Scoring system: use and not use from the future to present

Abstract

We appreciated the letter from Wohlgemut and colleagues regarding the TIC score that we recently published in Critical Care [1, 2]. They highlight the value of this score for the early detection of traumatic coagulopathy, and recognize its ease of use upon hospital admission [2]. However, they challenged several points in our discussion regarding their Bayesian network score [3]. Briefly, they highlight the flexibility in modelling continuous variables, as well as the improved discrimination and calibration of their model. They suggest that prediction of coagulopathy is possible even with missing variables, which the TIC score theoretically cannot do. Finally, they suggest it may no longer be necessary to compromise model performance to achieve a simpler, more user-friendly model, due to advances in user interface design and user experience. It should be noted that we have deliberately chosen to compare our results with the model described by Yet B et al. because the model had good performance metrics and provides a realistic view of the causality of trauma-induced coagulopathy [3].

Their method relies on a set of data including clinical observation, physiological parameters, radiological findings and laboratory values being implemented automatically in a software application, enabling clinical decision making. Unfortunately, this does not currently match the reality of contemporary hospital care [4]. Furthermore, they argue that the Bayesian network scoring system can handle missing variables and estimate them from pre-existing data. We have chosen to include only pre-hospital parameters in our model, as they are immediately available at the time of admission or even during the pre-hospital phase of care. In our model, no parameters are missing at admission, except in rare cases when capillary hemoglobin measurement is not available. The score we described can be easily calculated mentally, driving immediate decision-making for the trauma patient [1]. The author's final point regarding the strength of Bayesian network analysis also reveals its weakness: weak diffusion. To our knowledge, the model has not been published and is therefore not reproducible by other centers, with the added difficulty of understanding and executing this analysis for the physician unfamiliar with such a model. There is also the “black box” issue, of using a model that obscures the weight of each variable, and its associated under-utilization in the medical field [5]. A simpler—but still accurate—scoring system not only provides a better understanding of the presence of trauma-induced coagulopathy, but also enables rapid and easy implementation of corrective therapy.

In conclusion, models such as Bayesian networks are currently rarely used, even though they represent a highly promising tool for the future of medicine. Until these techniques are ready for prime time, we believe it is still useful to have simple, pragmatic and easy-to-use tools to help doctors anticipate patients' needs.

No datasets were generated or analysed during the current study.

1. Brac L, Levrat A, Vacheron C-H, Bouzat P, Delory T, David J-S. Development and validation of the tic score for early detection of traumatic coagulopathy upon hospital admission: a cohort study. Crit Care. 2024;28:168.

   Article PubMed PubMed Central Google Scholar

2. Wohlgemut JM, Pisirir E, Stoner RS, et al. Bayesian networks may allow better performance and usability than logistic regression. Crit Care. 2024;28:234.

   Article PubMed PubMed Central Google Scholar

3. Yet B, Perkins Z, Fenton N, Tai N, Marsh W. Not just data: a method for improving prediction with knowledge. J Biomed Inform. 2014;48:28–37.

   Article PubMed Google Scholar

4. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.

   Article PubMed Google Scholar

5. Kyrimi E, Dube K, Fenton N, et al. Bayesian networks in healthcare: what is preventing their adoption? Artif Intell Med. 2021;116: 102079.

   Article PubMed Google Scholar

Download references

We thanks Dr Kenji Inaba (Department of surgery, University of Southern California, Los Angeles, USA) for proofreading and correcting the English version of the article.

None.

Authors and Affiliations

1. Département d’Anesthésie Réanimation, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Lyon, France

   Charles-Hervé Vacheron & Jean Stéphane David

2. CIRI-Centre International de Recherche en Infectiologie (Team PHE3ID), Univ Lyon, Inserm, U1111, CNRS, UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, 46 Allée d’Italie, 69007, Lyon, France

   Charles-Hervé Vacheron

3. Department of Intensive Care, Annecy‐Genevois Hospital, Annecy, France

   Louis Brac & Albrice Levrat

4. Research on Healthcare Performance RESHAPE, INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France

   Jean Stéphane David

Authors

1. Charles-Hervé VacheronView author publications

   You can also search for this author in PubMed Google Scholar

2. Louis BracView author publications

   You can also search for this author in PubMed Google Scholar

3. Albrice LevratView author publications

   You can also search for this author in PubMed Google Scholar

4. Jean Stéphane DavidView author publications

   You can also search for this author in PubMed Google Scholar

Contributions

Charles-Hervé VACHERON: This author helped in writing the first draft of the manuscript, reviewing the manuscript; Jean Stephane DAVID, Albrice LEVRAT & Louis BRAC: These authors helped in reviewing the manuscript.

Corresponding author

Correspondence to Louis Brac.

Ethics approval

Not applicable.

Consent for publication

Not applicable.

Competing interests

JSD did lectures and consulting for LFB (Les Ullis, France).

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

Cite this article

Vacheron, CH., Brac, L., Levrat, A. et al. Scoring system: use and not use from the future to present. Crit Care 28, 303 (2024). https://doi.org/10.1186/s13054-024-05081-0

Download citation

• Received: 23 July 2024

• Accepted: 27 August 2024

• Published: 13 September 2024

• DOI: https://doi.org/10.1186/s13054-024-05081-0

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Sorry, a shareable link is not currently available for this article.

Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative