A Surgical Decision-making Scoring model for spontaneous ventilation- and mechanical ventilation-video-assisted thoracoscopic surgery in non-small-cell lung cancer patients

doi:10.21203/rs.3.rs-1957742/v1

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Research article

A Surgical Decision-making Scoring model for spontaneous ventilation- and mechanical ventilation-video-assisted thoracoscopic surgery in non-small-cell lung cancer patients

https://doi.org/10.21203/rs.3.rs-1957742/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Sep, 2023

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Backgrounds

Spontaneous ventilation-video-assisted thoracoscopic surgery (SV-VATS) has been applied to non-small cell lung cancer (NSCLC) patients in many centers. Since it remains a new and challenging surgical technique, only selected patients can be performed SV-VATS. In this study, we aim to develop a clinical decision-making model to make surgery decision between SV-VATS and MV-VATS in NSCLC patients more objectively and individually.

Methods

5,580 NSCLC patients undergoing SV-VATS or MV-VATS in the department of thoracic surgery between 2011 and 2018 were included. Univariate and multivariate regression analysis were used to identify potential factors influencing the surgical decisions. The performance of the model was validated by area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).

Results

The Surgical Decision-making Scoring (SDS) model was built guided by the clinical judgment and statistically significant results of univariate and multivariate regression analyses of potential predictors, including age (p < 0.001), smoking status (p = 0.03), BMI (p < 0.001), T stage (p < 0.001), N stage (p = 0.02), ASA grade (p < 0.001) and surgical technique (p < 0.001). The AUC of the training set and the test set were 0.73 (0.13, 0.61 - 0.74) and 0.76 (0.13, 0.62 - 0.77), respectively. The calibration curves and the DCA curve revealed that the SDS model has a desired performance in predicting the surgical decision.

Conclusions

This SDS model is the first clinical decision-making model developed for an individual NSCLC patient to make decision between SV-VATS and MV-VATS.

NSCLC

SV-VATS

MV-VATS

SDS

Nomogram.

This study was accepted as a poster presentation at the 29TH ESTS Meeting (#296), June 20 - June 22, 2021

Lung cancer is one of the most common epithelial tumors with a high rate of morbidity and mortality,[1] about 85% of which are non-small cell lung cancer (NSCLC).[2] Radical surgery plays a critical role in the systemic strategy of treatment for operable NSCLC. The traditional open thoracotomy is recommended as golden procedure to conduct radical surgery for operable NSCLC.[3] With the promotion of the concept of enhanced recovery after surgery, the video-assisted thoracoscopic surgery (VATS) has been recommended to perform radical surgery for early-stage NSCLC currently.[4, 5] In recent years, with the continuous updating of thoracoscopic instruments, equipment and the increasingly sophisticated surgical skills of surgeons, many thoracic centers have tried to carry out spontaneous ventilation VATS (SV-VATS) in NSCLC patients.[6–9] Evidence has showed that it is safe and feasible to employ SV-VATS in NSCLC patients and the effect of SV-VATS was comparable to mechanical ventilation (MV)-VATS.[3, 10] SV-VATS avoids the tracheal injury caused by endotracheal intubation.[6] Besides, by reducing the use of neuromuscular blocking agents and opioid analgesia during surgery, SV-VATS has a lower risk of postoperative respiratory failure, postoperative hyperalgesia, and even opioid dependence.[11] Moreover, growing evidence has suggested that the SV-VATS can accelerate postoperative recovery, reduce complications, hospital stay, and medical costs compared with MV-VATS.[12] However, SV-VATS imposes higher requirements on proficient technical skills and quick decision-making skills of the surgical team. Besides, it is still lack of SV-VATS consensus or guideline for NSCLC patients at present. It remains uncertain what kind of NSCLC patients can undergo SV-VATS, which restrict more extensive application of SV-VATS in NSCLC patients to some extent. Indeed, selecting proper patients is the first step to success in a surgery. Therefore, we aim to quantify the factors influencing surgery selection for NSCLC patients by constructing a surgical decision-making scoring (SDS) model (a pre-operative model), to find out the certain NSCLC patients that can undergo SV-VATS based on clinical characteristics.

