Evidence map›Paper›PMID 42535204›Full record

ArticleCureus2026

Determinants of Poor Outcomes in Acute Poisoning at a Tertiary Care Hospital in an Industrial City of Eastern India: A Retrospective Observational Study.

Murtaza A Khan, Saurabh Pathak, Md Khalid Jung Khan, Nibedita Mishra

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Murtaza A KhanInternal Medicine, Tata Main Hospital, Jamshedpur, IND.
Saurabh PathakNephrology, Tata Main Hospital, Jamshedpur, IND.
Md Khalid Jung KhanGeneral Medicine, Tata Main Hospital, Jamshedpur, IND.
Nibedita MishraGeneral Medicine, Tata Main Hospital, Jamshedpur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndustrial cities of eastern India combine dense urban populations with proximity to agricultural hinterlands and access to pharmaceutical, household, and agricultural toxic agents, creating a mixed exposure profile. Published data from such settings remain sparse. This study characterised the clinical profile of acute poisoning at an industrial-urban tertiary care centre and evaluated factors associated with escalation of care.

methodsThis retrospective observational study included 322 eligible poisoning admissions identified from departmental records at a tertiary care centre in industrial eastern India between January 2023 and March 2025. The prespecified composite endpoint comprised intensive care unit (ICU) admission, mechanical ventilation, vasopressor support, or in-hospital death. Because all patients meeting the composite endpoint were admitted to the ICU, the endpoint was numerically equivalent to ICU admission in this cohort. Multivariable logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate associated factors and assess exploratory discrimination.

resultsMedian age was 29 years (IQR 21-39; mean 31.6 ± 13.7 years); 182 (56.5%) were female. Suicidal intent was documented in 151 patients (46.9%). Drug overdose (n=100, 31.1%; comprising 99 pharmaceutical overdoses and one alcohol-ingestion case) and corrosive ingestion (n=85, 26.4%) were the leading agent categories. A composite poor outcome was identified in 114 (35.4%) patients. Six patients died, giving an in-hospital mortality of 1.9% (6/322). On multivariable regression, lower Glasgow Coma Scale (GCS) score (odds ratio (OR) 2.73 per unit decrease, 95% confidence interval (CI) 1.64-4.55, p<0.001) and lower peripheral oxygen saturation (SpO₂; OR 1.14 per unit decrease, 95% CI 1.05-1.24, p=0.002) were independently associated with the composite endpoint, which was numerically equivalent to ICU admission in this cohort. The shock index had the largest point-estimate area under the ROC curve (area under the curve (AUC) 0.751, 95% CI 0.693-0.811), followed by heart rate (AUC 0.736) and GCS score (AUC 0.708); no formal pairwise comparison of AUCs was performed.

conclusionIn this single-centre cohort, pharmaceutical overdose and household chemical ingestion were the most frequently recorded poisoning categories, alongside pesticide, rodenticide, and occupational chemical exposures. Lower admission GCS and SpO₂ were associated with the institution-dependent escalation-of-care endpoint. These findings should be considered hypothesis-generating and require validation using objective clinical outcomes in prospective multicentre cohorts.

Indexed as

acute poisoningaluminium phosphidecorrosive ingestiondrug overdoseindustrial eastern indiajharkhandlogistic regressionorganophosphatepoor outcome predictorsroc analysis

Identifiers

PMID42535204
PMCPMC13423574

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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.