PurposeEpilepsy is a debilitating disease that can lead to series of social and psychological issues, impairing the quality of life of people with epilepsy (PWE). This survey aimed to investigate the awareness, attitudes, and first-aid knowledge of epilepsy in university studentsMethodThis cross-sectional study was conducted in Henan Province, China between January 1 and April 30, 2022. Students majored in education, medicine, science and engineering from 8 universities attended the study. The survey questionnaire comprised 28 questions covering 4 sections: demographic characteristics, awareness of epilepsy, attitudes toward PWE and knowledge of first aid for seizures.ResultsA total of 2376 university students completed the questionnaire. 94.7% heard of epilepsy. In the first aid knowledge section, individual question was correctly answered by at least 50% students, 9.3% students correctly answered all questions. Attitude toward PWE was independently (R2 =0.108, F=73.227, p < 0.001) associated with both awareness of epilepsy (B=0.411, p < 0.001) and first aid knowledge of epilepsy (B=0.047, p = 0.001). Among the three majors, medical students had more positive attitudes toward PWE than students majored in education, science and engineering (p < 0.05). However, medical students performed worse among the groups when answering the first aid knowledge questions.ConclusionThis survey showed that university students in Central China had a good awareness of epilepsy. For medical students, improvements are necessary for the awareness of the first aid knowledge for seizure.

Background ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. Methods We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test’s accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. Results Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. Conclusion ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

Objective The purpose of this study was to characterize real-world studies (RWSs) registered at ClinicalTrials.gov to help investigators better conduct relevant research in clinical practice. Methods A retrospective analysis of 944 studies was performed on February 28, 2023. Results A total of 944 studies were included. The included studies involved a total of 48 countries. China was the leading country in terms of the total number of registered studies (37.9%, 358), followed by the United States (19.7%, 186). Regarding intervention type, 42.4% (400) of the studies involved drugs, and only 9.1% (86) of the studies involved devices. Only 8.5% (80) of the studies mentioned both the detailed study design type and data source in the “Brief Summary”. A total of 49.4% (466) of studies had a sample size of 500 participants and above. Overall, 63% (595) of the studies were single-center studies. A total of 213 conditions were covered in the included studies. One-third of the studies (32.7%, 309) involved neoplasms (or tumors). China and the United States were very different regarding the study of different conditions. Conclusion Although the pandemic has provided new opportunities for RWSs, the rigor of scientific research still needs to be emphasized. Special attention needs to be given to the correct and comprehensive description of the study design in the Brief Summary of registered studies, thereby promoting communication and understanding. In addition, deficiencies in ClinicalTrials.gov registration data remain prominent.

Abstract Background The application of artificial intelligence (AI) and large language models (LLMs) in the medical sector has become increasingly common. The widespread adoption of electronic health record (EHR) platforms has created demand for the efficient extraction and analysis of unstructured data, which are known as real-world data (RWD). The rapid increase in free-text data in the medical context has highlighted the significance of natural language processing (NLP) with regard to extracting insights from EHRs, identifying this process as a crucial tool in clinical research. The development of LLMs that are specifically designed for biomedical and clinical text mining has further enhanced the capabilities of NLP in this domain. Despite these advancements, the utilization of LLMs specifically in clinical research remains limited. Objective This study aims to assess the feasibility and impact of the implementation of an LLM for RWD extraction in hospital settings. The primary focus of this research is on the effectiveness of LLM-driven data extraction as compared to that of manual processes associated with the electronic source data repositories (ESDR) system. Additionally, the study aims to identify challenges emerging in the context of LLM implementation and to obtain practical insights from the field. Methods The researchers developed the ESDR system, which integrates LLMs, electronic case report forms (eCRFs) and EHRs. The Paroxysmal Atrial Tachycardia Project, a single-center retrospective cohort study, served as a pilot case. This study involved deploying the ESDR system on the hospital local area network (LAN). Localized LLM deployment utilized the Chinese open-source ChatGLM model. The research design compared the AI-assisted process with manual processes associated with the ESDR in terms of accuracy rates and time allocation. Five eCRF forms, predominantly including free-text content, were evaluated; the relevant data focused on 630 subjects, in which context a 10% sample (63 subjects) was used for assessment. Data collection involved electronic medical and prescription records collected from 13 departments. Results While the discharge medication form achieved 100% data completeness, some free-text forms exhibited data completeness rates below 20%. The AI-assisted process was associated with an estimated efficiency improvement of 80.7% in eCRF data transcription time. The AI data extraction accuracy rate was 94.84%, and errors were related mainly to localized Chinese clinical terminology. The study identified challenges pertaining to prompt design, prompt output consistency, and prompt output verification. Addressing limitations in terms of clinical terminology and output inconsistency entails integrating local terminology libraries and offering clear examples of output format. Output verification can be enhanced by probing the model's reasoning, assessing confidence on a scale, and highlighting relevant text snippets. These measures mitigate challenges that can impede our understanding of the model's decision-making process with regard to extensive free-text documents. Conclusions This research enriches academic discourse on LLMs in the context of clinical research and provides actionable recommendations for the practical implementation of LLMs for RWD extraction. By offering insights into LLM integration in the context of clinical research systems, the study contributes to the task of establishing a secure and efficient framework for digital clinical research. The continuous evolution and optimization of LLM technology are crucial for its seamless integration into the broader landscape of clinical research.

