A cost-effective and video-based method for continuous cardiac output monitoring: design and clinical validation
LinqianZhao1,2
XiaoyaXie3
WenjiaoWu1,4
NannanZhou5
YuexiuChen6
LihangZhu7
SongliHu1
ChaominWu2
TingLi2
YinboZhong1
WeidongWu1
YuanyuanYao1
ZexinChen8
MinYan1
XingChen3
QiwenYu2✉,9Emailyuqw@zju.edu.cn
FengjiangZhang1✉Emailzrzfj@zju.edu.cn
1Department of Anaesthesiology, the Second Affiliated Hospital of ZhejiangUniversity School of MedicineHangzhouChina
2Department of Anaesthesiology, the Fourth Affiliated Hospital of School of Medicine, and International School of MedicineInternational Institutes of Medicine, Zhejiang UniversityYiwuChina
3Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education of China, Zhejiang Provincial Key Laboratory of Cardio- Cerebral Vascular Detection Technology and Medicinal Effectiveness AppraisalZhejiang UniversityHangzhouChina
4Department of AnaesthesiologyThe First Affiliated Hospital of Bengbu Medical UniversityBengbuChina
5Ningbo Medical Center Lihuili HospitalNingboChina
6Department of Anesthesiology, Women’s HospitalZhejiang University School of MedicineHangzhouChina
7Department of Clinical Engineeringthe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
8Scientific Research Department, the Second Affiliated Hospital of ZhejiangUniversity School of MedicineHangzhouChina
9Research Center for Frontier Fundamental StudiesZhejiang labHangzhouChina
Linqian Zhao1,2, Xiaoya Xie3, Wenjiao Wu1,4, Nannan Zhou5, Yuexiu Chen6, Lihang Zhu7, Songli Hu1, Chaomin Wu2, Ting Li2, Yinbo Zhong1, Weidong Wu1, Yuanyuan Yao1, Zexin Chen8, Min Yan1, Xing Chen3, Qiwen Yu9*z,2and Fengjiang Zhang1*
1 Department of Anaesthesiology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
2 Department of Anaesthesiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
3 Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education of China, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
4 Department of Anaesthesiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
5 Ningbo Medical Center Lihuili Hospital, Ningbo, China.
6 Department of Anesthesiology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China.
7 Department of Clinical Engineering, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
8 Scientific Research Department, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
9 Research Center for Frontier Fundamental Studies, Zhejiang lab, Hangzhou, China
z,2orresponding to: yuqw@zju.edu.cn (Qiwen Yu) and zrzfj@zju.edu.cn (Fengjiang Zhang)
Abstract
Background
Continuous monitoring of cardiac output (CO) is limited by expensive consumables and equipment, which is rarely used in resource-limited settings. The aim of this study was to develop a novel method based on video analysis technique (called “video-based method”) to estimate CO deriving from ubiquitous smartphones.
Methods
Videos of minimal invasive radial arterial waveform were recorded and analyzed by a smartphone. CO values obtained from the FloTrac/Vigileo system, and video-based method were collected simultaneously. Further, we compared the performance of video-based method with commercial FloTrac/Vigileo system for clinical validation. Concordance and interchangeability of CO measurements were assessed using the intra-class correlation coefficient (ICC), mean error and the Bland-Altman analysis (B-A). Furthermore, the ability of detecting directional changes in CO values was also evaluated.
Results
In the clinical validation section, eleven patients who received general anesthesia were included, with a total amount of 3,892 pairs of CO values acquired. In B-A, the average CO value extracted by video-based method was 0.11 (95%CI, 0.10–0.14; p < 0.001) L/min, which was lower than that provided by the FloTrac/Vigileo system. Strong concordance (ICC = 0.87; p < 0.001) and acceptable interchangeability (mean error = 27.65%) were achieved, which indicated excellent clinical validity. Additionally, the sensitivity for significant directional changes was 92%, and the specificity was 62%.
Conclusions
A video-based method for cost-effective and continuous CO monitoring was successfully developed, which evaluated continuous CO values by analyzing arterial pressure waveform of recording monitor videos in real time. This video-based method provided extraordinary clinical performance, thus offering a cost-effective alternative for real-time CO monitoring.
