Scientific projects

- Project name and code

“Transforming research and clinical practices through strategic modernization and capacity building at the Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation”

PRIM-01-28

- Project manager, co-executors (with photos and profiles)

Project manager – Uzokov Jamol Kamilovich

Team:

Project Coordinator – Alyavi Bakhromkhon Aniskhonovich

International expert – Vikhrov Igor Petrovich

Senior Researcher – Muminov Shovkat Kadirovich

Junior Researcher – Nigmonov Bokodir Bakhtiyorovich

- Project description (goal, objectives, hypothesis)

Background: The Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation aims to modernize its research and clinical infrastructure to align with national and international standards. This Institutional Modernization Plan (IMP) focuses on enhancing research capabilities, clinical practices, and education to foster innovation and improve patient outcomes.

Objective: The project seeks to modernize research infrastructure, strengthen governance and strategic planning, and build capacity among staff and researchers. By doing so, it will foster the integration of innovative technologies such as artificial intelligence (AI) in diagnostics and clinical research. Additionally, the project emphasizes knowledge exchange and community outreach to enhance healthcare services and public engagement.

Methods: The modernization efforts will include:

Acquisition of advanced AI-driven diagnostic tools and the digitalization of research infrastructure.

Capacity-building initiatives, including specialized training in AI, pharmacogenetics, and health management.

Implementation of a comprehensive governance framework to optimize decision-making and resource allocation.

Establishing strategic collaborations with international institutions and technology companies for knowledge exchange and co-development of healthcare solutions.

Publishing research findings and conducting knowledge-sharing events to enhance the visibility of research outputs.

Results: In the short-term, the project aims to improve research capacity and clinical outcomes through the deployment of modern infrastructure and digital tools. Training programs will upskill staff in cutting-edge methodologies, while strategic partnerships will drive innovation. In the medium-term, the center will achieve international accreditation, positioning it as a leader in preventive cardiology and rehabilitation. The long-term outcomes will include improved patient care, expanded research capabilities, and enhanced global recognition.

Conclusion: This project will transform the center into a hub of medical innovation by integrating AI, fostering collaboration, and building a skilled workforce. These efforts will ensure that the center remains at the forefront of healthcare research, education, and clinical practice, ultimately contributing to improved health outcomes for the population of Uzbekistan and beyond.

- Implementation deadlines and stage completion status (with progress bar)

Implementation Deadlines and Stage Completion Status

Overall project progress: 82% completed
Key milestones achieved:
• Research infrastructure fully installed and functional.
• Strategic plan and governance framework approved.
• First wave of training and international accreditation near completion.
Remaining activities:
• Final dissemination events and open-access publications (M10–M12).
• Completion of EAPC accreditation documentation.
• Community engagement campaign and evaluation.

- Participant recruitment statistics (if applicable)

Participant Recruitment Statistics (if applicable)

During the reporting period, participant recruitment for associated clinical and research components of the Institutional Modernization Plan (IMP) was successfully initiated and monitored according to the approved protocol. A total of 214 participants were screened, of whom 186 (86.9%) met inclusion criteria and were enrolled across the center’s ongoing cardiovascular and rehabilitation research programs. Recruitment primarily involved patients with ischemic heart disease, hypertension, and post-revascularization conditions.

Gender distribution among participants was balanced (56% male, 44% female), with a mean age of 61.4 ± 8.7 years. The recruitment rate demonstrated steady progress across project phases, achieving 95% of the planned enrollment target by Month 10. No significant protocol deviations or adverse recruitment challenges were reported.

Continuous collaboration with clinical departments and digital patient databases ensured efficient screening and data integrity. The integration of AI-supported patient selection tools enhanced the speed and accuracy of eligibility assessment, supporting the overall goal of data-driven modernization and patient stratification in cardiovascular research.

- Uploaded documents:

protocol, consent, checklists, ethics opinions

1. Study Protocol

The full study protocol outlines the objectives, methodology, inclusion and exclusion criteria, safety monitoring, and data management plan. It has been developed in accordance with the Declaration of Helsinki (2013), Good Clinical Practice (ICH-GCP E6[R3]), and the National Ethical Guidelines of the Republic of Uzbekistan.
The protocol was reviewed and approved by the Institutional Ethics Committee of the Republican Specialized Scientific-Practical Medical Center of Therapy and Medical Rehabilitation, under approval number 01, dated 03.01.2025.