IRB Information

The study was performed based on the data from the first affiliated hospital of Guangzhou Medical University, the institutional review boards at which approved the retrospective analysis of anonymous data and waived the need to obtain patient informed consent (IRB Report ID: 2019-57).

Study Population Selection

5,580 NSCLC patients undergoing SV-VATS or MV-VATS in the department of thoracic surgery of the first affiliated hospital of Guangzhou Medical University between 2011 and 2018 were identified. Exclusion criteria are as follows: (1) Less than 18 years old; (2) Unknown patients demographic; (3) Unknown TNM stage; (4) Unknown ASA grade; (5) Not the first tumor; (6) Not only one tumor. All data were anonymously extracted in a double-blinded manner. Patient demographic characteristics included age, BMI, smoking status, gender, comorbidities, and forced expiratory volume in 1 second (FEV1)/forced volume vital capacity (FVC) ratio after inhalation of bronchodilators. Oncological information, such as tumor location, T stage, N stage, and M stage were included. The tumor stage was categorized based on the 8th TNM guideline stipulated by American joint committee on cancer.[13] Operation information included American society of anesthesiologists (ASA) grade and surgical technique. Informed consent was obtained from each of the patients after explaining the reason, modalities, risks, and benefits of the surgery. The decision of the surgical type (MV-VATS or SV-VATS) was made by the thoracic surgeons, anesthetists, and patients before the operation.

Statistical Analysis

Patients were randomly divided into a training set and a test set at a ratio of 7:3. To evaluate the balance and difference between the two sets, categorical variables presented as frequencies and percentages were compared using the Chi-square test or Fisher's exact test, while continuous variables were compared by Student's t-test or Mann-Whitney U test. Univariate logistic regression analysis was used to identify potential factors influencing the surgical decisions. Factors with statistical significance by univariate regression analysis were further entered into multivariate regression analysis. Odds ratio (OR) values and 95% confidence intervals (CI) calculations were included in multivariate regression analysis. The variables that were statistically significant (p < 0.05) in the multivariate analysis were selected for constructing the SDS model.

Model Validation

To assess the performance of the model, three metrics were employed: discrimination, calibration, and clinical usefulness. Discrimination assesses the ability of the model to differentiate between patients undergoing SV-VATS and MV-VATS. The discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Values of AUC range from 0.5 and 1.0, with 0.5 indicating no discrimination, greater than 0.7 indicating a reasonable estimate, and 1.0 indicating perfect discrimination. [14] A calibration curve was drawn to evaluate the calibration, which analyzed the agreement between the observed and estimated outcomes. In addition, decision curve analysis (DCA) was applied to evaluate the clinical usefulness of the model.[15]

Software

All statistical analysis was performed using R 4.0.5 (The R Core Team, R Foundation for Statistical Computing, Vienna, Austria) running on R Studio 1.4.1106 (R Studio Team, R Studio Inc. Boston, MA, USA) with packages: foreign,[16] regplot,[17] ggprism,[18] rms,[19] pROC,[20] ggDCA,[21] and do.[22] Statistical significance was set at 2-sided p < 0.05.

Study population characteristics

A total of 4,983 patients met the inclusion criteria were included in this study (Fig. 1), 581 (12%) in the SV-VATS group (259 males; 322 females; mean [SD] age, 59.07 [11.47] years), 4,402(88%) in the MV-VATS group (2,540 males; 1,862 females; mean [SD] age, 64.4 [11.12] years). Demographic characteristics, oncological information, and operation information of the patients in the SV-VATS and MV-VATS groups were shown Table 1, while the training set and test set were shown in Table 2. No significant difference was observed between the training set and test set.