Key Words:
Cardiac output
Cost-effective monitoring
Hemodynamic monitoring
Video analysis
Waveform extraction
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1. Introduction
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Adequate perioperative fluid management is beneficial to the postoperative outcomes of patients.[1] Therapy with fluids is aimed at obtaining optimizing the cardiac output (CO), ultimately enhancing tissue oxygen delivery and improving outcome.[2–5] In this regard, advanced hemodynamic parameters, such as CO, have been proposed for perioperative goal-directed therapy, which helps prevent unnecessary fluid administration.[6–8]Minimally invasive hemodynamic monitoring devices, such as the FloTrac/Vigileo system (Edwards Lifesciences LLC, Irvine, CA, USA), has gained global recognition and popularity compared with invasive devices like transpulmonary thermodilution.[9–12] The FloTrac/Vigileo system offers accurate and robust CO evaluations using only an arterial catheter, but it requires expensive stand-alone monitors (≥ 10,000 USD) and specific disposable sensors (≥ 80 USD).[13] Their availability and use are limited in remote operating rooms and critical care units in the developing and developed countries.
To overcome this, a novel smartphone app, Capstesia (Galenic App, Vitoria-Gasteiz, Spain) was developed. It can estimate CO and PPV by a digital picture of the arterial blood pressure waveform taken from any patient monitor screen.[14–16] However, Capstesia is limited to analyzing a single image at a time and does not have the capability for continuous monitoring. Each step of the operation process including capturing images, intercepting data, and uploading, can lead to delays in the diagnosis, which cannot be applied in emergencies and rapidly changing situations.[17] Moreover, the application depends the internet connection and cannot be used in remote areas or extreme situations like in helicopters.
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Herein, we developed a video-based method for cost-effective and continuous CO monitoring, which evaluated CO values by analyzing arterial pressure waveform of monitor videos in real time. Further, we aimed to validate its performance with the commercially available and commonly used system in adult patients under general anesthesia.2. Materials and Methods
2.1 Design of video-based method for CO monitoring
The video-based method for CO monitoring comprises two technologies: video waveform extraction and hemodynamic monitoring algorithm (Fig. 1A). Specifically, the video of monitor is recorded by in-built camera of smartphone and transmitted to a laptop for waveform extraction in real time. Then, the extracted blood pressure data is imported into the customized 'Hemodynamic Monitoring' program on laptop to obtain advanced hemodynamic parameters (Fig. S1).
2.1.1 Video waveform extraction
This study employs a mobile device's camera to capture the screen image of a monitor displaying invasive arterial waveform data, thereby facilitating the digital extraction of the patient arterial waveform for continuous evaluation of advanced hemodynamics (for specific technical process, see S2 in Supporting Information).
2.1.2 Hemodynamic monitoring algorithm
Based on uncalibrated pulse contour analysis (UPCA) and two-element Windkessel model, a CO value is derived from the initial pulse waveform and demographic parameters (age, body height and weight, and gender) to calibrate the internal calculation function. The dicrotic notch of the arterial blood pressure waveform is clearly identified, which is recognized as an aortic valve closing point. As a result, the area under the curve (AUC) integrated from the systolic portion is calculated to estimate the stroke volume. Then, CO was calculated as it is the production of heart and strike volume. (for detailed calculation procedures, see S3 in Supporting Information).
Fig. 1
(A) Design of video-based method for CO monitoring. (B) Clinical validation of video-based method for CO monitoring.
2.2 Clinical validation of a video-based method for CO monitoring
2.2.1 Patients
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The local ethics committee approved this prospective observational study (2nd Affiliated Hospital, School of Medicine, Zhejiang University, China) on June 30, 2023, and registered on clinicaltrials.gov under NCT05961358 on July 6, 2023, before inclusion of the first patient. A
We obtained informed consent from all patients before the commencement of the study.Between July 2023 and December 2023, fourteen patients undergoing general anesthesia for elective surgeries at the Second Affiliated Hospital of Zhejiang University were included in the study. We included ASA I, II, and III patients of both sexes who were in the age group of 18–65 years and required invasive arterial blood pressure measurement for their care. Exclusion criteria included: (1) intraoperatively spontaneous breathing, (2) prone position, (3) body weight < 40kg, (4) arrhythmia or basic line heart rate > 100 times min− 1, (5) aortic regurgitation or requiring intra-aortic balloon pump intraoperatively, and (6) peripheral vascular disease.
