Evidence map›Paper›PMID 40130129›Full record

ArticleCureus2025

Perioperative Mortality Rate in a Low-Resource Non-governmental Organisation Setting.

Yuki Julius Ng, Ravindranath Shroff

Abstract read
In one paragraph

Article in Cureus, 2025. 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
–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

1 citing paper in PubMed.

  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

2 authors.

Yuki Julius NgGeneral Surgery, International Medical University, Kuala Lumpur, MYS.
Ravindranath ShroffGeneral Surgery, Vivekananda Memorial Hospital, Sargur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction A significant portion of the global population lacks access to safe and affordable surgical and anaesthesia care, with the majority of those affected residing in low- and middle-income countries. As a low-middle-income country and one of the most populated nations, India faces substantial challenges in addressing this healthcare gap. We aimed to descriptively assess the perioperative mortality rate (POMR) of a low-resource non-governmental organisation hospital in India and assess the preparedness of surgical services. Methodology We performed a retrospective study by collecting the surgical volume from the operation theatre registry. All recorded deaths about surgery during the timeframe of data collection and investigated each death. We have also assessed the hospital's preparedness for surgical services. Results The operation theatre registry recorded 1,860 patients over five years who underwent major operations. The perioperative mortality was three (0.16%). The case mix done under obstetrics and gynaecology was 1,046 (56.2%), general surgery at 614 (33.0%), and orthopaedics at 200 (10.8%). After adjustment, we found that our average surgical volume per year was 448 (95% CI: 391.52-504.96). Emergency surgeries had a relative risk of 1.9 (95% CI: 0.172-20.87) higher than elective surgeries. Our situational analysis of surgical preparedness shows that Vivekananda Memorial Hospital (VMH) is a well-prepared hospital to provide 32 out of 42 essential surgeries, as described by the Disease Control Priorities. This hospital fulfilled the infrastructure and equipment required for an ideal 100-bed hospital, except for having a blood bank. Conclusion In a low-resource non-governmental organisation setting such as VMH, POMR can meet global standards. Surgical care is accessible and affordable to the poorest of the population.

Indexed as

general surgeryglobal surgerylow-resource settingnon-governmental organizationperioperative mortality

Identifiers

PMID40130129
PMCPMC11931452

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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.