A Predictive Model for Thiamine Responsive Disorders Among Infants and Young Children: Results from a Prospective

Taryn J Smith1, Charles D Arnold1, Philip R Fischer2

  • 1Institute for Global Nutrition and Department of Nutrition, University of California Davis, Davis, CA.

The Journal of Pediatrics
|February 18, 2024
PubMed

Insights

Infants and young children with suspected thiamine deficiency disorders (TDDs) often respond well to thiamine treatment. A predictive model identified key signs, but high prevalence suggests empirical thiamine use in high-risk areas.

Area of Science:

  • Pediatrics
  • Nutritional Science
  • Infectious Disease Epidemiology

Background:

  • Thiamine deficiency disorders (TDDs) pose a significant health risk to infants and young children.
  • Early diagnosis and treatment are crucial for favorable outcomes.
  • Identifying predictive factors for thiamine responsiveness can aid clinical decision-making.

Purpose of the Study:

  • To develop a predictive model for identifying thiamine responsive disorders (TRDs) in hospitalized infants and young children.
  • To assess the response to therapeutic thiamine in a high-risk population.

Main Methods:

  • A cohort of 449 children (21 days to <18 months) in Lao People's Democratic Republic with suspected TDDs received parenteral thiamine.
  • Clinical assessments and recovery were monitored for 72 hours post-thiamine initiation.
  • Logistic regression models identified predictors of TRD, with model performance evaluated by AUC.

Main Results:

  • 60.8% of enrolled children had a confirmed TRD.
  • Key predictors of TRD included exclusive/predominant breastfeeding, hoarse voice, cyanosis, lack of eye contact, and absence of recent diarrhea.
  • The predictive model achieved an AUC of 0.82 (95% CI: 0.78, 0.86).

Conclusions:

  • The majority of children with suspected TDDs showed a positive response to thiamine.
  • Five clinical features effectively predicted TRD, but the high prevalence supports empirical thiamine administration for all suspected cases in similar settings.
  • Further research is needed to refine and validate the predictive model for broader application.
Abstract

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