Clinical Epidemiology最新文献

Purpose: The study aims to explore the association between patients diagnosed with inflammatory bowel disease (IBD) in childhood or youth and mental health disorders.

Methods: The study is a register-based cohort study of patients with IBD-onset before 25 years of age and matched references. They were followed until 30 years of age. The incidence rate and incidence rate ratio (IRR) for a wide spectrum of mental health disorders were assessed based on diagnostic codes from the Danish National Patient Registry, reimbursed prescriptions for psychotropic medications, and composite measures combining diagnosis and medication. Furthermore, the relative excess risk due to interaction (RERI) for parental educational level and parental mental health disorders were estimated.

Results: A total of 4904 patients with Crohn's disease (CD), 5794 with ulcerative colitis (UC), and 94,802 matched references were identified. Patients with CD-onset before age 18 had a higher risk of anxiety disorders (IRR 1.58 (CI95%: 1.33-1.86)), while patients with CD-onset between age 18 to 24 had a higher risk of both anxiety and mood disorders. Patients with UC-onset before age 18 had a higher risk of anxiety disorders (IRR: 1.39 (CI95%: 1.19-1.64)). In general, patients with IBD had a higher risk of receiving psychotropic medication. Parental education had a subadditive interaction with the risk of emotional disorders for both patients with CD and UC, while maternal mental health disorders had a subadditive interaction for patients with UC.

Conclusion: Patients with CD and UC have a higher risk of mental health disorders, primarily due to an elevated risk of emotional disorders and a higher use of psychotropic medication. Surprisingly, the study demonstrated subadditive effect of parental education and for patients with UC maternal mental health disorders on the risk of emotional disorders.

Background: Psychiatric inpatients face an increased risk of deep vein thrombosis (DVT) due to their psychiatric conditions and pharmacological treatments. However, research focusing on this population remains limited.

Methods: This study analyzed 17,434 psychiatric inpatients at Huzhou Third Municipal Hospital, incorporating data on demographics, psychiatric diagnoses, physical illnesses, laboratory results, and medication use. Predictive models for DVT were developed using logistic regression, random forest, support vector machine (SVM), and XGBoost (Extreme Gradient Boosting). Feature importance was assessed using the random forest model.

Results: The DVT incidence among psychiatric inpatients was 1.6%. Predictive model performance, measured by the area under the curve (AUC), showed logistic regression (0.900), random forest (0.885), SVM (0.890), and XGBoost (0.889) performed well. Logistic regression and random forest models exhibited optimal overall performance, while XGBoost excelled in recall. Significant predictors of DVT included elevated D-dimer levels, age, Alzheimer's disease, and Madopar use.

Conclusion: Psychiatric inpatients require vigilance for DVT risk, with factors like D-dimer levels and age serving as critical indicators. Machine learning models effectively predict DVT risk, enabling early detection and personalized prevention strategies in clinical practice.

Purpose: Infection in general is a frequent and serious complication after hip fracture (HF) surgery. Multimorbidity in HF patients is associated with elevated infection risk. It remains unclear whether multimorbidity interacts with HF surgery to increase infection risk beyond their individual effects.

Methods: Using Danish registries, we identified 92,599 patients ≥65 years surgically treated for HF 2004 to 2018 and an age- and sex-matched comparison cohort from the background population without HF (n=462,993). Multimorbidity was defined using the Charlson Comorbidity Index in categories no, moderate, or severe. We computed incidence rates (IR) of any kind of hospital-treated infection within 1 month and 1 year with 95% confidence intervals and estimated the attributable proportion (in %) based on differences in IRs.

Results: The IR of infection within 1 month was 181 (176-186) per 100 person years in HF patients with no multimorbidity and 9 (95% CI 8-9) in the comparison cohort with no multimorbidity. The IRs were 240 (234-246) and 302 (291-313) in HF patients with moderate and severe multimorbidity compared with 17 (16-18) and 31 (30-33) in the comparison cohort with same multimorbidity level. The attributable proportion indicates that 21% and 33% of the IR among HF patients with moderate and severe multimorbidity, respectively, was explained by interaction. Similar interactions were observed within 1 year.