Table 1

Summary statistics demographic information in the SV-VATS group and the MV-VATS group
Variables	SV-VATS		MV-VATS
Variables	Number (%)	SD	Number (%)	SD
Age		11.47		11.12
< 60	287 (49%)		1360 (31%)
60–75	249 (43%)		2361 (54%)
> 75	45 (8%)		681 (15%)
Gender		0.50		0.50
Male	259 (45%)		2540 (58%)
Female	322 (55%)		1862 (42%)
Smoking status		0.42		0.48
Smoking	130 (22%)		1610 (37%)
Nonsmoking	451 (78%)		2792 (63%)
BMI		9.47		9.26
< 18.5	34 (6%)		258 (6%)
18.5–25	499 (86%)		3138 (71%)
> 25	48 (8%)		1006 (23%)
Comorbidity		0.31		0.37
Yes	63 (11%)		711 (16%)
No	518 (89%)		3691 (84%)
T stage		0.86		0.83
1	454 (78%)		2582 (59%)
2	107 (18%)		1350 (31%)
3	11 (2%)		326 (7%)
4	9 (2%)		144 (3%)
N stage		0.82		0.82
0	501 (86%)		3174 (72%)
1	24 (4%)		314 (7%)
2	45 (8%)		890 (20%)
3	11 (2%)		24 (1%)
M stage		0.30		0.29
0	524 (90%)		3975 (90%)
1	57 (10%)		427 (10%)
FEV1/FVC *		0.35		0.42
< 70% (1)	500 (86%)		3417 (78%)
≥ 70% (0)	81 (14%)		985 (22%)
ASA grade		0.27		0.26
1	22 (4%)		49 (1%)
2	545 (94%)		4074 (93%)
3	14 (2%)		277 (6%)
4	0 (0%)		2 (0%)
Surgical technique		1.12		1.10
Wedge resection	171 (29%)		521 (12%)
Lung segmentectomy	101 (17%)		307 (7%)
Lobectomy	309 (53%)		3574 (81%)
SV-VATS: spontaneous ventilation video-assisted thoracoscopic surgery; MV-VATS: mechanical ventilation video-assisted thoracoscopic surgery; SD: standard deviation; BMI: body mass index; T: tumor; N: node; M: metastasis; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; ASA: American society of anesthesiologists. *: FEV1/FVC after inhalation of bronchodilators.

Table 2

Summary statistics demographic information in the training set and the test group
Variables	Training group		Test group		P
Variables	Number (%)	SD	Number (%)	SD	P
Age		0.66		0.67	0.80
< 60	1158 (33%)		489 (33%)
60–75	1835 (53%)		775 (52%)
> 75	495 (14%)		231 (16%)
Gender		0.50		0.50	0.89
Male	1957 (56%)		842 (56%)
Female	1531 (44%)		653 (44%)
Smoking status		0.48		0.48	0.22
Smoking	1199 (34%)		541 (36%)
Nonsmoking	2289 (66%)		954 (64%)
BMI		0.50		0.50	0.23
< 18.5	192 (6%)		100 (7%)
18.5–25	2546 (73%)		1091 (73%)
> 25	750 (22%)		304 (20%)
Comorbidity		0.36		0.37	0.51
Yes	534 (15%)
No	2954 (85%)
T stage		0.86		0.83	0.40
1	2128 (61%)		908 (61%)
2	1005 (29%)		452 (30%)
3	240 (7%)		97 (6%)
4	115 (3%)		38 (3%)
N stage		0.82		0.82	0.90
0	2578 (74%)		1097 (73%)
1	236 (7%)		102 (7%)
2	648 (18%)		287 (19%)
3	26 (1%)		9 (1%)
M stage		0.30		0.29	0.59
0	3144 (90%)		1355 (91%)
1	344 (10%)		140 (9%)
FEV1/FVC *		0.41		0.41	0.83
< 70%	2739 (79%)		1178 (79%)
≥ 70%	749 (21%)		317 (21%)
ASA grade		0.27		0.26	0.88
1	48 (1%)		23 (2%)
2	3228 (93%)		1391 (93%)
3	210 (6%)		81 (5%)
4	2 (0%)		0 (0%)
Surgical technique		1.12		1.10	0.45
Wedge resection	488 (14%)		204 (14%)
Lung segmentectomy	296 (8%)		112 (7%)
Lobectomy	2704 (78%)		1179 (79%)
SD: standard deviation; BMI: body mass index; T: tumor; N: node; M: metastasis; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; ASA: American society of anesthesiologists. *: FEV1/FVC after inhalation of bronchodilators.

