Evidence map›Paper›PMID 40777138›Full record

ArticleJournal of medical signals and sensors2025

Designing a Software for Registry of Pregnant Women with Heart Disease in Iran and Preliminary Results.

Mahdi Kalani, Fateme Mahdikhoshouei, Parvin Bahrami, Amirreza Sajjadieh Khajouei, Minoo Movahedi, Shima Mehdipour, Marzieh Rezvani Habibabadi

Abstract readCase Reports
In one paragraph

Article in Journal of medical signals and sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Mahdi KalaniTabiban Roshanandish Houshmand Co., Isfahan, Iran.
Fateme MahdikhoshoueiTabiban Roshanandish Houshmand Co., Isfahan, Iran.
Parvin BahramiDepartment of Cardiology, Alzahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran.
Amirreza Sajjadieh KhajoueiDepartment of Cardiology, Alzahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran.
Minoo MovahediDepartment of Obstetrics and Gynecology, Alzahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran.
Shima MehdipourIsfahan University of Medical Sciences, Isfahan, Iran.
Marzieh Rezvani HabibabadiTabiban Roshanandish Houshmand Co., Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart disease in pregnancy is an important health issue worldwide which needs precise care to improve pregnant women health care and reduce maternal mortality rate (MMR). As we know registries play an important role in improvement of health care, so we decided to design a software to take the first step for having a national registry for pregnant women with heart disease in Iran and classify them in a more effective way to reduce mismanagements. A windows-based software with C# language programming was designed and implemented by a group of specialists included two experienced cardiologists, a skilled gynecologist, and a proficient medical doctor programmer. Since the launch of the software, information for 500 pregnant women with heart disease has been entered. The most common types of heart disease in order were congenital heart disease, prosthetic heart valves, valvular disease, and cardiomyopathies. The software developed by our team provides a comprehensive and efficient tool for managing patients with heart disease in pregnancy. The use of this software can help identify high-risk patients early on, leading to better patient outcomes and ultimately contributing to the global goal of reducing MMR. In the field of pregnant women with heart disease, gathering large and accurate data over time can be utilized in artificial intelligence for analysis.

Indexed as

Artificial intelligenceheart diseasematernal mortality ratepregnancypregnancy registrysoftware

Identifiers

PMID40777138
PMCPMC12331175

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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.