        ▼
Clinical Assessment
        │
        ▼
Laboratory Tests
        │
        ▼
ECG / Echo / Imaging
        │
        ▼
Electronic Data Entry
        │
        ▼
Digital CKM Registry Database
        │
        ├────────► Clinical Dashboard
        ├────────► Patient Follow-up
        ├────────► Quality Improvement Reports
        ├────────► Research Dataset
        └────────► Ministry of Health Reports

 

 

Cardiometabolic Education Meeting with Kiama Kiama

Overview

A strategic meeting was held with Kiama Kiama to discuss the importance of cardiometabolic health education among its members. The session aimed to raise awareness of the growing prevalence of cardiometabolic diseases and to identify practical approaches to prevention and management through community engagement.


Objectives

  • To educate members on the causes and consequences of cardiometabolic diseases.

  • To identify key risk factors contributing to these conditions.

  • To develop a plan for regular education, screening, and lifestyle support.


Understanding Cardiometabolic Diseases

Cardiometabolic diseases include a group of conditions such as hypertension, type 2 diabetes, obesity, and high cholesterol, which increase the risk of heart disease and stroke. These conditions often coexist and share common risk factors that can be prevented or managed through lifestyle changes.


Key Risk Factors

Modifiable Factors

  • Unhealthy diet (high in sugar, salt, and saturated fats)

  • Physical inactivity or sedentary lifestyle

  • Tobacco use

  • Excessive alcohol consumption

  • Overweight and obesity

  • Chronic stress and poor sleep patterns

Non-Modifiable Factors

  • Age (risk increases with age)

  • Family history of cardiometabolic diseases

  • Genetic predisposition

  • Ethnicity (some groups have higher susceptibility)


Discussion Highlights

  • Emphasized the need for regular health screening (blood pressure, glucose, and cholesterol).

  • Promoted balanced nutrition emphasizing fruits, vegetables, and whole grains.

  • Advocated for community-based physical activity programs.

  • Highlighted mental health and stress management as part of overall cardiometabolic care.

  • Agreed to develop educational workshops and awareness materials tailored to the needs of Kiama Kiama members.


Action Plan

  1. Launch a series of cardiometabolic education workshops.

  2. Partner with local health providers for screening and early detection programs.

  3. Establish peer support groups to encourage lifestyle modification.

  4. Conduct follow-up meetings to assess progress and share outcomes.


Outcome

 

The meeting concluded with a shared commitment to strengthen cardiometabolic awareness, promote preventive behaviors, and create a healthier community through sustained education and engagement.

Thursday, 30 October 2025 19:20

30TH OCTOBER 2025 MEETING

Written by

 

 

Cardiometabolic Clinic – Mtwapa Session Report

On 30th October 2025, the Cardiometabolic Clinic at Mtwapa held another successful support and follow-up session, attended by 72 members from various community programs. The meeting took place at the S J Medical Centre, bringing together patients, caregivers, and health professionals committed to improving cardiometabolic health through education, monitoring, and shared experiences.

Purpose of the Meeting

The session focused on:

  • Reviewing progress among members living with hypertension, diabetes, dyslipidemia, and obesity.

  • Strengthening lifestyle modification practices — nutrition, exercise, sleep, and medication adherence.

  • Offering personalized counseling and follow-up on laboratory and clinical results.

  • Encouraging community-based peer support and active participation in Cardiometabolic Support Groups.

Highlights

  • A total of 72 participants actively engaged in discussions and follow-up clinics.

  • Educational talks covered key topics such as blood pressure monitoring, glycemic control, and risk-factor modification.

  • Members shared personal experiences on diet, medication adherence, and mental wellness.

  • New members were registered and oriented to the clinic’s structured follow-up program.

Outcome

 

The meeting enhanced awareness and strengthened commitment to integrated cardiometabolic care within the Mtwapa community. It also reinforced the clinic’s vision of community-driven prevention and management of chronic diseases through continuous education, group support, and collaboration between clinicians and patients.

 

 INTRODUCTION TO CARDIOMETABOLIC

Welcome to the Health Africa Conference Program at St. John's Clinic, Mtwapa

At the heart of this initiative is our commitment to specialized, patient-centered, and evidence-based programs that not only manage but aim to halt or reverse disease progression. This program will showcase the clinic’s cutting-edge and holistic approach to chronic care, anchored in lifestyle transformation, precision medicine, and community empowerment.