2.2.2 Perioperative management and hemodynamic monitoring
General anesthesia was induced with individualized dosing of propofol and sufentanil[18]. A non-depolarizing neuromuscular blocker was given for endotracheal intubation with video laryngoscopy. Anesthesia maintenance was conducted by inhalation of sevoflurane and intravenous administration of propofol as remifentanil, aiming to maintain the Bispectral Index (BIS) value within the target range between 40 and 60. Fluids were administered under the clinical judgment of the attending anesthesiologist using the FloTrac/Vigileo system. The radial artery catheter was connected to three backend devices (Fig. 1B):
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1)
The FloTrac/Vigileo system (denoted as “Vigileo”): The radial artery catheter was connected to the Vigileo monitor through the FloTrac sensor. As recommended by the manufacturer, the patient demographic data (height, weight, age, and gender) were input, and the system provided results, including CO. Then, data were recorded on a USB drive.
2)
The video-based method (denoted as “Video”): The same radial artery catheter was connected to the CARESCAPE Monitor B650 (GE Healthcare, Chicago, IL, USA) for invasive blood pressure monitoring. Meanwhile, a Huawei HONOR 30 smartphone (Huawei Technologies Co., Ltd., Shenzhen, China) was employed for screen recording. The image processing program on the laptop was designed to extract the blood pressure waveform in real time, and the cardiac output (CO) values were estimated using the hemodynamic monitoring algorithm at the same time (Fig. S3). To avoid parallax errors, the smartphone was mounted facing and in parallel to the screen of the monitor at about 15 cm.
3)
The “Direct” Hemodynamic system (denoted as “Direct”): The "Direct" Hemodynamic System is a newly developed system designed by the authors for original arterial waveform acquisition directly. The system consists of a pressure transducer, a signal acquisition device enabled with Bluetooth, and the customized computer program "Hemodynamic Monitoring" (Fig. S6). To evaluate the accuracy of Hemodynamic Monitoring algorithm and feasibility of video waveform extraction, we designed and fabricated the system as an intermediary to compare it with the FloTrac/Vigileo system and the video-based method.
2.2.3 Data collection
For each patient, characteristics (age, gender, height, weight), ASA classification and surgical type were collected. We collected instantaneously t three sets of CO values at 20-second intervals: 1) CO values provided by the FloTrac/Vigileo system (denoted as “COvigileo”); 2) CO values obtained with video-based method (denoted as “COvideo”); 3) CO values extracted by original invasive arterial waveform (denoted as “COdirect”); Any time point with incomplete data will be excluded.
2.2.4 Sample size estimation
The expected CO value measured of the Vigileo was 5.15 L/min, while it was 5.30 L/min by Video in a preliminary experiment. An agreement threshold value of ± 0.50 L/min was set for CO.[19] In order to ascertain a power of 90% and a confidence interval of 95%, we planned to acquire 2185 paired values i.e., around 10 patients would have been enrolled. Assuming that valid data can be collected from each patient for 80 minutes (two-thirds of the surgical time), each pair of value could be collected in every 20 second, resulting in a total number of 240 paired values. Thus, a minimum number of 10 patients are required.
2.2.5 Statistical analysis
Statistical analysis was performed in MedCalc® Statistical Software version 20.022 (MedCalc Software Ltd, Ostend, Belgium) and SPSS 22.0 (IBM Corporation, Armonk, NY, USA). We accepted p < 0.05 as significant. Categorical variables were expressed as percentages and 95% confidence intervals (95% CI), and continuous variables as standard deviation (SD) or medians and interquartile ranges.
