Pragmatic and Observational Research最新文献

Background: Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems.

Methods: We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events.

Results: We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease.

Conclusion: Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related mortality worldwide and accounts for 90% of all primary liver cancers. Chronic inflammation is the hallmark across most prevalent etiologies among which HBV is the leading cause worldwide (33%), followed by alcohol (30%), HCV (21%), other factors like non-alcoholic steatohepatitis linked to insulin resistance/metabolic syndrome, and obesity associated inflammation (16%). Deregulation of the tightly controlled immunological network leads to liver disease, including chronic infection, autoimmunity, and tumor development. While inflammation drives oncogenesis in the liver, HCC also recruits ICOS+ FOXP3+ Tregs and MDSCs and upregulates immune checkpoints to induce a state of immunosuppression in the tumor microenvironment. As such, research is focused on targeting and modulating the immune system to treat HCC. The Checkmate 040 and Keynote 224 studies established the role of immunotherapy in the treatment of patients with HCC. In Phase I and II trials, nivolumab and pembrolizumab demonstrated durable response rates of 15-20% and were subsequently approved as second-line agents after sorafenib. Due to the success of the IMbrave 150 and HIMALAYA trials, which examined the combination of atezolizumab/bevacizumab and tremelimumab/durvalumab, respectively, the FDA approved these regimens as first-time treatment options for patients with advanced HCC. The encouraging results of immunotherapy in the management of HCC has led researchers to evaluate if combination with locoregional therapies may result in a synergistic effect. Real-world studies represent an invaluable tool to assess and verify the applicability of clinical trials in the bedside setting with a more varied patient population. We herein review current real-life use of ICIs in the management of HCC and highlight some of the ongoing clinical trials that are expected to change current recommended first-line treatment in the near future.

Background: The current flows of the SSN represent the set of interest whose interconnection alone justifies the current study. These flows can be interconnected with other sources, institutional or otherwise, in order to answer well-defined questions.

Objective: The objective of the study is to verify, through the analysis of administrative databases, any differences in the consumption of health resources between biological off-patent originator drugs and biosimilars in real clinical practice, with particular reference to the rheumatology area.

Methods: Through the use of assisted databases (BDA) of ATS Pavia we evaluated the differences in terms of consumption of health resources related to the different drugs under analysis. Annual and daily costs were calculated by total patient cost, stratified for different treatments, considering the sum of total costs for the prescriptions of drugs subject to the analysis. Another objective was to evaluate the adherence of the drugs of interest, by utilizing specific indicators (MPR).

Results: A total of 145 patients were analyzed. Among enrolled patients, 26.9% of users were treated with a biosimilar drug, while 73.1% with a biologic originator. Adherence is higher if it is considered the population treated with biosimilar drugs (82.1%). Total cost (including drug prescriptions, hospitalizations, outpatient services, tests for any cause) during the observation period of 1 year is 14,274.08. 87.7% of the total is attributable to drugs. Non-hospitalized patients are the least expensive, whether they were treated with biologics or biosimilars.

Conclusion: In our sample, biosimilar drugs tend to be underused: the treatment of a patient with a chronic autoimmune disease is a clinical process that involves many health professionals, and a criticality could also derive from the difficult communication between the various professional figures who get involved with the whole patient treatment.

Objective: Stratifying disease severity in patients with non-alcoholic steatohepatitis (NASH) is essential for appropriate treatment and long-term management. Liver biopsy is the reference standard for fibrosis severity in NASH, but less invasive methods are used, eg, Fibrosis-4 Index (FIB-4) and vibration-controlled transient elastography (VCTE), for which reference thresholds for no/early fibrosis and advanced fibrosis are available. We compared subjective physician assessment of NASH fibrosis versus reference thresholds to understand classification in a real-world setting.

Methods: Data were drawn from Adelphi Real World NASH Disease Specific Programme^TM conducted in France, Germany, Italy, Spain and UK in 2018. Physicians (diabetologists, gastroenterologists, hepatologists) completed questionnaires for five consecutive NASH patients presenting for routine care. Physician-stated fibrosis score (PSFS) based on available information was compared with clinically defined reference fibrosis stage (CRFS) determined retrospectively using VCTE and FIB-4 data and eight reference thresholds.

Results: One thousand two hundred and eleven patients had VCTE (n = 1115) and/or FIB-4 (n = 524). Depending on thresholds, physicians underestimated severity in 16-33% (FIB-4) and 27-50% of patients (VCTE). Using VCTE ≥12.2, diabetologists, gastroenterologists and hepatologists underestimated disease severity in 35%, 32%, and 27% of patients, respectively, and overestimated fibrosis in 3%, 4%, and 9%, respectively (p = 0.0083 across specialties). Hepatologists and gastroenterologists had higher liver biopsy rates than diabetologists (52%, 56%, 47%, respectively).

Conclusion: PSFS did not consistently align with CRFS in this NASH real-world setting. Underestimation was more common than overestimation, potentially leading to undertreatment of patients with advanced fibrosis. More guidance on interpreting test results when classifying fibrosis is needed, thereby improving management of NASH.

