A big-data approach to producing descriptive anthropometric references: a feasibility and validation study of paediatric growth charts.

BACKGROUND: Both national and WHO growth charts have been found to be poorly calibrated with the physical growth of children in many countries. We aimed to generate new national growth charts for French children in the context of huge datasets of physical growth measurements routinely collected by office-based health practitioners. METHODS: We recruited 32 randomly sampled primary care paediatricians and ten volunteer general practitioners from across the French metropolitan territory who used the same electronic medical records software, from which we extracted all physical growth data for the paediatric patients, with anonymisation. We included measurements from all children born from Jan 1, 1990, and aged 1 month to 18 years by Feb 8, 2018, with birthweight greater than 2500 g, to which an automated process of data cleaning developed to detect and delete measurement or transcription errors was applied. Growth charts for weight and height were derived by using generalised additive models for location, scale, and shape with the Box-Cox power exponential distribution. We compared the new charts to WHO growth charts and existing French national growth charts, and validated our charts using growth data from recent national cross-sectional surveys. FINDINGS: After data cleaning, we included 1 458 468 height and 1 690 340 weight measurements from 238 102 children. When compared with the existing French national and WHO growth charts, all height SD and weight percentile curves for the new growth charts were distinctly above those for the existing French national growth charts, as early as age 1 month, with an average difference of -0·75 SD for height and -0·50 SD for weight for both sexes. Comparison with national cross-sectional surveys showed satisfactory calibration, with generally good fit for children aged 5-6 years and 10-11 years in height and weight and small differences at age 14-15 years. INTERPRETATION: We successfully produced calibrated paediatric growth charts by using a novel big-data approach applied to data routinely collected in clinical practice that could be used in many fields other than anthropometry. FUNDING: The French Ministry of Health; Laboratoires Guigoz-General Pediatrics section of the French Society of Pediatrics-Pediatric Epidemiological Research Group; and the French Association for Ambulatory Pediatrics.

Modular Knowledge-Based Decision Support System Dedicated to a Cooperative Decision to Prevent Cardiovascular Diseases.

Despite the success of artificial intelligence solutions in the recent years, physicians are still reticent to use integrated functionalities to support their decision. Methods used to create these functionalities can be divided into two groups, each being associated to different questions. Data-based methods are seen as black boxes for which it is impossible to understand how the decision is taken; knowledge-based methods need to rely on formalized knowledge sources on the basis of evidence, which can be discussed and criticized by physicians for their use in real life. This paper presents a new modular decision support system for the prevention of cardiovascular diseases, based on knowledge and on cooperative decision between the patient and the physician. The decision support system is based on two layers: (i) the first layer is a knowledge-based module which generates automatically patient profile, and prevention strategies associated to the profile; (ii) the second layer is a dynamic collaborative graphic user interface which displayed information about the risks of treatment adherence failure, personalized motivation and follow-up strategies. In the future, we aim at assessing the platform in real life.

Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk.

The prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g. diabetes). Hence, French national clinical practice guidelines to quantify the cardiovascular risk is able to deal with more clinical situations than the SCORE. The goal of this paper is to formalize knowledge extracted from these guidelines and implement the rules so that they can be used into an auto-assessing tool of cardiovascular risk. Formalization followed five steps and was conducted under the guidance of medical experts. It resulted into a decision tree fed by eight decision variables. Evaluation of the accuracy of the decision tree showed 80% of agreement with an expert in medical informatics in predicting the cardiovascular risk level for 15 different clinical situations. Discrepancies correspond to the knowledge gaps within Clinical Practice Guidelines. We intend to extend the implementation of the decision tree to a complete tool, for allowing patient to auto-assess their cardiovascular risk. This tool will be integrated into a platform providing recommendations adapted to the calculated level of cardiovascular risk.

Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus.

BACKGROUND: Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization. OBJECTIVE: To obtain a consensus about the priority target conditions for algorithms monitoring children's growth. METHODS: We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating), with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists) as well as parent associations. We asked experts in the pilot (n = 11), reading (n = 8) and rating (n = 60) groups to complete the list of diagnostic classification of the European Society for Paediatric Endocrinology and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition. RESULTS: Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement). CONCLUSION: This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.