Coding, Recording and Incidence of Different Forms of Coronary Heart Disease in Primary Care

To evaluate the coding, recording and incidence of coronary heart disease (CHD) in primary care electronic medical records.Data were drawn from the UK General Practice Research Database. Analyses evaluated the occurrence of 271 READ medical diagnostic codes, including categories for 'Angina', 'Myocardial Infarction', 'Coronary Artery Bypass Grafting' (CABG), 'percutaneous transluminal coronary angioplasty' (PCTA) and 'Other Coronary Heart Disease'. Time-to-event analyses were implemented to evaluate occurrences of different groups of codes after the index date.Among 300,020 participants aged greater than 30 years there were 75,197 unique occurrences of coronary heart disease codes in 24,244 participants, with 12,495 codes for incident events and 62,702 for prevalent events. Among incident event codes, 3,607 (28.87%) were for angina, 3,262 (26.11%) were for MI, 514 (4.11%) for PCTA, 161 (1.29%) for CABG and 4,951 (39.62%) were for 'Other CHD'. Among prevalent codes, 20,254 (32.30%) were for angina, 3,644 (5.81%) for MI, 34,542 (55.09%) for 'Other CHD' and 4,262 (6.80%) for CABG or PCTA. Among 3,685 participants initially diagnosed exclusively with 'Other CHD' codes, 17.1% were recorded with angina within 5 years, 5.6% with myocardial infarction, 6.3% with CABG and 8.6% with PCTA. From 2000 to 2010, the overall incidence of CHD declined, as did the incidence of angina, but the incidence of MI did not change. The frequency of CABG declined, while PCTA increased.In primary care electronic records, a substantial proportion of coronary heart disease events are recorded with codes that do not distinguish between different clinical presentations of CHD. The results draw attention to the need to improve coding practice in primary care. The results also draw attention to the importance of code selection in research studies and the need for sensitivity analyses using different sets of codes

Radiotherapy dose-distribution to the perirectal fat space (PRS) is related to gastrointestinal control-related complications.

Traditionally rectal symptoms following pelvic/prostate radiotherapy are correlated to the dosimetry of the anorectum or a substructure of this. It has been suggested that the perirectal fat space (PRS) surrounding the rectum may also be relevant. This study considers the delineation and dosimetry of the PRS related to both rectal bleeding and control-related toxicity. Initially, a case-control cohort of 100 patients from the RADAR study were chosen based on presence/absence of rectal control-related toxicity. Automated contouring was developed to delineate the PRS. 79 of the 100 auto-segmentations were considered successful. Balanced case-control cohorts were defined from these cases. Atlas of Complication Incidence (ACI) were generated to relate the DVH of the PRS with specific rectal symptoms; rectal bleeding and control-related symptoms (LENT/SOM). ACI demonstrated that control-related symptoms were related to the dose distribution to the PRS which was confirmed with Wilcoxon rank sum test (p < 0.05). To the authors knowledge this is the first study implicating the dose distribution to the PRS to the incidence of control-related symptoms of rectal toxicity

Recovery of Salivary Function: Contralateral Parotid-sparing Intensity-modulated Radiotherapy versus Bilateral Superficial Lobe Parotid-sparing Intensity-modulated Radiotherapy.

