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- Wang, X, et al.
(författare)
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Kongcun Town Asymptomatic Intracranial Artery Stenosis study in Shandong, China: cohort profile
- 2020
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:7, s. e036454-
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Tidskriftsartikel (refereegranskat)abstract
- The population-based Kongcun Town Asymptomatic Intracranial Artery Stenosis (KT-aICAS) study aims to investigate the prevalence of aICAS and major cardiovascular risk factors (CRFs) or biomarkers related to the development and prognosis of aICAS.ParticipantsThe KT-aICAS study included 2311 rural residents who were aged ≥40 years and living in Kongcun Town, Shandong Province, China. Baseline examination was conducted from October 2017 to October 2018, during which information on demographics, socioeconomics, personal and family medical history, and lifestyle factors was collected through face-to-face interviews, physical examination and blood tests. aICAS was initially screened using transcranial Doppler examination and then diagnosed using magnetic resonance angiography. Atherosclerosis in carotid arteries was diagnosed via carotid ultrasonography. High-resolution MRI was further used to evaluate the vessel wall of aICAS. Neuropsychological assessments were performed in the participants diagnosed with aICAS and the age-matched and sex-matched controls.Findings to dateOf the 2311 participants, 2027 (87.7%) completed the diagnostic procedure and aICAS was detected in 154 persons, resulting in an overall prevalence of 7.6%. The prevalence of aICAS increased with advancing age from 5.1% in participants aged 40–49 years to 12.7% in those aged ≥70 years (p<0.001). aICAS was detected in 305 intracranial arteries, including 221 (72.5%) in the anterior circulation and 84 (27.5%) in the posterior circulation (p<0.001). In addition, major CRFs were highly prevalent among middle-aged and elderly rural dwellers who were free of clinical stroke.Future plansFollow-up examinations will be performed every 3 years following the baseline examination. This study will increase our knowledge about the natural history of aICAS and facilitate studies of aICAS-associated disorders among rural-dwelling Chinese adults, such as ischaemic stroke and vascular cognitive impairment.Trial registration numberChiCTR1800017197.
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- Xu, L, et al.
(författare)
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Trends in Psychotropic Medication Prescriptions in Urban China From 2013 to 2017: National Population-Based Study
- 2021
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Ingår i: Frontiers in psychiatry. - : Frontiers Media SA. - 1664-0640. ; 12, s. 727453-
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Psychotropic medications are commonly used for treating mental disorders; however, there is currently no study on how commonly they are used in China. This study reported the trends in psychotropic medications prescriptions in urban China.Methods: A national population-based study was conducted using the China Health Insurance Research Association database to estimate the period prescription prevalence of 11 major classes of psychotropic medications annually during 2013–2017. The World Health Organization Anatomical Therapeutic Chemical (ATC) classification codes were used to identify psychotropic medications.Results: The prescription prevalence of any psychotropic medication increased from 8.110% (8.106–8.114%) in 2013 to 11.362% (11.357–11.366%) in 2017. The prescription prevalence of six classes increased significantly during 2013–2017, including sedatives-hypnotics (from 3.177 to 5.388%), anxiolytics (from 1.436 to 2.200%), antiepileptic drugs (from 1.416 to 2.140%), antipsychotics (from 0.809 to 1.156%), antidepressants (from 0.891 to 1.045%), and psycholeptic polypills (from 0.682 to 0.866%). The prescription prevalence of antidementia drugs increased from 0.069 to 0.122%, and mood stabilizers increased from 0.029 to 0.037%, although not statistically significant. The prescription prevalence of nootropic drugs, attention deficit hyperactivity disorder (ADHD) medications and drugs used in the treatment of addictive disorders was largely stable. Psychotropic medication prescription increased with age for all classes except for ADHD medications and mood stabilizers.Conclusion: Increasing trends in prescription prevalence were observed for most classes of psychotropic medications in urban China, although the prevalence was still lower than that in most developed countries. Further research is warranted to explore the potential treatment gap between China and most developed countries.
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- Mei, J, et al.
(författare)
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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
- 2020
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:12, s. e044028-
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
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