Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Real-world economic evaluation of prospective rapid whole-genome sequencing compared to a matched retrospective cohort of critically ill pediatric patients in the United States

Abstract

There is an increasing demand for supporting the adoption of rapid whole-genome sequencing (rWGS) by demonstrating its real-world value. We aimed to assess the cost-effectiveness of rWGS in critically ill pediatric patients with diseases of unknown cause. Data were collected prospectively of patients admitted to the Nicklaus Children’s Hospital’s intensive care units from March 2018 to September 2020, with rWGS (N = 65). Comparative data were collected in a matched retrospective cohort with standard diagnostic genetic testing. We determined total costs, diagnostic yield (DY), and incremental cost-effectiveness ratio (ICER) adjusted for selection bias and right censoring. Sensitivity analyses explored the robustness of ICER through bootstrapping. rWGS resulted in a diagnosis in 39.8% while standard testing in 13.5% (p = 0.026). rWGS resulted in a mean saving per person of $100,440 (SE = 26,497, p < 0.001) and a total of $6.53 M for 65 patients. rWGS in critically ill pediatric patients is cost-effective, cost-saving, shortens diagnostic odyssey, and triples the DY of traditional approaches.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study flowchart.
Fig. 2: Cost-effectiveness acceptability curves for rapid whole-genome sequencing and standard diagnostic genetic testing.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from Nicklaus’ Children hospital, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Nicklaus’ Children hospital.

Code availability

The STATA codes used in this study are available from the authors upon request.

References

  1. Ontario Health (Quality). Genome-wide sequencing for unexplained developmental disabilities or multiple congenital anomalies: a health technology assessment. Ont Health Technol Assess Ser. 2020;20:1–178.

    Google Scholar 

  2. Lionel AC, Costain G, Monfared N, Walker S, Reuter MS, Hosseini SM, et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet Med. 2018;20:435–43. https://doi.org/10.1038/gim.2017.119.

    Article  CAS  PubMed  Google Scholar 

  3. Friedman JM, Bombard Y, Cornel MC, Fernandez CV, Junker AK, Plon SE, et al. Genome-wide sequencing in acutely ill infants: genomic medicine’s critical application? Genet Med. 2019;21:498–504. https://doi.org/10.1038/s41436-018-0055-z.

    Article  CAS  PubMed  Google Scholar 

  4. Alam K, Schofield D. Economic evaluation of genomic sequencing in the paediatric population: a critical review. Eur J Hum Genet. 2018;26:1241–7. https://doi.org/10.1038/s41431-018-0175-6.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Farnaes L, Hildreth A, Sweeney NM, Clark MM, Chowdhury S, Nahas S, et al. Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization. npj Genom Med. 2018;3:10. https://doi.org/10.1038/s41525-018-0049-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rare Diseases Europe. What is a rare disease? Eurordis-Rare Diseases. Survey of the delay in diagnosis for 8 rare diseases In Europe (‘EURORDISCARE 2’) diseases and countries number of respondents by country. Europe. www.Eurodis.org. Accessed 27 April 2021.

  7. Shashi V, McConkie-Rosell A, Rosell B, Schoch K, Vellore K, McDonald M, et al. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet Med. 2014;16:176–82. https://doi.org/10.1038/gim.2013.99.

    Article  CAS  PubMed  Google Scholar 

  8. Bloss CS, Zeeland AASV, Topol SE, Darst BF, Boeldt DL, Erikson GA, et al. A genome sequencing program for novel undiagnosed diseases. Genet Med. 2015;17:995–1001. https://doi.org/10.1038/gim.2015.21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sanford EF, Clark MM, Farnaes L, Williams MR, Perry JC, Ingulli EG, et al. Rapid whole genome sequencing has clinical utility in children in the PICU*. Pediatr Crit Care Med. 2019;20:1007–20. https://doi.org/10.1097/PCC.0000000000002056.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Willig LK, Petrikin JE, Smith LD, Saunders CJ, Thiffault I, Miller NA, et al. Whole-genome sequencing for identification of Mendelian disorders in critically ill infants: a retrospective analysis of diagnostic and clinical findings. Lancet Respir Med. 2015;3:377–87. https://doi.org/10.1016/S2213-2600(15)00139-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dimmock DP, Clark MM, Gaughran M, Cakici JA, Caylor SA, Clarke C, et al. An RCT of rapid genomic sequencing among seriously ill infants results in high clinical utility, changes in management, and low perceived harm. Am J Hum Genet. 2020;107:942–52. https://doi.org/10.1016/j.ajhg.2020.10.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Soden SE, Saunders CJ, Willig LK, Farrow EG, Smith LD, Petrikin JE, et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci Transl Med. 2014;6:265ra168–265ra168. https://doi.org/10.1126/scitranslmed.3010076.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dimmock D, Caylor S, Waldman B, Benson W, Ashburner C, Carmichael JL, et al. Project Baby Bear: Rapid precision care incorporating rWGS in 5 California children’s hospitals demonstrates improved clinical outcomes and reduced costs of care. Am J Hum Genet. 2021;108:1231–8. https://doi.org/10.1016/j.ajhg.2021.05.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Global. Whole exome and whole genome sequencing. 2021. https://static.cigna.com/assets/chcp/pdf/coveragePolicies/medical/mm_Future/mm_0519_coveragepositioncriteria_exome_genome_sequence.pdf. Accessed 27 April 2021.

