Evidence map›Paper›PMID 40443477›Full record

ArticleTrauma surgery & acute care open2025

Identifying those at risk: predicting patient factors associated with worse EGS outcomes.

Komal Abdul Rahim, Namra Qadeer Shaikh, Maryam Pyar Ali Lakhdir, Asma Altaf Hussain Merchant, Noreen Afzal, Saad Bin Zafar Mahmood, Saqib Kamran Bakhshi, Mushyada Ali, Zainab Samad, Adil H Haider

Abstract read
In one paragraph

Article in Trauma surgery & acute care open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Komal Abdul RahimCentre of Excellence in Trauma and Emergencies, The Aga Khan University, Karachi, Sindh, Pakistan.ORCID 0000-0002-0790-6644
Namra Qadeer ShaikhDean's Office, Medical College, The Aga Khan University, Karachi, Sindh, Pakistan.
Maryam Pyar Ali LakhdirCommunity Health Sciences, The Aga Khan University, Karachi, Sindh, Pakistan.
Asma Altaf Hussain MerchantMedical College, The Aga Khan University, Karachi, Pakistan.ORCID 0000-0002-0393-2079
Noreen AfzalDepartment of Surgery, The Aga Khan University, Karachi, Sindh, Pakistan.
Saad Bin Zafar MahmoodDepartment of Medicine, The Aga Khan University Medical College Pakistan, Karachi, Sindh, Pakistan.
Saqib Kamran BakhshiDepartment of Neurosurgery, The Aga Khan University, Karachi, Sindh, Pakistan.
Mushyada AliDepartment of Medicine, The Aga Khan University, Karachi, Sindh, Pakistan.
Zainab SamadDepartment of Medicine, The Aga Khan University, Karachi, Sindh, Pakistan.
Adil H HaiderDean of Medical College and Professor of Surgery, The Aga Khan University, Karachi, Sindh, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Comorbidity has a detrimental impact on Emergency General Surgery (EGS) outcomes. In lesser-developed countries with inconsistent documentation of comorbid conditions, undiagnosed and progressively worsening comorbidities can worsen EGS outcomes. We aimed to discern the comorbidity index as a predictor of complications and inpatient mortality in EGS using a large South Asian sample population. Materials and methods: Data of adult patients with AAST-defined EGS diagnoses at primary index admission from 2010 to 2019 were retrieved. Patients were categorized into predefined EGS groups using ICD-9 CM codes. Primary exposure was comorbidity using the Charlson Comorbidity Index (CCI). The primary outcome was inpatient mortality, and the secondary outcome was complication status. Multiple logistic and Cox regression with Weibull distribution was performed. Results: Analysis of 32 280 patients showed a mean age of 40.06±16.87 years. Overall comorbidity, inpatient mortality, and complication rates were 44.6%, 2.42% and 36.37%, respectively. Patients with moderate CCI had the highest complications (AOR 6.61, 95% CI 5.91, 7.37), and severe comorbidity had the highest hazards (AOR 3.79, 95% CI 2.89, 4.98). Male gender, increasing age, emergent admission status, and lack of insurance were associated with moderate and severe CCI, resulting in prolonged length of stay (5.72 and 5.83 days), reduced survival time (20.04 and 21.95 days), and higher mortality rates (10.52% and 9.48%). Conclusions: We identified predictive patient-level factors associated with higher CCI and worse EGS outcomes. Our findings can help stratify population subsets at risk of worse outcomes, provide valuable insight into disease progression, and aid decision-making in EGS patients. Level of Evidence: III.

Indexed as

emergency general surgerySurgical outcomes

Identifiers

PMID40443477
PMCPMC12121602

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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.