Evidence map›Paper›PMID 37568517›Full record

ArticleJournal of clinical medicine2023

Development of a Cardiovascular Disease Risk Prediction Model: A Preliminary Retrospective Cohort Study of a Patient Sample in Saudi Arabia.

Khaled Alabduljabbar, Mohammed Alkhalifah, Abdulaziz Aldheshe, Abdulelah Bin Shihah, Ahmed Abu-Zaid, Edward B DeVol, Norah Albedah, Haifa Aldakhil, Balqees Alzayed, Ahmed Mahmoud and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Guideline
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 2 countries.

Khaled AlabduljabbarDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.ORCID 0000-0002-6323-2525
Mohammed AlkhalifahDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
Abdulaziz AldhesheDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
Abdulelah Bin ShihahDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.ORCID 0000-0001-7800-8529
Ahmed Abu-ZaidCollege of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia.
Edward B DeVolDepartment of Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
Norah AlbedahDepartment of Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.ORCID 0000-0002-9571-4668
Haifa AldakhilDepartment of Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.ORCID 0000-0002-6710-933X
Balqees AlzayedDepartment of Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
Ahmed MahmoudDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
Abdullah AlkhenizanDepartment of Family Medicine & Polyclinics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
King Faisal Specialist Hospital & Research Centre · SAAlfaisal University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Saudi Arabia has an alarmingly high incidence of cardiovascular disease (CVD) and its associated risk factors. To effectively assess CVD risk, it is essential to develop tailored models for diverse regions and ethnicities using local population variables. No CVD risk prediction model has been locally developed. This study aims to develop the first 10-year CVD risk prediction model for Saudi adults aged 18 to 75 years. The electronic health records of Saudi male and female patients aged 18 to 75 years, who were seen in primary care settings between 2002 and 2019, were reviewed retrospectively via the Integrated Clinical Information System (ICIS) database (from January 2002 to February 2019). The Cox regression model was used to identify the risk factors and develop the CVD risk prediction model. Overall, 451 patients were included in this study, with a mean follow-up of 12.05 years. Thirty-five (7.7%) patients developed a CVD event. The following risk factors were included: fasting blood sugar (FBS) and high-density lipoprotein cholesterol (HDL-c), heart failure, antihyperlipidemic therapy, antithrombotic therapy, and antihypertension therapy. The Bayesian information criterion (BIC) score was 314.4. This is the first prediction model developed in Saudi Arabia and the second in any Arab country after the Omani study. We assume that our CVD predication model will have the potential to be used widely after the validation study.

Indexed as

cardiac outcomescardiovascular diseasecardiovascular risk assessmentprimary health careretrospectiverisk prediction modelSaudi ArabiaSaudi population

Identifiers

PMID37568517
PMCPMC10419869
OpenAlexW4385576385

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.