  15. Aetna. Subject: genetic testing. 2021. http://www.aetna.com/cpb/medical/data/100_199/0140.html. Accessed 27 April 2021.

  16. UnitedHealthcare. Whole exome and whole genome sequencing UnitedHealthcare ® commercial medical policy whole exome and whole genome sequencing. 2021. https://www.uhcprovider.com/content/dam/provider/docs/public/policies/comm-medical-drug/whole-exome-and-whole-genome-sequencing.pdf.

  17. Genetic Testing - Medical Clinical Policy Bulletins | Aetna. http://www.aetna.com/cpb/medical/data/100_199/0140.html.

  18. Personalized Medicine Coalition - Advocates for precision medicine: understanding genomic testing utilization and coverage in the US. http://www.personalizedmedicinecoalition.org/Resources/Understanding_Genomic_Testing_Utilization_and_Coverage_in_the_US. Accessed 27 April 2021.

  19. Sabatini LM, Mathews C, Ptak D, Doshi S, Tynan K, Hegde MR, et al. Genomic sequencing procedure microcosting analysis and health economic cost-impact analysis. J Mol Diagnostics. 2016;18:319–28. https://doi.org/10.1016/j.jmoldx.2015.11.010.

    Article  Google Scholar 

  20. Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA, Chaib H, et al. Clinical interpretation and implications of whole-genome sequencing. JAMA. 2014;311:1035. https://doi.org/10.1001/jama.2014.1717.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Febres-Aldana CA, Pelaez L, Wright MS, Maher OM, Febres-Aldana AJ, Sasaki J, et al. A case of UDP-galactose 4′-epimerase deficiency associated with dyshematopoiesis and atrioventricular valve malformations: an exceptional clinical phenotype explained by altered n-glycosylation with relative preservation of the leloir pathway. Mol Syndromol. 2020;11:320–30. https://doi.org/10.1159/000511343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Elixhauser Comorbidity Software, Version 3.7. https://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. Accessed 27 April 2021.

  23. Thompson NR, Fan Y, Dalton JE, Jehi L, Rosenbaum BP, Vadera S, et al. A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality. Med Care. 2015;53:374–9. https://doi.org/10.1097/MLR.0000000000000326.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sekhon J. The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods. Box-Steffensmeier J, Brady H, Collier D, eds Oxford Handbook of Political Methodology Oxford Oxford University Press. 2009. https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199286546.001.0001/oxfordhb-9780199286546-e-11.

  25. Li X, Shen C. Doubly robust estimation of causal effect. Circ Cardiovasc Qual Outcomes. 2020;13:e006065. https://doi.org/10.1161/CIRCOUTCOMES.119.006065.

    Article  PubMed  Google Scholar 

  26. Rosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987;82:387–94. https://doi.org/10.2307/2289440.

    Article  Google Scholar 

  27. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89:846–66. https://doi.org/10.1080/01621459.1994.10476818.

    Article  Google Scholar 

  28. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for nonignorable drop-out using semiparametric nonresponse models. J Am Stat Assoc. 1999;94:1096–120. https://doi.org/10.1080/01621459.1999.10473862.

    Article  Google Scholar 

  29. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962–73. https://doi.org/10.1111/j.1541-0420.2005.00377.x.

    Article  PubMed  Google Scholar 

  30. Robins J, Sued M, Lei-Gomez Q, Rotnitzky A. Comment: performance of double-robust estimators when “inverse probability” weights are highly variable. Stat Sci. 2007;22:544–59. https://doi.org/10.1214/07-STS227D.

    Article  Google Scholar 

  31. Li J, Vachani A, Epstein A, Mitra N. A doubly robust approach for cost-effectiveness estimation from observational data. Stat Methods Med Res. 2018;27:3126–38. https://doi.org/10.1177/0962280217693262.