BACKGROUND:Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU).METHODS:The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost.RESULTS:The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784-0.797 and 0.691-0.780, respectively, and the corresponding accuracy ranges were 0.723-0.725 and 0.641-0.718, respectively.CONCLUSIONS:The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.

Background:eSources consist of data that were initially documented in an electronic structure. Typically, an eSource encompasses the direct acquisition, compilation, and retention of electronic information (such as electronic health records [EHRs] or wearable devices), which serves to streamline clinical research. eSources have the potential to enhance the accuracy of data, promote patient safety, and minimize expenses associated with clinical trials. An opinion study published in September 2020 by TransCelerate outlined a road map for the future application of eSource technology and identified 5 key areas of challenges. The background of this study concerns the use of eSource technology in clinical research. Objective:The aim of this study was to present challenges and possible solutions for the implementation of eSource technology in real-world studies by summarizing team experiences and lessons learned from an eSource record (ESR) project. Methods:After initially developing a simple prototype of the ESR software that can be demonstrated systematically, the researchers conducted in-depth interviews and interacted with different stakeholders to obtain guidance and suggestions. The researchers selected 5 different roles for interviewees: regulatory authorities, pharmaceutical company representatives, hospital information department employees, medical system providers, and clinicians. Results:After screening all consultants, the researchers concluded that there were 25 representative consultants. The hospital information department needs to implement many demands from various stakeholders, which makes the existing EHR system unable to meet all the demands of eSources. The emergence of an ESR is intended to divert the burden of the hospital information department from the enormous functional requirements of the outdated EHR system. The entire research process emphasizes multidisciplinary and multibackground expert opinions and considers the complexity of the knowledge backgrounds of personnel involved in the chain of clinical source data collection, processing, quality control, and application in real-world scenarios. To increase the readability of the results, the researchers classified the main results in accordance with the paragraph titles in "Use of Electronic Health Record Data in Clinical Investigations," a guide released by the US Food and Drug Administration. Conclusions:This study introduces the requirement dependencies of different stakeholders and the challenges and recommendations for designing ESR software when implementing eSource technology in China. Experiences based on ESR projects will provide new insights into the disciplines that advance the eSource research field. Future studies should engage patients directly to understand their experiences, concerns, and preferences regarding the implementation of eSource technology. Moreover, involving additional stakeholders, including community health care providers and social workers, will provide valuable insights into the challenges and potential solutions across various health care settings.

Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.

BACKGROUND Although the association between Helicobacter pylori (H. pylori) infection and hepatic encephalopathy (HE) has been confirmed through some research, the results of these relevant studies still remain controversial. We conducted an updated meta-analysis based on published studies to address this issue. METHODS A systematic search was conducted, reviewing all studies about the association between H. pylori infection and HE, through November 2021. The outcome measures were presented as odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS In total, 13 studies provided data from 2784 subjects. H. pylori infection increased the risk of HE by 32% (OR = 2.32, 95% CI: 1.78-3.04). The effect became greater after hepatic encephalopathy was divided into overt HE and minimal hepatic encephalopathy (MHE) (HE OR = 2.66, 95% CI: 2.01-3.51, MHE OR = 1.74, 95% CI: 1.10-2.76). After H. pylori eradication, the risk of HE was reduced by 64%. CONCLUSIONS H. pylori infection is significantly associated with HE, and the infection rate of H. pylori also increases with the severity of HE. Eradication of H. pylori has a protective effect on HE. Therefore, it is necessary to eradicate H. pylori in HE treatments.

