645 - Data Analytics and KMC Research: A Case Study

In a perspective to support longitudinal studies, a use case of a visual data analytics platform associated with the KMC intervention is presented. KMC as a comprehensive intervention has characteristics that require continuous monitoring, assessment, and evaluation. The data obtained throughout the process is very complex and requires modeling and curation to ensure the quality and longevity of the data. Thus, analysis based on this data will have great potential value for KMC worldwide.

Objective: to highlight the value of the data generated into the process of caring for this fragile population and the potential of these data for improving the quality of KMC worldwide.

Design/Methods: two specific cases are presented • A database of 50,000 individuals from their family environment, pregnancy, birth, stages up to 40 weeks gestational age, and up to 3, 6, 9 and 12 months corrected age. These data correspond to three Kangaroo programs under study over 25 years of operation (updated to 2022). • A cohort of 450 individuals (200 preterm KMC, 200 incubator, 50 term) with data from pregnancy to 1 year of corrected age, and a comprehensive review at 18-20 years of corrected age including MRI, DTI, TMS, health status, socioeconomic background, and neurodevelopmental testing.

Results: Based on a data model structured by process steps over time, and a set of visual analytical tools oriented to KMC, longitudinal studies such as [1][2][3] (see Fig. 1) have been conducted and published (

1. Tools for the exploration of growth data in a cohort of kangaroo infants during their first year of life. In2017 IEEE Workshop on Visual Analytics in Healthcare (VAHC) 2017 Oct 1 (pp. 9-16). IEEE.


2. "Kangaroo mother care had a protective effect on the volume of brain structures in young adults born preterm." Acta Paediatrica 111.5 (2022): 1004-1014.

This data modeling and these tools are highly valued by analysts.