Saliva is an easy to obtain bodily fluid that is specific to the oral environment. It can be used for metabolomic studies as it is representative of the overall wellbeing of an organism, as well as mouth health and bacterial flora. The metabolomic structure of saliva varies greatly depending on the bacteria present in the mouth as they produce a range of metabolites. In this study we have investigated the metabolomic profiles of human saliva that were obtained using 1H NMR (nuclear magnetic resonance) analysis. 48 samples of saliva were collected from 16 healthy subjects over 3 days. Each sample was split in two and the first half treated with an oral rinse, while the second was left untreated as a control sample. The 96 1H NMR metabolomic profiles obtained in the dataset are affected by three factors, namely 16 subjects, 3 sampling days and 2 treatments. These three factors contribute to the total variation in the dataset. When analysing datasets from saliva using traditional methods such as PCA (principal component analysis), the overall variance is dominated by subjects' contributions, and we cannot see trends that would highlight the effect of specific factors such as oral rinse. In order to identify these trends, we used methods such as MSCA (multilevel simultaneous component analysis) and ASCA (ANOVA simultaneous component analysis), that provide variance splits according to the experimental factors, so that we could look at the particular effect of treatment on saliva. The analysis of the treatment effect was enhanced, as it was isolated from the overall variance and assessed without confounding factors. © 2011 Springer Science+Business Media, LLC.
Purpose: To identify symptom clusters and predisposing factors associated with long-term symptoms and health-related quality of life (HRQOL) following radiotherapy in men with prostate cancer. Methods: Patient-reported outcomes (PROs) data from the Medical Research Council RT01 radiotherapy with neoadjuvant androgen deprivation therapy (ADT) trial of 843 patients were used. PROs were collected over 5 years with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI) and the 36-Item Short-Form Health Survey (SF-36). Symptom clusters were explored using hierarchical cluster analysis (HCA). The association of treatment dose, baseline patient characteristics and early symptom clusters with the change in severity of PROs over three years was investigated with multivariate linear mixed effects models. Results: Seven symptom clusters of three or more symptoms were identified. The clusters were stable over time. The longitudinal profiles of symptom clusters showed the onset of acute symptoms during treatment for all symptom clusters and significant recovery by six months. Some clusters such as Physical Health and Sexual Function were adversely affected more than others by ADT, and were less likely to return to pre-treatment levels over time. Older age was significantly associated with decreased long-term Physical Function, Physical Health and Sexual Function (p<0.001). Both baseline and acute symptom clusters were significant antecedents for impaired function and HRQOL at three years. Conclusions: Men with poorer physical function and health prior to or during treatment were more likely to report poorer PROs at year three. Early assessment using PROs and lifestyle interventions should be employed to identify those with higher needs and provide targeted rehabilitation and symptom management.
Lemanska Agnieszka, Byford Rachel C., Cruickshank Clare, Dearnaley David P., Ferreira Filipa, Griffin Clare, Hall Emma, Hinton William, de Lusignan Simon, Sherlock Julian, Faithfull Sara Linkage of the CHHiP randomised controlled trial with primary care data: a study investigating ways of supplementing cancer trials and improving evidence-based practice, In: BMC Medical Research Methodology20(198)
Background Randomised controlled trials (RCTs) are the gold standard for evidence-based practice. However, RCTs can have limitations. For example, translation of findings into practice can be limited by design features, such as inclusion criteria, not accurately reflecting clinical populations. In addition, it is expensive to recruit and follow-up participants in RCTs. Linkage with routinely collected data could offer a cost-effective way to enhance the conduct and generalisability of RCTs. The aim of this study is to investigate how primary care data can support RCTs. Methods Secondary analysis following linkage of two datasets: 1) multicentre CHHiP radiotherapy trial (ISRCTN97182923) and 2) primary care database from the Royal College of General Practitioners Research and Surveillance Centre. Comorbidities and medications recorded in CHHiP at baseline, and radiotherapy-related toxicity recorded in CHHiP over time were compared with primary care records. The association of comorbidities and medications with toxicity was analysed with mixed-effects logistic regression. Results Primary care records were extracted for 106 out of 2811 CHHiP participants recruited from sites in England (median age 70, range 44 to 82). Complementary information included longitudinal body mass index, blood pressure and cholesterol, as well as baseline smoking and alcohol usage but was limited by the considerable missing data. In the linked sample, 9 (8%) participants were recorded in CHHiP as having a history of diabetes and 38 (36%) hypertension, whereas primary care records indicated incidence prior to trial entry of 11 (10%) and 40 (38%) respectively. Concomitant medications were not collected in CHHiP but available in primary care records. This indicated that 44 (41.5%) men took aspirin, 65 (61.3%) statins, 14 (13.2%) metformin and 46 (43.4%) phosphodiesterase-5-inhibitors at some point before or after trial entry. Conclusions We provide a set of recommendations on linkage and supplementation of trials. Data recorded in primary care are a rich resource and linkage could provide near real-time information to supplement trials and an efficient and cost-effective mechanism for long-term follow-up. In addition, standardised primary care data extracts could form part of RCT recruitment and conduct. However, this is at present limited by the variable quality and fragmentation of primary care data.
