Criteria for identifying acute respiratory events based on FEV1 decline in home spirometry for lung transplant patients.

Background: Lung transplantation is a critical treatment for end-stage lung diseases, but long-term survival is challenged by graft rejection and infection. The detection of adverse respiratory events depends on home spirometry, which can exhibit greater fluctuations than laboratory tests and may not provide timely alerts. Our LT-FollowUp system offers an internet-based platform for daily FEV1 monitoring. This paper explores whether a new algorithm using LT-FollowUp data can detect the clinically significant FEV1 declines that predict adverse respiratory events.

Methods: A retrospective cohort study of lung transplant patients from the University of Tokyo Hospital was conducted using LT-FollowUp. The accuracy of the algorithm was evaluated using a nested case-crossover study comparing FEV1 declines before acute respiratory events with control periods, and a nested case-time-control study comparing cases with matched controls to adjust for time trends and bias.

Results: Of the 95 patients included in this study, 21 experienced acute respiratory events. The odds ratios derived from conditional logistic regression in the nested case-crossover study and the conditional logistic regression in the nested case-time-control study are 5.42 × 10⁵ and 1, respectively. There is a clear association between abnormal FEV1 decline and acute respiratory events. No clear time trend is observed.

Conclusion: The proposed algorithm using LT-FollowUp data shows promise for the real-time detection of respiratory events in lung transplant patients, potentially facilitating early interventions that may prevent chronic lung allograft dysfunction. Further validation in larger, multi-centre studies is needed to confirm these findings and enhance clinical utility.