Observation on the Effect of Non-Invasive Ventilator Combined with Conventional Therapy in the Treatment of Chronic Obstructive Pulmonary Disease Complicated with Respiratory Failure

Objective:To explore the clinical effect of a non-invasive ventilator combined with conventional therapy in the treatment of patients with chronic obstructive pulmonary disease(COPD)combined with respiratory failure.Methods:68 patients with COPD combined with respiratory failure treated in our hospital from September 2021 to October 2023 were selected as the research subjects.Using the random number table method,they were divided into a control group and an experimental group of 34 cases each.The control group received conventional symptomatic treatment,and the experimental group received non-invasive ventilator treatment based on the control group.The clinical effects,blood gas indicators(partial pressure of carbon dioxide(PaCO_(2)),partial pressure of oxygen(PaO_(2)),arterial oxygen saturation(SaO_(2))),lung function(forced expiratory volume in 1 second(FEV1),forced vital capacity(FVC),6 min walking distance),complications,and inflammatory factor levels(c-reactive protein(CRP),interleukin-6(IL-6),neutrophil-to-lymphocyte ratio(NLR))of the two groups of patients were observed.Results:(1)The clinical efficacy of the patients in the experimental group(33/97.06%)was more significant as compared with the control group(25/73.53%)(P<0.05);(2)After treatment,the clinical efficacy of the two groups of patients in terms of FEV1,FEV1/FVC,6-minute walking distance,PaO_(2)and SaO_(2)all increased in the experimental group as compared to that of the control group(P<0.05);(3)After treatment,the PaCO_(2),CRP,IL-6,and NLR of the two groups of patients decreased,and the decrease in the experimental group was higher than that of the control group(P<0.05);(4)The patients’complication rate in the experimental group(2/5.88%)was lower as compared to that of the control group(9/26.46%)(P<0.05).Conclusion:Non-invasive ventilators combined with conventional therapy achieved good clinical results in treating patients with COPD and respiratory failure.

Data analysis guidelines for single‑cell RNA‑seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

Status and Analysis of 832 Investigator-Initiated Coronavirus-Related Clinical Studies

Objective:To contribute to the development of clinical research on novel coronavirus by analyzing the clinical research data of COVID-19.Methods:Searches were performed on the database of“National Health Insurance Information Platform Medical Research Registration Information System”using the keywords“COVID-19”and“Novel coronavirus.”The search was performed till 31 December 2022.This paper presents a statistical analysis of the status quo of the registered projects in terms of the number of registered projects,the types of projects,the levels of the institutions,the types of research,the intervention measures,the research design,the main objectives of the research,and so on.Results:A total of 823 investigator-initiated clinical studies of COVID-19 were documented,and the number of studies registered peaked on December 31,2020,and December 31,2022.Among them,there were 819 items from general medical research(99.5%),812 items from medical institutions(98.7%),and 713 items from Medical Grade III,and Class A hospitals(86.6%).Among these items,534(64.9%)were observational studies.The most common intervention method used was administering existing drugs,with 140 studies utilizing them.This data analysis also included 128 case-control studies and 247 treatment-oriented studies.Conclusion:Researchers in local medical institutions have been actively carrying out clinical research related to COVID-19.However,they should refer to registered research to avoid duplicate research.

基于Clinicaltrials.gov平台的口腔鳞状细胞癌临床研究注册特点分析

目的:基于Clinicaltrials.gov平台分析全球口腔鳞状细胞癌(oral squamous cell carcinoma,OSCC)的临床研究注册特点,了解研究现状和发展趋势,为OSCC临床研究和诊疗提供新思路。方法:通过挖掘Clinicaltrials.gov平台建库至2023年3月24日已注册的OSCC临床试验数据,从年度趋势、地域分布、临床分期、研究类型、受试者人数、经费来源、机构数量等多方面进行统计分析,归纳其临床研究特征。结果:共纳入OSCC临床注册研究332项,注册数量最多的3个国家是美国(188项,56.63%),中国(37项,11.14%)和法国(18项,5.42%)。332项研究中干预性研究和观察性研究分别占83.43%和16.57%,277项干预性试验中药物干预204项(73.65%),其中单克隆抗体药物研究79项(28.52%),顺铂60项(21.66%)。119项平行试验中105项(88.24%)采用随机方法,44项(36.97%)为盲法。332项研究中约1/3(111项,33.43%)归属于Ⅱ期临床试验,受试者人数在50以下的占比超过一半(177项,53.31%)。结论:OSCC的临床研究存在地区不均衡性,多中心大样本随机三盲对照的高质量临床研究不足,应加强临床试验注册的培训和审查,以促进OSCC临床试验高质量的开展,推动其治疗方案的优化。

