Clinical Epidemiology最新文献

Background: The Danish National Patient Registry (DNPR) is a central source of information on hospital contacts for the Danish population and is a key data source for health-related Danish registry studies. The data structure of DNPR was updated from DNPR2 to DNPR3 in 2019, where a key patient-type variable for classification of inpatient, outpatient, or emergency wards was removed. This affects how hospital contacts can be defined and compared across different calendar years.

Aim: To present and compare different algorithms to determine the type of hospital visit (inpatient, outpatient, or emergency) for all hospital visits in Denmark from 2006 to 2021 across DNPR2 and DNPR3.

Methods: The monthly number of hospital visits per 1000 citizens was presented for four different algorithms: 1) a validated approach suggested by Skjøth et al, 2) an approach suggested by the Danish Ministry of Health and Elderly, 3) the latter combined with patient type variables available in DNPR2 only, and 4) a consensus-driven algorithm introduced by Gregersen et al.

Results: Using the same algorithm for DNPR2 and DNPR3 yielded the most similar results across calendar years. The least variation across calendar years was observed for the approach suggested by the Danish Ministry of Health and Elderly, whereas the validated approach suggested by Skjøth et al was more in line with the patient-type variable previously used in DNPR2. When comparing the algorithms, the main difference in the number of hospital visits was observed for inpatient and emergency visits.

Conclusion: We recommend using the same algorithm across DNPR2 and DNPR3. The choice of algorithm should be based on the disease or patient group being studied and by considering how the approaches reflect reality and need in the actual study. We recommend the algorithm suggested by Skjøth et al for the specific clinical situations presented in this study.

Purpose: The g-formula offers a promising approach to analyze long-term dynamic asthma treatment trajectories. This study investigates whether the g-formula can simulate real-world asthma treatment trajectories and predicts subgroup differences in switching behavior.

Patients and methods: This retrospective cohort study identified individuals aged 16- to 45 years who initiated inhaled asthma medication in the Netherlands between 1994 and 2021, from the IADB.nl pharmacy dispensing database. We used the g-formula combined with logistic regression to predict treatment trajectories and their associations with various patient characteristics, such as age, sex, chronic drug treatment for atopic diseases (ATD), cardiovascular diseases (CVD), thyroid diseases, arthritis, diabetes, gastroesophageal reflux disease (GERD), mental health problems (MHP), and immunosuppressants.

Results: The simulations predicted 76% of individuals to switch treatment, on average 2.3 times, with the first switch occurring on average after 8.3 months, which agrees with the real-world observations (77%, 2.3 times and 7.9 months, respectively). Fewer 45-year-olds switched treatment compared to 16-year-olds (74% vs 78%, p < 0.001), but they switched earlier (8.1 vs 8.6 months, p < 0.001) and more frequently (2.4 vs 2.3 times, p < 0.001). Women were more likely to switch compared to men. Patients with ATD, CVD, MHP, or GERD switched significantly less often (p < 0.05).

Conclusion: The g-formula effectively simulates asthma treatment trajectories and found higher age, male sex, ATD, CVD, MHP, and GERD to decrease overall switching behavior. These patients might benefit from earlier intervention or closer monitoring to reduce delays in treatment progression.

Objective: Although the 5-item modified frailty index (mFI-5) has been found to be associated postoperative outcomes, there are limited studies examining its utility in urologic surgery. Our purpose is to evaluate the association between the mFI-5 and postoperative mortality and complications among patients undergoing urologic surgery.

Methods: This retrospective cohort study used the American College of Surgeons National Surgical Quality Improvement Program database from 2015 to 2020. All adult patients who underwent urologic procedures were included. The mFI-5 includes five items: hypertension, diabetes, congestive heart failure, chronic obstructive pulmonary disease, and physical function status. Each item is assigned one point, and an mFI-5 score of 2 or greater indicates frailty. The primary outcome was postoperative mortality, while secondary outcomes were postoperative complications. Propensity score analysis was employed to control for confounders.

Results: After propensity score matching, each group contained 55,322 surgical patients. The patients in the frailty group were at risks of in-hospital mortality (absolute risk increase [ARI] 0.29%) and higher postoperative complications, including acute myocardial infarction (ARI 0.25%), pneumonia (ARI 0.42%), sepsis (ARI 0.41%), and septic shock (0.2%). Compared to the non-frailty group, the length of hospital stay was higher in the frailty group.

Conclusion: Patients with an mFI-5 score of 2 or greater were associated with an increased risk of postoperative mortality and complications, including myocardial infarction, pneumonia, sepsis, and septic shock. The mFI-5 is a simple index that quickly identifies frail patients. This allows for the implementation of prehabilitation and nutritional strategies targeted at enhancing their physiological reserve and optimizing their surgical outcomes.

Background: Basic science evidence reveals interactions between the immune and bone systems. However, population studies linking infectious diseases and musculoskeletal (MSK) disorders are limited and inconsistent. We aimed to examine the risk of six main MSK disorders (osteoarthritis, rheumatoid arthritis, osteoporosis, gout, low back pain, and neck pain) following hospital-treated infections in a large cohort with long follow-up periods.

