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to observe a fluid responsiveness in patients who receives scheduled thoracoscopic pulmonary lobectomy

to observe a fluid responsiveness in patients who receives scheduled open pulmonary lobectomy(thoracotomy)

Fluid loading at defined period

* 500ml colloid solution infusion at 20min after thorax open for 30min. 500ml colloid administration is a kind of routine procedure during pulmonary lobectomy in our hospital. We just control the fluid loading timing for hemodynamic parameter records.

fluid loading

the patient group for scheduled thoracoscopic pulmonary lobectomy

thoracoscopic pulmonary lobectomy

the patient group for scheduled open pulmonary lobectomy

thoracotomy

Hyun Joo Ahn

There are some risks of pulmonary edema in patients undergoing pulmonary lobectomy with one lung ventilation. The overloading of fluid administration could be related to the development of pulmonary edema in patents after thoracic surgery. But fluid restriction may cause major organ hypoperfusion during the surgery. The purpose of this study is to evaluate the ability of stroke volume variation as an indicator for a fluid responsiveness in patient who receives pulmonary lobectomy via thoracotomy.

Perioperative fluid management during thoracic surgery is a significantly important, because it is quite difficult to prevent pulmonary edema due to the fluid overload and compromise perfusion of vital organ. So, it is essential to maintain optimal organ perfusion by appropriate fluid management during thoracic surgery. Stroke volume variation (SVV) is derived from pulse contour analysis and it is known that SVV ≥12\~15% correlate with fluid responsiveness, defined as a significant increase in cardiac output with fluid loading, dung two-lung ventilation. It is a parameter derived from changes in stroke volume (SV) that is according to the heart-lung interaction during mechanical ventilation. positive pressure ventilation induces cyclic changes in left ventricular SV that are related mainly to the expiratory decrease in right ventricular filling and ejection. This is a reflected by variations in the SV. However both ventilator issues, such as tidal volume, PEEP, chest and lung condition, and the cardiovascular condition, such as heart rate, rhythm, ventricular function, cardiac afterload, arterial compliance may affect SVV. Recently some studies reported that SVV could predict fluid responsiveness in mechanically ventilated patients under various conditions. But it is still unclear whether SVV could predict fluid responsiveness during one lung ventilation with the chest open via a thoracotomy. During one-lung ventilation, the shunted blood flow through the non-ventilated-lung dose not contribute to the generation of SVV. And with the chest opening by thoracotomy, the pressure generated by ventilator would not be transmitted to the pulmonary vessels but rather to the atmosphere. So, the purpose of this study is to evaluate the ability of SVV as an indicator a fluid responsiveness particularly in patients undergoing one-lung ventilation with thoracotomy and to found the optimal threshold value of SVV for fluid management during thoracic surgery.

Could the Stroke Volume Variation Predict a Fluid Responsiveness in Thoracotomy?

after surgery check chest x-ray It's the routine follow up after lobectomy.

check perioperative urine output, and check post op serum creatine

we are going to measure the SVV, SVI before and after fluid loading. Fluid responders were defined as patients demonstrating an increase in SVI ≥ 10% and non-responders as patients whose SVI changed \< 10%. Receiver operating characteristic (ROC) curves were generated for SVV of each group (responders and non responders). Threshold value of SVV was determined by considering values that yielded the greatest sensitivity and specificity from ROC curve

Samsung Medical Center

Associate professor

COVID-19

Disease Attributes

Respiratory System Agents

Participants with lung cancer/pulmonary nodules

wei geng, Phd

Yang Jin, MD

Yang Jin, Professor

To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.

