What are the current challenges of managing cancer pain and could digital technologies help?

OBJECTIVES: Pain remains a problem for people with cancer despite effective treatments being available. We aimed to explore current pain management strategies used by patients, caregivers and professionals and to investigate opportunities for digital technologies to enhance cancer pain management. METHODS: A qualitative study comprising semistructured interviews and focus groups. Patients with cancer pain, their caregivers and health professionals from Northeast Scotland were recruited from a purposive sample of general practices. Professionals were recruited from regional networks. RESULTS: Fifty one participants took part in 33 interviews (eight patients alone, six patient/caregiver dyads and 19 professionals) and two focus groups (12 professionals). Living with cancer was hard work for patients and caregivers and comparable to a 'full-time job'. Patients had personal goals which involved controlling pain intensity and balancing this with analgesic use, side effects, overall symptom burden and social/physical activities.Digital technologies were embraced by most patients, and made living life with advanced cancer easier and richer (eg, video calls with family). Technology was underutilised for pain and symptom management. There were suggestions that technology could support self-monitoring and communicating problems to professionals, but patients and professionals were concerned about technological monitoring adding to the work of managing illness. CONCLUSIONS: Cancer pain management takes place in the context of multiple, sometimes competing personal goals. It is possible that technology could be used to help patients share individual symptom experiences and goals, thus enhancing tailored care. The challenge is for digital solutions to add value without adding undue burden

Distinguishing variation in referral accuracy from referral threshold: analysis of a national dataset of referrals for suspected cancer

Objectives: To distinguish between variation in referral threshold and variation in accurate selection of patients for referral in fast-track referrals for possible cancer. To examine factors associated with threshold and accuracy and model the effects of changing thresholds. Design: Analysis of national data on cancer referrals from general practices in England over a five-year period. We developed a new method to estimate specificity of referral to complement existing sensitivity. We used bivariate meta-analysis to produce summary measures and described practices in relation to these. Setting: 5479 GP practices with data relating to more than 50 cancer cases diagnosed over the five years. Outcomes: Number of practices whose 95% confidence regions for sensitivity and specificity indicated that they were outliers in terms of either referral threshold or decision accuracy. Results: 2019 practices (36.8%) were outliers in relation to referral threshold compared to 1205 practices (22.0%) in relation to decision accuracy. Practice age profile, cancer incidence, and deprivation showed a modest association with decision accuracy but not with thresholds. If all practices shared the referral behaviour of those in the highest quintile of age-standardised referral rate there would be a 3.3% increase in cancers detected through fast track pathways at the cost of a 36.9% increase in urgent referrals. Conclusion: This new method permits variation in referral to be described more precisely and quality improvement activities to be targeted. Changing referral thresholds without increasing accuracy will result in modest effects on detection rates and a large increase in demand on diagnostic services

The use, quality and effectiveness of pelvic examination in primary care for the detection of gynaecological cancer : a systematic review

Background Urgent suspected cancer referral guidelines recommend that women with gynaecological cancer symptoms should have a pelvic examination (PE) prior to referral. We do not know to what extent GPs comply, their competency at PE, or if PE shortens the diagnostic interval. Objectives We conducted a systematic review of the use, quality and effectiveness of PE in primary care for women with suspected gynaecological cancer. Method PRISMA guidelines were followed. Three databases were searched using four terms: PE, primary care, competency and gynaecological cancer. Citation lists of all identified papers were screened independently for eligibility by two reviewers. Data extraction was performed in duplicate and independently. Paper quality was assessed using the relevant Critical Appraisal Skills Programme checklist. Emergent themes and contrasting issues were explored in a narrative ecological synthesis. Main Findings Twenty papers met the inclusion criteria. 52% or less of women with suspicious symptoms had a PE. No papers directly explored GPs’ competence at performing PE. Pre-referral PE was associated with reduced diagnostic delay and earlier stage diagnosis. Ecological synthesis demonstrated a complex interplay between patient and practitioner factors and the environment in which examination is performed. Presenting symptoms are commonly misattributed by patients and practitioners resulting in misdiagnosis and lack of PE. Conclusion We do not know if pre-referral PE leads to better outcomes for patients. PE is often not performed for women with gynaecological cancer symptoms, and evidence that it may result in earlier stage of diagnosis is weak. More research is needed

