Clinical Epidemiology最新文献

Background and aims: Lipid metabolism is altered in systemic sclerosis (SSc), mediating activation of immune cells and fibroblasts. However, it is unclear whether altered lipid profile is associated with a risk of developing SSc. We aimed to assess the association between lipid profile and risk of incident SSc.

Methods: From a Korean nationwide database, individuals without SSc who underwent national health check-ups in 2009 were selected and followed-up through 2019. Serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride were measured on the health check-up date in 2009. Individuals who developed SSc during follow-up were identified. Multivariable Cox models were performed to estimate the risk of incident SSc according to TC, HDL-C, LDL-C, and triglyceride levels, respectively.

Results: Of the 9,894,996 individuals selected, 1355 individuals developed SSc during a mean follow-up of 9.2 years (incidence rate=1.49 per 100,000 person-years). Levels of TC (adjusted hazard ratio [aHR] 0.959, 95% confidence interval [CI] 0.945-0.974), HDL-C (aHR 0.968, 95% CI 0.950-0.987), LDL-C (aHR 0.968, 95% CI 0.952-0.983) were inversely associated with the risk of incident SSc, whereas no significant association was observed between levels of triglyceride (aHR 1.004, 95% CI 0.998-1.011) and risk of incident SSc.

Conclusion: Serum levels of TC, HDL-C, and LDL-C were inversely associated with the risk of incident SSc. Our findings provide new insights that altered lipid profile could be considered a non-causal biomarker associated with incident SSc, which could help early diagnosis. The underlying mechanism for this association needs further studies.

Objective: Numerous pharmacological interventions are now under investigation for the treatment of the 2019 coronavirus pandemic (COVID-19), and the evidence is rapidly evolving. Our aim is to evaluate the comparative efficacy and safety of these drugs.

Methods: We searched for randomized clinical trials (RCTs) on the efficacy and safety of novel oral antivirals for the treatment of hospitalized COVID-19 patients until November 30, 2022, including baricitinib, ivermectin (IVM), favipiravir (FVP), chloroquine (CQ), lopinavir and ritonavir (LPV/RTV), hydroxychloroquine (HCQ), and hydroxychloroquine plus azithromycin (HCQ+AZT). The main outcomes of this network meta-analysis (NMA) were in-hospital mortality, adverse event (AE), recovery time, and improvement in peripheral capillary oxygen saturation (SpO2). For dichotomous results, the odds ratio (OR) was used, and the 95% confidence interval (CI) was determined. We also used meta-regression to explore whether different treatments affected efficacy and safety. STATA 15.0 was used to conduct the NMA. The research protocol was registered with PROSPERO (#CRD 42023415743).

Results: Thirty-six RCTs, with 33,555 hospitalized COVID-19 patients, were included in this analysis. First, we compared the efficacy of different novel oral antivirals. Baricitinib (OR 0.56, 95% CI: 0.35 to 0.90) showed the highest probability of being the optimal probiotic species in reducing in-hospital mortality and suggested that none of the interventions reduced AE better than placebo. In terms of safety outcomes, IVM ranked first in improving the recovery time of hospitalized COVID-19 patients (mean difference (MD) -1.36, 95% CI: -2.32 to -0.39). In addition, patients were most likely to increase SpO2 (OR 1.77, 95% CI: 0.09 to 3.45). The meta-regression revealed no significant differences between participants using different novel oral antivirals in all outcomes in hospitalized COVID-19 patients.

Conclusion: Currently, baricitinib has reduced in-hospital mortality in hospitalized COVID-19 patients, with moderate certainty of evidence. IVM appeared to be a safer option than placebo in improving recovery time, while FVP was associated with increased SpO2 safety outcomes. These preliminary evidence-based observations should guide clinical practice until more data are made public.

Purpose: Distinguishing ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) is crucial in acute myocardial infarction (AMI) research due to their distinct characteristics. However, the accuracy of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for STEMI and NSTEMI in Taiwan's National Health Insurance (NHI) database remains unvalidated. Therefore, we developed and validated case definition algorithms for STEMI and NSTEMI using ICD-10-CM and NHI billing codes.

Patients and methods: We obtained claims data and medical records of inpatient visits from 2016 to 2021 from the hospital's research-based database. Potential STEMI and NSTEMI cases were identified using diagnostic codes, keywords, and procedure codes associated with AMI. Chart reviews were then conducted to confirm the cases. The performance of the developed algorithms for STEMI and NSTEMI was assessed and subsequently externally validated.

