Clinical and economic evaluation of a proteomic biomarker preterm birth risk predictor: cost-effectiveness modeling of prenatal interventions applied to predicted higher-risk pregnancies within a large and diverse cohort

Julja Burchard, Glenn R. Markenson, George R. Saade, Louise C. Laurent, Kent D. Heyborne, Dean V. Coonrod, Corina N. Schoen, Jason K. Baxter, David M. Haas, Sherri A. Longo, Scott A. Sullivan, Sarahn M. Wheeler, Leonardo M. Pereira, Kim A. Boggess, Angela F. Hawk, Amy H. Crockett, Ryan Treacy, Angela C. Fox, Ashoka D. Polpitiya, Tracey C. FleischerThomas J. Garite, J. Jay Boniface, John A.F. Zupancic, Gregory C. Critchfield, Paul E. Kearney

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objectives: Preterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment. Methods: The ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N = 847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and multimodal management (risk predictor/case management with pharmacological treatment) (RP-MM, active). In the active arms, only subjects stratified as higher risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects’ gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher’s exact test for neonatal morbidity/mortality (significance, p <.05). Results: The model predicted improvements for all outcomes. RP-CM decreased neonatal and maternal hospital stay by 19% (p =.029) and 8.5% (p =.001), respectively; neonatal costs’ point estimate by 16% (p =.098); and moderate-to-severe neonatal morbidity/mortality by 29% (p =.025). RP-MM strengthened observed reductions and significance. Point estimates of benefit did not differ by race/ethnicity. Conclusions: Modeled evaluation of a biomarker-based test-and-treat strategy in a diverse population predicts clinically and economically meaningful improvements in neonatal and maternal outcomes.

Original languageEnglish (US)
Pages (from-to)1255-1266
Number of pages12
JournalJournal of Medical Economics
Volume25
Issue number1
DOIs
StatePublished - 2022
Externally publishedYes

Keywords

  • Premature birth
  • biomarker–blood
  • clinical utility
  • cost-effectiveness
  • microsimulation model
  • preterm birth
  • protein biomarker risk predictor

ASJC Scopus subject areas

  • Health Policy

Fingerprint

Dive into the research topics of 'Clinical and economic evaluation of a proteomic biomarker preterm birth risk predictor: cost-effectiveness modeling of prenatal interventions applied to predicted higher-risk pregnancies within a large and diverse cohort'. Together they form a unique fingerprint.

Cite this