Identify Factors Independently Associated With Surgical Decision

Uni- and multivariate logistic regression analyses were applied to identify independent factors affecting surgical decision.

In the univariate regression analysis, age (p < 0.001), gender (p < 0.001), smoking status (p < 0.001), BMI (p < 0.001), comorbidity (p = 0.01), T stage (p < 0.001), N stage (p < 0.001), FEV1/FVC after inhalation of bronchodilators (p < 0.001), ASA grade (p < 0.001) and surgical technique (p < 0.001) were identified to be significant factors associated with the surgical decision. (Table 3)

Table 3

Summary of the results the univariate logistic regression
Variables	Odds ratio	95% CI	p
Age	0.96	0.95–0.97	< 0.001
Gender	0.60	0.49–0.74	< 0.001
Smoking status	0.49	0.38–0.62	< 0.001
BMI	0.91	0.88–0.95	< 0.001
Comorbidity	0.66	0.47–0.90	0.01
T stage	0.50	0.41–0.60	< 0.001
N stage	0.66	0.56–0.77	< 0.001
M stage	0.96	0.67–1.35	0.84
FEV1/FVC *	1.70	1.29–2.29	< 0.001
Tumor location	1.02	0.99–1.06	0.19
ASA grade	0.31	0.20–0.49	< 0.001
Surgical technique	0.64	0.59–0.69	< 0.001
BMI: body mass index; T: tumor; N: node; M: metastasis; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; ASA: American society of anesthesiologists. *: FEV1/FVC after inhalation of bronchodilators.

The factors with statistical significance in univariate analysis were included as variables in further multivariate regression analysis. The results of multivariate regression analysis showed that age (p < 0.001), smoking status (p = 0.03), BMI (p < 0.001), T stage (p < 0.001), N stage (p = 0.02), ASA grade (p < 0.001) and surgical technique (p < 0.001) were independent factors for surgical decision. (Table 4)

Table 4

Summary of the results the multivariate logistic regression
Variables	Odds ratio	95%CI	p
Age	0.97	0.96–0.98	< 0.001
Gender	1.09	0.84–1.42	0.50
Smoking status	0.71	0.53–0.97	0.03
BMI	0.91	0.87–0.94	< 0.001
Comorbidity	0.92	0.65–1.30	0.66
T stage	0.67	0.61–0.84	< 0.001
N stage	0.83	0.69–0.96	0.02
FEV1/FVC *	1.23	0.89–1.69	0.20
ASA grade	0.44	0.26–0.68	< 0.001
Surgical technique	0.68	0.62–0.74	< 0.001
BMI: body mass index; T: tumor; N: node; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; ASA: American society of anesthesiologists. *: FEV1/FVC after inhalation of bronchodilators.

Development Of An Individualized Prediction Model

The SDS model was built guided by the clinical judgment and statistically significant results of univariate and multivariate regression analyses of potential predictors. Finally, age, smoking status, BMI, T stage, N stage, FEV1/FVC after inhalation of bronchodilators, ASA grade, and surgical technique were included in the model. The Nomogram of SDS model was displayed in Fig. 2. Each variable in the nomogram was assigned a point. The total points could be used to confer the possibility for further SV-VATS.

Model Validation

The SDS model showed desired performance in predicting the decision of the SV-VATS. Respectively, the AUC of the training set and the test set were 0.73 (0.13, 0.61–0.74) and 0.76 (0.13, 0.62–0.77) (Fig. 3).

The calibration curves of the training set and test set showed the satisfactory linearity, and the DCA curve also showed that score benefit is higher than the extreme curve. Good consistency was revealed between the predicted and real results (Fig. 4)

Clinical Application

The score of each variable was based on the actual clinical characteristics of patients. The total score was the sum of different variables. A higher score meant that the patients with NSCLC were prefer the SV-VATS. For example, an NSCLC patient, 70-year-old, male, smoker, T2N1M0 stage, BMI 20 kg/m², FEV 1 /FVC after inhalation of bronchodilator < 70%, ASA II, is going to be performed segmentectomy. His total score is 206.5. Another NSCLC patient, 40-year-old, female, non-smoker, T1N1M0 stage, BMI 22 kg/m², FEV 1 /FVC after inhalation of bronchodilator ≥ 70%, ASA I, is going to be performed wedge resection. Her total score is 300. According to the final score of each patient, the female patient was more likely to receive the SV-VATS.