There is a growing interest in real world evidence when developing antineoplastic drugs owing to the shorter length of time and low costs compared to randomised controlled trials. External validity of studies in the regulatory phase can be enhanced by complementing randomised controlled trials with real world evidence. Furthermore, the use of real world evidence ensures the inclusion of patients often excluded from randomised controlled trials such as the elderly, certain ethnicities or those from certain geographical areas. This review explores approaches in which real world data may be integrated with randomised controlled trials. One approach is by using big data, especially when investigating drugs in the antineoplastic setting. This can even inform artificial intelligence thus ensuring faster and more precise diagnosis and treatment decisions. Pragmatic trials also offer an approach to examine the effectiveness of novel antineoplastic drugs without evading the benefits of randomised controlled trials. A well-designed pragmatic trial would yield results with high external validity by employing a simple study design with a large sample size and diverse settings. Although randomised controlled trials can determine efficacy of antineoplastic drugs, effectiveness in the real world may differ. The need for pragmatic trials to help guide healthcare decision-making led to the development of trials within cohorts (TWICs). TWICs make use of cohorts to conduct multiple randomised controlled trials while maintaining characteristics of real world data in routine clinical practice. Although real world data is often affected by incomplete data and biases such as selection and unmeasured biases, the use of big data and pragmatic approaches can improve the use of real world data in the development of antineoplastic drugs that can in turn steer decision-making in clinical practice.

The approach to peripheral pulmonary lesions (PPL) has been evolving continuously. Advanced bronchoscopic navigational techniques have improved the airway-based approaches to these lesions. Robotic Assisted Bronchoscopy (RAB) can be considered the current pinnacle of this evolution; allowing for a safer approach to sampling lesions previously considered outside of bronchoscopic reach. We present a comprehensive review of the changing epidemiology of lung cancer and the importance of early tissue sampling, the evolution of sampling and navigational bronchoscopic techniques, technical considerations and evidence pertaining to the use of RAB, and adjunct techniques in the diagnosis of lung cancer. Complications and future applications of RAB are also discussed.

Introduction: Electronic medical records (EMRs) maintained in primary care in the UK and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database: the Optimum Patient Care Research Database (OPCRD).

Methods and participants: The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socio-economic status. Patients have a mean duration of 11.7 years' follow-up (SD 17.50), with a majority having key summary data from birth to last data entry. Data for the OPCRD are collected incrementally monthly and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality-improvement programmes provided to GP surgeries, the OPCRD also includes patient-reported outcomes from a range of disease-specific validated questionnaires, with over 66,000 patient responses on asthma, COPD, and COVID-19. Further, bespoke data collection is possible by working with GPs to collect new research via patient-reported questionnaires.

Findings to date: The OPCRD has contributed to over 96 peer-reviewed research publications since its inception encompassing a broad range of medical conditions, including COVID-19.

Conclusion: The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to embedded cluster-randomised trials. Advantages of the OPCRD over other EMR databases are its large size, UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique collection of patient-reported information on respiratory health.

Real world data comprise information on health care that is derived from multiple sources outside typical clinical research settings. This review focuses on what real world evidence tells us about problems with the diagnosis of chronic obstructive pulmonary disease (COPD), problems with the initial and follow-up pharmacological and non-pharmacological management, problems with the management of exacerbations and problems with palliative care. Data from real world studies show errors in the management of COPD with delays to diagnosis, lack of confirmation of the diagnosis with spirometry, lack of holistic assessment, lack of attention to smoking cessation, variable adherence to management guidelines, delayed implementation of appropriate interventions, under-recognition of patients at higher risk of adverse outcomes, high hospitalisation rates for exacerbations and poor implementation of palliative care. Understanding that these problems exist and considering how and why they occur is fundamental to developing solutions to improve the diagnosis and management of patients with COPD.

Background: Many allergic rhinitis (AR) patients have moderate/severe persistent disease. MP-AzeFlu (Dymista^®) comprises intranasal azelastine hydrochloride and fluticasone propionate in a novel formulation delivered in a single device.

Objective: This prospective, noninterventional study assessed the effectiveness of MP-AzeFlu (one spray/nostril twice daily; azelastine hydrochloride = 548 μg; fluticasone propionate = 200 μg) on relieving AR symptom severity.

Methods: A visual analogue scale (VAS; 0 mm [not at all bothersome] to 100 mm [very bothersome]) was used during a 42-day MP-AzeFlu treatment period by 161 persistent AR (PER) patients in routine clinical practice in Sweden. Patients also assessed their sleep quality.

Results: VAS scores decreased from baseline during the treatment period and patients achieved a clinically relevant VAS score cutoff before Day 7, with 89.3% reporting well or partly controlled symptoms on Day 1. VAS score decreased from 61.4 ± 22.4 mm (baseline) to 32.1 ± 24.6 mm on Day 28 and 26.1 ± 24.3 mm on Day 42 (both p < 0.0001), an overall reduction from baseline on Day 42 of 38.1 ± 28.2 mm. The percentage of patients with very good/good sleep quality increased from 3.7%/28.6% on Day 0 to 16.5%/51.5% on Day 42.

Conclusion: MP-AzeFlu provides effective, rapid control of PER assessed by VAS in a real-world clinical setting in Sweden. Symptom improvement was observed at Day 1, sustained for 42 days, and associated with improved sleep quality. MP-AzeFlu significantly improved the QoL of the patients and was well tolerated.