Introduction: Incentive spirometry (IS) is commonly used for increasing postoperative IS inspiratory capacity (ISIC) after open heart surgery (OHS). However, little is known about the serial changes in ISIC and their predictive factors.
Objective: The aim of this study is to identify the postoperative ISIC changes relative to preoperative ISIC after OHS, and determine their predictors, including patient characteristics factors and IS performance parameters such as inspiration volumes (ISv) and frequencies (ISf).
Methods: This is a prospective study with blinding procedures involving 95 OHS patients, aged 52.8±11.5 years, whose ISIC was measured preoperatively (PreopISIC) until fifth postoperative day (POD), while ISv and ISf monitored with an electronic device from POD1-POD4. Regression models were used to identify predictors of POD1 ISIC, POD2- POD5 ISIC increments, and the odds of attaining PreopISIC by POD5.
Results: The ISIC reduced to 41% on POD1, increasing thereafter to 57%, 75%, 91%, and 106% from POD2-POD5 respectively. Higher PreopISIC (B=-0.01) significantly predicted lower POD1 ISIC, and, together with hyperlipedemia (B=11.52), which significantly predicted higher POD1 ISIC, explained 13% of variance. ISv at relative percentages of PreopISIC from POD1-POD4 (B=0.60, B=0.56, B=0.49, B=0.50) significantly predicted ISIC of subsequent PODs with variances at 23%, 24%, 17% and 25% respectively, but no association was elicited for ISf. IS performance findings facilitated proposal of a postoperative IS therapy target guideline. Higher ISv (B=0.05) also increased odds of patients recovering to preoperative ISIC on POD5 while higher PreopISIC (B=- 0.002), pain (B=-0.72) and being of Indian race (B=-1.73) decreased its odds.
Conclusion: ISv appears integral to IS therapy efficacy after OHS and the proposed therapy targets need further verification through randomized controlled trials.
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Quant Imaging Med Surg
January 2025
School of Computer and Control Engineering, Yantai University, Yantai, China.
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Assistant professor, ISIC Institute of Rehabilitation Sciences, New Delhi, India.
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Elife
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Laboratory of Biophysical Chemistry of Macromolecules, Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The field of medical image segmentation powered by deep learning has recently received substantial attention, with a significant focus on developing novel architectures and designing effective loss functions. Traditional loss functions, such as Dice loss and Cross-Entropy loss, predominantly rely on global metrics to compare predictions with labels. However, these global measures often struggle to address challenges such as occlusion and nonuni-form intensity.
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Laboratory of Synthesis and Natural Products (LSPN), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, EPFL-SB-ISIC-LSPN, BCH 5304, CH-1015 Lausanne, Switzerland.
In the dyotropic rearrangement of molecules with semiflexible structures, characterized by a freely rotating static C-C bond, the formation of a mixture of products is common due to the coexistence of several energetically comparable conformers. Herein, we report that it is possible to modulate the shifting groups by adjusting the metal's coordination sphere in Pd-based dyotropic rearrangement. In the presence of a catalytic amount of Pd(II) salt, the reaction of γ-hydroxyalkenes or γ,δ-dihydroxyalkenes with Selectfluor affords fluorinated tetrahydropyranols or 6,8-dioxabicyclo[3.
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