BACKGROUND:Ultrasound elastography (US-E) has been shown superior to the conventional US in diagnosing benign and malignant breast lesions. In contrast, the role of US-E in the differentiation of breast invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) has been poorly described.OBJECTIVE:This study was designed to examine the diagnostic value of US-E in the differentiation of IDC and DCIS.METHODS:Medical records of all patients who underwent preoperative US-E evaluation and were diagnosed with IDC or DCIS at our hospital from April-December 2019 were retrieved and analyzed. Those who had prior surgical treatment, chemotherapy or radiotherapy were excluded.RESULTS:Twenty women with DCIS and 111 women with IDC were included in this study. There were no significant differences in age, maximum lesion diameter and tumor volume between the two groups. While shear wave velocity (SWV) inside the lesion and in the surrounding tissue, strain ratio and tumor area ratio were not substantially different between the two groups, SWV at the edge of the lesion was significantly higher in IDC cases, which had an AUC value of 0.66 with a sensitivity of 65.8% and a specificity of 60.0% for the differential diagnosis of IDC and DCIS.CONCLUSION:Edge SWV is significantly higher in IDC than that in DCIS, which had a moderate diagnostic value for the differentiation of IDC and DCIS, similar to the performance of diffusion-weighted magnetic resonance imaging as reported in the literature. In terms of cost-effectiveness, US-E could be very useful while waiting for further evaluations to determine whether US-E combined with other diagnostic modalities improves the diagnostic performance.

Background Electronic sources (eSources) can improve data quality and reduce clinical trial costs. Our team has developed an innovative eSource record (ESR) system in China. This study aims to evaluate the efficiency, quality, and system performance of the ESR system in data collection and data transcription. Methods The study used time efficiency and data transcription accuracy indicators to compare the eSource and non-eSource data collection workflows in a real-world study (RWS). The two processes are traditional data collection and manual transcription (the non-eSource method) and the ESR-based source data collection and electronic transmission (the eSource method). Through the system usability scale (SUS) and other characteristic evaluation scales (system security, system compatibility, record quality), the participants’ experience of using ESR was evaluated. Results In terms of the source data collection (the total time required for writing electronic medical records (EMRs)), the ESR system can reduce the time required by 39% on average compared to the EMR system. In terms of data transcription (electronic case report form (eCRF) filling and verification), the ESR can reduce the time required by 80% compared to the non-eSource method (difference: 223 ± 21 s). The ESR accuracy in filling the eCRF field is 96.92%. The SUS score of ESR is 66.9 ± 16.7, which is at the D level and thus very close to the acceptable margin, indicating that optimization work is needed. Conclusions This preliminary evaluation shows that in the clinical medical environment, the ESR-based eSource method can improve the efficiency of source data collection and reduce the workload required to complete data transcription.

Background: An eSource generally includes the direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. There has been some eSource-related research progress in relatively large projects. However, most of these studies focused on technical explorations to improve interoperability among systems to reuse retrospective data for research. Few studies have explored source data collection and quality control during prospective data collection from a methodological perspective. Objective: This study aimed to design a clinical source data collection method that is suitable for real-world studies and meets the data quality standards for clinical research and to improve efficiency when writing electronic medical records (EMRs). Methods: On the basis of our group's previous research experience, TransCelerate BioPharm Inc eSource logical architecture, and relevant regulations and guidelines, we designed a source data collection method and invited relevant stakeholders to optimize it. On the basis of this method, we proposed the eSource record (ESR) system as a solution and invited experts with different roles in the contract research organization company to discuss and design a flowchart for data connection between the ESR and electronic data capture (EDC). Results: The ESR method included 5 steps: research project preparation, initial survey collection, in-hospital medical record writing, out-of-hospital follow-up, and electronic case report form (eCRF) traceability. The data connection between the ESR and EDC covered the clinical research process from creating the eCRF to collecting data for the analysis. The intelligent data acquisition function of the ESR will automatically complete the empty eCRF to create an eCRF with values. When the clinical research associate and data manager conduct data verification, they can query the certified copy database through interface traceability and send data queries. The data queries are transmitted to the ESR through the EDC interface. The EDC and EMR systems interoperate through the ESR. The EMR and EDC systems transmit data to the ESR system through the data standards of the Health Level Seven Clinical Document Architecture and the Clinical Data Interchange Standards Consortium operational data model, respectively. When the implemented data standards for a given system are not consistent, the ESR will approach the problem by first automating mappings between standards and then handling extensions or corrections to a given data format through human evaluation. Conclusions: The source data collection method proposed in this study will help to realize eSource's new strategy. The ESR solution is standardized and sustainable. It aims to ensure that research data meet the attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available standards for clinical research data quality and to provide a new model for prospective data collection in real-world studies.

