Validation of the 'paediatric extracorporeal membrane oxygenation prediction' model in a UK extracorporeal membrane oxygenation centre.

INTRODUCTION

There is a need for a universal risk-adjustment model that may be used regardless of the indication and nature of neonatal or paediatric extracorporeal membrane oxygenation support. The 'paediatric extracorporeal membrane oxygenation prediction' model appeared to be a promising candidate but required external validation.

METHODS

We performed a validation study using institutional database of extracorporeal membrane oxygenation patients (2008-2019). We used the published paediatric extracorporeal membrane oxygenation prediction score calculator to derive estimated mortality based on the model in this cohort of patients in our institutional database. We used standardized mortality ratio, area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test in 10 deciles to assess model performance.

RESULTS

We analysed 154 extracorporeal membrane oxygenation episodes in 150 patients. About 53% of the patients were full term (age ⩽30 days and gestation at birth ⩾37 weeks) neonates. The commonest category of extracorporeal membrane oxygenation support was cardiac (42%). The overall in-paediatric intensive care unit mortality was 37% (57/154) and the in-hospital mortality was 42% (64/154). Distribution of estimated mortality risk was similar to the derivation study. The calculated standardized mortality ratio was 0.81 based on the paediatric extracorporeal membrane oxygenation prediction model of risk-adjustment. The area under the receiver operating characteristic curve was 0.55 (0.45-0.64) and Hosmer-Lemeshow-test p value <0.001 was unable to support goodness-of-fit.

CONCLUSION

This small single-centre study with a small number of events was unable to validate the paediatric extracorporeal membrane oxygenation prediction-model of risk-adjustment. Although this remains the most promising of all the available models, further validation in larger data sets and/or refinement may be required before widespread use.