Our flagship programs include:

·         Diabetes Reversal / Halt Program – targeting early and advanced cases with lifestyle and pharmacological interventions.

·         Obesity Reversal Program – addressing metabolic dysfunction and sustainable weight management.

·         Hypertension Reversal / Halt Program – focused on blood pressure control and vascular protection.

·         Chronic Kidney Disease (CKD) Halt / Stabilization Program – slowing disease progression and improving renal outcomes.

·         Erectile Dysfunction   Recovery Program in cardiometabolic health – restoring function and confidence through cardiometabolic rehabilitation.

·         Lipid Disorders Reversal / Control Program – optimizing lipid profiles to reduce cardiovascular risk.

·         Neurodegeneration Disease in Cardiometabolic Support and Management Program – improving cognitive outcomes and quality of life for at-risk populations.

Together, these programs reflect our vision of transforming chronic care from lifelong management to achievable recovery and control

 

OUR APPOACH

  

1. Diabetes Reversal / Halt Program

Approach:
This program aims to either reverse early-stage type 2 diabetes or halt its progression through a multidisciplinary strategy that includes:

  • Personalized nutrition therapy (low glycemic index, anti-inflammatory diet)
  • Supervised physical activity
  • Regular blood glucose monitoring
  • Weight management
  • Medication de-escalation (if appropriate)
  • Patient education and behavioral counseling

Goal: Restore insulin sensitivity, reduce dependency on medication, and prevent complications.

 

2. Obesity Reversal Program

Approach:
This program tackles the root causes of obesity using:

  • Comprehensive lifestyle modification plans
  • Caloric restriction and meal planning
  • Hormonal and metabolic assessments
  • Behavioral therapy
  • Medical treatment or referrals for bariatric evaluation where needed

Goal: Achieve sustainable weight loss, reduce cardiometabolic risks, and improve quality of life.

 

3. Hypertension Reversal / Halt Program

Approach:
This focuses on reducing blood pressure without over-reliance on medication through:

  • Salt and sodium intake reduction
  • DASH (Dietary Approaches to Stop Hypertension) diet
  • Stress reduction (e.g., mindfulness, yoga)
  • Physical activity and weight loss
  • Monitoring and gradual medication tapering (if clinically safe)

Goal: Normalize blood pressure or prevent further elevation, reduce risk of stroke and heart disease.

 

4. Chronic Kidney Disease (CKD) Halt / Stabilization Program

Approach:
This program aims to slow the progression of CKD by:

  • Tight control of blood pressure and diabetes
  • Dietary modifications (e.g., low protein, low phosphate diets)
  • Use of kidney-protective drugs (ACEIs/ARBs)
  • Avoidance of nephrotoxic agents
  • Regular eGFR and creatinine monitoring

Goal: Prevent advancement to end-stage renal disease and delay the need for dialysis.

 

5. Erectile Dysfunction in Cardiometabolic Recovery Program

Approach:
This program integrates sexual health with metabolic control by:

  • Screening for underlying cardiometabolic issues (diabetes, hypertension, obesity)
  • Lifestyle intervention to restore vascular health
  • Testosterone and hormonal evaluations
  • Use of PDE5 inhibitors when appropriate
  • Psychosexual counseling

Goal: Restore sexual function as a marker of vascular recovery and overall metabolic improvement.


6. Lipid Disorders Reversal / Control Program

Approach:
The focus is on correcting dyslipidemia (high LDL, low HDL, high triglycerides) through:

  • Therapeutic lifestyle changes (TLC diet, omega-3 fatty acids)
  • Weight loss and increased physical activity
  • Pharmacotherapy where needed (statins, ezetimibe, PCSK9 inhibitors)
  • Nutraceuticals and supplements

Goal: Achieve target lipid levels, reduce atherosclerosis risk and prevent cardiovascular events.

 

7. Neurodegeneration Disease in Cardiometabolic Support and Management Program

Approach:
Recognizing the link between metabolic syndrome and cognitive decline, this program includes:

  • Early cognitive screening
  • Anti-inflammatory and brain-healthy diets (e.g., MIND or Mediterranean diet)
  • Cardiometabolic control (BP, glucose, lipids)
  • Cognitive therapy and memory support strategies
  • Use of neuroprotective agents and vitamins (e.g., B12, folate)

Goal: Delay or prevent cognitive decline, improve brain health in patients with metabolic diseases.

Friday, 08 August 2025 13:59

REPORTS

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Intro

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Introduction

Overview

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