Conclusion: Multimorbidity and HF surgery interact synergistically, which substantially increases the infection risk. The interaction effect increased with multimorbidity level. Our findings highlight the potential benefits of implementing more targeted and personalized preventive initiatives for multimorbid patients.

Purpose: The Danish National Patient Register (DNPR) is an important data source for research providing detailed information on all hospital contacts in Denmark. With the transition from the second version of the DNPR (DNPR2) to the third version (DNPR3) in early 2019, the patient type variable (inpatient, elective outpatient, acute outpatient) was removed. This study proposes and evaluates algorithms to classify hospital contacts into these categories in DNPR3, aiming for consensus in data interpretation for researchers using Danish registries.

Patients and methods: We analyzed somatic public hospital contacts in Denmark from 2017 to 2020, with 20,882,018 unique contacts in DNPR2 and 27,694,584 in DNPR3. Several classification algorithms were developed and assessed, including department-based, contact-based, and hybrid methods, to infer patient types in DNPR3 based on contact features, such as duration and admission type. In DNPR3, where the true patient type is unknown, proxy labels were used to train classification algorithms.

Results: Compared to the true patient type variable in DNPR2, our department-based classifier showed high positive predictive values (PPVs) and sensitivities in DNPR2 with PPVs ranging from 95.6 to 99.5 and sensitivities ranging from 94.1 to 99.6 across patient types. The hybrid approach showed improved PPVs and sensitivities for acute (PPV = 97.3, sensitivity = 96.8) and elective (PPV = 99.8, sensitivity = 99.9) outpatients. In both DNPR2 and DNPR3 high agreement between contact-based classification algorithms was obtained indicating robustness in our classification methods which suggests the presence of inherent patterns in the data.

Conclusion: Our study shows that all presented classification methods are suitable for categorizing patient types in DNPR2 depending on the available data and furthermore demonstrated robustness, supporting their suitability for classification in DNPR3. Future research should explore advanced techniques and comprehensive department classification for enhanced accuracy and applicability.

Background: A systematic relational assessment of the global, regional, and national Ischemic heart disease (IHD) burden and its attributable risk factors is essential for developing more targeted prevention and intervention strategies.

Methods: The GBD 2021 comparative risk assessment framework was employed to evaluate stroke burden attributable to environmental, behavioral, metabolic, and dietary risk factors, and a total of 25 risk factors were included. Specifically, we used the joinpoint regression model, decomposition analysis, and systematic fixed-effects analysis to reveal the global, regional, and national burden of IHD attributable to these 25 risk factors and their exposure value across 204 countries and territories with different socio-demographic index (SDI) levels from different perspectives.

Results: Joinpoint regression revealed similar trends in summary exposure value (SEV) and attributable burdens for 25 IHD risk factors. From 1990 to 2021, SEV rankings increased for 12/25 risk factors, decreased for 10/25, and remained unchanged for 3/25. Decomposition analysis indicated that from 1990 to 2021, low SDI countries experienced the most significant increase in IHD burden attributable to 25 risk factors due to population growth, while upper-middle and high SDI countries were most affected by population aging, and high SDI countries demonstrated the greatest reduction in IHD burden attributed to epidemiological changes. Panel data analysis elucidated the impact of SEV, SDI, and quality-of-care index (QCI) on attributable IHD burden.

Conclusion: This study emphasizing the critical role of risk factor control. Tailored interventions and exploration of country-specific factors are crucial for effectively reducing the global IHD burden.

Background: Lymphoid-lineage cancers (LC; International Classification of Diseases, 10^th edition [ICD10] C81.x-C90.x, C91.1-C91.9, C95.1, C95.7, C95.9, D47.2, D47.9B, and E85.8A) share many epidemiological and clinical features, which favor meta-learning when developing medical artificial intelligence (mAI). However, access to large, shared datasets is largely missing and limits mAI research.

Aim: Creating a large-scale data repository for patients with LC to develop data-driven hematology.

Methods: We gathered electronic health data and created open-source processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research.