Results: The algorithm that defined STEMI as any STEMI ICD code in the first three diagnosis fields had the highest performance, with a sensitivity of 93.6% (95% confidence interval [CI], 91.7-95.2%), a positive predictive value (PPV) of 89.4% (95% CI, 87.1-91.4%), and a kappa of 0.914 (95% CI, 0.900-0.928). The algorithm that used the NSTEMI ICD code listed in any diagnosis field performed best in identifying NSTEMI, with a sensitivity of 82.6% (95% CI, 80.7-84.4%), a PPV of 96.5% (95% CI, 95.4-97.4), and a kappa of 0.889 (95% CI, 0.878-0.901). The algorithm that included either STEMI or NSTEMI ICD codes listed in any diagnosis field showed excellent performance in defining AMI, with a sensitivity of 89.4% (95% CI, 88.2-90.6%), a PPV of 95.6% (95% CI, 94.7-96.4%), and a kappa of 0.923 (95% CI, 0.915-0.931). External validation confirmed these algorithms' efficacy.

Conclusion: Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.

Background: It is unclear whether colorectal cancer screening history, regardless of stage, is an independent predictor of survival, and if the screening advantage persists after diagnosis. 32 099 patients with colorectal cancer were enrolled in this population-based cohort study.

Methods: We used data from the Taiwan Cancer Registry on patients with a first-time diagnosis of colorectal cancer between 2013 and 2015. In addition, we utilized data from a nationwide database of colorectal cancer screening programs to evaluate patients' screening histories, and sourced outcome data from the National Death Registry, tracking patients up to the last day of 2019.

Results: Compared with fecal immunochemical testing (FIT)-positive patients with a follow-up examination, the adjusted hazard ratios (95% confidence intervals) for death from colorectal cancer were 1.40 (1.26-1.56) for FIT-positive patients without a follow-up examination, 1.63 (1.48-1.78) for FIT-negative patients, and 1.76 (1.65-1.89) for never screened patients. The adjusted hazard ratios for the FIT-positive patients with a follow-up examination increased when diagnosis was delayed by more than 12 months and were 1.2 after a 2-year delay. The adjusted hazard ratios for FIT-negative patients were approximately 2.0, decreased rapidly to 1.6, and stabilized after the 9th time-to-diagnosis month.

Conclusion: In colorectal cancer patients, screening history prior to diagnosis is an independent prognostic factor, regardless of cancer stage or other variables. This study recommends that physicians take screening history into account during diagnosis to optimize follow-up and management for patients at higher risk.

Purpose: To evaluate how comprehensively wrist fractures can be tracked from the national medical registers, and to propose a method for complementing the register data using time stamps of wrist radiography visits recorded in the radiological image archive.

Patients and methods: For the Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE) cohort of 14220 post-menopausal women, we analysed the data from the Care Register for Health Care, Register for Primary Health Care Visits, self-reports, radiological image archive PACS, and patient records to identify the wrist fractures occurred between 2011 and 2021. Using this gold standard of fractures, we validated the coverage of the registers and image archive and created algorithms to automatically identify fracture events from the registers and/or metadata of wrist radiography visits.

Results: We show that wrist fractures cannot be comprehensively identified based on national registers. To remedy this, our proposed method of combining register and image archive data can lift the coverage from 81% to 94% and reduce false discoveries from 6% to 2%.

Conclusion: The proposed method offers a more reliable way of gathering fracture information. Comprehensive fracture identification is essential in many research settings, such as incidence statistics, prevention studies, and risk assessment models.

Purpose: Linked health-care registries and high coverage in Nordic countries lend themselves well to epidemiologic research. Given its relatively high incidence in Western Europe, complexity in diagnosis, and challenges in registration, multiple myeloma (MM) was selected to compare registries in Denmark, Finland, and Sweden.

Patients and methods: Data were obtained from four archetypal registries in each country (spanning January 2005-October 2018): National Patient Registry (NPR), Prescribed Drug Registry (PDR), Cancer Registry (CR), and Cause of Death Registry. Patients newly diagnosed with MM who received MM-specific treatment were included. PDR/NPR treatment records were used to assess incident NPR cases. The registration quality of MM-specific drugs in the PDR of each country was also evaluated.

Results: In Denmark, only 6% of patients in the NPR were not registered in the CR; in Sweden, it was 16.9%. No systematic differences were identified that could explain this discrepancy. In Denmark, lenalidomide and bortezomib were registered in the NPR with high coverage, but less expensive drugs typically given in combination with bortezomib were not covered in any of the registries. In Finland and Sweden, bortezomib records were not identified in the PDR, but some were in the NPR; other drugs had good coverage in the PDR.

Conclusions: The registries evaluated in this study can be used to identify the MM population; however, given the gaps in MM registration in the Finnish and Swedish CRs, Danish registries provide the most comprehensive datasets for research on treatment patterns for MM.

Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.

Patients and methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.

Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.

Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.