The standardization degree of traditional Chinese medicine clinical data in the real world is low and heterogeneous data aggregation among institutions is difficult, which leads to the difficulty of sharing clinical data and scientific research data of traditional Chinese medicine. This paper designs and implements a real world traditional Chinese medicine clinical scientific research information electronic medical record sharing system. The system consists mainly of two subsystems, namely electronic medical record collection system and electronic medical record integration system. The collection system can collect, normalize and structured storage inpatient electronic medical records, outpatient electronic medical records and cloud platform electronic medical records. The integration system can integrate heterogeneous data from different traditional Chinese medicine diagnostic and treatment institutions to realize the sharing of traditional Chinese medicine clinical and research data.

Objective To investigate the existing barriers and recommendations of real-world data (RWD) standardisation for clinical research through a qualitative study on different stakeholders. Design This qualitative study involved five types of stakeholders based on five interview outlines. The data analysis was performed using the constructivist grounded theory analysis process. Setting Eight hospitals, four hospital system vendors, three big data companies, six medical products companies and four regulatory institutions were included. Participants In total, 62 participants from 25 institutions were interviewed through purposive sampling. Results The findings showed that the lack of clinical applicability in existing terminology standards, lack of generalisability in existing research databases, and lack of transparency in existing data standardisation process were the barriers of data standardisation of RWD for clinical research. Enhancing terminology standards by incorporating locally used clinical terminology, reducing burden in the usage of terminology standards, improving generalisability of RWD for research by using clinical data models, and improving traceability to source data for transparency might be feasible suggestions for solving the current problems. Conclusions Efficient and reliable data standardisation of RWD for clinical research can help generate better evidence used to support regulatory evaluation of medical products. This research suggested enhancing terminology standards by incorporating locally used clinical terminology, reducing burden in the usage of terminology standards, improving generalisability of RWD for research by using clinical data models, and improving traceability to source data for transparency to guide efforts in data standardisation in the future.

Background As researchers are increasingly interested in real-world studies (RWSs), improving data collection efficiency and data quality has become an important challenge. An electronic source (eSource) generally includes direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. Although there are already large projects on eSource technology, there is a lack of experience in using eSource technology to implement RWSs. Our team designed and developed an eSource record (ESR) system in China. In a preliminary prospective study, we selected a cosmetic medical device project to evaluate ESR software’s effect on data collection and transcription. As the previous case verification was simple, we plan to choose more complicated ophthalmology projects to further evaluate the ESR. Objective We aimed to evaluate the data transcription efficiency and quality of ESR software in retrospective studies to verify the feasibility of using eSource as an alternative to traditional manual transcription of data in RWS projects. Methods The approved ophthalmic femtosecond laser project was used for ESR case validation. This study compared the efficiency and quality of data transcription between the eSource method using ESR software and the traditional clinical research model of manually transcribing the data. Usability refers to the quality of a user’s experience when interacting with products or systems including websites, software, devices, or applications. To evaluate the system availability of ESR, we used the System Usability Scale (SUS). The questionnaire consisted of the following 2 parts: participant information and SUS evaluation of the electronic medical record (EMR), electronic data capture (EDC), and ESR systems. By accessing log data from the EDC system previously used by the research project, all the time spent from the beginning to the end of the study could be counted. Results In terms of transcription time cost per field, the eSource method can reduce the time cost by 81.8% (11.2/13.7). Compared with traditional manual data transcription, the eSource method has higher data transcription quality (correct entry rate of 2356/2400, 98.17% vs 47,991/51,424, 93.32%). A total of 15 questionnaires were received with a response rate of 100%. In terms of usability, the average overall SUS scores of the EMR, EDC, and ESR systems were 50.3 (SD 21.9), 51.5 (SD 14.2), and 63.0 (SD 11.3; contract research organization experts: 69.5, SD 11.5; clinicians: 59.8, SD 10.2), respectively. The Cronbach α for the SUS items of the EMR, EDC, and ESR systems were 0.591 (95% CI −0.012 to 0.903), 0.588 (95% CI −0.288 to 0.951), and 0.785 (95% CI 0.576-0.916), respectively. Conclusions In real-world ophthalmology studies, the eSource approach based on the ESR system can replace the traditional clinical research model that relies on the manual transcription of data.

BackgroundCoronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors.MethodsFrom February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability.ResultsIn 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P=0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed.ConclusionIn COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome.