Lemanska Agnieszka, Faithfull Sara, Liyanage Harshana, Otter Sophie, Romanchikova Marina, Sherlock Julian, Smith Nadia, Thomas Spencer, Lusignan Simon de Primary Care Prostate Cancer Case Ascertainment, In: Studies in health technology and informatics270pp. 1369-1370
Although routine healthcare data are not collected for research, they are increasingly used in epidemiology and are key real-world evidence for improving healthcare. This study presents a method to identify prostate cancer cases from a large English primary care database. 19,619 (1.3%) men had a code for prostate cancer diagnosis. Codes for medium and high Gleason grading enabled identification of additional 94 (0.5%) cases. Many studies do not report codes used to identify patients, and if published, the lists of codes differ from study to study. This can lead to poor research reproducibility and hinder validation. This work demonstrates that carefully developed comprehensive lists of clinical codes can be used to identify prostate cancer; and that approaches that do not solely rely on clinical codes such as ontologies or data linkage should also be considered.
Assessing fitness and promoting regular physical activity can improve health outcomes and early recovery in prostate cancer. This is however, underutilised in clinical practice. The cardiopulmonary exercise test (CPET) is increasingly being used pre-treatment to measure aerobic capacity and peak oxygen consumption (VO2peak - a gold standard in cardiopulmonary fitness assessment). However, CPET requires expensive equipment and may not always be appropriate. The Siconolfi step test (SST) is simpler and cheaper, and could provide an alternative.
The aim of this study was to evaluate the validity and reliability of SST for predicting cardiopulmonary fitness in men with prostate cancer. Men were recruited to this two-centre study (Surrey and Newcastle, United Kingdom) after treatment for locally advanced prostate cancer. They had one or more of three risk factors: elevated blood pressure, overweight (BMI ˃ 25), or androgen deprivation therapy (ADT). Cardiopulmonary fitness was measured using SST and cycle ergometry CPET, at two visits three months apart. The validity of SST was assessed by comparing it to CPET. The VO2peak predicted from SST was compared to the VO2peak directly measured with CPET. The reliability of SST was assessed by comparing repeated measures. Bland-Altman analysis was used to derive limits of agreement in validity and reliability analysis.
Sixty-six men provided data for both SST and CPET. These data were used for validity analysis. 56 men provided SST data on both visits. These data were used for reliability analysis. SST provided valid prediction of the cardiopulmonary fitness in men ˃ 60 years old. The average difference between CPET and SST was 0.64 ml/kg/min with non-significant positive bias towards CPET (P = 0.217). Bland-Altman 95% limits of agreement of SST with CPET were ± 7.62 ml/kg/min. SST was reliable across the whole age range. Predicted VO2peak was on average 0.53 ml/kg/min higher at Visit 2 than at Visit 1 (P = 0.181). Bland-Altman 95% limits of agreement between repeated SST measures were ± 5.84 ml/kg/min.
SST provides a valid and reliable alternative to CPET for the assessment of cardiopulmonary fitness in older men with prostate cancer. Caution is advised when assessing men 60 years old or younger because the VO2peak predicted with SST was significantly lower than that measured with CPET.
To assess the feasibility and acceptability of a community pharmacy lifestyle intervention to improve physical activity and cardiovascular health of men with prostate cancer. To refine the intervention.
Phase II feasibility study of a complex intervention.
Nine community pharmacies in the UK.
Community pharmacy teams were trained to deliver a health assessment including fitness, strength and anthropometric measures. A computer algorithm generated a personalised lifestyle prescription for a homebased programme accompanied by supporting resources. The health assessment was repeated 12 weeks later and support phone calls were provided at weeks 1 and 6.
