Evidence map›Paper›PMID 42504371›Full record

ArticleCureus2026

Comparison of Pre-transfer Risk Assessment Tools Used for Critically Ill Patient Transport: A Retrospective Study.

Aruvi Poomali, Ajay Ambalakkatte, Sreelal Tp, Abhijit Mohanan, Nithyanand Madathil, Chippy Babu, Liza P Kadavana, Nizamudheen Kollanthodukayil

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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

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

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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

8 authors.

Aruvi PoomaliDevelopment Research, Indian Council of Medical Research, Nagpur, IND.
Ajay AmbalakkatteTrauma and Emergency, All India Institute of Medical Sciences, Nagpur, Nagpur, IND.
Sreelal TpCommunity Medicine, Manipal University College Malaysia, Melaka, MYS.
Abhijit MohananCritical Care, Dr. Moopen's Medical College, Meppadi, IND.
Nithyanand MadathilEmergency Medicine Nursing, Ungoofaaru Regional Hospital, Raa Atoll, MDV.
Chippy BabuNursing, Bowden Derra Park Nursing Home, Cornwall, GBR.
Liza P KadavanaEmergency Medicine Nursing, King Saud Medical City, Riyadh, SAU.
Nizamudheen KollanthodukayilEmergency Medicine, National Ambulance, Sharjah, ARE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective The objective of this study was to compare three scoring systems for pre-transfer risk assessment to enable the efficient utilization of transport infrastructure available while avoiding adverse events during interhospital patient transport. Methods This was a retrospective, cross-sectional study conducted in a tertiary care hospital situated in an aspirational district in Southern India. Trained personnel used three pre-transfer risk assessment tools to stratify the medical records of patients transported from the hospital during the study period. The data were analyzed to assess the sensitivity, specificity, and positive and negative likelihood ratios of the scoring systems. Results A total of 200 patient transports were included in the study. Cardiac emergency was the most common clinical diagnosis. A total of 35 patients had at least one major adverse event during transport. The Risk Score for Transport Patients (RSTP) classified 127 patients as high risk, the Transport Triage Tool (TTT) classified 133 as high risk, and the National Early Warning Score (NEWS2)-based tool classified 123 patients as high risk. Sensitivity and specificity of the RSTP scoring system to predict a major adverse event during transport were 77% and 39%, respectively. It also had a positive likelihood ratio of 1.27. Conclusion While the RSTP scoring system has the highest sensitivity and positive predictive value, further research is required to validate its performance in the Indian context.

Indexed as

critically ill patient transportcritically ill transportemergency medicineinterhospital transportprehospital caretriage

Identifiers

PMID42504371
PMCPMC13401515

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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.