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Abstract

Background

The potential clinical value of driving pressure (DP) and mechanical power (MP) as digital biomarkers for predicting postoperative pulmonary complications (PPC) has been emphasized. Although several studies have explored this association, evidence from clinical cohorts involving large patient populations remains limited.

Methods

A retrospective cohort study was conducted between October 2004 and May 2023 on patients who underwent OLV surgery. The association between time-weighted median dynamic DP or MP and PPC was analyzed using multivariate logistic regression models adjusted for confounders. Additionally, risk threshold analysis was conducted to propose thresholds for an increased risk of PPC.

Results

Among the 3386 (using plateau pressure; Pplat) or 4951 (using peak inspiratory pressure; PIP) patients included, PPC occurrence was 19.31 % and 17.43 %, respectively. The mean of the time-weighted median DP derived from Pplat (DP-Pplat) and MP derived from Pplat (MP-Pplat) were 14.6 cmH 2O and 7.1 J/min, while DP-PIP and MP-PIP were 17.2 cmH 2O and 6.4 J/min. Both calculation methods showed that DP had an independent association with the occurrence of PPC, with an odds ratio of 1.047 [95 % CI 1.019–1.075, p < 0.05] and 1.036 [95 % CI 1.013–1.059, p < 0.05] using Pplat and PIP, respectively. However, MP was not found to be independently associated with PPC using either method, with an odds ratio of 1.033 [95 % CI 0.980–1.089, p = 0.226] and 1.048 [95 % CI 0.992–1.106, p = 0.092] using Pplat and PIP, respectively. The risk threshold for DP-Pplat was 15 cmH 2O, whereas for DP-PIP, it was 18 cmH 2O.

Conclusions

In this OLV surgery population, a DP-Pplat-limited mechanical ventilation strategy of 15 cmH 2O or DP-PIP of 18 cmH 2O was associated with lower risk of PPC.

Details

Title
Associations of driving pressure and mechanical power with postoperative pulmonary complications in one-lung ventilated surgery
Author
Yang, Hyun-Lim; Park, Seong-A; Lee, Hong Yeul; Lee, Hyeonhoon 1 ; Ryu, Ho-Geol; Lee, Hyung-Chul 1 ; Lee, Sang-Min; Lee, Jinwoo

 Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea 
Section
Original Contribution
Publication year
2025
Publication date
Sep 2025
Publisher
Elsevier Limited
ISSN
09528180
e-ISSN
18734529
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3246956273
Copyright
© 2025 Elsevier Inc.