Pragmatic and Observational Research最新文献

Background: Routinely collected electronic healthcare records (EHRs) document many details of a person's health, including demographics, preventive services, symptoms, tests, disease diagnoses and prescriptions. Although not collected for research purposes, these data provide a wealth of information which can be incorporated into epidemiological investigations, and records can be analysed to understand a range of important health questions. We aimed to understand the use of routinely collected health data in epidemiological studies relating to three of the most common chronic respiratory conditions, namely: asthma, chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD). We also characterised studies using EHR data to investigate respiratory diseases more generally, relative to cardiovascular disease and COVID-19, to understand trends in the use of these data.

Methods: We conducted a search of the Scopus database, to identify original research articles (irrespective of date) which used data from one of the following most frequently used UK EHR databases: Clinical Practice Research Datalink (including General Practice Research Database (CPRD's predecessor)), The Health Improvement Network and QResearch, defined through the presence of keywords. These databases were selected as they had been previously included in the works of Vezyridis and Timmons.

Findings: A total of 716 manuscripts were included in the analysis of the three chronic respiratory conditions. The majority investigated either asthma or COPD, whilst only 28 manuscripts investigated ILD. The number of publications has increased for respiratory conditions over the past 10 years (888% increase from 2000 to 2022) but not as much as for cardiovascular diseases (1105%). These data have been used to investigate comorbidities, off-target effects of medication, as well as assessing disease incidence and prevalence. Most papers published across all three domains were in journals with an impact factor less than 10.

Purpose: Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes.

Patients and methods: In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records. They were categorized into success (n=479) and failure (n=391) groups based on average blood pressure control efficacy. Predictive models were constructed using six machine learning algorithms. Evaluation metrics encompassed the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis assessed feature contributions to efficacy.

Results: All six machine learning models demonstrated superior predictive performance. Following variable reduction, the model predicting amlodipine efficacy was reconstructed using these algorithms, with the light gradient boosting machine (LightGBM) model achieving the highest overall performance (AUC = 0.803). Notably, amlodipine showed enhanced efficacy in patients with low platelet distribution width (PDW) values, as well as high hematocrit (HCT) and thrombin time (TT) values.

Conclusion: This study utilized machine learning to predict amlodipine's effectiveness in hypertension treatment, pinpointing key factors: HCT, PDW, and TT levels. Lower PDW, along with higher HCT and TT, correlated with enhanced treatment outcomes. This facilitates personalized treatment, particularly for hospitalized hypertensive patients undergoing amlodipine monotherapy.

Obesity is currently considered a global epidemic, with rising prevalence worldwide and rather pessimistic projections. Based on its close interconnection with various co-morbidities, such as diabetes mellitus and cardiovascular disease, obesity is associated with significant increases in morbidity and mortality, while it also poses a substantial economic burden for national healthcare systems. Apparently, the majority of individuals classified as obese do not achieve adequate weight loss with the adoption of a healthy lifestyle intervention, including dietary modification and physical activity. Fortunately, during the last decade, a significant progress in pharmacotherapy of obesity has been observed, with the introduction of agents that have gained approval from regulatory authorities, namely semaglutide, liraglutide and tirzepatide, due to their impressive results in body weight reduction, alongside their beneficial, pleiotropic effects. The aim of the present review article is to discuss on evidence retrieved from real-world studies regarding the efficacy of those agents in obesity treatment, with emphasis on cost-effectiveness data, towards an effort to tackle efficiently the progression of obesity epidemic.

Purpose: Facial basal cell carcinoma (BCC) poses significant challenges due to its potential for local destruction and impact on quality of life (QoL). Continuous research is necessary to identify novel factors influencing the quality of life within this demographic across diverse cultural settings. The aims of this study were to translate, culturally adapt, and validate the Lithuanian version of Skin Cancer Index, subsequently utilizing this questionnaire in the pilot phase of the study to achieve the following: (1) identify the differences in short- and long-term QoL, (2) establish empirical correlations between SCI scores and aesthetic facial regions, evaluate the potential differences between age, gender, and tumor size groups.

Patients and methods: A prospective longitudinal study was conducted with 100 consecutive patients. The SCI was translated into Lithuanian language, with a rigorous assessment of its psychometric properties to confirm validity. Alongside hypothesis testing, a detailed analysis of variables was conducted. Statistical techniques, including t-tests and ANOVA, were employed to compare scores across demographic and clinical groups, with effect size calculations for further interpretation.

Results: Our findings demonstrate that the Lithuanian SCI successfully fulfills the criteria established by the COSMIN checklist. Surgical treatment for facial BCC notably enhances QoL, particularly evident six months post-surgery. Analysis of SCI scores identified demographic and clinical factors associated with lower QoL, including female gender, treatment with skin plasty, and tumor sites in aesthetically sensitive areas like the cheek, nose, and eyelid.

Conclusion: The Lithuanian version of the SCI is a reliable and valid tool for assessing QoL in facial BCC patients. Our findings underscore the global relevance of understanding the multifactorial influences on QoL in BCC patients. Early diagnosis, less invasive treatment approaches, and tailored post-operative care are crucial in minimizing the psychological, social, and appearance-related burdens of facial BCC.

Background: In total joint replacement procedures, surgeons have increasingly adopted advanced multi-layer, watertight closure. The objective of the study was to compare the clinical and economic outcomes for advanced multi-layer, watertight closure patients to those with conventional closure with sutures and skin staples.

