JMIR Publications

ABSTRACT

Background:

LMS-based parameterization of anthropometric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of LMS-based tools for use by clinicians and clinical researchers currently restricts broader application.

Objective:

Describe deployment of calculators for LMS-based growth charts and examples of their utilization for patient care delivery, clinical research, and quality improvement projects.

Methods:

The publicly accessible PediTools (https://peditools.org/) website of clinical calculators was developed to allow LMS-based calculations on anthropometric measurements of individual patients. Similar calculations were applied in a retrospective study of a population of patients from seven Massachusetts neonatal intensive care units (NICUs) to compare inter-hospital growth outcomes (change in weight Z-score from birth to discharge, ∆Z weight) and their association with gestational age at birth. At one hospital, a bundle of quality improvement interventions targeting improved growth was implemented and outcomes assessed prospectively via monitoring of ∆Z weight pre- and post-intervention.

Results:

The PediTools website was launched in January 2012 and as of June 2019 receives over 500,000 pageviews per month with users from over 21 countries. A retrospective analysis of 7,975 patients at seven Massachusetts NICUs born between 2006 and 2011 at 23 to 34 completed weeks gestation identified an overall ∆Z weight from birth to discharge of -0.81 (_P_ < .001), indicating a substantial loss of weight Z-score from birth to discharge. However, the degree of ∆Z weight differed significantly by hospital, ranging from -0.56 to -1.05 (_P_ < .001). Also identified was the association between worse growth outcomes and lower gestational age at birth, and that the degree of association between ∆Z weight and gestation at birth also differed by hospital. At one hospital, implementing a bundle of interventions targeting growth resulted in a significant and sustained reduction in loss of weight Z-score from birth to discharge.

Conclusions:

LMS-based anthropometric measurement calculation tools on a public web site have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated worse growth at lower gestational age and inter-hospital variation in growth outcomes. Change in weight Z-score has proven useful as an outcome measure for monitoring clinical quality improvement. We also announce the release of open source computer code written in R to allow other clinicians and clinical researchers to easily perform similar analyses.