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Background Navigating the world of qualitative thematic analysis can be challenging. This is compounded by the fact that detailed descriptions of methods are often omitted from qualitative discussions. While qualitative research methodologies are now mature, there often remains a lack of fine detail in their description both at submitted peer reviewed article level and in textbooks. As one of research’s aims is to determine the relationship between knowledge and practice through the demonstration of rigour, more detailed descriptions of methods could prove useful. Rigour in quantitative research is often determined through detailed explanation allowing replication, but the ability to replicate is often not considered appropriate in qualitative research. However, a well described qualitative methodology could demonstrate and ensure the same effect. Methods This article details the codebook development which contributed to thematic analysis of qualitative data. This analysis formed part of a mixed methods multiphase design research project, with both qualitative and quantitative inquiry and involving the convergence of data and analyses. This design consisted of three distinct phases: quantitative, qualitative and implementation phases. Results and conclusions This article is aimed at researchers and doctoral students new to thematic analysis by describing a framework to assist their processes. The detailed description of the methods used supports attempts to utilise the thematic analysis process and to determine rigour to support the establishment of credibility. This process will assist practitioners to be confident that the knowledge and claims contained within research are transferable to their practice. The approach described within this article builds on, and enhances, current accepted models.

Statin therapy is effective for the prevention of atherosclerotic cardiovascular disease and is widely prescribed, but there are persisting concerns that statin therapy might frequently cause muscle pain or weakness. We aimed to address these through an individual participant data meta-analysis of all recorded adverse muscle events in large, long-term, randomised, double-blind trials of statin therapy. Randomised trials of statin therapy were eligible if they aimed to recruit at least 1000 participants with a scheduled treatment duration of at least 2 years, and involved a double-blind comparison of statin versus placebo or of a more intensive versus a less intensive statin regimen. We analysed individual participant data from 19 double-blind trials of statin versus placebo (n=123 940) and four double-blind trials of a more intensive versus a less intensive statin regimen (n=30 724). Standard inverse-variance-weighted meta-analyses of the effects on muscle outcomes were conducted according to a prespecified protocol. Among 19 placebo-controlled trials (mean age 63 years [SD 8], with 34 533 [27·9%] women, 59 610 [48·1%] participants with previous vascular disease, and 22 925 [18·5%] participants with diabetes), during a weighted average median follow-up of 4·3 years, 16 835 (27·1%) allocated statin versus 16 446 (26·6%) allocated placebo reported muscle pain or weakness (rate ratio [RR] 1·03; 95% CI 1·01–1·06). During year 1, statin therapy produced a 7% relative increase in muscle pain or weakness (1·07; 1·04–1·10), corresponding to an absolute excess rate of 11 (6–16) events per 1000 person-years, which indicates that only one in 15 ([1·07–1·00]/1·07) of these muscle-related reports by participants allocated to statin therapy were actually due to the statin. After year 1, there was no significant excess in first reports of muscle pain or weakness (0·99; 0·96–1·02). For all years combined, more intensive statin regimens (ie, 40–80 mg atorvastatin or 20–40 mg rosuvastatin once per day) yielded a higher RR than less intensive or moderate-intensity regimens (1·08 [1·04–1·13] vs 1·03 [1·00–1·05]) compared with placebo, and a small excess was present (1·05 [0·99–1·12]) for more intensive regimens after year 1. There was no clear evidence that the RR differed for different statins, or in different clinical circumstances. Statin therapy yielded a small, clinically insignificant increase in median creatine kinase values of approximately 0·02 times the upper limit of normal. Statin therapy caused a small excess of mostly mild muscle pain. Most (>90%) of all reports of muscle symptoms by participants allocated statin therapy were not due to the statin. The small risks of muscle symptoms are much lower than the known cardiovascular benefits. There is a need to review the clinical management of muscle symptoms in patients taking a statin. British Heart Foundation, Medical Research Council, Australian National Health and Medical Research Council.

Background Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset. Methods Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves. Results There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601). Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. Conclusions The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year.

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional “soft” functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results. •We contribute finely-resolved high-dimensional functional modes for fMRI analysis.•Those are trained on millions of varied fMRI functional brain volumes, using a sparse matrix factorisation algorithm. The total training size is 2.4TB.•These Dictionaries of Functional Modes (DiFuMo) are multi-scale, with a number of functional networks ranging from 64 to 1024.•Our benchmarks reveal the importance of using high-dimensional “soft” continuous-valued functional atlases when extracting image-derived phenotypes.•We provide an anatomical name to each of the modes of the DiFuMo atlases. Those are available at https://parietal-inria.github.io/DiFuMo/.

Background Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. Methods In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients’ discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). Results The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. Conclusions For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

The purpose of this study was to determine whether racial or other demographic characteristics were associated with declining surgery for early stage gastric cancer. Patients with clinical stage I-II gastric adenocarcinoma were identified from the NCDB. Multivariable logistic models identified predictors for declining resection. Patients were stratified based on propensity scores, which were modeled on the probability of declining. Overall survival was evaluated using the Kaplan-Meier method. Of 11,326 patients, 3.68% (n = 417) declined resection. Patients were more likely to refuse if they were black (p < 0.001), had Medicaid or no insurance (p < 0.001), had shorter travel distance to the hospital (p < 0.001) or were treated at a non-academic center (p = 0.001). After stratification, patients who declined surgery had worse overall survival (all strata, p < 0.001). Racial and sociodemographic disparities exist in the treatment of potentially curable gastric cancer, with patients who decline recommended surgery suffering worse overall survival. [Display omitted] •Of 11,326 stage I-II gastric cancer patients from the NCDB, 3.68% declined surgery.•Black patients and those with Medicaid or no insurance were more likely to decline.•Patients who declined a gastrectomy had 4.6 times worse five-year overall survival.

