Total intravenous anesthesia decreases hospital stay but not incidence of postoperative pulmonary complications after lung resection surgery: a propensity score matching study

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Abstract

Background

There is no consensus regarding the superiority of volatile or total intravenous anesthesia (TIVA) in reducing the incidence of postoperative pulmonary complications (PPCs) after lung resection surgery (LRS). Thus, the aim of this study was to investigate the different anesthetic regimens and the incidence of PPCs in patients who underwent LRS. We hypothesized that TIVA is associated with a lower incidence of PPCs than volatile anesthesia.

Methods

This was a retrospective cohort study of patients who underwent LRS at Taipei Veterans General Hospital between January 2016 and December 2020. The patients’ charts were reviewed and data on patient characteristics, perioperative features, and postoperative outcomes were extracted and analyzed. The patients were categorized into TIVA or volatile anesthesia groups and their clinical data were compared. Propensity score matching was performed to reduce potential selection bias. The primary outcome was the incidence of PPCs, whereas the secondary outcomes were the incidences of other postoperative events, such as length of hospital stay (LOS) and postoperative nausea and vomiting (PONV).

Results

A total of 392 patients each were included in the TIVA and volatile anesthesia groups. There was no statistically significant difference in the incidence of PPCs between the volatile anesthesia and TIVA groups. The TIVA group had a shorter LOS ( p < 0.001) and a lower incidence of PONV than the volatile anesthesia group (4.6% in the TIVA group vs. 8.2% in the volatile anesthesia group; p = 0.041). However, there were no significant differences in reintubation, 30-day readmission, and re-operation rates between the two groups.

Conclusions

There was no significant difference between the incidence of PPCs in patients who underwent LRS under TIVA and that in patients who underwent LRS under volatile anesthesia. However, TIVA had shorter LOS and lower incidence of PONV which may be a better choice for maintenance of anesthesia in patients undergoing LRS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-023-02260-4.

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Most cited references 23

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Mary E Charlson, Peter Pompei, Kathy L. Ales … (1987)

The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.

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Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

Ralph D'Agostino (1998)

In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.

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Author and article information

Contributors

Chien‑Kun Ting: ckting2@gmail.com

Journal

Journal ID (nlm-ta): BMC Anesthesiol

Journal ID (iso-abbrev): BMC Anesthesiol

Title: BMC Anesthesiology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2253

Publication date (Electronic): 17 October 2023

Publication date PMC-release: 17 October 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 345

Affiliations

[1 ]Department of Anesthesiology, Taipei Veterans General Hospital, ( https://ror.org/03ymy8z76) No. 201, Sec. 2, Shih-pai Rd, 11217 Taipei, Taiwan

[2 ]School of Medicine, National Yang Ming Chiao Tung University, ( https://ror.org/00se2k293) Taipei, Taiwan

[3 ]Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, ( https://ror.org/00se2k293) 112 Taipei, Taiwan, ROC

[4 ]Department of Surgery, Division of Thoracic Surgery, Taipei Veterans General Hospital, ( https://ror.org/03ymy8z76) Taipei, Taiwan

[5 ]Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, ( https://ror.org/00se2k293) 112304 Taipei, Taiwan

Article

Publisher ID: 2260

DOI: 10.1186/s12871-023-02260-4

PMC ID: 10580638

PubMed ID: 37848832

SO-VID: c338bfbe-fe36-4886-a691-6d95965da47c

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 8 May 2023

Date accepted : 26 August 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004663, Ministry of Science and Technology, Taiwan;

Award ID: MOST109-2314-B-075-047-MY3

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ScienceOpen disciplines: Anesthesiology & Pain management

Keywords: anesthesia,postoperative pulmonary complications,lung resection surgery,volatile anesthesia,total intravenous anesthesia

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ScienceOpen disciplines: Anesthesiology & Pain management

Keywords: anesthesia, postoperative pulmonary complications, lung resection surgery, volatile anesthesia, total intravenous anesthesia

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Total intravenous anesthesia decreases hospital stay but not incidence of postoperative pulmonary complications after lung resection surgery: a propensity score matching study

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Cardiovascular Innovations and Applications

Most cited references 23

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

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