To assess the level of concordance, we used intraclass correlation coefficients (ICCs), considering values of 0.75 to indicate strong agreement. We performed Bland–Altman analysis the bias and variability between methods.[14] The 95% limits of agreement (LOA) determined as:
. And 95% CI was calculated for the upper and lower limit of agreement. The mean error or percentage error was calculatedas:
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in which SD is the standard deviation of the differences.[19] The interchangeability of the two methods is deemed acceptable when the mean error or percentage error is equal to or below 30%.[20]
To assess the ability of detecting directional changes in CO values, We defined three periods of COvigileo slope change: the stable period (COvigileo slope ≤ + 5% and ≥ -5%), the increasing period (COvigileo slope < + 5%), and the decreasing period (COvigileo slope) < -5%).[11] Inconsistent directional changes were considered unacceptable when the negative intraclass correlation coefficient (ICC) was observed. Sensitivity and specificity for directional change was calculated as:
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in which TP is change in both systems, TN is no change in both systems, FP is change in COvideo only, FN is change in COvigileo only.
3. Results
3.1 Participant characteristics
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Between July 2023 and December 2023, 14 patients were eligible for analysis. 3 patients were excluded (data lost in 1 patient and data digitization errors in 2). Finally, 11 patients under went final analysis and a total amount of 3,892 pairs of CO values were analyzed. Patients had a mean age of 51 year., 63% were male, 27% had known ASA 1, 63% known ASA 2 and 9% known ASA 3. The demographic and surgical profiles of patients are summarized in Table S2 Cardiac output measured by the commercial FloTrac/Vigileo system (COvigileo) were presented in Fig. S7 and Table S3, which ranged from 2.3 to 13.7 L/min (mean 5.3 ± 1.4 L/min).3.2 Clinical validation of video-based method for CO monitoring
3.2.1 Feasibility of video waveform extraction
In this part, we designed to demonstrate the technical feasibility of video extracting-waveform by assessing the concordance between COvideo and COdirect. Between COvideo and COdirect, the correlation was strong (ICC = 0.893; 95% CI, 0.887–0.900; P < 0.001). In B-A, the COvideo was 0.02 L/min on average lower (95% CI, 0.00–0.04) than the COdirect. And the mean error 24.64%, which indicated acceptable interchangeability (Fig. 2A and Table 1).
3.2.2 Accuracy of hemodynamic monitoring algorithm
In this part, we aimed to prove the accuracy of the algorithm by assessing the concordance between COvigileo and COdirect. Between COvigileo and COdirect, the correlation was strong (ICC = 0.908; 95% CI, 0.903–0.914; P < 0.001). In B-A, the COdirect was 0.09 L min− 1 on average lower (95% CI, 0.08–0.11) than the COvigileo. And the mean error was 22.17%, which indicated acceptable interchangeability (Fig. 2B and Table 1).
3.2.3 Clinical validation of the video-based method
Integrating video waveform extraction and hemodynamic monitoring algorithm, we archived video-based CO monitoring. We compared COvigileo and COvideo to confirm the eventually clinical performance of the method and identify the source of error. Between COvigileo and COvideo, the correlation was strong (ICC = 0.867; 95% CI,0.858–0.874; P < 0.001). In B-A, the COvideo was 0.11 L/min on average lower (95% CI, 0.10 to 0.14) than the COvigileo. And the mean error 27.65%, which indicated acceptable interchangeability (Fig. 2C and Table 1).
3.2.4 The ability to detect directional changes in CO values
To assess the ability to detect directional changes in CO values, we compared the change slopes of CO values among COvigileo, COdirect, and COvideo.
During 59 stable periods (COvigileo slope ≤ + 5% and ≥ -5%), the averaged slopes for COvigileo, COdirect, and COvideo were 0.000 ± 0.021, 0.000 ± 0.031 and − 0.002 ± 0.034 (p = 0.903). Acceptable differences in slope compared with the reference were 53 (90%) for both COdirect and COvideo.
During 49 increasing periods (COvigileo slope > + 5%), the averaged slopes for COvigileo, COdirect, and COvideo were 0.336 ± 0.295, 0.182 ± 0.257 and 0.255 ± 0.329 (p = 0.039). Acceptable differences in slope compared with the reference were 36 (73%) for both COdirect and COvideo.
During 56 decreasing periods (COvigileo slope < -5%), the averaged slopes for COvigileo, COdirect, and COvideo were − 0.250 ± 0.228, -0.143 ± 0.213 and − 0.150 ± 0.229 (p = 0.019). Acceptable differences in slope compared with the reference were 37 (73%) for COdirect and 38 (73%) for COvideo.