    Article  PubMed  Google Scholar 

  32. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47:626–33. https://doi.org/10.1097/MLR.0b013e31819432e5.

    Article  PubMed  Google Scholar 

  33. Sun JW, Bourgeois FT, Haneuse S, Hernández-Díaz S, Landon JE, Bateman BT, et al. Development and validation of a pediatric comorbidity index. Am J Epidemiol. 2021;190:918–27. https://doi.org/10.1093/aje/kwaa244.

    Article  PubMed  Google Scholar 

  34. Petrikin JE, Cakici JA, Clark MM, Willig LK, Sweeney NM, Farrow EG, et al. The NSIGHT1-randomized controlled trial: rapid whole-genome sequencing for accelerated etiologic diagnosis in critically ill infants. NPJ Genom Med. 2018;3:6 https://doi.org/10.1038/s41525-018-0045-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Diagnosis Related Group (DRG). https://hmsa.com/portal/provider/zav_pel.fh.DIA.650.htm. Accessed 27 April 2021.

  36. Krantz ID, Medne L, Weatherly JM, Wild KT, Biswas S, Devkota B, et al. Effect of whole-genome sequencing on the clinical management of acutely ill infants with suspected genetic disease: a randomized clinical trial. JAMA Pediatr. 2021;175:1218–26. https://doi.org/10.1001/jamapediatrics.2021.3496.

    Article  PubMed  Google Scholar 

  37. Kingsmore SF, Cakici JA, Clark MM, Gaughran M, Feddock M, Batalov S, et al. A randomized, controlled trial of the analytic and diagnostic performance of singleton and trio, rapid genome and exome sequencing in ill infants. Am J Hum Genet. 2019;105:719–33. https://doi.org/10.1016/j.ajhg.2019.08.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Dragojlovic N, Elliott AM, Adam S, van Karnebeek C, Lehman A, Mwenifumbo JC, et al. The cost and diagnostic yield of exome sequencing for children with suspected genetic disorders: a benchmarking study. Genet Med. 2018;20:1013–21. https://doi.org/10.1038/gim.2017.226.

    Article  PubMed  Google Scholar 

  39. Ellsworth KA, Caylor S, Benson W, Ashburner C, Carmichael J, Cham E, et al. Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: Oral Presentations. Eur J Hum Genet. 2020;28:1–140. https://doi.org/10.1038/s41431-020-00740-6.

    Article  Google Scholar 

  40. Project Baby Bear Final Report; 2020. https://radygenomics.org/wp-content/uploads/2020/07/PBB-Final-Report_07.14.20.pdf.

  41. Smith HS, Swint JM, Lalani SR, de Oliveira Otto MC, Yamal JM, Russell HV, et al. Exome sequencing compared with standard genetic tests for critically ill infants with suspected genetic conditions. Genet Med. 2020;22:1303–10. https://doi.org/10.1038/s41436-020-0798-1.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge Rene Medina for their contribution to the data extraction and Jennifer McCafferty for assistance in Baby Manatee program development.

Funding

This study was supported by Florida Department of Heath State Appropriation, Cornelia T. Bailey Foundation, Sanford Health and Nicklaus Children’s Health Care Foundation.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: VD, DS2. Data curation: PE, MJ, DS, AG, PJ, MDB, BT, JS, AJ, DS2. Formal Analysis: VD, AB, YH, RM, PE, MJ, DS, AG, DS2. Funding Acquisition: VD, DS2. Investigation: PE, MJ, DS, AG, PJ, MDB, BT, JS, AJ, DS2. Methodology: VD, AB, YH, PE, AG, PJ, MDB, BT, JS, AJ, DS2. Project administration: PE, MJ, DS, DS2. Resource: PE, PJ, MDB, BT, JS, AJ. Supervision: VD, PE, DS2. Validation: PE, MJ, DS, AG, PJ, MDB, BT, JS, AJ, DS2. Writing – original draft: VD, AB, YH, DS, PE, AG, DS2. Writing – review & editing: VD, AB, YH, DS, PE, AG, PJ, MDB, BT, JS, AJ, DS2. (DS = Diana Soler, DS2 = Daria Salyakina).

Corresponding author

Correspondence to Vakaramoko Diaby.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The data used for analyses within the project were de-identified.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diaby, V., Babcock, A., Huang, Y. et al. Real-world economic evaluation of prospective rapid whole-genome sequencing compared to a matched retrospective cohort of critically ill pediatric patients in the United States. Pharmacogenomics J 22, 223–229 (2022). https://doi.org/10.1038/s41397-022-00277-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-022-00277-5

This article is cited by

Search

Quick links