Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

BACKGROUND: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.

Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.

OBJECTIVE: To investigate the spatial patterns of the prevalence, awareness, treatment, and control rates of dyslipidemia at the provincial level in China. METHODS: A national and provincial representative cross-sectional survey was conducted among 178,558 Chinese adults in 31 provinces in mainland China in 2018-2019, using a multi-stage, stratified, cluster-randomized sampling design. Subjects, as households, were selected, followed by a home visit to collect information. Both descriptive and linear regression procedures were applied in the analyses. RESULTS: The overall prevalence of dyslipidemia was 35.6%, and wide geographic variations of prevalence, treatment, and control rates of dyslipidemia were identified among 178,558 eligible participants with a mean age of 55.1 ± 13.8 years. The highest-lowest difference regarding the provincial level prevalence rates were 19.7% vs. 2.1% for high low-density lipoprotein cholesterol, 16.7% vs. 2.5% for high total cholesterol, 35.9% vs. 5.4% for high triglycerides, and 31.4% vs. 10.5% for low high-density lipoprotein cholesterol. The treatment rate of dyslipidemia was correlated with the socio-demographic index ( P < 0.001), urbanization rate ( P = 0.01), and affordable basic technologies and essential medicines ( P < 0.001). CONCLUSION Prevailing dyslipidemia among the Chinese population and its wide geographic variations in prevalence, treatment, and control suggest that China needs both integrated and localized public health strategies across provinces to improve lipid management.
Humans ; Adult ; Middle Aged ; Aged ; Cross-Sectional Studies ; Prevalence ; East Asian People ; Dyslipidemias/prevention & control* ; China/epidemiology* ; Cholesterol, HDL ; Risk Factors ; Triglycerides

The mechanisms underlying autophagic defects in nonalcoholic steatohepatitis (NASH) remain largely unknown. We aimed to elucidate the roles of hepatic cyclooxygenase 1 (COX1) in autophagy and the pathogenesis of diet-induced steatohepatitis in mice. Human nonalcoholic fatty liver disease (NAFLD) liver samples were used to examine the protein expression of COX1 and the level of autophagy. Cox1^Δhepa mice and their wildtype littermates were generated and fed with 3 different NASH models. We found that hepatic COX1 expression was increased in patients with NASH and diet-induced NASH mice models accompanied by impaired autophagy. COX1 was required for basal autophagy in hepatocytes and liver specific COX1 deletion exacerbated steatohepatitis by inhibiting autophagy. Mechanistically, COX1 directly interacted with WD repeat domain, phosphoinositide interacting 2 (WIPI2), which was crucial for autophagosome maturation. Adeno-associated virus (AAV)-mediated rescue of WIPI2 reversed the impaired autophagic flux and improved NASH phenotypes in Cox1^Δhepa mice, indicating that COX1 deletion-mediated steatohepatitis was partially dependent on WIPI2-mediated autophagy. In conclusion, we demonstrated a novel role of COX1 in hepatic autophagy that protected against NASH by interacting with WIPI2. Targeting the COX1-WIPI2 axis may be a novel therapeutic strategy for NASH.

Cardiac ion channelopathies encompass a set of inherited or acquired conditions that are due to dysfunction in ion channels or their associated proteins, typically in the presence of structurally normal hearts. They are associated with the development of ventricular arrhythmias and sudden cardiac death. The aim of this review is to provide a historical perspective and recent advances in the research of the cardiac ion channelopathies, Brugada syndrome, long QT syn‑ drome and catecholaminergic polymorphic ventricular tachycardia, in Hong Kong, China. In particular, recent works on the development of novel predictive models incorporating machine learning techniques to improve risk strati‑ fication are outlined. The availability of linked records of affected patients with good longitudinal data in the public sector, together with multidisciplinary collaborations, implies that ion channelopathy research efforts have advanced significantly.

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of cur‑ rent ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Emergence of the colistin resistance gene,mcr-1,has attracted worldwide attention.Despite the prevalence of mcr-1-positive Escherichia coli(MCRPEC)strains in human carriage showing a significant decrease between 2016 and 2019,genetic differences in MCRPEC strains remain largely unknown.We therefore conducted a comparative genomic study on MCRPEC strains from fecal samples of healthy human subjects in 2016 and 2019.We identified three major differences in MCRPEC strains between these two time points.First,the insertion sequenceISApll1 was often deleted and the percentage of mcr-1-carrying IncI2 plasmids was increased in MCRPEC strains in 2019.Second,the antibiotic resistance genes(ARGs),aac(3)-Ⅳa and blaCTX-M-1,emerged and coexisted with mcr-1 in 2019.Third,MCRPEC strains in 2019 contained more viru-lence genes,resulting in an increased proportion of extraintestinal pathogenic E.coli(ExPEC)strains(36.1％)in MCRPEC strains in 2019 compared to that in 2016(10.5％),implying that these strains could occupy intestinal ecological niches by competing with other commensal bacteria.Our results suggest that despite the significant reduction in the prevalence of MCRPEC strains in humans from 2016 to 2019,MCRPEC exhibits increased resistance to other clinically important ARGs and contains more virulence genes,which may pose a potential public health threat.