Influence of thoracoscopic operations on pulmonary function and postoperative recovery

Author:

Wu Di1,Zhao Honglin1,Li Xuanguang1,Dong Ming1,Li Xin1,Liu Jinghao1,Liu Minghui1,Wang Jing2,Xia Chunqiu1,Yan Tingzhi1,Liu Hongyu2,Chen Jun1

Affiliation:

1. Tianjin Medical University General Hospital

2. Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital

Abstract

Abstract Background Nowadays, the age of patients qualified for lung surgery has been lowered. These people have increasingly higher requirements for postoperative quality of life, which is closely related to pulmonary function (PF). Therefore, it’s meaningful to analyze the effects of different surgical methods on postoperative PF and postoperative recovery. Methods A total of 171 patients underwent thoracoscopic lung surgery were selected in our study: unilateral lobectomy (UL), unilateral sublobectomy (USL), and other surgical method (OSM). Other operations included unilateral/bilateral lobectomy and/or sublobectomy. Study indicators included patient general condition, PF and recovery condition. Results The USL was in the best condition during and after surgery. Then was OSM, USL was the worst. All the thoracoscopic operations significantly reduced PF and the loss of PF in the UL was significantly higher than USL. In the long-term observation, the loss of PF after lobectomy was almost the same. The compensatory capacity of the upper lobe was better than the lower lobe. The prediction models of PF cannot fully reflect the actual situation after operation. Our PF loss assessment table based on clinical data was constructed to correct existing models. Conclusions Surgeries had significant effects on PF, but these changes gradually disappeared after operation. The greatest affect were PEF and PEF%, the least were MVV and MVV%. The compensatory function of the upper lobe was better than lower one. The loss of PF in multiple lung tissue resection is equivalent to lobectomy. Our postoperative PF assessment table can better reflect the PF than existing prediction models.

Publisher

Research Square Platform LLC

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