In summary, the sensitivity for significant directional changes between COvigileo and COvideo was 92%, and the specificity was 62% (Table 2).
Fig. 2
Statistical result (n = 3892). (A) Correlation (left) and Bland - Altman analysis (right) between COdirect and COvideo. (B) Correlation (left) and Bland - Altman analysis (right) between COvigileo and COdirect. (C) Correlation (left) and Bland - Altman analysis (right) between COvigileo and COvideo. ICC: Intraclass Correlation Coefficient. COvigileo: Cardiac output (CO) values provided by the FloTrac/Vigileo system; COdirect: CO values extracted by original invasive arterial waveform; COvideo: CO values provided by the video-based method. The solid horizontal line (right) represents the mean value difference between the methods. The upper dashed line shows the upper compliance limit value (+ 1.96 SD). The lower dashed line (right) shows the value of the lower compliance limit (-1.96 SD).
Values compared | ICC (95% CI) | Mean of the Differences | SD of the Differences | Limits of Agreement | Error Percentage (1.96 * SD / Mean) |
|---|---|---|---|---|---|
COvigileo and COdirect | 0.908 (0.903–0.914) | -0.09 | 0.59 | 1.06; -1.25 | 22.17% |
COdirect and COvideo | 0.893 (0.887–0.900) | -0.02 | 0.65 | 1.25; -1.29 | 24.64% |
COvigileo and COvideo | 0.867 (0.858–0.874) | -0.12 | 0.73 | 1.32; 1.56 | 27.65% |
Vigileo | Video | P | Agreement | |
|---|---|---|---|---|
Stable CO n = 59 | ||||
CO slope | 0.000 ± 0.021 | -0.002 ± 0.034 | p = 0.579 | 53 (90%) |
Increasing CO n = 56 | ||||
CO slope | 0.336 ± 0.295 | 0.255 ± 0.329 | p = 0.048 | 37 (66%) |
Decreasing CO n = 49 | ||||
CO slope | -0.250 ± 0.228 | -0.150 ± 0.229 | p = 0.004 | 36 (73%) |
4. Discussion
Cardiac output (CO) monitoring can assess cardiac function and circulation status, guiding drug and fluid administration, which can help prevent unnecessary fluid load due to the curvilinearity of the Frank-Starling relationship.[21, 22] To date, the thermodilution technique using pulmonary artery catheter (PAC) has been regarded as the gold standard for CO monitoring.[23] However, it is invasive, technically challenging, time-consuming, and has been proven to be associated with increased morbidity and mortality rates.[24, 25] For these reasons, the minimally invasive hemodynamic monitoring devices, such as the FloTrac/Vigileo system, have been validated against thermodilution.[12, 26] Herein, the FloTrac/Vigileo system, which utilizes pressure waveform characteristics for CO monitoring, was employed as the reference standard for CO monitoring. In previous research, a 30% limit of mean error was deemed acceptable for interchangeability between the two methods.[20] In this work, the mean error between the video-based method and the FloTrac/Vigileo system was 27.65%, demonstrating that the method can serve as an alternative to the expensive and specific FloTrac/Vigileo system. Notably, the cost of video-based CO monitoring is less than 20 USD, compared to hundreds of dollars for a single use, making it particularly suitable for developing and developed countries (for detailed cost, see S7 in Supporting Information).
Capstesia, a smartphone application, can calculate CO by a snapshot of the arterial pressure waveform. Previous researches have compared the performance of CO monitoring between Capstesia and the FloTrac/Vigileo system, reaching contradictory conclusions.[27, 28] In this work, with the aim of continuous and automatic CO monitoring, we upgraded from photo-based to video-based method and from manual interception to intelligent identification of waveform. The video-based method demonstrated excellent clinical validation compared with Capstesia (ICC = 0.57 and mean error = 61.91%).
