Growth response to medical intervention in an observational real-life cohort of children with growth delay: A causal inference methods study

Background

Studies on growth response to medical interventions in children with growth delay are mainly randomized controlled trials (RCTs) and observational studies. RCTs establish cause-effect relationships under controlled conditions but limited generalizability to real-life. Conversely, observational studies reflect real-life conditions but are subject to confounding factors and bias. Data regarding catch-up growth and associated factors in children who do not benefit from medical treatment are lacking.

Objectives

To evaluate the causal impact of etiological treatment (ET) versus a wait-and-see approach (WS) on one-year catch-up growth in children with growth delay of different etiologies and to identify factors associated with good catch-up growth independent of the intervention.

Study design

Retrospective cohort of children referred for growth delay, defined by height < -2 SD, height < -2 SD from target height, or growth velocity < 0 SD.

Causal inference methods, including inverse probability treatment weighting (IPTW), and weighted Poisson regression with robust variance, were used to estimate the average treatment effect in the treated and relative risk, respectively. A variable selection procedure was applied using backward selection by Akaike Information Criterion.

Results

We included 198 children: 100 with ET and 98 with WS. Main diagnosis was secondary growth failure (42.4 %). Good catch-up growth was more frequent with ET versus WS (31.31% vs. 3.54 %, p < 0.05). Patients in the ET group had a 10.3 times higher chance of achieving one-year good catch-up growth. Young age and low growth velocity at baseline were associated with better catch-up growth, independently of the intervention.

Conclusion

ET significantly improves growth catch-up in children with growth delay. Young age and low growth velocity are associated with good catch-up growth, regardless of intervention. The use of IPTW strengthen the validity of the findings in a real-world setting, despite the retrospective study design. This approach highlights the effectiveness of interventions and the robustness of findings through advanced statistical adjustments.