Decision support tool for the management of severe trauma: Traumamatrix.
In collaboration with Jean-Pierre Nadal, the Traumabase group, APHP (Public Assistance – Hospitals of Paris) especially with Tobias Gauss, Sophie Hamada, Jean-Denis Moyer and passionate students! Short slide presentation.
Major trauma is defined as any injury that endangers the life or the functional integrity of a person. The Global Burden of Disease working group of the WHO has recently shown that major trauma in its various manifestations, from road traffic accidents, interpersonal violence, self-harm to falls, remains a public health challenge and major source of mortality and handicap around the world. Hopefully, it has also been shown that management of major trauma based on standardized and protocol based care improves prognosis of patients especially for the two main causes of death in major trauma i.e., hemorrhage and traumatic brain injury. The classic pathway of a traumatized patient takes place in several stages : from the site of the accident where the patient is taken care of by the ambulance to the transfer to an intensive care unit (ICU) for immediate interventions and finally to the comprehensive care at the hospital. To be effective, patient management protocols require adjustments to the individual patient and clinical context on one hand and to the organizational context and trauma system on the other hand.
However, evidence shows that patient management even in mature trauma systems often exceeds acceptable time frames, and despite existing guidelines deviations from protocol-based care are often observed. These deviations lead to a high variability in care and are associated with bad outcome such as inadequate hemorrhage control or delayed transfusion. Two main factors explain these observations. First, decision-making in trauma care is particularly demanding, because it requires rapid and complex decisions under time pressure in a very dynamic and multi-player environment characterized by high levels of uncertainty and stress. Second, being a complex and multiplayer process, trauma care is affected by fragmentation. Fragmentation is often the result of loss or deformation of information. This disruptive influence prevents providers to engage with each other and commit to the care process.In order to respond to this challenge, our program has set the ambitious goal to develop a trauma decision support tool, the TraumaMatrix. The program aims to provide an integrative decision support and information management solution to clinicians for the first 24 hours of major trauma management. This program is divided into three steps.
Based on a detailed and high quality trauma database, Step 1 consists in developing the mathematical tools and models to predict trauma specific outcomes and decisions. This step raises considerable scientific and methodological challenges.
Step 2 will use these methods to apply them to develop in close cooperation with trauma care experts the decision support tool and develop a user friendly and ergonomic interface to be used by clinicians.
Step 3 will further develop the tool and interface and test in real-time its impact on clinician decision making and patient outcome.
Hypothesis: The global program TraumaMatrix stands for the hypothesis that an integrative, interactive decision support tool relying on advanced machine learning based on detailed and heterogenous clinical data can considerably improve patient care and survival in major trauma.
Originality: Firstly, the proposal relies on an unlimited access to a unique database: the Traumabase (www.traumabase.eu). With the objective of evaluating and improving the care of trauma patients, 15 French Trauma centers have decided to collaborate to collect detailed, high quality clinical data from the scene of the accident to discharge from the hospital. The resulting database, the Traumabase has prospectively gathered more than 14000 trauma admissions data, and new cases are permanently recruited. The granularity of the collected data makes this observatory unique in Europe. The present consortium takes strategic advantage of an unrestricted access to this database to propose an innovative response to the public health challenge of major trauma.
Secondly, to the best of our knowledge, such a trauma information platform currently does not exist. To develop and design an interactive, real-time, probabilistic decision-support and information management platform constitutes a major conceptual and scientific innovation. No proof of concept study exists that evaluates this approach on a large scale for complex medical decisions such as trauma care.Thirdly, handling trauma patients requires complex and multiplayer strategies and the medical community recognizes the need to adopt and develop new methods to eliminate preventable deaths and disabilities. Thus, the community is willing to make use of a large amount of data for diagnosis, decision-support and treatment.
Lastly, from the statistical point of view, the proposal will develop innovative methods to tackle the important scientific challenge of handling highly heterogeneous data, with a large number of missing data. Indeed, despite the high quality of the Traumabase, since data collection is carried out by data technicians, there are many missing values that occur for different reasons (impossibility to make the measurement for technical issues or because of the patient’s state, no time to record the measure, etc.). Current data analysis tools and predictive models cannot be applied with restrictions. To develop innovative methods allowing exploitation of missing data, heterogenous coding and complex structure is an important scientific contribution. Any development in this field will be useful and applicable to a large array of scientific sectors.
The project provides thus a unique opportunity for trans-disciplinary research and collaboration bringing together mathematical, methodological, technological, cognitive and medical expertise to design innovative methodological solutions to respond to complex challenges and improve patient care.
Current Works: We are mainly in step 1. This subject feeds many statistical research problems. I am particularly interested in problematics of causal inference with missing values and performing exploratory analysis and predictive models with missing values with many missing values (with different coding: NA for Not Applicable, Imp for impossible, NR for Not Recorded, NM for Not Made..) as well as both continuous and categorical data. We start step 2.
Here a list of some papers and also projects/internship done with interns or polytechnique students.
- Paper. Logistic regression with missing data (Useful for propensity scores and IPW with missing values) joint work with Wei Jiang, Marc Lavielle
- Paper. Supervised learning/random forest with missing data – joint work with Nicolas Prost, Erwan Scornet, Gael Varoquaux
- Paper. Imputation when data is MNAR data – joint work with Claire Boyer, Aude Sportisse)
- On going. Causal inference with missing data in X? joint work with Imke Mayer, Stefan Wager
- Paper. Average Treatment effect. Effect of Fibrinogen administration on early mortality in traumatic haemorrhagic shock: a propensity score analysis. Hamada, S., Beauchesne J (Polytechnique student), et al.
- On going. poster. Prescriptive Treatment. Presciption of the Fibrinogene. J Beauchesne (Polytechnique student).
- On going. poster. Heterogeneous Treatment effect. Effect of the Tranexomic acid for head trauma. T Alves De Sousa, JP Nadal, T Gauss, JD Moyer, I Mayer
- On going. Prospective assessment of the prediction of haemorrhagic shock. M Pichon.
- Internships/Projects (Polytechnique): Descriptive statistics, recoding; creation of a Shiny interface to explore the data, unsupervised clustering, prediction, causal inference etc. (Antoine Ogier, Alexandre Claude Marc Saillard, )
Distributed matrix completion for medical databases
This is a joint work with Geneviève Robin (PhD student at Polytechnique), François Husson (Professor at Agrocampus Ouest) and Balasubramanian Narasimhan (Senior Researcher at Stanford University). Personalized medical care relies on comparing new patients profiles to existing medical records, in order to predict patients treatment response or risk of disease based on their individual characteristics, and adapt medical decisions accordingly. The chances of finding profiles similar to new patients, and therefore of providing them better treatment, increase with the number of individuals in the database. For this reason, gathering the information contained in the databases of several hospitals promises better care for every patient. However, there are technical and social barriers to the aggregation of medical data. The size of combined databases often makes computations and storage intractable, while institutions are usually reluctant to share their data due to privacy concerns and proprietary attitudes. Both obstacles can be overcome by turning to distributed computations, which consists in leaving the data on sites and distributing the calculations, so that hospitals only share some intermediate results instead of the raw data. This could solve the privacy problem and reduce the cost of calculations by splitting one large problem into several smaller ones. The general project is described in Narasimhan et. al. (2017). As it is often the case, the medical databases are incomplete. One aim of the project is to impute the data of one hospital using the data of the other hospitals. This could also be an incentive to encourage the hospitals to participate in the project and to share their summaries of their data.