Evidence map›Paper›PMID 34955288›Full record

ArticleSurgery2022

Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older.

Majed El Hechi, Anthony Gebran, Hamza Tazi Bouardi, Lydia R Maurer, Mohamad El Moheb, Daisy Zhuo, Jack Dunn, Dimitris Bertsimas, George C Velmahos, Haytham M A Kaafarani

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Artificial intelligence in gynecologic and obstetric emergencies.International journal of emergency medicine · 2025
    Review
  5. Article
  6. Review
  7. Review
  8. Review
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

10 authors at 3 institutions in 1 country.

Majed El HechiDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA; Center for Outcomes and Patient Safety in Surgery, Massachusetts General Hospital, Boston, MA. Electronic address: https://twitter.com/majedhechi.
Anthony GebranDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA; Center for Outcomes and Patient Safety in Surgery, Massachusetts General Hospital, Boston, MA. Electronic address: https://twitter.com/AnthonyGebran.
Hamza Tazi BouardiMassachusetts Institute of Technology, Cambridge, MA.
Lydia R MaurerDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA; Center for Outcomes and Patient Safety in Surgery, Massachusetts General Hospital, Boston, MA. Electronic address: https://twitter.com/LMaurerMD.
Mohamad El MohebDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA; Center for Outcomes and Patient Safety in Surgery, Massachusetts General Hospital, Boston, MA. Electronic address: https://twitter.com/ElmohebM.
Daisy ZhuoInterpretable AI, Boston, MA.
Jack DunnInterpretable AI, Boston, MA.
Dimitris BertsimasMassachusetts Institute of Technology, Cambridge, MA; Interpretable AI, Boston, MA. Electronic address: https://twitter.com/dbertsim.
George C VelmahosDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA.
Haytham M A KaafaraniDivision of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA; Center for Outcomes and Patient Safety in Surgery, Massachusetts General Hospital, Boston, MA. Electronic address: hkaafarani@mgh.harvard.edu.
Twitter (United States) · USMassachusetts General Hospital · USMassachusetts Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient.

methodsAll patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years.

resultsA total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively).

conclusionThe Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Indexed as

Wounds, NonpenetratingWounds, PenetratingAgedArtificial IntelligenceBenchmarkingFemaleHospital MortalityHumansInjury Severity ScoreMaleRetrospective Studies

Identifiers

PMID34955288
PMCPMC9131296
OpenAlexW4200546736

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.