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PediTools Electronic Growth Chart Calculators: Applications in Clinical Care, Research, and Quality Improvement
Joseph H Chou1,2, Sergei Roumiantsev1,2, Rachana Singh3,4
1Massachusetts General Hospital, Boston, MA, United States.
Insights
Electronic pediatric growth chart calculators using the lambda-mu-sigma (LMS) method are now widely available. These tools aid in precise growth metric quantification, clinical research, and quality improvement initiatives for better patient care.
Area of Science:
- Pediatric growth monitoring and assessment
- Clinical informatics and data analysis
- Neonatal intensive care unit (NICU) outcomes
Background:
- Traditional paper-based pediatric growth charts present limitations in precise growth metric quantification.
- Limited availability of electronic growth chart calculators hinders broader clinical and research application.
- Parameterization methods, such as lambda-mu-sigma (LMS), offer advanced growth quantitation.
Purpose of the Study:
- To evaluate the deployment and utility of electronic growth chart calculators employing the LMS parameterization method.
- To demonstrate the application of these tools in patient care, clinical research, and quality improvement.
- To provide open-source code for similar analyses by clinicians and researchers.
Main Methods:
- Development of the publicly accessible PediTools website for LMS-based anthropometric calculations.
- Retrospective analysis of growth outcomes (change in weight Z-score) in 7975 neonatal patients across 7 NICUs.
- Prospective assessment of quality improvement interventions targeting growth at a single hospital.
Main Results:
- The PediTools website has achieved significant global usage since its 2012 launch.
- A retrospective analysis revealed significant interhospital variation in weight Z-score changes and an association with gestational age.
- Quality improvement interventions at one NICU led to a significant and sustained reduction in weight Z-score loss.
Conclusions:
- LMS-based anthropometric calculation tools are widely adopted and valuable for clinical research and quality monitoring.
- Weight Z-score change serves as a potential outcome measure for assessing clinical quality improvement in neonates.
- The study highlights the importance of accessible tools for analyzing growth trajectories and interhospital variations.
Background:
Parameterization of pediatric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of growth chart calculators for use by clinicians and clinical researchers currently restricts broader application.
Objective:
The aim of this study was to assess the deployment of electronic calculators for growth charts using the lambda-mu-sigma (LMS) parameterization method, with examples of their utilization for patient care delivery, clinical research, and quality improvement projects.
Methods:
The publicly accessible PediTools 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 7 Massachusetts neonatal intensive care units (NICUs) to compare interhospital growth outcomes (change in weight Z-score from birth to discharge [∆Z weight]) and their association with gestational age at birth. At 1 hospital, a bundle of quality improvement interventions targeting improved growth was implemented, and the outcomes were assessed prospectively via monitoring of ∆Z weight pre- and postintervention.
Results:
The PediTools website was launched in January 2012, and as of June 2019, it received over 500,000 page views per month, with users from over 21 countries. A retrospective analysis of 7975 patients at 7 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). 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 inferior growth outcomes and lower gestational age at birth, as well as that the degree of association between ∆Z weight and gestation at birth also differed by hospital. At 1 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 website have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated inferior growth at lower gestational age and interhospital variation in growth outcomes. Change in weight Z-score has potential utility 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.
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