The main aims are as follows:

1. To establish a medical big data platform for multi-modal information fusion of common tumors and major infectious diseases (lung cancer/pulmonary nodules, tuberculosis, and COVID-19) based on the existing pathological image features and clinical multi-omics information database: The medical big data platform supports the acquisition of the patient's clinical electronic medical records (including routine clinical detection), full view digital section of pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology, pathology, and associated graphic data reports and multimodal medical treatment data. We aim to realize the storage, sharing, fusion computing, privacy protection, and security supervision of multi-modal and cross-scale biomedical big data. Our work will open up key business processes and links across regions, across hospitals, between different terminals, between hospitals and doctors, and between departments, so as to promote continuous data accumulation and knowledge precipitation in hospitals and promote medical collaboration.
2. To create a multimodal information fusion database with pathologic features, imaging features, multi-omics (pathologic, genomic, transcriptome, metabolome, proteomics, etc.), and clinical information of patients at different stages of lung cancer/pulmonary nodules, tuberculosis, and COVID-19. The database scale includes multimodal data of at least 600 lung cancer/pulmonary nodules, 200 tuberculosis, and 200 COVID-19 patients. Moreover, there will be more than 10 biomarkers significantly related to the diagnosis and treatment of patients with lung cancer/pulmonary nodules, tuberculosis and COVID-19 were excavated through association analysis, providing parameters for artificial intelligence model construction.
3. We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an ARTIFICIAL intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).

Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

Research and Development of an Artificial Intelligence Technology System for Digital Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).

① Benign nodule

② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.

The outcome of clinical diagnosis of patients with pulmonary tuberculosis (Positive/Negative).

The outcome of clinical diagnosis of patients with COVID-19 (Positive/Negative).

The treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows.

CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm.

PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline.

PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography).

SD (stable disease): Neither PR nor PD.

Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).

effective treatment： Improved total time to recovery, resolution of fever, cough remission, and pneumonia severity.

ineffective treatment： The above conditions have not improved or patients go die.

Treatment cure： patients with bacteriologically confirmed TB at the beginning of treatment who were smear- or culture-negative in the last month of treatment and on at least one previous occasion.

Treatment completer: patients who completed treatment without evidence of failure but with no record to show that sputum smear or culture results in the last month of treatment and on at least one previous occasion were negative.

Treatment success: The sum of cured and treatment completed.

Treatment failure: patients whose sputum smear or culture is positive at month 5 or later during treatment.

Treatment relapse: Patients who were declared cured or treatment completed at the end of their most recent course of TB treatment, and are now diagnosed with a recurrent episode of TB. This can be either a true relapse or a new episode of TB caused by reinfection.

Patient died.

The time interval between the date of treatment initiation and disease progression (Months) of patients with lung cancer/pulmonary nodules.

The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.

Whole-genome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of exhaled air condensate samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Transcriptome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Metabolomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Proteomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomicstissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

forced expiratory volume in one second (FEV1) for lung volume

carbon monoxide diffusion capacity (DLCO) for pulmonary diffusion function.

St. George's Respiratory Questionnaire total score(0-3989.4), St. George's Respiratory Questionnaire symptoms score(0-662.5); St. George's Respiratory Questionnaire impacts score(0-2117.8); St. George's Respiratory Questionnaire activity score(0-1209.1). The higher the score, the worse the lung.

Serum tumor markers including Carcinoembryonic Antigen (ug/L), Cytokeratin 19 fragment , Squamous Cell Carcinoma Antigen(ug/L), Nervous specific enolase (U/mL), Tissue Polypeptide Specific Antigen(ug/L), Cancer antigen 125 (U/mL), Cancer antigen 15-3 (U/mL), Bombesin (U/mL), The stomach secrete ty (U/mL), β2-microglobulin (U/mL).

Etiological detection including Mycoplasma, Chlamydia, Viruses, Bacteria (especially Mycobacterium tuberculosis), and Fungi. (Positive/Negative)

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Could the Stroke Volume Variation Predict a Fluid Responsiveness in Thoracotomy?	NCT02331056	Pulmonary Neopl... Pulmonary Tuber...	fluid loading thoracoscopic p... thoracotomy	20 Years - 75 Years	Samsung Medical Center
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.	NCT05046366	Artificial Inte... Deep Learning Pathology, Mole... Medical Informa... Database Lung Cancer Pulmonary Tuber... Covid19		18 Years - 90 Years	Wuhan Union Hospital, China

Spots Global Cancer Trial Database for pulmonary tuberculosis