Bayesian hierarchical vector autoregressive models for patient-level predictive modeling

Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts

Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data

Background: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of exacerbations of chronic obstructive pulmonary disease (COPD) with a view to instituting timely treatment. However, current algorithms to identify exacerbations result in frequent false positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving quality of predictions. Objective: To establish if machine learning techniques applied to telemonitoring datasets improve prediction of hospital admissions, decisions to start steroids, and to determine if the addition of weather data further improves such predictions. Methods: We used daily symptoms, physiological measures and medication data, with baseline demography, COPD severity, quality of life, and hospital admissions from a pilot and large randomised controlled trial of telemonitoring in COPD. In addition, we linked weather data from the UK Meteorological Office. We used feature selection and extraction techniques for time-series to construct up to 153 predictive patterns (features) from symptom, medication, and physiological measurements. The resulting variables were used for the construction of predictive models fitted to training sets of patients and compared to common algorithms. Results: We had a mean 363 days of telemonitoring data from 135 patients. The two most practical traditional score-counting algorithms, restricted to cases with complete data resulted in AUC estimates of 0.60 [CI 95% 0.51, 0.69] and 0.58 [0.50, 0.67] for predicting admissions based on a single day’s readings. However, in a real-world scenario allowing for missing data, with greater numbers of patient daily data and hospitalisations (N = 57,150, N+=17), the performance of all the traditional algorithms fell, including those based on two days data. One of the most frequently used algorithms performed no better than chance. Machine learning models demonstrated significant improvements; the best machine learning algorithm based on 57,150 episodes resulted in an aggregated AUC = 0.73 [0.67, 0.79]. Addition of weather data measurements resulted in a negligible improvement in the predictive performance of the best model (AUC = 0.74 [0.69, 0.79]). In order to achieve an 80% true positive rate (sensitivity), the traditional algorithms were associated with an 80% false positive rate: our algorithm halved this rate to approximately 40% (specificity approximately 60%). The machine learning algorithm was moderately superior to the best standard algorithm (AUC = 0.77 [0.74, 0.79] v AUC = 0.66 [0.63, 0.68]) at predicting the need for steroids. Conclusions: The early detection and management of COPD remains an important goal given the huge personal and economic costs of the condition. Machine learning approaches, which can be tailored to an individual’s baseline profile and can learn from experience of the individual patient are superior to existing predictive algorithms show promise in achieving this goal

What do guidelines and systematic reviews tell us about the management of medically unexplained symptoms in primary care?

Medically unexplained symptoms (MUS) are symptoms for which the origin remains unclear despite adequate history taking, physical examination, and additional investigations.1 An estimated 3–11% of patients visiting general practice repeatedly consult their GP for MUS.2,3 MUS exist along a continuum ranging from self-limiting symptoms, to recurrent and persistent symptoms, through to symptom disorders.4 Although there are various terms for the condition, for example unexplained physical symptoms, functional symptoms, or somatoform symptoms, we have chosen to use MUS in this article because this is the most frequently used term. This review aims to address current problems with the management of undifferentiated MUS; specific syndromes within the MUS spectrum, such as chronic fatigue syndrome and irritable bowel syndrome, are excluded from discussion. Patients with persistent MUS suffer from their symptoms, are functionally impaired, and are at risk of potentially harmful additional testing and treatment.5 Furthermore, these patients commonly express dissatisfaction with the medical care they receive during their illness.6 They feel stigmatised and not taken seriously.7 GPs often experience patients with persistent MUS as difficult and frustrating to manage.8 In addition, MUS are associated with reduced health-related quality of life, higher healthcare and social costs, and costs associated with lost productivity.9,10 The effects of many treatment strategies have been studied in recent decades. However, not all interventions are acceptable or feasible in routine primary care. In the light of the central role of the GP in managing MUS, we will discuss the importance of consultation skills and the effects of specific treatments in primary care. We will do this by way of a narrative review using available national guidelines and Cochrane Reviews in this field