116 men who completed treatment for prostate cancer.
The feasibility and acceptability of the intervention and the delivery model were assessed by evaluating study processes (rate of participant recruitment, consent, retention and adverse events), by analysing delivery data and semi-structured interviews with participants and by focus groups with pharmacy teams. Physical activity (measured with accelerometry at baseline, 3 and 6 months) and patient reported outcomes (activation, dietary intake and quality of life) were evaluated. Change in physical activity was used to inform the sample size calculations for a future trial.
Out of 403 invited men, 172 (43%) responded and 116 (29%) participated. Of these, 99 (85%) completed the intervention and 88 (76%) completed the 6-month follow-up (attrition 24%). Certain components of the intervention were feasible and acceptable (eg, community pharmacy delivery), while others were more challenging (eg, fitness assessment) and will be refined for future studies. By 3 months, moderate to vigorous physical activity increased on average by 34 min (95% CI 6 to 62, p=0.018), but this was not sustained over 6 months.
The community pharmacy intervention was feasible and acceptable. Results are encouraging and warrant a definitive trial to assess the effectiveness of the refined intervention.
Lemanska Agnieszka, Byford Rachel C., Cruickshank Clare, Deamaley David P., Ferreira Filipa, Griffin Clare, Hall Emma, Hinton William, Lusignan Simon de, Sherlock Julian, Faithfull Sara Linkage of the CHHiP randomised controlled trial with primary care data. A study investigating ways of supplementing cancer trials and improving evidence-based practice., In: BMC Medical Research Methodology
Background: Randomised controlled trials (RCTs) are the gold standard for evidence-based practice. However, RCTs can have limitations. For example, translation of findings into practice can be limited by design features, such as inclusion criteria, not accurately reflecting clinical populations. In addition, it is expensive to recruit and follow-up participants in RCTs. Linkage with routinely collected data could offer a cost-effective way to enhance the conduct and generalisability of RCTs. The aim of this study is to investigate how primary care data can support RCTs. Methods: Secondary analysis following linkage of two datasets: 1) multicentre CHHiP radiotherapy trial (ISRCTN97182923) and 2) primary care database from the Royal College of General Practitioners Research and Surveillance Centre. Comorbidities and medications recorded in CHHiP at baseline, and radiotherapy-related toxicity recorded in CHHiP over time were compared with primary care records. The association of comorbidities and medications with toxicity was analysed with mixed-effects logistic regression. Results: Primary care records were extracted for 106 out of 2811 CHHiP participants recruited from sites in England (median age 70, range 44 to 82). Complementary information included longitudinal body mass index, blood pressure and cholesterol, as well as baseline smoking and alcohol usage but was limited by the considerable missing data. In the linked sample, 9 (8%) participants were recorded in CHHiP as having a history of diabetes and 38 (36%) hypertension, whereas primary care records indicated incidence prior to trial entry of 11 (10%) and 40 (38%) respectively. Concomitant medications were not collected in CHHiP but available in primary care records. This indicated that 44 (41.5%) men took aspirin, 65 (61.3%) statins, 14 (13.2%) metformin and 46 (43.4%) phosphodiesterase-5-inhibitors at some point before or after trial entry. Conclusions: People develop new comorbidities and commence medications after enrolment to trials. Data recorded in primary care are a rich resource and linkage could provide near real-time information to supplement trials and an efficient and cost-effective mechanism for long-term follow-up. In addition, standardised primary care data extracts could form part of RCT recruitment and conduct. However, this is at present limited by the variable quality and fragmentation of primary care data.
Background: Education literature worldwide is replete with studies evaluating the effectiveness of Multiple Mini Interviews (MMIs) in admissions to medicine but <1% of published studies have been conducted in selection to nursing and midwifery programmes. Objectives: To examine the predictive validity of MMIs using end of programme clinical and academic performance indicators of pre-registration adult, child, and mental health nursing and midwifery students. Design and setting: A cross-sectional cohort study at one university in the United Kingdom. Participants: A non-probability consecutive sampling strategy whereby all applicants to the September 2015 pre-registration adult, child, mental health nursing and midwifery programmes were invited to participate. Of the 354 students who commenced year one, 225 (64%) completed their three-year programme and agreed to take part (adult 120, child 32, mental health nursing 30 and midwifery 43). Methods: All applicants were interviewed using MMIs with six and seven station, four-minute models deployed in nursing and midwifery student selection respectively. Associations between MMI scores and the cross-discipline programme performance indicators available for each student at this university at the end of year three: clinical practice (assessed by mentors) and academic attainment (dissertation mark) were explored using multiple linear regression adjusting for applicant age, academic entry level, discipline and number of MMI stations. Results: In the adjusted models, students with higher admissions MMI score (at six and seven stations) performed better in clinical practice (p < 0.001) but not in academic attainment (p = 0.122) at the end of their three-year programme. Conclusion: These findings provide the first report of the predictive validity of MMIs for performance in clinical practice using six and seven station models in nursing and midwifery programmes. Further evidence is required from both clinical and academic perspectives from larger, multi-site evaluations.