Preliminary Exploration of the Initial Diagnostic Prediction Model of Moderate Coronavirus Disease 2019 (2019-nCoV) Based on Clinical Data

Objective: To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establish prediction models for predicting the probability of the first diagnosis of 2019-nCoV. Methods: A total of 81 suspected cases and 87 confirmed cases of moderate 2019-nCoV diagnosed initially in the isolation wards of the First People’s Hospital of Wuhu and the People’s Hospital of Wuwei and Wuhan Caidian Module Hospital with the help of our hospital doctors were gathered, and retrospectively analyzed. Results: The most common symptoms were fever (76.79%) and cough (64.29%) in the total of 168 cases. The median age was 45 (35 - 56) years old in the 87 confirmed cases of moderate 2019-nCoV, older than the median age 36 (29 - 50) in the 81 suspected cases. There were significant more in the former than in the latter in the incidence of myalgia, ground-glass opacity (GGO), invasions of lesion in the peripheral lobes, vascular thickening and bronchial wall thickening, interlobular septal thicking, and small pulmonary nodules. On the contrary, there were less in the former than in the latter in the total number of leukocytes and neutrophils in blood routine examination and the levels of procalcitonin (PCT). Two groups were statistically significantly different (P < 0.05). Multivariate logistic regression analysis showed that age, fever, myalgia, GGO, vascular thickening and bronchial wall thickening, invasions of lesion in the peripheral lobes were independent factors for identification of 2019-nCoV, and the total number of leukocytes, cough, pharyngalgia and headache were negatively related. The established mathematical equation for predicting model for predicting the probability of the first diagnosis of 2019-nCoV is: P = ex/(1 + ex), x = −6.226 + (0.071 × ages) + (1.720 × fever) + (2.858 × myalgia) + (2.131 × GGO) + (3.000 × vascular thickening and bron-chial wall thickening) + (3.438 × invasions of lesion in the peripheral lobes) + (−0.304 × the number of leukocytes) + (−1.478 × cough) + (−1.830 × pharyngalgia) + (−2.413 × headache), where e is a natural logarithm. The area under the ROC curve (AUC) of this model was calculated to be 0.945 (0.915 - 0.976). The sensitivity is 0.920 and the specificity is 0.827 when the appropriate critical point is 0.360. Conclusions: A mathematical equation prediction model for predicting the probability of the first diagnosis of 2019-nCoV can be established based on the initial diagnostic clinical data of moderate 2019-nCoV. The prediction model is a good assistant diagnostic method for its high accurateness.

Immunotherapy for advanced hepatocellular carcinoma:From clinical trials to real-world data and future advances

Hepatocellular carcinoma(HCC)is a leading cause of cancer-associated mortality worldwide.HCC is an inflammation-associated immunogenic cancer that frequently arises in chronically inflamed livers.Advanced HCC is managed with systemic therapies;the tyrosine kinase inhibitor(TKI)sorafenib has been used in 1st-line setting since 2007.Immunotherapies have emerged as promising treatments across solid tumors including HCC for which immune checkpoint inhibitors(ICIs)are licensed in 1st-and 2nd-line treatment setting.The treatment field of advanced HCC is continuously evolving.Several clinical trials are investigating novel ICI candidates as well as new ICI regimens in combination with other therapeutic modalities including systemic agents,such as other ICIs,TKIs,and anti-angiogenics.Novel immunotherapies including adoptive cell transfer,vaccine-based approaches,and virotherapy are also being brought to the fore.Yet,despite advances,several challenges persist.Lack of real-world data on the use of immunotherapy for advanced HCC in patients outside of clinical trials constitutes a main limitation hindering the breadth of application and generalizability of data to this larger and more diverse patient cohort.Consequently,issues encountered in real-world practice include patient ineligibly for immunotherapy because of contraindications,comorbidities,or poor performance status;lack of response,efficacy,and safety data;and cost-effectiveness.Further real-world data from high-quality large prospective cohort studies of immunotherapy in patients with advanced HCC is mandated to aid evidence-based clinical decision-making.This review provides a critical and comprehensive overview of clinical trials and real-world data of immunotherapy for HCC,with a focus on ICIs,as well as novel immunotherapy strategies underway.

