Evidence map›Paper›PMID 36051315›Full record

ReviewGlobal heart2022

Leveraging Clinical Digitized Data to Understand Temporal Characteristics and Outcomes of Acute Myocardial Infarctions at a Tertiary Care Medical Centre in Pakistan from 1988-2018 - Methods and Results.

Zainab Samad, Ali Aahil Noorali, Awais Farhad, Safia Awan, Nada Qaiser Qureshi, Minaz Mawani, Mushyada Ali, Laiba Masood, Ghufran Adnan, Linda K Shaw and 6 more

Abstract readReview
In one paragraph

Review in Global heart, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Temporal trends and regional disparities in mortality from acute myocardial infarction in Brazil: a time series study, 2015-2024.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2026
    Article
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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

16 authors.

Zainab SamadDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0003-2422-3199
Ali Aahil NooraliDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0002-5112-9571
Awais FarhadDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0001-8833-4088
Safia AwanDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0001-8284-7800
Nada Qaiser QureshiDepartment of Medicine, Aga Khan University, Karachi, Pakistan.
Minaz MawaniDepartment of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA.ORCID 0000-0002-2298-8385
Mushyada AliDepartment of Medicine, Aga Khan University, Karachi, Pakistan.
Laiba MasoodDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0002-0908-3813
Ghufran AdnanDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0001-7623-3802
Linda K ShawIndependent Biostatistical Consultant, USA.
Fahim Haider JafaryDepartment of Cardiology, Tan Tock Seng Hospital, Singapore.
Salim S ViraniDivision of Cardiology, Department of Medicine, Baylor College of Medicine, US.ORCID 0000-0001-9541-6954
Eric J VelazquezSection of Cardiology, Department of Medicine, Yale School of Medicine, Yale University, New Haven, CO, USA.ORCID 0000-0003-2245-7477
Zulfiqar BhuttaInstitute of Global Health and Development, Aga Khan University, Karachi, Pakistan.ORCID 0000-0003-0637-599X
Gerald S BloomfieldDivision of Cardiology, Department of Medicine, Duke University, Duke Global Health Institute, Duke Clinical Research Institute, Durham, NC, USA.ORCID 0000-0002-7176-1611
Javed TaiDepartment of Medicine, Aga Khan University, Karachi, Pakistan.ORCID 0000-0001-6138-7294

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Few data exist on trends in acute myocardial infarction (AMI) patterns spanning recent epidemiological shifts in low middle-income countries (LMICs). To understand temporal disease patterns of AMI characteristics and outcomes between 1988-2018, we used digitized legacy clinical data at a large tertiary care centre in Pakistan. Methods: We reviewed digital health information capture systems maintained across the Aga Khan University Hospital and obtained structured elements to create a master dataset. We included index admissions of patients >18 years that were discharged between January 1, 1988, and December 31, 2018, with a primary discharge diagnosis of AMI (using ICD-9 diagnoses). The outcome evaluated was in-hospital mortality.Clinical characteristics derived from the electronic database were validated against chart review in a random sample of cases ( Results: The final population consisted of 14,601 patients of which 30.6% (n = 4,470) were female, 52.4% (n = 7,651) had ST elevation MI and 47.6% (n = 6,950) had non-ST elevation MI. The median (IQR) age at presentation was 61 (52-70) years. Overall unadjusted in-hospital mortality was 10.3%. Across the time period, increasing trends were noted for the following characteristics: age, proportion of women, prevalence of hypertension, diabetes, proportion with NSTEMI (all p Conclusions: The patterns of AMI have changed over the last three decades with a concomitant decline in in-hospital mortality at a tertiary care centre in Pakistan. Clinical digitized data presents a unique opportunity for gaining insights into disease patterns in LMICs.

Indexed as

Myocardial InfarctionFemaleHospital MortalityHumansMalePakistanTertiary Care CentersTertiary Health CareElectronic Health RecordsHealth Care Outcome AssessmentMyocardial InfarctionQuality of Health CareRisk Factors

Identifiers

PMID36051315
PMCPMC9389955

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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.