It is well established that exercise and lifestyle behaviours improve men's health outcomes from prostate cancer. With 3.8 million men living with the disease worldwide, the challenge is creating accessible intervention approaches that lead to sustainable lifestyle changes. We carried out a phase II feasibility study of a lifestyle intervention delivered by nine community pharmacies in the United Kingdom to inform a larger efficacy study. Qualitative interviews explored how men experienced the intervention, and these data are presented here.
Community pharmacies delivered a multicomponent lifestyle intervention to 116 men with prostate cancer. The intervention included a health, strength, and fitness assessment, immediate feedback, lifestyle prescription with telephone support, and reassessment 12 weeks later. Three months after receiving the intervention, 33 participants took part in semistructured telephone interviews.
Our framework analysis identified how a teachable moment can be created by a community pharmacy intervention. There was evidence of this when men's self‐perception was challenged and coupled to a positive interaction with a pharmacist. Our findings highlight the social context of behaviour change with men identifying how their lifestyle choices were negotiated within their household. There was a ripple effect as lifestyle behaviours made a positive impact on friends and family.
The teachable moment is not a serendipitous opportunity but can be created by an intervention. Our study adds insight into how community pharmacists can support cancer survivors to make positive lifestyle behaviour changes and suggests a role for doing rather than just telling.
Patient reported outcome measures (PROMs) are increasingly being used in research to explore experiences of cancer survivors. Techniques to predict symptoms, with the aim of providing triage care, rely on the ability to analyse trends in symptoms or quality of life and at present are limited. The secondary analysis in this study uses a statistical method involving the application of autoregression (AR) to PROMs in order to predict symptom intensity following radiotherapy, and to explore its feasibility as an analytical tool. The technique is demonstrated using an existing dataset of 94 prostate cancer patients who completed a validated battery of PROMs over time. In addition the relationship between symptoms was investigated and symptom clusters were identified to determine their value in assisting predictive modeling. Three symptom clusters, namely urinary, gastrointestinal and emotional were identified. The study indicates that incorporating symptom clustering into predictive modeling helps to identify the most informative predictor variables. The analysis also showed that the degree of rise of symptom intensity during radiotherapy has the ability to predict later radiotherapy-related symptoms. The method was most successful for the prediction of urinary and gastrointestinal symptoms. Quantitative or qualitative prediction was possible on different symptoms. The application of this technique to predict radiotherapy outcomes could lead to increased use of PROMs within clinical practice. This in turn would contribute to improvements in both patient care after radiotherapy and also strategies to prevent side effects. In order to further evaluate the predictive ability of the approach, the analysis of a larger dataset with a longer follow up was identified as the next step.
Purpose: To investigate the role of symptom clusters in the analysis and utilisation of Patient-Reported Outcome Measures (PROMs) for data modelling and clinical practice. To compare symptom clusters with scales, and explore their value in PROMs interpretation and symptom management. Methods: A dataset called RT01 (ISCRTN47772397) of 843 prostate cancer patients was used. PROMs were reported with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI). Symptom clusters were explored with hierarchical cluster analysis (HCA) and average linkage method (correlation >0.6). The reliability of the Urinary Function Scale was evaluated with Cronbach's Alpha. The strength of the relationship between the items was investigated with Spearman's correlation. Predictive accuracy of the clusters was compared to the scales by receiver operating characteristic (ROC) analysis. Presence of urinary symptoms at 3 years measured with the Late Effects on Normal Tissue: Subjective, Objective, Management tool (LENT/SOM) was an endpoint. Results: Two symptom clusters were identified (Urinary Cluster and Sexual Cluster). The grouping of symptom clusters was different than UCLA-PCI Scales. Two items of the Urinary Function Scales (“Number of pads” and “Urinary leak interfering with sex”) were excluded from the Urinary Cluster. The correlation with the other items in the scale ranged from 0.20-0.21 and 0.31-0.39 respectively. Cronbach's Alpha showed low correlation of those items with the Urinary Function Scale (0.14-0.36 and 0.33-0.44 respectively). All Urinary Function Scale items were subject to a ceiling effect. Clusters had better predictive accuracy, AUC = 0.70-0.65, while scales AUC = 0.67-0.61. Conclusion: This study adds to the knowledge on how cluster analysis can be applied for the interpretation and utilisation of PROMs. We conclude that multiple-item scales should be evaluated and that symptom clusters provide an adaptive and study specific approach for modelling and interpretation of PROMs.
