Evidence map›Paper›PMID 36207680›Full record

ArticleBMC cardiovascular disorders2022

Estimation of myocardial infarction death in Iran: artificial neural network.

Mohammad Asghari-Jafarabadi, Kamal Gholipour, Rahim Khodayari-Zarnaq, Mehrdad Azmin, Gisoo Alizadeh

Open access · goldAbstract read
In one paragraph

Article in BMC cardiovascular disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

5 authors at 2 institutions in 2 countries.

Mohammad Asghari-JafarabadiCabrini Research, Cabrini Health, Melbourne, VIC, 3144, Australia.ORCID 0000-0003-3284-9749
Kamal GholipourTabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.ORCID 0000-0002-6595-4150
Rahim Khodayari-ZarnaqDepartment of Health Policy and Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.ORCID 0000-0003-1626-4505
Mehrdad AzminNon-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.ORCID 0000-0003-2316-4326
Gisoo AlizadehTabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran. g.alizadeh.1369@gmail.com.ORCID 0000-0002-7753-0988
Tabriz University of Medical Sciences · IRTehran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExamining past trends and predicting the future helps policymakers to design effective interventions to deal with myocardial infarction (MI) with a clear understanding of the current and future situation. The aim of this study was to estimate the death rate due to MI in Iran by artificial neural network (ANN).

methodsIn this ecological study, the prevalence of diabetes, hypercholesterolemia over 200, hypertension, overweight and obesity were estimated for the years 2017-2025. ANN and Linear regression model were used. Also, Specialists were also asked to predict the death rate due to MI by considering the conditions of 3 conditions (optimistic, pessimistic, and probable), and the predicted process was compared with the modeling process.

resultsDeath rate due to MI in Iran is expected to decrease on average, while there will be a significant decrease in the prevalence of hypercholesterolemia 1.031 (- 24.81, 26.88). Also, the trend of diabetes 10.48 (111.45, - 132.42), blood pressure - 110.48 (- 174.04, - 46.91) and obesity and overweight - 35.84 (- 18.66, - 5.02) are slowly increasing. MI death rate in Iran is higher in men but is decreasing on average. Experts' forecasts are different and have predicted a completely upward trend.

conclusionThe trend predicted by the modeling shows that the death rate due to MI will decrease in the future with a low slope. Improving the infrastructure for providing preventive services to reduce the risk factors for cardiovascular disease in the community is one of the priority measures in the current situation.

Indexed as

HypercholesterolemiaMyocardial InfarctionHumansIranMaleNeural Networks, ComputerObesityOverweightRisk FactorsArtificial neural networkDeath rateEstimationIranMyocardial infarction

Identifiers

PMID36207680
PMCPMC9547455
OpenAlexW4303427382

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Registered trials

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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.