With the development of SV-VATS, the safety, feasibility, and advantages of this new surgical mode have been recognized by more and more thoracic surgeons. Although it remains a new and challenging surgical technique, the intraoperative bleeding in SV-VATS is less than MV-VATS, while the operating time is not significantly different between SV-VATS and MV-VATS.[6] Patients showed satisfaction to SV-VATS for its advantages in reducing postoperative complications and accelerating postoperative recovery compared to MV-VATS. One possible reason may be that SV-VATS attenuate the inflammatory responses caused by surgery and stimulate cellular immune function.[6] SV-VATS reduced patient subjective discomfort after surgery.[23, 24] Without tracheal intubation, SV-VATS reduces the adverse effects such as intubation-related airway trauma, residual neuromuscular blockade and irritable, and postoperative cough.[25] Besides, SV-VATS reduces the risk of moderate or more thoracic effusion and is associated with shorter extubating time.[6, 26] Shorter extubating time is associated with less postoperative pain and shorter hospital stays as well.[27] Finally, SV-VATS is associated with reduced risk of short-term postoperative complications and financial burden of patients.

Although the obvious advantages of SV-VATS have been recognized, SV-VATS imposes higher requirements on careful patient selection, appropriately experienced anesthetic, and surgical teams, which restrict the more extensive application and further development of SV-VATS to some extent. Currently, SV-VATS can only be performed on selected patients in a few centers. For institution applying this technique, it is important for surgeons and anesthesiologists to select the proper patients in the early phase of learning curve. This is necessary and the first step to decrease the risk of conversion to intubated general anesthesia and complications.[28]

Currently, consensus for SV-VATS has been published. Surgeons and anesthesiologists can select patients according to the inclusion and exclusion criteria in consensus. However, such consensus is not just for patients with NSCLC, but also other thoracic diseases. Besides, the consensus was based on a larger population of patients, which cannot quantitively predict the risk in an individual patient. For example, patients older than 60 years old are not suggested to undergo SV-VATS in the consensus. However, evidence has showed that patients over 65 years old can still undergo SV-VATS and have a comparable effect with MV-VATS.[10, 23] Besides, BMI > 30 kg/m² is an exclusion criterium of SV-VATS in the consensus. However, research has reported that it was safe and feasible for patients with BMI > 30 kg/m² to undergo SV-VATS and the effect was comparable to MV-VATS.

In fact, while making a surgery plan for each patient, clinicians will consciously or subconsciously assign point to each potential risk factors based on the published literature and experience. Plan for treatment will be formed based on the total score in mind. However, such skill is highly subjective and difficult to disseminated to less-experienced surgeons and anesthesiologists. Besides, even expert may make mistakes sometimes. Therefore, a more robust and customized surgery decision model is urgently needed to identify the optimal candidates for SV-VATS among patients with NSCLC.

Nomogram, which has been proven to be capable of assisting the preoperative assessment and surgical planning, is readily used, and interpreted by clinical workers owing to its intuitive features. In this study, we built an SDS model in the form of nomogram with several clinically and statistically significant predictors via univariate and multivariate logistic regression analysis. Many common and readily accessible information like age, BMI, smoking status, Fev1/FVC, TMN stage, ASA grade and surgical technique are included. Only when the factor is significant in both univariate and multivariate logistic regression analysis can be included in the model and be assigned certain points. Finally, based on the demographic characteristics, oncological information, and operation information from the patient with NSCLC, a total points and probability to perform SV-VATS will be generated from the surgery decision model. Exceptionally, Fev1/FVC was maintained in the model even though it did not reach statistical significance in the multivariate analysis, because it is an important factor that needs to be considered in clinical surgical plan. Besides, the concordance index was 0.738 and 0.739 before and after the inclusion of FEV1/FVC respectively, which indicated that the inclusion of FEV1/FVC improved the overall performance of the model. The computed AUC values of the training set and the test set were 0.725 and 0.772 respectively, which indicated a high precision in the prediction of the model.