AB047.Quebec database of clinical data and biological material for research on uveal melanoma

Background:Sharing biological material and clinical data from patients with uveal melanoma.Methods:Uveal melanoma is the most common intraocular malignancy in the adult population.Because uveal melanoma is primarily a sporadic cancer and familial cases are rare,it is difficult to prevent or detect it.Despite effective treatment of ocular tumors,more than 50%of patients develop incurable liver metastases mainly in the 5-10 years following the detection of the primary tumor.This cancer is relatively rare and the obtained biopsies are very small.About 20 samples are taken each year in Quebec.This provincial infrastructure is made of biological material from donors with uveal melanoma and a large clinical database.Collected tumor biopsies are used for culturing cell lines and the creation of a DNA/RNA library used for genomic and genetic studies.Results:This infrastructure plays an important role in the achievement of various research programs for a better understanding of genetic and environmental factors involved in the development of melanoma and the spread of metastasis.It allows collaboration with other researchers at a provincial,national and international level in order to make progress in basic and clinical research on uveal melanoma.Conclusions:The biological material and clinical data of this infrastructure are available upon request to VHRN members whose research project was approved by the ethics committee of the institution.

Construction of databases:advances and significance in clinical research

Widely used in clinical research, the database is a new type of data management automation technology and the most efficient tool for data management. In this article, we first explain some basic concepts, such as the definition, classification, and establishment of databases. Afterward, the workflow for establishing databases, inputting data, verifying data, and managing databases is presented. Meanwhile, by discussing the application of databases in clinical research, we illuminate the important role of databases in clinical research practice. Lastly, we introduce the reanalysis of randomized controlled trials(RCTs) and cloud computing techniques, showing the most recent advancements of databases in clinical research.

The global registry of COVID-19 clinical trials: indicating the design of traditional Chinese medicine clinical trials

Objective:To analyze the registration information of Corona Virus Disease 2019(COVID-19)related clinical trials from all the clinical trial registry accepted by the International Committee of Medical Journal Editors(ICMJE).Methods:All the database of ICMJE-accepted clinical trial registry platform were searched for COVID-19 related clinical trials that registered from December 8,2019 to February 19,2020.Results:All the database totally contained data of 209 COVID-19 related clinical trials,including 66(31.6%)traditional Chinese medicine(TCM)related studies and 143(68.4%)non-TCM related studies.176(84.2%)clinical trials were registered in ChiCTR,as 33(15.8%)were registered in ClinicalTrials.gov.There were 152(72.7%)intervention trials,including 9(4.3%in total)preventive trials,136(65.1%in total)therapeutic trials and 7(3.3%in total)convalescent trials.Conclusion:At the present stage,clinical studies are still aiming at exploring the effective treatment of COVID-19,and the existing treatments need more high-quality clinical evidence supporting.Traditional Chinese medicine may have played an important role in all stages of the epidemic preventing and control,and the enthusiasm of carrying out scientific research has also been greatly improved.The relevant policies of clinical trial registration should be popularized among researchers,including traditional Chinese medicine researchers.And the methodological quality of clinical trials’design urges improving.

Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

Radiological clinical trials: Proposal of a problem-finding questionnaire to improve study success

AIM To develop a survey to help define the main problems in radiological clinical trials. METHODS Since 2006, we have managed seven different radiological clinical trials recruiting patients in academic and non-academic centres. We developed a preliminary questionnaire using a four-round Delphi approach to identify problems occurring in radiological clinical trials run at our centre. We investigated the recruitment experience, involvement of all multi-disciplinary team members and main obstacles to completing the projects. A final round of Delphi processes elucidated solutions to the identified problems.RESULTS Among 19/20(95%) respondents, 10(53%) were young physicians(under 35 years old), and the respondents included non-faculty members, fellows, residents, and undergraduate students. Ninety-four percent(18/19) of respondents showed interest in conducting clinical trials. On a scale of 1 to 10, the problems with higher/worse scores(8-9) were related to technical or communication problems. The most frequent problems across all studies were technical problems related to clinical trial equipment, insufficient willingness to participate, obstacles to understanding the design of electronic-case report form and extra work.CONCLUSION The developed questionnaire identified the main recurring problems in radiological clinical trials as perceived by endusers and helped define possible solutions that are mostly related to having dedicated clinical trial research staff.

Application of AI and IoT in Clinical Medicine:Summary and Challenges

The application of artificial intelligence(AI)technology in the medical field has experienced a long history of development.In turn,some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth.With the development of advanced technologies such as the Internet of Things(IoT),cloud computing,big data,and 5G mobile networks,AI technology has been more widely adopted in the medical field.In addition,the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way.In this work,we examine the technical basis of IoT,cloud computing,big data analysis and machine learning involved in clinical medicine,combined with concepts of specific algorithms such as activity recognition,behavior recognition,anomaly detection,assistant decision-making system,to describe the scenario-based applications of remote diagnosis and treatment collaboration,neonatal intensive care unit,cardiology intensive care unit,emergency first aid,venous thromboembolism,monitoring nursing,image-assisted diagnosis,etc.We also systematically summarize the application of AI and IoT in clinical medicine,analyze the main challenges thereof,and comment on the trends and future developments in this field.

Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials

Current minimization programs do not permit full control over different aspects of minimization algorithm such as distance or probability measures and may not allow for unequal allocation ratios. This article describes the implementation of “MinimPy” an open-source minimization program in Python programming language, which provides full customizetion of minimization features. MinimPy supports naive and biased coin minimization together with various new and classic distance measures. Data syncing is provided to facilitate minimization of multicenter trial over the network. MinimPy can easily be modified to fit special needs of clinical trials and in particular change it to a pure web application, though it currently supports network syncing of data in multi-center trials using network repositories.

The Assessment of Non-Linear Effects in Clinical Research

Background: Novel models for the assessment of non-linear data are being developed for the benefit of making better predictions from the data. Objective: To review traditional and modern models. Results, and Conclusions: 1) Logit and probit transformations are often successfully used to mimic a linear model. Logistic regression, Cox regression, Poisson regression, and Markow modeling are examples of logit transformation;2) Either the x- or y-axis or both of them can be logarithmically transformed. Also Box Cox transformation equations and ACE (alternating conditional expectations) or AVAS (additive and variance stabilization for regression) packages are simple empirical methods often successful for linearly remodeling of non-linear data;3) Data that are sinusoidal, can, generally, be successfully modeled using polynomial regression or Fourier analysis;4) For exponential patterns like plasma concentration time relationships exponential modeling with or without Laplace transformations is a possibility. Spline and Loess are computationally intensive modern methods, suitable for smoothing data patterns, if the data plot leaves you with no idea of the relationship between the y- and x-values. There are no statistical tests to assess the goodness of fit of these methods, but it is always better than that of traditional models.

Non-Invasive Pressure Support Ventilation in Major Lung Resection for High Risk Patients: Does It Matter?

Background and purpose: Patients with severely impaired pulmonary function have an increased operative risk for major lung resection. The clinical benefits of pre- and perioperative, non-invasive pressure support ventilation (NIPSV) have up to now not been extensively evaluated. Patients with severely reduced pulmonary function were investigated in this prospective and randomised single centre clinical trial. Methods: Standard pulmonary evaluation was performed in all patients before major lung resection. To predict postoperative pulmonary function, a lung perfusion-ventilation scan was carried out. All patients enrolled in the study were instructed preoperatively on how to use a NIPSV respirator. Af-ter lung resection patients were randomised either for continuation of NIPSV or for standard treatment. Results: Of the 52 patients assessed, 21 patients met the inclusion criteria for the study protocol. Predicted mean postoperative FEV1 was 1.10 L (range 0.92 - 1.27 L). Lobectomy was performed in 14 patients, pneumonectomy in 6 patients and a segmentectomy in 1 patient. No inhospital deaths occurred. Pulmonary complications (reintubation, pneumonia) were more frequent in the NIPSV group than in the control group (3 patients versus 1 patient), without statistical significance (p = 0.31). Conclusions: We observed no mortality and a low morbidity in this high risk group. Postoperative continuation of NIPSV had no beneficial effect on the clinical outcome. Preoperative conditioning with NIPSV, however, seems to be a suitable tool for patients with severely impaired pulmonary function. This study may serve therefore as basis for further investigations for the potential clinical benefits of prophylactic NIPSV in major lung surgery.

Forecasting patient demand at urgent care clinics using explainable machine learning

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows.The delays arising from inadequate staffing levels during these periods have been linked with adverse clinical outcomes.Previous research into forecasting patient flows has mostly used statistical techniques.These studies have also predominately focussed on short‐term forecasts,which have limited practicality for the resourcing of medical personnel.This study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient presentations.Our research uses datasets covering 10 years from two large urgent care clinics to develop long‐term patient flow forecasts up to one quarter ahead using a range of state‐of‐the‐art algo-rithms.A distinctive feature of this study is the use of eXplainable Artificial Intelligence(XAI)tools like Shapely and LIME that enable an in‐depth analysis of the behaviour of the models,which would otherwise be uninterpretable.These analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVID‐19 pandemic lockdowns and to identify the most impactful variables,resulting in valuable insights into their performance.The results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting,into an ensemble,delivered the most accurate and consistent solutions on average.This approach generated improvements in the range of 16%-30%over the existing in‐house methods for esti-mating the daily patient flows 90 days ahead.

Clinical utility of non-invasive tests to predict clinical outcomes in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease(NAFLD)is the leading cause of chronic liver disease,and the fastest-growing cause of hepatocellular carcinoma(HCC)worldwide(1,2).As the number of people with NAFLD is too great to perform surveillance in all,risk-stratification strategies are increasingly important to identify those at the highest risk of decompensation and HCC(3,4).