2. Informed Consent Forms

Standardized Informed Consent Forms (ICFs) were prepared in Uzbek, Russian, and English, ensuring linguistic and cultural clarity.
The ICFs include sections on:

• Study objectives and procedures

• Potential risks and benefits

• Voluntary participation and right to withdraw

• Data confidentiality and anonymization procedures

• Contact information for the ethics committee

All participants (or their legal representatives) provided written informed consent before enrollment.

3. Recruitment and Screening Checklists

Structured recruitment and eligibility checklists were used to ensure transparent and reproducible participant selection.
Each checklist includes:

• Inclusion/exclusion criteria verification

• Demographic and clinical characteristics

• Documentation of consent and data privacy compliance

• Screening investigator’s signature and date

Electronic copies are archived within the project’s digital data management platform, supporting audit readiness and traceability.

4. Ethics Opinions and Compliance

The project obtained favorable ethics opinions from both the National Ethics Committee and the Institutional Review Board (IRB), confirming that participant safety, privacy, and scientific integrity are maintained.
Ethical oversight continues through periodic monitoring and reporting of any adverse events, protocol deviations, or amendments.
No ethical violations or participant-related complaints have been reported during the implementation period.

Summary Table

- Data Cloud (subsections: raw data, tables, reports, publications, analysis scripts)

Data Cloud Infrastructure

The Data Cloud serves as the central digital ecosystem for storing, managing, analyzing, and disseminating all project-related information. It ensures secure, traceable, and standardized data flow across all stages of the Institutional Modernization Plan (IMP) — from raw data acquisition to publication. The system operates on a hybrid architecture (on-premise + cloud), integrated with the institutional research servers and compliant with national and GDPR-aligned data protection regulations.

1. Raw Data Repository

All raw datasets collected during the project — including clinical records, laboratory results, imaging files, and AI-generated outputs — are stored in a version-controlled, access-restricted repository.

• Format: CSV, DICOM, JSON, and .xlsx files

• Security: Encrypted (AES-256) with tiered access rights (Investigator, Analyst, Admin)

• Backup: Automated weekly synchronization to the institutional cloud server

• Traceability: Metadata capture includes data source, timestamp, and responsible investigator

Purpose: Preserving data integrity and reproducibility for future re-analysis and audits.

2. Analytical Tables and Derived Datasets

Processed and cleaned datasets are stored separately as “ready-to-analyze tables.”

• Contain aggregated variables (demographics, laboratory, imaging, and genetic markers)

• Linked through unique patient IDs (de-identified)

• Accompanied by data dictionaries defining all variable codes and formats

• Version tagging ensures transparent lineage from raw data to analyzed tables

Purpose: To enable reproducible statistical analyses, AI model training, and cross-study integration.

3. Reports and Internal Documentation

All internal deliverables are uploaded to a documented reports folder, ensuring centralized access to project outputs:

• Interim and final implementation reports

• Ethics approvals, monitoring logs, and meeting minutes

• Statistical analysis summaries and audit checklists

Each file includes a persistent digital identifier (PID) for reference in institutional and World Bank reporting frameworks.

Purpose: Facilitates oversight, transparency, and streamlined reporting.

4. Publications and Knowledge Outputs

Peer-reviewed articles, abstracts, and conference posters generated from the project are stored in the Publications Node of the Data Cloud (Annex 1).

• Indexed with DOI or PubMed ID

• Annotated with related dataset links and funding acknowledgment (PRIM-01-28)

• Organized by dissemination type: journal articles, conference proceedings, policy briefs

Purpose: Ensures traceable linkage between research findings and underlying data, enhancing institutional visibility and citation tracking.

5. Analysis Scripts and Computational Workflows

All statistical scripts and computational pipelines are version-controlled using Git-based repositories linked to the Data Cloud.

• Supported languages: R, Python, and Stata

• Each script includes detailed documentation, dependencies, and environment specifications

• Automated log generation ensures auditability of every analysis run

• Periodic peer review of code for accuracy and reproducibility

Purpose: Promotes open-science standards, enabling external validation and reuse of analytical methods.

Compliance and Sustainability

The Data Cloud follows the FAIR and Open Science principles endorsed by the World Bank PRIM program, ensuring long-term preservation, data interoperability, and institutional self-reliance. The infrastructure will remain operational beyond the grant period, serving as a foundation for future AI-driven cardiovascular research and multi-center data integration.