Patients receiving cancer treatment often have one or more co-morbid conditions that are treated pharmacologically. Co-morbidities are recorded in clinical trials usually only at baseline. However, co-morbidities evolve and new ones emerge during cancer treatment. The interaction between multi-morbidity and cancer recovery is significant but poorly understood. Purpose:
To investigate the effect of co-morbidities (e.g. cardiovascular and diabetes) and medications (e.g. statins, antihypertensives, metformin) on radiotherapy-related toxicity and long-term symptoms in order to identify potential risk factors. The possible protective effect of medications such as statins or antihypertensives in reducing radiotherapy-related toxicity will also be explored. Methods:
Two datasets will be linked. 1) CHHiP (Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy for Prostate Cancer) randomised control trial. CHHiP contains pelvic symptoms and radiation-related toxicity reported by patients and clinicians. 2) GP (General Practice) data from RCGP RSC (Royal College of General Practitioners Research and Surveillance Centre). The GP records of CHHiP patients will be extracted, including cardiovascular co-morbidities, diabetes and prescription medications. Statistical analysis of the combined dataset will be performed in order to investigate the effect. Conclusions:
Linking two sources of healthcare data is an exciting area of big healthcare data research. With limited data in clinical trials (not all clinical trials collect information on co-morbidities or medications) and limited lengths of follow-up, linking different sources of information is increasingly needed to investigate long-term outcomes. With increasing pressures to collect detailed information in clinical trials (e.g. co-morbidities, medications), linkage to routinely collected data offers the potential to support efficient conduct of clinical trials.
Background: Universities in the United Kingdom (UK) are required to incorporate values based recruitment (VBR) into their healthcare student selection processes. This reflects an international drive to strengthen the quality of healthcare service provision. This paper presents novel findings in relation to the reliability and predictive validity of multiple mini interviews (MMIs); one approach to VBR widely being employed by universities. Objectives: To examine the reliability (internal consistency) and predictive validity of MMIs using end of Year One practice outcomes of under-graduate pre-registration adult, child, mental health nursing, midwifery and paramedic practice students. Design: Cross-discipline evaluation study. Setting: One university in the United Kingdom. Participants: Data were collected in two streams: applicants to A) The September 2014 and 2015 Midwifery Studies programmes; B) September 2015 adult; Child and Mental Health Nursing and Paramedic Practice programmes. Fifty-seven midwifery students commenced their programme in 2014 and 69 in 2015; 47 and 54 agreed to participate and completed Year One respectively. 333 healthcare students commenced their programmes in September 2015. Of these, 281 agreed to participate and completed their first year (180 adult, 33 child and 34 mental health nursing and 34 paramedic practice students). Methods: Stream A featured a seven station four-minute model with one interviewer at each station and in Stream B a six station model was employed. Cronbach’s alpha was used to assess MMI station internal consistency and Pearson’s moment correlation co-efficient to explore associations between participants’ admission MMI score and end of Year one clinical practice outcomes (OSCE and mentor grading). Results: Stream A: Significant correlations are reported between midwifery applicant’s MMI scores and end of Year One practice outcomes. A multivariate linear regression model demonstrated that MMI score significantly predicted end of Year One practice outcomes controlling for age and academic entry level: coefficients 0.195 (p = 0.002) and 0.116 (p = 0.002) for OSCE and mentor grading respectively. In Stream B no significant correlations were found between MMI score and practice outcomes measured by mentor grading. Internal consistency for each MMI station was ‘excellent’ with values ranging from 0.966–0.974 across Streams A and B. Conclusion: This novel, cross-discipline study shows that MMIs are reliable VBR tools which have predictive validity when a seven station model is used. These data are important given the current international use of different MMI models in healthcare student selection processes.