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ItemPatterns of pain in patients with advanced cancerPHILIP, JENNIFER ( 2000)Extensive literature detailing pain in cancer has been published in the past 15 years with point prevalence studies for populations, effects of interventions and general pain management approaches receiving most attention. The experience of pain over time for individual patients has not been studied. There is value in identifying patterns that pain may follow in order to predict future pain levels for a particular patient. Aims: This prospective longitudinal study of patients with cancer, for whom treatment is palliative, seeks to determine if pain reported by patients follows characteristic patterns. The study took place in the context of a larger epidemiological study of symptoms, quality of life, health service utilization and the quality of information regarding cancer and treatment for patients and their carers. Within this epidemiological study, an observational study of pain intensity, characteristics and analgesic levels was undertaken over a 6 month period. The investigators enrolled 202 participants, with 165 participants completing data for evaluation. Method: Data series were examined as a whole group, then individual participants' time series data were examined for visual and mathematical patterns. A series of mathematical models were fitted to successive pain scores based on the assumption that current pain scores are not independent of previous pain scores. Results: As a group, mean pain levels did not alter over the 6 months, but differences between individuals within the group were marked. Considered as a whole, 20.50/0 reported using paracetamol at assessment, and just 16.3% used slow release morphine. Visual examination of pain levels over time revealed three broad groups of patients: those for whom pain scores were always high, those for whom pain scores were always low, and those who had fluctuating pain scores. Those with consistently high pain scores were more likely to have bone metastases (p <0.05), arthritis (p <0.005), to describe pain as burning (p <0.003), stabbing (p <0.001), present with light touch (p <0.001), than the fluctuating or consistently low pain scores groups. In addition these patients used more paracetamol, codeine, slow release morphine and morphine mixture (p < 0.001), though not nonsteroidal anti-inflammatory drugs. There was no significant difference in gender, treatments planned or survival between the groups. A series of mathematical models were fitted to each individuals’ time series data in an attempt to simply describe patterns within the data. The model that most followed the plotted data points with the least variation of points from that model was a 'cubic' pattern for 95% of patients. This pattern is best described as fluctuating, typically with an initial increase of pain early in the study, followed by some lessening of pain, to again increase more steeply towards the end of the study. The degree to which this model 'fitted' or the mean residual square value for all time series ranged from r2=0.02 - 1.0 (median r2=0.38, mode r2=1.0). For 22 subjects the mean residual square value for cubic modeling was r2=0.8 or greater indicating an excellent correlation between the data and the model. These patients were significantly more likely to be treated with radiotherapy (p <0.01), codeine (p <0.05) and morphine mixture (p <0.05), and had a poorer ECOG performance status (p <0.001) and shorter survival (p <0.05) than the other patients. Therefore a cubic model may assist to predict future pain levels for patients with poor performance status. Clinical Implications: The results of this study have implications for the clinician caring for patients with advanced cancer, enabling forecasting of likely future pain levels for a particular patient. This information will assist when planning analgesic interventions, frequency of consultations and resource allocation for a patient, as well as interpreting information gleaned from analgesic trials. Conclusion: Mean pain scores for a whole population with advanced cancer do not change considerably with time, but for individuals marked fluctuations are apparent. Patients with a poor performance status, arthritis, bone metastases and describing certain pain descriptors are more likely to have consistently high pain levels. For 95% of patients a cubic function model best fitted their time series data, and this model is likely to be most reliable in forecasting pain levels when patients have a poor performance status.
ItemCoaching patients on achieving cardiovascular health: the COACH program a patient targeted strategy for the secondary prevention of coronary heart diseaseVale, Margarite Julia ( 2002)It is well recognised that there is a treatment gap in the management of risk factors in coronary heart disease (CHD) - a gap between what is known from published evidence and what is actually practised. Despite major advances in scientific evidence for aggressive risk factor management, only a minority of patients with CHD are achieving the target levels for their modifiable coronary risk factors. Strategies to address the treatment gap have been usually aimed at the physician and these have often been ineffective. Few strategies have been directed at the patient. Patient targeted strategies can be subdivided into those that permit the prescribing of medication ('competitive' with usual care) such as secondary prevention clinics or disease management programs, and those where support staff do not have prescribing rights ('cooperative' with usual care). Although intuitively it may appear that any program providing attention to patients would result in improvements in risk factor levels, published work shows that only the competitive programs resulted in significant improvement in coronary risk factor status. All of the cooperative programs failed to effect an improvement in risk factor status. While competitive programs are clearly effective, they risk alienating usual medical care and in a competitive environment may be counterproductive. There is a role for a cooperative program in an environment where primary care is competitive. This has been the rationale for the development of The Coach Program to bridge the treatment gap in CI-ID. The Coach Program was not founded on sociological or psychological theory. It is an empirical technique developed by the PhD Candidate on the basis of the Candidate's experience as a secondary school teacher. Although there is no coherent theory of coaching, coaching has been used in clinical medicine to improve doctor-patient interaction in the consultation process, assist patients to cope with painful procedures, for exercise training of patients to improve medical conditions and in staff teaching. Thus far, coaching has not been applied and evaluated in chronic disease management such as for the achievement of specific secondary prevention goals. The Coach Program is a training program for patients with CHD in which a health professional coach trains patients to aggressively pursue the target levels for their particular coronary risk factors. The coach is hospital-based and uses the telephone and mailouts to provide regular coaching sessions to patients after discharge from hospital. Coaching is directed at the patient and not at the treating doctor. Patients are coached to know their risk factor levels, know the target levels for their risk factors and how to achieve the target levels for their risk factors. Patients are persuaded to go to their own doctor(s) and ask for appropriate prescription of medication(s). Coaching also trains patients to follow appropriate lifestyle measures. The Coach Program has been validated by two randomised controlled trials. Pilot project carried out at St. Vincent's Hospital only by the PhD Candidate, a qualified dietitian. This study targeted cholesterol levels only, with the aim of achieving a TC < 4.5 mmol/L. At the end of the 6 month intervention, 107 patients who were coached achieved a mean TC (95%CI) of 5.00 (4.82-5.17) mmol/L versus 5.54 (5.36-5.72) mmol/L in 112 usual care patients (P<0.0001). Multivariate analysis showed that being coached was of equal magnitude in its effect on TC as was prescription of lipid-lowering medication. The Coach Program achieved a significantly greater ΔTC than usual care alone: mean ΔTC (95%CI) 0.54 (0.42 to 0.65) mmol/L (n=398) in The Coach Program group versus 0.18 (0.07 to 0.29) mmol/L (n=394) in the usual care group (P<0.0001). Thus, the reduction in TC from baseline to 6 months post-randomisation was 0.36 (95%CI: 0.20 to 0.52) mmol/L greater in The Coach Program group than in the usual care group. Coaching produced substantial improvements in most of the other coronary risk factors and in the patient's quality of life. The results of these two randomised controlled trials prove that coaching, delivered as The Coach Program, is a highly effective strategy in reducing TC and many other coronary risk factors in patients with CHD.