In addition to the determination of CO values, the detection of CO directional changes was also realized. Previous researches have defined a threshold of 10% for directional changes and unacceptable discordances as a difference > 20% between the two slopes or as a negative ICC.[11, 29] In this work, we obtained CO values at 20-second intervals instead of 1 minute, thus offering a steeper and more discernible trend of change. A threshold of 5% for directional changes was selected and only negative ICC as unacceptable discordance was considered. As shown in Table 2, the agreement between directional changes in COvideo and COvigileo is 77%, which is higher during stable periods (90%) while lower during unstable periods (70%). This difference may be attributed to the longer response time of our hemodynamic monitoring algorithm compared to the FloTrac/Vigileo system. However, a time interval of just 20 seconds does not lead to significant evaluation errors and mistreatment.
Though distinctive features were demonstrated above, this work has its limitations. This proof-of-concept study was conducted at a single center with a relatively small sample size and does not compare with the gold standard (PAC). Therefore, it requires further clinical validation, particularly with the inclusion of a fluid challenge test.[15] Additionally, real-time extraction of CO is performed on a laptop currently. However, the migration of such method from a laptop to mobile platform is technically feasible, as the requirements for computational resources and capacity of this method are relatively low. Besides, the customized program in laptop is developed using Python, which could be migrated to mobile platform (Android or iOS) through the implementation of frameworks such as Kivy and BeeWare. Therefore, the video-based CO monitoring will be universally applicable, rather than limited to specific smartphones or operating systems. Efforts have been made to the development of such APP in our future works.
In our subsequent research plans, we will further compare CO from the video-based method with the gold standard (CO from the PAC). We will also extract and analyze various waveforms from videos, such as the respiratory waveform, to diagnose issues like patient-ventilator trigger asynchrony and tube tightness.[30] Additionally, AI opens a new window for intelligent monitoring and advanced healthcare in our future works. Firstly, for waveform extraction, AI could be incorporated with the optimization of image processing, thus allowing enhanced graphic correction, reflection handling, and other related aspects. Secondly, with a larger dataset, AI could be leveraged to improve the accuracy of the current algorithms. Besides, predicting hypotension over time using AI is also scheduled. We envision integrating waveform extraction and analysis as a smart assistant technology for anesthesiologists, facilitating diagnosis, monitoring, and automatic alerts. The integration of an early warning system could also reduce fatigue-related safety risks and enhance remote monitoring, thus providing significant benefits to anesthesia, especially in remote and underdeveloped areas. Ultimately, video-based and multi-functional intelligent monitoring systems are poised to become an integral part of anesthesiologist’s daily works, paving the way for a new era in anesthesia monitoring.
In conclusion, a video-based method for cost-effective and continuous cardiac output (CO) monitoring was successfully developed. This method evaluates continuous CO values by analyzing the arterial pressure waveform from recorded monitor videos in real time. Additionally, it can serve as an alternative to the expensive and specialized FloTrac/Vigileo system.
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Acknowledgement
The authors acknowledge the collaboration and commitment of all investigators and their staff. The authors also thank all the patients who participated in this trial, and all participating institutions, particularly the research nurses and assistants.
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Author Contribution
ZLQ and XXY contributed equally. Full access to all of the data in the study and responsibility for the integrity of the data and the accuracy of the data analysis: ZLQ, XXY, YQW, ZFJ. Technology development and improvement: XXY, YQW, WWJ, ZNN. Study conception and design: WWD, ZYB, YYY, CZX, ZFJ, YM, CX. Data acquisition, analysis, and interpretation: all authors. Writing of the first draft of the manuscript: ZLQ, XXY. Critical revision of the manuscript: ZLH, YM. Approval of the final version to be published: all authors.
Founding
This research was supported by National Natural Science Foundation of China (No. 82172064 to CX) and Natural Science Foundation of Zhejiang Province (No. LY23H090015 to ZFJ). The funder had no role in the design and conduct of the study; collection, analysis, or interpretation of the data; preparation, revision, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
Data generated may be shared upon reasonable request to the corresponding author and approval from ethics committee.
Declarations
Ethical statement
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The study was approved by the Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine (Prof Zhiying Wu) on June 30, 2023, and registered on clinicaltrials.gov under NCT05961358 on July 6, 2023, before inclusion of the first patient. We obtained informed consent from all patients before the commencement of the study.Consent for publication
Not applicable
Declaration of competing interest
s
The authors declare that they have no conflict of interests.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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