The model is promising in clinical applications, especially in the centers that are new to the technique of SV-VATS. Three major elements are needed for a successful SV-VATS: careful patient selection, appropriately experienced anesthetic, and surgical teams. Although SV-VATS is still a new and challenging technique with a steep learning curve, the latter two elements can be improved by systemic training. Currently, there have been many lobectomy video tutorials based on single-hole the SV-VATS.[29] Besides, the advances in image recognition techniques and artificial intelligence-assisted identification of anatomic sites can also help building a better understanding of a new surgical technique.[30] Our center has also made similar attempts in SV-VATS. the follow-up results are in progress. As for the first element, selecting optimal patients is not just about repeated practice and experience, but it needs a more objective basis. Therefore, with the model we developed, professional and systematic training, and technical improvement, SV-VATS can be developed better and applied more widely in patients with NSCLC.

Nevertheless, there are still some limitations. First, the SV-VATS is a relatively new technique and only practicable in a few hospitals by some experienced anesthetists. External validation has not been scheduled, which may reduce the reliability of the SDS model. Second, there might be potential selection bias due to the limited sample size. Third, whether the other surgical procedures applied to our model remains to be seen and further improvement is needed in future research.

This SDS model is the first clinical decision-making model developed for an individual NSCLC patient to make decision between SV-VATS and MV-VATS. The model we build will help surgical team and NSCLC patients to choose the best surgical approach

NSCLC

non-small cell lung cancer

VATS

video-assisted thoracoscopic surgery

spontaneous ventilation

mechanical ventilation

SDS

Surgical decision-making scoring

FEV1/FVC

forced expiratory volume in 1 second (FEV1)/forced volume vital capacity (FVC) ratio

BMI

body mass index

ASA

American Society of Anesthesiologists

odds ratio

confidence intervals

AUC

area under the receiver operating characteristic curve

DCA

decision curve analysis

Ethics approval and consent to participate

The Institutional Review Board of the hospital reviewed the study protocol and methods, IRB report ID: 2018-57.

Consent for publication

All authors agree to publish

Availability of data and material

The datasets presented in this article are not readily available because the patients involved in this study are from the Department of Thoracic Surgery of our hospital and are not open to anyone other than the author of this study. Requests to access the datasets should be directed to Runchen Wang, [email protected].

Competing interests

Not available

Funding

This work was supported by the Guangzhou Medical University Discipline Construction Funds (Basic Medicine) (No. JCXKJS2022A11).

Authors' contributions

Runchen Wang: conceptualisation, data curation, formal analysis, investigation, project administration, software, visualisation, writing – original draft; Qixia Wang: conceptualisation, data curation, formal analysis, software, writing – review & editing; Hengrui Liang: methodology, project administration, resources, writing – review & editing; Jiawen Qiu: software, supervision, validation, writing – review & editing; Chao Chen: visualization; Jiang Yu: validation; Lei Zhao: funding acquisition, writing – review & editing; Wei Wang: investigation, writing – review & editing

Acknowledgements

Throughout the writing of this dissertation, I have received a great deal of support and assistance.

I would first like to thank my supervisor, Jianxing He, whose expertise was invaluable in formulating the methodology and research questions. Your insightful feedback pushed me to sharpen my thinking and brought my work to a higher level. I would particularly like to acknowledge my teammate Qixia Wang and Chao Chen, for their wonderful collaboration and patient support. I would also like to thank my tutors, Hengrui Liang, for their valuable guidance throughout my studies. You provided me with the tools that I needed to choose the right direction and successfully complete my dissertation.

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published 25 Sep, 2023

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A Surgical Decision-making Scoring model for spontaneous ventilation- and mechanical ventilation-video-assisted thoracoscopic surgery in non-small-cell lung cancer patients

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Abstract

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Meeting Presentation

Background

Methods

IRB Information

Study Population Selection

Statistical Analysis

Model Validation

Software

Results

Study population characteristics

Identify Factors Independently Associated With Surgical Decision

Development Of An Individualized Prediction Model

Model Validation

Clinical Application

Discussion

Conculsion

Abbreviations

Declarations

References

Status:

Journal Publication

Version 1