Methods: We analysed data from 502,409 UK Biobank participants. Participants free of specific MSK disorders at baseline were included in each analysis. Hospital-treated infections before baseline were identified using national inpatient data, while incident MSK outcomes were ascertained from inpatient records, primary care, and death registers. Participants with prior infections were propensity score matched (1:5) with those without. Hazard ratios (HRs) and absolute rate differences (ARDs) with 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. To assess potential reverse causality due to delayed diagnosis of preexisting illness, analyses were repeated excluding MSK disorder cases that occurred within the first 5 and 10 years post-baseline.

Results: A hospital-treated infection was associated with increased risks of all six MSK disorders, with particularly strong associations for osteoporosis (HR, 1.55 [1.48-1.63]; ARD, 1.48 [95% CI 1.29-1.68] per 1000 person-years) and rheumatoid arthritis (HR, 1.53 [1.41-1.65]; ARD, 0.58 [0.46-0.71] per 1000 person-years), while other disorders showed HRs of 1.28-1.32. Bacterial and viral infections showed similar associations, with MSK infections (generally stronger risk) and other locations both linked to increased risks. Associations remained significant even for incident cases that occurred more than 10 years post-baseline.

Conclusion: Hospital-treated infections are associated with long-term MSK disorder risks, regardless of pathogen type or disorder nature (inflammatory or degenerative). Long-term monitoring and care for MSK health in patients with prior hospital-treated infections are recommended.

Purpose: This study applied machine learning (ML) and explainable artificial intelligence (XAI) to predict changes in HbA1c levels, a critical biomarker for monitoring glycemic control, within 12 months of initiating a new antidiabetic drug in patients diagnosed with type 2 diabetes. It also aimed to identify the predictors associated with these changes.

Patients and methods: Electronic health records (EHR) from 10,139 type 2 diabetes patients in North Karelia, Finland, were used to train models integrating randomized controlled trial (RCT)-derived HbA1c change values as predictors, creating offset models that integrate RCT insights with real-world data. Various ML models-including linear regression (LR), multi-layer perceptron (MLP), ridge regression (RR), random forest (RF), and XGBoost (XGB)-were evaluated using R² and RMSE metrics. Baseline models used data at or before drug initiation, while follow-up models included the first post-drug HbA1c measurement, improving performance by incorporating dynamic patient data. Model performance was also compared to expected HbA1c changes from clinical trials.

Results: Results showed that ML models outperform RCT model, while LR, MLP, and RR models had comparable performance, RF and XGB models exhibited overfitting. The follow-up MLP model outperformed the baseline MLP model, with higher R² scores (0.74, 0.65) and lower RMSE values (6.94, 7.62), compared to the baseline model (R²: 0.52, 0.54; RMSE: 9.27, 9.50). Key predictors of HbA1c change included baseline and post-drug initiation HbA1c values, fasting plasma glucose, and HDL cholesterol.

Conclusion: Using EHR and ML models allows for the development of more realistic and individualized predictions of HbA1c changes, accounting for more diverse patient populations and their heterogeneous nature, offering more tailored and effective treatment strategies for managing T2D. The use of XAI provided insights into the influence of specific predictors, enhancing model interpretability and clinical relevance. Future research will explore treatment selection models.

Purpose: Occupational physical activity (OPA) has been linked to adverse pregnancy outcomes, although findings are not consistent. This paper describes the PRECISE Occupational Cohort, designed with the purpose to obtain comprehensive information on OPA with objective measurements and prospective information on pregnancy-related discomforts and sick leave among pregnant employees in Denmark.

Methods: A total of 1556 pregnant participants were included between January 2023 and June 2024 from six obstetric departments in relation to the first trimester ultrasound scan. Information on OPA, pregnancy-related discomforts and sick leave was collected by repeated weekly questionnaires. Additionally, a subgroup of 327 pregnant participants and 90 non-pregnant co-workers were invited for repeated objective measurements, and/or workplace observations. A total of 603 accelerometer measurements from 412 unique participants, and 138 workplace observations were obtained from 102 unique participants. Time spent standing, walking and forward bending was acquired by accelerometers, and information on lifting and person-handlings was quantified by observations. All participants covered 197 occupational codes.

Results: A total of 1008 pregnant participants on average responded to the weekly questionnaires from pregnancy weeks 12-40. High frequencies of pregnancy discomforts were reported throughout pregnancy, and on average only 11% reported no discomforts. Pregnancy-related sick leave increased throughout pregnancy, peaking in pregnancy week 29, where 26% reported at least one day of pregnancy-related sick leave in the past week.

Conclusion: This cohort provides unique repeated measurements with comprehensive information about pregnant employees across many jobs, disclosing high levels of pregnancy discomforts and sick leave throughout pregnancy. The information will enable investigation of the associations of OPA, pregnancy-related discomforts and sick leave on a more detailed level than now. The objective measurements with novel information on OPA will contribute to the development of quantitative Job Exposure Matrices enabling investigation of the association between OPA and adverse pregnancy outcomes in larger populations, with the potential to strengthen preventive guidelines.

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.