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The Veterinary record2021; 189(5); e136; doi: 10.1002/vetr.136

Equine simplified acute physiology score: Personalised medicine for the equine emergency patient.

Abstract: Scoring models are useful tools that guide the attending clinician in gauging the severity of disease evolution and in evaluating the efficacy of treatment. There are few tools available with this purpose for the non-human patient, including horses. We aimed (i) to adapt the simplified acute physiology score 3 (SAPS-3) model for the equine species, reaching a margin of accuracy greater than 75% in the calculation of the probability of survival/death and (ii) to build a decision tree that helps the attending veterinarian in assessment of the clinical evolution of the equine patient. Methods: From an initial pool of 5568 medical records from University-based Veterinary Hospitals, a final cohort of 1000 was further mined manually for data extraction. A set of 19 variables were evaluated and tested by five machine learning data mining algorithms. Results: The final scoring model, named EqSAPS for equine simplified acute physiology score, reached 91.83% of correct estimates (post hoc) for probability of death within 24 hours upon hospitalization. The area under receiver operating characteristic curve for outcome 'death' was 0.742, while for 'survival' was 0.652. The final decision tree was able to refine prognosis of patients whose EqSAPS score suggested 'death'. Conclusions: EqSAPS is a useful tool to gauge the severity of the clinical presentation of the equine patient.
© 2021 British Veterinary Association.
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Publication Date: 2021-02-19 PubMed ID: 33729604DOI: 10.1002/vetr.136Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study describes the creation and validation of an equine-specific scoring model, known as the Equine Simplified Acute Physiology Score (EqSAPS), which can accurately predict the mortality rate of horses in emergency care situations. This tool can assist veterinary medical professionals in assessing severity of illness and guiding treatment.

Research Aim

  • The research focused on two major goals. The first was to adapt the Simplified Acute Physiology Score 3 (SAPS-3) model, typically used for human patients, to the specific needs of equine patients. The goal was to achieve over 75% accuracy in calculating the probability of survival or death.
  • The second goal was to create a decision tree tool that would aid veterinarians in evaluating the clinical progression of the horse patient.

Methods

  • The team evaluated 5568 medical records from University-based veterinary hospitals, from which a final sample of 1000 cases were carefully analyzed.
  • They gathered and assessed 19 variables from these records.
  • Five different machine learning data mining algorithms were used to test and analyze these data.

Results

  • The newly created EqSAPS model showed a success rate of 91.83% in its estimates, particularly in predicting the probability of a horse’s death within 24 hours of hospitalization.
  • The model’s receiver operating characteristic, a measure of its accuracy for binary decision-making, was 0.742 for predicting death and 0.652 for predicting survival.
  • The research team also successfully developed a decision tree as a complementary tool which could refine the prognosis of patients whose EqSAPS score indicated a likelihood of death.

Conclusion

  • The research concluded that the EqSAPS scoring model could be crucial in assessing the severity of clinical presentations in equine patients and can aid veterinarians in deciding the course of treatment for critical cases.

Cite This Article

APA
de Barros AMC, Silva AFR, Zibordi M, Spagnolo JD, Corrêa RR, Belli CB, de Camargo MM. (2021). Equine simplified acute physiology score: Personalised medicine for the equine emergency patient. Vet Rec, 189(5), e136. https://doi.org/10.1002/vetr.136

Publication

The Veterinary record
ISSN: 2042-7670
NlmUniqueID: 0031164
Country: England
Language: English
Volume: 189
Issue: 5
Pages: e136

Researcher Affiliations

de Barros, Aline de Matos Curvelo
  • School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil.
Silva, Ana Flávia Rocha
  • School of Zootechnics and Food Engineering, University of São Paulo, Pirassununga, Brazil.
Zibordi, Miriam
  • School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil.
Spagnolo, Julio David
  • Veterinary Hospital, Large Animals Surgery Section, School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil.
Corrêa, Rodrigo Romero
  • Department of Surgery, School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil.
Belli, Carla B
  • Department of Clinics, School of Veterinary Medicine, University of São Paulo, São Paulo, Brazil.
de Camargo, Maristela Martins
  • Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.

MeSH Terms

  • Animals
  • Horses
  • Intensive Care Units
  • Precision Medicine / veterinary
  • Prognosis
  • ROC Curve
  • Retrospective Studies
  • Simplified Acute Physiology Score

Grant Funding

  • Coordenau00e7u00e3o de Aperfeiu00e7oamento de Pessoal de Nu00edvel Superior- Brasil (CAPES)

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Citations

This article has been cited 2 times.
  1. Cummings CO, Krucik DDR, Price E. Clinical predictive models in equine medicine: A systematic review. Equine Vet J 2023 Jul;55(4):573-583.
    doi: 10.1111/evj.13880pubmed: 36199162google scholar: lookup
  2. Akbarein H, Taaghi MH, Mohebbi M, Soufizadeh P. Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review. Vet Med Sci 2025 May;11(3):e70315.
    doi: 10.1002/vms3.70315pubmed: 40173266google scholar: lookup
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