Single Spin Asymmetry ANA_N in Polarized Proton-Proton Elastic Scattering at s=200\sqrt{s}=200 GeV

We report a high precision measurement of the transverse single spin asymmetry $A_N$ at the center of mass energy $\sqrt{s}=200$ GeV in elastic proton-proton scattering by the STAR experiment at RHIC. The $A_N$ was measured in the four-momentum transfer squared $t$ range $0.003 \leqslant |t| \leqslant 0.035$ \GeVcSq, the region of a significant interference between the electromagnetic and hadronic scattering amplitudes. The measured values of $A_N$ and its $t$-dependence are consistent with a vanishing hadronic spin-flip amplitude, thus providing strong constraints on the ratio of the single spin-flip to the non-flip amplitudes. Since the hadronic amplitude is dominated by the Pomeron amplitude at this $\sqrt{s}$, we conclude that this measurement addresses the question about the presence of a hadronic spin flip due to the Pomeron exchange in polarized proton-proton elastic scattering.Comment: 12 pages, 6 figure

Measurement of the parity-violating longitudinal single-spin asymmetry for W±W^{\pm} boson production in polarized proton-proton collisions at s=500\sqrt{s} = 500 GeV

We report the first measurement of the parity violating single-spin asymmetries for midrapidity decay positrons and electrons from $W^{+}$ and $W^{-}$ boson production in longitudinally polarized proton-proton collisions at $\sqrt{s}=500$GeV by the STAR experiment at RHIC. The measured asymmetries, $A^{W^+}_{L}=-0.27\pm 0.10\;({\rm stat.})\pm 0.02\;({\rm syst.}) \pm 0.03\;({\rm norm.})$ and $A^{W^-}_{L}=0.14\pm 0.19\;({\rm stat.})\pm 0.02 \;({\rm syst.})\pm 0.01\;({\rm norm.})$, are consistent with theory predictions, which are large and of opposite sign. These predictions are based on polarized quark and antiquark distribution functions constrained by polarized DIS measurements.Comment: 6 pages, 4 figures, submitted to Physics Review Letter

High pTp_{T} non-photonic electron production in pp+pp collisions at s\sqrt{s} = 200 GeV

We present the measurement of non-photonic electron production at high transverse momentum ($p_T >$ 2.5 GeV/$c$) in $p$ + $p$ collisions at $\sqrt{s}$ = 200 GeV using data recorded during 2005 and 2008 by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The measured cross-sections from the two runs are consistent with each other despite a large difference in photonic background levels due to different detector configurations. We compare the measured non-photonic electron cross-sections with previously published RHIC data and pQCD calculations. Using the relative contributions of B and D mesons to non-photonic electrons, we determine the integrated cross sections of electrons ($\frac{e^++e^-}{2}$) at 3 GeV/$c < p_T <~$10 GeV/$c$ from bottom and charm meson decays to be ${d\sigma_{(B\to e)+(B\to D \to e)} \over dy_e}|_{y_e=0}$ = 4.0$\pm0.5$({\rm stat.})$\pm1.1$({\rm syst.}) nb and ${d\sigma_{D\to e} \over dy_e}|_{y_e=0}$ = 6.2$\pm0.7$({\rm stat.})$\pm1.5$({\rm syst.}) nb, respectively.Comment: 17 pages, 17 figure

Evolution of the differential transverse momentum correlation function with centrality in Au+Au collisions at sNN=200\sqrt{s_{NN}} = 200 GeV

We present first measurements of the evolution of the differential transverse momentum correlation function, {\it C}, with collision centrality in Au+Au interactions at $\sqrt{s_{NN}} = 200$ GeV. {\it C} exhibits a strong dependence on collision centrality that is qualitatively similar to that of number correlations previously reported. We use the observed longitudinal broadening of the near-side peak of {\it C} with increasing centrality to estimate the ratio of the shear viscosity to entropy density, $\eta/s$, of the matter formed in central Au+Au interactions. We obtain an upper limit estimate of $\eta/s$ that suggests that the produced medium has a small viscosity per unit entropy.Comment: 7 pages, 4 figures, STAR paper published in Phys. Lett.