Methods: Patients aged ≥18 years were included in the study if they underwent total joint arthroplasty of the hip or knee as an elective, primary, inpatient procedure between January 2014 and March 2019. Cohorts having advanced multi-layer, watertight closure or conventional closure were compared using multivariable regression analysis of surgical site infections, length of stay, operating room time, procedure time, discharge status, readmissions, reoperations, and hospital emergency department visits.

Results: A total of 1828 patients received at least one total hip or knee replacement, of which 434 (23.7%) had advanced multi-layer, watertight closure and 1394 (76.3%) had conventional closure. Unadjusted time to readmission, when occurring, was considerably longer following advanced multi-layer, watertight closure (89.9 vs 51.1 days, p < 0.0001), and a lower proportion of the advanced multi-layer, watertight closure cohort required reoperation within 90 days (0.0% vs 2.6%, p < 0.0001). Adjusted mean hospital length of stay was approximately half of a day shorter for advanced multi-layer, watertight closure patients (1.10 vs 1.65 days; p < 0.001), and they were also more likely to be discharged to home (Odds Ratio: 4.61; p = 0.002).

Conclusion: Among patients undergoing total hip and knee arthroplasty in a highly optimized real-world clinical practice, advanced multi-layer, watertight closure was associated with significantly shorter inpatient length of stay and increased likelihood of being discharged to home compared with conventional closure. These findings suggest that advanced multi-layer, watertight closure is a valuable component of an optimal workflow for total hip or knee replacement, and may be especially valuable for high-risk patients.

Lenvatinib received its initial approval in 2018 for the treatment of advanced hepatocellular carcinoma. It has since emerged as the preferred first line agent, supported by non-inferiority data from the REFLECT trial. Notably, lenvatinib exhibits a more favorable toxicity profile and a higher response rate compared to sorafenib. Despite the approval of immunotherapy in 2020, specifically the combination of atezolizumab and bevacizumab following the IMbrave150 trial, tyrosine kinase inhibitors remain an indispensable class of agents in the landscape of hepatocellular carcinoma treatment. This comprehensive review delves into various facets of lenvatinib utilization in hepatocellular carcinoma, shedding light on real-world data, addressing challenges, and providing insights into strategies to overcome these obstacles.

Background: Lack of body mass index (BMI) measurements limits the utility of claims data for bariatric surgery research, but pre-operative BMI may be imputed due to existence of weight-related diagnosis codes and BMI-related reimbursement requirements. We used a machine learning pipeline to create a claims-based scoring system to predict pre-operative BMI, as documented in the electronic health record (EHR), among patients undergoing a new bariatric surgery.

Methods: Using the Optum Labs Data Warehouse, containing linked de-identified claims and EHR data for commercial or Medicare Advantage enrollees, we identified adults undergoing a new bariatric surgery between January 2011 and June 2018 with a BMI measurement in linked EHR data ≤30 days before the index surgery (n=3226). We constructed predictors from claims data and applied a machine learning pipeline to create a scoring system for pre-operative BMI, the B3S3. We evaluated the B3S3 and a simple linear regression model (benchmark) in test patients whose index surgery occurred concurrent (2011-2017) or prospective (2018) to the training data.

Results: The machine learning pipeline yielded a final scoring system that included weight-related diagnosis codes, age, and number of days hospitalized and distinct drugs dispensed in the past 6 months. In concurrent test data, the B3S3 had excellent performance (R² 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R² 0.750, 95% CI 0.686-0.799) and calibration but both aspects were inferior to the B3S3. Findings in prospective test data were similar.

Conclusion: The B3S3 is an accessible tool that researchers can use with claims data to obtain granular and accurate predicted values of pre-operative BMI, which may enhance confounding control and investigation of effect modification by baseline obesity levels in bariatric surgery studies utilizing claims data.

Purpose: Associations between systemic glucocorticoid (SGC) exposure and risk for adverse outcomes have spurred a move toward steroid-sparing treatment strategies. Real-world changes in SGC exposure over time, after the introduction of steroid-sparing treatment strategies, reveal areas of successful risk mitigation as well as unmet needs.

Patients and methods: A population-based ecological study was performed from the Optimum Patient Care Research Database to describe SGC prescribing trends of steroid-sparing treatment strategies in primary care practices before and after licensure of biologics in the United Kingdom from 1990 to 2019. Each analysis year included patients aged ≥5 years who were registered for ≥1 year with a participating primary care practice. The primary analysis was SGC exposure, defined as total cumulative SGC dose per patient per year, for asthma, severe asthma, chronic obstructive pulmonary disease (COPD), nasal polyps, Crohn's disease, rheumatoid arthritis, ulcerative colitis, and systemic lupus erythematosus. Secondary outcomes were percentages of patients prescribed SGCs and number of SGC prescriptions per patient per year.

Results: The number of patients who met study inclusion criteria ranged from 219,862 (1990) to 1,261,550 (2019). At the population level, patients with asthma or COPD accounted for 67.7% to 73.2% of patients per year with an SGC prescription. Over three decades, decreases in SGC total yearly dose ≥1000 mg have been achieved in multiple conditions. Patients with COPD prescribed SGCs increased from 5.8% (1990) to 34.8% (2017). SGC prescribing trends for severe asthma, Crohn's disease, and ulcerative colitis show decreased prescribing trends after the introduction of biologics.

Conclusion: Decreases in total yearly SGC doses have been shown in multiple conditions; however, for conditions such as severe asthma and COPD, an unmet need remains for increased awareness of SGC burden and the adoption or development of SGC-sparing alternatives to reduce overuse.