Purpose This study aimed to establish and evaluate the usefulness of a simple, practical, and easy-to-promote machine learning model based on ultrasound imaging features for diagnosing breast cancer (BC). Materials and Methods Logistic regression, random forest, extra trees, support vector, multilayer perceptron, and XG Boost models were developed. The modeling data set of 1345 cases was from a tertiary class A hospital in China. The external validation data set of 1965 cases were from 3 tertiary class A hospitals and 2 primary hospitals. The area under the receiver operating characteristic curve (AUC) was used as the main evaluation index, and pathological biopsy was used as the gold standard for evaluating each model. Diagnostic capability was also compared with that of clinicians. Results Among the six models, the logistic model showed superior diagnostic efficiency, with an AUC of 0.771 and 0.906 and Brier scores of 0.181 and 0.165 in the test and validation sets, respectively. The AUCs of the clinician diagnosis and the logistic model were 0.913 and 0.906. Their AUCs in the tertiary class A hospitals were 0.915 and 0.915, respectively, and were 0.894 and 0.873 in primary hospitals, respectively. Conclusion The externally validated logical model can be used to distinguish between malignant and benign breast lesions in ultrasound images. Compared with clinician diagnosis, the logistic model has better diagnostic efficiency, making it potentially useful to assist in screening, particularly in lower level medical institutions. Trial Registration http://www.clinicaltrials.gov. ClinicalTrials.gov ID: NCT03080623.

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Objective To provide a basis for further optimizing the diagnosis and treatment strategies of severe and critical corona virus disease 2019 (COVID-19) by investigating and analyzing the epidemiological and clinical characteristics of the death cases. Methods The epidemiological and clinical characteristics of 47 death cases obtained from Huoshenshan Hospital in Whuhan, Hubei Province were retrospectively analyzed. Results All the patients developed initial symptoms in Wuhan. The time from onset to admission was (12.60±5.60) days. Most of them were male (68.09%) with non-nosocomial infection (91.49%), advanced age (>60 years, 89.36%). Over half of the cases (51.06%) reported a history of contact with suspected or confirmed patients, and comorbidity of chronic diseases (70.21%). Multiple organ dysfunction syndrome (MODS) occurred in 29 cases (61.70%) with heart failure (51.06%) and renal failure (36.17%). The main clinical symptoms included fever, fatigue, dyspnea and cough. At admission,most cases were severe (55.32%) or critical (42.55%), and the in-hospital survival was longer for the severe than for the critical (P=0.02). 76.59% of the patients received invasive mechanical ventilation, and they had a longer in-hospital survival than those with non-invasive mechanical ventilation (P<0.05). Conclusions This group of cases occurred during the peak of the COVID-19 outbreak in China, characterized by male, elder and history of chronic diseases. Acute respiratory distress syndrome (ARDS) caused by COVID-19 was responsible for patients' death, and MODS manifestated by heart and kidney failure also implicated in the process. Disease severity and invasive mechanical ventilation were related to in-hospital survival. DOI: 10.11855/j.issn.0577-7402.2020.05.02

Objective: The aim of this study was to provide recommendations for improving the design of subsequent studies through analysis of the registered coronavirus disease 2019 (COVID-19) clinical trials.Methods: A retrospective analysis of 189 trial retrievals achieved on 20 February 2020.Results: A total of 189 trials are included in the study. There were 69.3% interventional studies, 21.7% observational studies, 5.3% diagnostic tests and 3.7% other studies. The following statistics are provided only for the interventional studies. Severity of disease: 5.3% light and common type, 17.6% severe and critically ill and 59.6% with no restricted classification. Medication use: 51.1% Western medicine, 32.1% Chinese medicine, 10.7% blood related product and 6.1% non-drug therapy. The median and inner quantile range of the sample sizes included in these studies: 104 (IQR: 60, 200). Primary outcome type most used: 45.8% with clinical characteristics and 21.4% with virological. Study design characteristics: 71% of all studies were randomized, 5% of all studies were blinded, 18% of all studies were multicenter and 76% of all studies were single center.Conclusion: Although many COVID-19 studies include randomization in their design, the lack of additional double-blind and placebo-controlled elements in their designs result in a less robust evaluation of intervention safety and efficacy. Furthermore, similar or repeated research and small sample studies that have less promise in gains of new information have possibly led to a shortage of recruitable patients and become a barrier to the completion of large multicenter clinical trial studies.

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Results Of the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively. Conclusion The machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.