Pragmatic and Observational Research最新文献

Background: Although the estimated prevalence of non-alcoholic steatohepatitis (NASH) in Italy is 4-6%, little is known about patient characteristics and care pathways.

Aim: To describe patient characteristics and management approaches for patients with NASH or suspected NASH in Italy.

Methods: Data were drawn from the Adelphi Real World NASH Disease Specific Programme™, a cross-sectional survey of endocrinologists and gastroenterologists in Italy from January to March 2018. Physicians completed questionnaires for their next five consecutively consulting patients with NASH or suspected NASH. Analyses were descriptive.

Results: Seventy-six physicians provided data on 380 patients. The mean age was 58.5 ± 11.1 years and the mean body mass index was 31.8 ± 5.5 kg/m². A total of 231 patients (61%) had no/non-advanced fibrosis as evaluated by liver biopsy or non-invasive tests. Common diagnostic assessments were cholesterol, hemoglobin A1c, absence of viral hepatitis, and alcohol assessment. At diagnosis, 87% (n=322/372) and 45% (n=169/372) of patients received an ultrasound and liver biopsy, respectively. Overall, 88% of patients were referred from primary to secondary care. Obesity (81%) and type 2 diabetes (62%) were the most commonly recorded comorbidities, with 70% of patients having ≥3 comorbidities. Vitamin E (13%) and GLP-1 receptor agonists (13%) were the most prescribed guideline-recommended treatments for all patients.

Conclusion: Patients with NASH in Italy had high levels of obesity and comorbidities, while diagnosis and treatment frequently were not according to guidelines. Our data show an unmet need for more targeted diagnosis and treatment in Italian patients with NASH, in order to optimize outcomes.

Purpose: Observational research provides valuable insights into treatments used in patient populations in real-world settings. However, confounding is likely to occur if there are differences in patient characteristics associated with both the exposure and outcome between the groups being evaluated. One approach to reduce confounding and facilitate unbiased comparisons is inverse probability of treatment weighting (IPTW) using propensity scores. Machine learning (ML) and entropy balancing can potentially be used in generating propensity scores for IPTW, but there is limited literature on this application. We aimed to assess the feasibility of applying these methods for reducing confounding in observational studies. These methods were assessed in a study comparing cardiovascular outcomes in adults with type 2 diabetes and established atherosclerotic cardiovascular disease taking once-weekly glucagon-like peptide-1 receptor agonists or dipeptidyl peptidase-4 inhibitors.

Methods: We applied advanced methods to generate the propensity scores compared to the original logistic regression method in terms of covariate balance. After calculating weights, a weighted Cox proportional hazards model was used to calculate the sample average treatment effect. Support Vector Classification, Support Vector Regression, XGBoost, and LightGBM were the ML models used. Entropy balancing was also performed on features identified in the original cardiovascular outcomes study.

Results: Accuracy (range: 0.71 to 0.73), area under the curve (0.77 to 0.79), precision (0.53 to 0.60), recall (0.66 to 0.68), and F1 score (0.60 to 0.64) were similar between all of the advanced propensity score methods and traditional logistic regression. Among ML models, only XGBoost achieved balance in all measured baseline characteristics between the two treatment groups, closely approximating the performance of the original logistic regression. Entropy balancing weights provided the best performance among all models in balancing baseline characteristics, achieving near perfect balancing.

Conclusion: Among the advanced methods examined, entropy balancing weights performed the best for optimizing balancing and can produce similar results compared to traditional logistic regression.

Purpose: Endodontic inflammation typically results from root canal infections and sensitizations to filling materials after root canal treatment (RCT), potentially leading to systemic inflammation and disease. We therefore aimed to characterize the inflammatory alterations after RCT as well as the inflammatory molecule levels following tooth extraction or renewed RCT.

Patients and methods: All (a total of 2585) walk-in patients with or without RCT history were included in this retrospective study. During the 3-year observation period, blood levels of RANTES/CCL5 (regulated on activation, normal T-cell expressed and secreted/chemotactic cytokine ligand 5), C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukin-10 (IL-10) were measured before and after tooth extraction or renewed RCT. Control patients did not undergo any dental treatment.

Results: A total 49.38% of patients had a history of at least one RCT. In RCT patients, RANTES/CCL5 values were significantly reduced by both tooth extraction (p=0.03) and renewed dental RCT (p=0.038), while a non-significant increase was observed in untreated patients. TNF-α levels were reduced by tooth extraction (p=0.014) but not by renewed RCT and no intervention. CRP levels were not significantly changed by either treatment. Tooth extraction significantly lowered IFN-γ levels (p=0.003), while in control patients, IFN-γ levels did not change. IL-10 levels were non-significantly reduced by tooth extraction (p=0.061). In a subcohort of RCT patients, the lymphocyte transformation test revealed an allergic reaction to at least one of the root filling materials in 39.46% of patients, with raw gutta percha (56%) and eugenol (19%) being frequent triggers.

Conclusion: Here, we demonstrate the involvement of root-treated teeth in inflammatory processes, as tooth extraction and renewed RCT could significantly reduce individual cytokine levels. Our data support the use of biomarkers for in vivo monitoring of treatment success.

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.