Nearly 50,000 incident cancer cases are reported in Veterans Affairs (VA) Central Cancer Registry (VACCR) annually. This article provides an updated report of cancer incidence recorded in VACCR. Data were obtained from VACCR for incident cancers diagnosed in the VA health care system, focusing on 2010 data. Cancer incidence among VA patients is described by anatomical site, sex, race, stage, and geographic location, and was compared to the general U.S. cancer population. In 2010, among 46,170 invasive cancers, 97% were diagnosed among men. Approximately 80% of newly diagnosed patients were white, 19% black, and less than 2% were other minority races. Median age at diagnosis was 65 years. The three most frequently diagnosed cancers among VA were prostate (29%), lung/bronchus (18%), and colon/rectum (8%). Melanoma and kidney/renal pelvis tied for fourth (4%), and urinary bladder tied for sixth with liver and intrahepatic bile duct (3.4%). Approximately 23% of prostate, 21% of lung/bronchus, and 31% of colon/rectum cancers were diagnosed with Stage I disease. The overall invasive cancer incidence rate among VA users was 505.8 per 100,000 person-years. Although the composition of the VA population is shifting and includes a larger number of women, registry data indicate that incident cancers in VA in 2010 were most similar to those observed among U.S. men. Consistent reporting of VACCR data is important to provide accurate estimates of VA cancer incidence. This information can be used to plan efforts to improve quality of cancer care and access to services.

Background Much of the research on high-cost patients in healthcare has taken a static approach to studying what is actually a dynamic process. High-cost patients often utilize services across multiple sectors along care pathways, but due to the cross-sectional nature of many study designs, we lack a clear understanding of the temporal relationship between high-cost spending in community and acute care. Studying care trajectories for high cost patients with cardiovascular disease (CVD) can shed light on the dynamic interplay between community-based and acute care along the care continuum, and provide information about signals in community care that may indicate future elective and urgent hospitalizations. Methods Using linked health administrative data in Ontario, Canada, 74,683 incident cases with cardiovascular disease were identified between the years 2009 and 2011. Patients were followed for 36 months (total study duration 2009–2014) until the first urgent or elective admission to hospital for a heart-related condition. We used an extended Cox survival model with competing risks to study the relationship between high-cost spending in community care with two mutually exclusive outcomes: urgent or elective hospitalizations. Results Elective hospitalizations were most clearly signaled by a high-cost utilization of community-based specialist services in the month prior to hospital admission (hazard ratio 9.074, p  < 0.0001), while urgent hospitalizations were signaled by high cost usage across all community-based sectors of care (from general practitioner & specialist visits, home care, laboratory services and emergency department (ED) usage). Urgent hospitalizations were most clearly signaled by high cost usage in ED in the month prior to hospital admission (hazard ratio 2.563, p  < 0.0001). Conclusion By studying the dynamic nature of patient care trajectories, we may use community-based spending patterns as signals in the system that can point to future and elective hospitalizations for CVD. These community-based spending signals may be useful for identifying opportunities for intervention along the care trajectory, particularly for urgent CVD patients for whom future hospitalizations are difficult to anticipate.

Medical educators and patients are turning to YouTube to teach and learn about medical conditions. These videos are from authors whose credibility cannot be verified & are not peer reviewed. As a result, studies that have analyzed the educational content of YouTube have reported dismal results. These studies have been unable to exclude videos created by questionable sources and for non-educational purposes. We hypothesize that medical education YouTube videos, authored by credible sources, are of high educational value and appropriately suited to educate the public. Credible videos about cardiovascular diseases were identified using the Mayo Clinic's Center for Social Media Health network. Content in each video was assessed by the presence/absence of 7 factors. Each video was also evaluated for understandability using the Suitability Assessment of Materials (SAM). User engagement measurements were obtained for each video. A total of 607 videos (35 hours) were analyzed. Half of all videos contained 3 educational factors: treatment, screening, or prevention. There was no difference between the number of educational factors present & any user engagement measurement (p NS). SAM scores were higher in videos whose content discussed more educational factors (p<0.0001). However, none of the user engagement measurements correlated with higher SAM scores. Videos with greater educational content are more suitable for patient education but unable to engage users more than lower quality videos. It is unclear if the notion \"content is king\" applies to medical videos authored by credible organizations for the purposes of patient education on YouTube.

IntroductionPercutaneous coronary intervention (PCI) aims to provide instant relief of symptoms, and improve functional capacity and prognosis in patients with coronary artery disease. Although patients may experience a quick recovery, continuity of care from hospital to home can be challenging. Within a short time span, patients must adjust their lifestyle, incorporate medications and acquire new support. Thus, CONCARDPCI will identify bottlenecks in the patient journey from a patient perspective to lay the groundwork for integrated, coherent pathways with innovative modes of healthcare delivery. The main objective of the CONCARDPCI is to investigate (1) continuity of care, (2) health literacy and self-management, (3) adherence to treatment, and (4) healthcare utilisation and costs, and to determine associations with future short and long-term health outcomes in patients after PCI.Methods and analysisThis prospective multicentre cohort study organised in four thematic projects plans to include 3000 patients. All patients undergoing PCI at seven large PCI centres based in two Nordic countries are prospectively screened for eligibility and included in a cohort with a 1-year follow-up period including data collection of patient-reported outcomes (PRO) and a further 10-year follow-up for adverse events. In addition to PROs, data are collected from patient medical records and national compulsory registries.Ethics and disseminationApproval has been granted by the Norwegian Regional Committee for Ethics in Medical Research in Western Norway (REK 2015/57), and the Data Protection Agency in the Zealand region (REG-145-2017). Findings will be disseminated widely through peer-reviewed publications and to patients through patient organisations.Trial registration number NCT03810612.