Results: We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. Among other, data include treatments (ie 21,750 cytoreductive treatment plans, 21.3M outpatient prescriptions, and 12.7M in-hospital administrations), biochemical analyses (77.3M), comorbidity (14.8M ICD10 codes), pathology codes (4.5M), treatment procedures (8.3M), surgical procedures (1.0M), radiological examinations (3.3M), vital signs (18.3M values), and survival data. We herein describe the data infrastructure and exemplify how DALY-CARE has been used for molecular studies, real-world evidence to evaluate the efficacy of care, and mAI deployed directly into EHR systems.

Conclusion: The DALY-CARE data resource allows for the development of near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC while facilitating streamlining of health data infrastructure across cohorts and medical specialties.

Purpose: This study aims to investigate the potential association between non-selective RET kinase inhibitors and thyroid dysfunction (TD) by conducting a pharmacovigilance analysis using data from the US FDA Adverse Event Reporting System (FAERS).

Methods: Data for non-selective RET MKIs were obtained from the FAERS database, spanning the first quarter of 2015 to the fourth quarter of 2023. Disproportionality analysis was used to quantify the AE signals associated with non-selective RET MKIs and to identify TD AEs. Subgroup analyses and multivariate logistic regressions were used to assess the factors influencing the occurrence of TD AEs. Time-to-onset (TTO) analysis and the Weibull Shape Parameter (WSP) test were also performed.

Results: Descriptive analysis revealed an increasing trend in TD adverse events linked to non-selective RET MKIs, with a notable proportion of serious reactions reported. Disproportionality analysis using ROR, PRR, BCPNN, and EBGM algorithms consistently demonstrated a positive association between Sunitinib, Cabozantinib, and Lenvatinib with TD adverse events. Subgroup analyses highlighted differential susceptibility to TD based on age, gender, and weight, with varying patterns observed for each inhibitor. Logistic regression analyses identified factors independently influencing the occurrence of TD adverse events, emphasizing the importance of age, gender, and weight in patient stratification. Time-to-onset analysis indicated early manifestation of TD adverse events following treatment with non-selective RET MKIs, with a decreasing risk over time.

Conclusion: The results of our study indicate a correlation between the use of non-selective RET MKIs and the occurrence of TD AEs. This may provide support for the clinical monitoring and risk identification of non-selective RET MKIs. Nevertheless, further clinical studies are required to substantiate the findings of this study.

Purpose: The present study aims to evaluate the changes in healthcare utilization following population-based screening for diabetes mellitus (DM) using point-of-care HbA1c measurement in the Viborg Screening Program (VISP) cohort, which invites all 67-year-olds in Viborg, Denmark, for cardiovascular disease (CVD) and DM screening.

Patients and methods: We conducted a cohort study using data from VISP and Danish national health registers. The study included 2386 individuals invited to VISP from August 1, 2014, to May 31, 2017. Exclusion criteria were non-attenders, those with prior DM, and those with missing HbA1c measurements. Pre- and post-screening healthcare utilization was analyzed, stratified by HbA1c levels: <42 mmol/mol (normal), 42-48 mmol/mol (pre-DM), and ≥48 mmol/mol (DM). Statistical analyses were performed using Poisson and logistic regression models to compare ratios of healthcare utilization before and after screening.

Results: Of the participants, 16.5% had pre-DM, and 3.4% had DM. Screening resulted in increased general physician contacts across all HbA1c groups, the highest increase was seen in the DM group with a pre- vs post-screening odds ratio [OR] of 3.25 (95% CI: 1.06-9.95) and a relative odds ratio [ROR] of 2.70 (0.87-8.39). Also, in this group, the OR for having ≥1 HbA1c measurement one year pre- vs post-screening was 5.56 (2.77 -11.14) and 26.8% (17.6-37.9) started glucose-lowering treatment within two years post-screening. Despite expectations, healthcare utilization did not decrease among those with normal HbA1c levels.

Conclusion: Population-based screening for DM and CVD among 67-year-olds resulted in increased healthcare utilization, particularly among those with screen-detected DM and pre-DM. The anticipated reduction in healthcare utilization among individuals with normal HbA1c levels was not observed. These findings highlight the potential for screening to enhance disease management and underscore the need for strategies to optimize healthcare resource use following screening, especially for individuals without DM.

Trial registration: NCT03395509.