Aims To establish whether there is a difference in recovery of salivary function with bilateral superficial lobe parotid-sparing intensity-modulated radiotherapy (BSLPS-IMRT) versus contralateral parotid-sparing IMRT (CLPS-IMRT) in patients with locally advanced head and neck squamous cell cancers.Materials and methods A dosimetric analysis was carried out on data from two studies in which patients received BSLPS-IMRT (PARSPORT II) or CLPS-IMRT (PARSPORT). Acute (National Cancer Institute, Common Terminology Criteria for adverse events - NCI CTCAEv3.0) and late (Late Effects of Normal Tissue- subjective, objective, management analytical - LENTSOMA and Radiation Therapy Oncology Group) xerostomia scores were dichotomised: recovery (grade 0-1) versus no recovery (≥grade 2). Incidence of recovery of salivary function was compared between the two techniques and dose-response relationships were determined by fitting dose-response curves to the data using non-linear logistic regression analysis.Results Seventy-one patients received BSLPS-IMRT and 35 received CLPS-IMRT. Patients received 65 Gy in 30 fractions to the primary site and involved nodal levels and 54 Gy in 30 fractions to elective nodal levels. There were significant differences in mean doses to contralateral parotid gland (29.4 Gy versus 24.9 Gy, P < 0.005) and superficial lobes (26.8 Gy versus 30.5 Gy, P = 0.02) for BSLPS and CLPS-IMRT, respectively. Lower risk of long-term ≥grade 2 subjective xerostomia (LENTSOMA) was reported with BSLPS-IMRT (odds ratio 0.50; 95% confidence interval 0.29-0.86; P = 0.012). The percentage of patients who reported recovery of parotid saliva flow at 1 year was higher with BSLPS-IMRT compared with CLPS-IMRT techniques (67.1% versus 52.8%), but the difference was not statistically significant (P = 0.12). For the whole parotid gland, the tolerance doses, D50, were 25.6 Gy (95% confidence interval 20.6-30.5), k = 2.7 (0.9-4.5) (CLPS-IMRT) and 28.9 Gy (26.1-31.9), k = 2.4 (1.4-3.4) (BSLPS-IMRT). For the superficial lobe, D50 were similar: BSLPS-IMRT 23.5 Gy (19.3-27.6), k = 1.9 (0.5-3.8); CLPS-IMRT 24.0 Gy (17.7-30.1), k = 2.1 (0.1-4.1).Conclusion BSLPS-IMRT reduces the risk of developing high-grade subjective xerostomia compared with CLPS-IMRT. The D50 of the superficial lobe may be a more reliable predictor of recovery of parotid function than the whole gland mean dose

Microbiota- and Radiotherapy-Induced Gastrointestinal Side-Effects (MARS) Study: A Large Pilot Study of the Microbiome in Acute and Late-Radiation Enteropathy.

Purpose Radiotherapy is important in managing pelvic cancers. However, radiation enteropathy may occur and can be dose limiting. The gut microbiota may contribute to the pathogenesis of radiation enteropathy. We hypothesized that the microbiome differs between patients with and without radiation enteropathy.Experimental Design: Three cohorts of patients (n = 134) were recruited. The early cohort (n = 32) was followed sequentially up to 12 months post-radiotherapy to assess early radiation enteropathy. Linear mixed models were used to assess microbiota dynamics. The late cohort (n = 87) was assessed cross-sectionally to assess late radiation enteropathy. The colonoscopy cohort compared the intestinal mucosa microenvironment in patients with radiation enteropathy (cases, n = 9) with healthy controls (controls, n = 6). Fecal samples were obtained from all cohorts. In the colonoscopy cohort, intestinal mucosa samples were taken. Metataxonomics (16S rRNA gene) and imputed metataxonomics (Piphillin) were used to characterize the microbiome. Clinician- and patient-reported outcomes were used for clinical characterization.Results In the acute cohort, we observed a trend for higher preradiotherapy diversity in patients with no self-reported symptoms (P = 0.09). Dynamically, diversity decreased less over time in patients with rising radiation enteropathy (P = 0.05). A consistent association between low bacterial diversity and late radiation enteropathy was also observed, albeit nonsignificantly. Higher counts of Clostridium IV, Roseburia, and Phascolarctobacterium significantly associated with radiation enteropathy. Homeostatic intestinal mucosa cytokines related to microbiota regulation and intestinal wall maintenance were significantly reduced in radiation enteropathy [IL7 (P = 0.05), IL12/IL23p40 (P = 0.03), IL15 (P = 0.05), and IL16 (P = 0.009)]. IL15 inversely correlated with counts of Roseburia and Propionibacterium.Conclusions The microbiota presents opportunities to predict, prevent, or treat radiation enteropathy. We report the largest clinical study to date into associations of the microbiota with acute and late radiation enteropathy. An altered microbiota associates with early and late radiation enteropathy, with clinical implications for risk assessment, prevention, and treatment of radiation-induced side-effects.See related commentary by Lam et al., p. 6280

Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists

Background Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop ‘indicator markers’ found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. Methods We examined EHRs of 5,843 patients in the General Practice Research Database, aged ≥30y, with a first coded diagnosis of RA between 2005 and 2008. Lists of indicator markers for RA were developed initially by panels of clinicians drawing up code-lists and then modified based on scrutiny of available data. The prevalence of indicator markers, and their temporal relationship to RA codes, was examined in patients from 3y before to 14d after recorded RA diagnosis. Findings Indicator markers were common throughout EHRs of RA patients, with 83.5% having 2 or more markers. 34% of patients received a disease-specific prescription before RA was coded; 42% had a referral to rheumatology, and 63% had a test for rheumatoid factor. 65% had at least one joint symptom or sign recorded and in 44% this was at least 6-months before recorded RA diagnosis. Conclusion Indicator markers of RA may be valuable for case definition in cases which do not yet have a diagnostic code. The clinical diagnosis of RA is likely to occur some months before it is coded, shown by markers frequently occurring ≥6 months before recorded diagnosis. It is difficult to differentiate delay in diagnosis from delay in recording. Information concealed in free text may be required for the accurate identification of patients and to assess the quality of care in general practice

Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.

Background and purpose Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning.Materials and methods Predictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models.Results The dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis.Conclusions The RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence

Clinical implementation of a knowledge based planning tool for prostate VMAT

Abstract Background A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinical plans and to improve plan quality and consistency. Methods A historical cohort of 97 VMAT prostate plans was interrogated using a RayStation script and used to develop a local model for predicting optimum average rectum dose based on individual anatomy. A preliminary validation study was performed whereby historical plans identified as “optimal” and “sub-optimal” by the local model were replanned in a blinded study by four experienced planners and compared to the original clinical plan to assess whether any improvement in rectum dose was observed. The predictive model was then incorporated into a RayStation script and used as part of the clinical planning process. Planners were asked to use the script during planning to provide a patient specific prediction for optimum average rectum dose and to optimise the plan accordingly. Results Plans identified as “sub-optimal” in the validation study observed a statistically significant improvement in average rectum dose compared to the clinical plan when replanned whereas plans that were identified as “optimal” observed no improvement when replanned. This provided confidence that the local model can identify plans that were suboptimal in terms of rectal sparing. Clinical implementation of the knowledge based planning tool reduced the population-averaged mean rectum dose by 5.6Gy. There was a small but statistically significant increase in total MU and femoral head dose and a reduction in conformity index. These did not affect the clinical acceptability of the plans and no significant changes to other plan quality metrics were observed. Conclusions The knowledge-based planning tool has enabled substantial reductions in population-averaged mean rectum dose for prostate VMAT patients. This suggests plans are improved when planners receive quantitative feedback on plan quality against historical data

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.

Purpose Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue-sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation.Methods and materials FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares-logistic regression [FPLS-LR] and functional principal component-logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate-response associations, assessed using bootstrapping.Results The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/-0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/-0.96, 0.79/-0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models.Conclusions FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling

Planning a cluster randomized trial with unequal cluster sizes: practical issues involving continuous outcomes

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screeening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes. METHODS: We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials. RESULTS: We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF) used in the sample size calculations has the best properties. CONCLUSION: Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (γ) of clusters actually recruit a proportion (τ) of subjects to be included (γ ≤ τ)"

Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study

<p>Abstract</p> <p>Background</p> <p>In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.</p> <p>Methods</p> <p>We challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker <it>K</it>^trans, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.</p> <p>Results</p> <p>ADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while <it>K</it>^trans, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, <it>K</it>^trans, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).</p> <p>Conclusions</p> <p>We have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.</p