Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

The prediagnostic general practitioner care of sarcoma patients: A real-world data study.

BACKGROUND: Limited understanding exists regarding early sarcoma symptoms presented during general practitioner (GP) consultations. The study explores GP visit patterns and recorded diagnoses in the 12 months preceding sarcoma diagnosis. METHODS: Sarcoma cases diagnosed from 2010 to 2020 were identified through the Netherlands Cancer Registry alongside general practice data. Sarcoma cases were age and gender matched to cancer-free controls (2:1 or 1:1 ratio). RESULTS: A total of 787 individuals with soft-tissue sarcoma (STS) and 188 individuals with bone sarcoma (BS) were identified. There was a significant difference in monthly GP contacts from 4 months to the last month before STS diagnosis, and 2 months before BS diagnosis between cases and controls. Most prevalent diagnoses recorded by the GP for STS cases included musculoskeletal neoplasm (26.6%), uncomplicated hypertension (15.6%), and cystitis/other urinary infections (12.2%). For BS cases, musculoskeletal neoplasm (42.8%), knee symptoms/complaints (9.7%), and shoulder symptoms/complaints (9.7%) were most frequent. CONCLUSIONS AND DISCUSSION: A significant difference in GP contacts between cases and controls preceding sarcoma diagnosis. STS cases were predominantly diagnosed with nonspecific symptoms, whereas BS cases with diagnoses more suggestive of BS. Better understanding of the prediagnostic trajectory could aid GPs in early identification of sarcoma.

Lessons learned from the development of a national registry on dementia care and support based on linked national health and administrative data.

Introduction: This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods: This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results: The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources. Conclusions: A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.

PURPOSE: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF. METHODS: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic. RESULTS: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES). CONCLUSION: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets. IMPLICATIONS FOR CANCER SURVIVORS: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial.

Varying severities of symptoms underline the relevance of personalized follow-up care in breast cancer survivors: latent class cluster analyses in a cross-sectional cohort.

PURPOSE: Insights into the severity of co-existing symptoms can help in identifying breast cancer survivors in need of symptom management. We aimed to identify subgroups of breast cancer survivors based on patterns of symptom severity, and characteristics associated with these subgroups. METHODS: We selected surgically treated stage I-III breast cancer survivors 1-5 years post-diagnosis from the Netherlands Cancer Registry (N = 876). We assessed experienced severity of fatigue, nausea, pain, dyspnea, insomnia, appetite, constipation, diarrhea, and emotional and cognitive symptoms through the EORTC-QLQ-C30 Quality of Life Questionnaire on a scale of 0-100. We determined subgroups of survivors using latent class cluster analyses (LCA) based on severity of co-existing symptoms and compared their mean severity to the age-matched female reference population to interpret clinical relevance. We assessed subgroup characteristics by multinomial logistic regression analyses. RESULTS: From 404 respondents (46%), three subgroups of survivors with distinct symptom severity were identified: low severity (n = 116, 28.7%), intermediate severity (n = 224, 55.4%), and high severity (n = 59, 14.6%). The low subgroup reported lower symptom severity than the general population; the intermediate subgroup reported a similar symptom severity, although scores for fatigue, insomnia, and cognitive symptoms were worse (small-medium clinical relevance). The high subgroup had worse symptom severity (medium-large clinical relevance). Compared to the intermediate subgroup, one (RRR: 2.75; CI: 1.22-6.19; p = 0.015) or more (RRR: 9.19; CI: 3.70-22.8; p = < 0.001) comorbidities were significantly associated with the high subgroup. We found no associated treatment characteristics. CONCLUSION: We identified distinct subgroups of breast cancer survivors based on symptom severity, underlining the relevance of further exploring personalized follow-up strategies.

Opportunities and obstacles in linking large health care registries: the primary secondary cancer care registry - breast cancer.

BACKGROUND: The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer. METHODS: The PSCCR - Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well. RESULTS: A collaboration agreement was signed in which the organisations retained data responsibility and accountability for 'their' registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients. CONCLUSIONS: We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries.

Evaluating quality of care at the end of life and setting best practice performance standards: a population-based observational study using linked routinely collected administrative databases.

BACKGROUND: A high percentage of people dying at home, and a low percentage of people being admitted to hospital and dying there are regarded as indicators of appropriate care at the end of life. However, performance standards for these quality indicators are often lacking, which makes it difficult to state whether an indicator score falls between the ranges of good or poor quality care. The aim of this study was to assess quality indicators concerning place of death and hospital care utilization in people with diseases relevant for palliative care, and to establish best practice performance standards based on indicator scores in 31 regions in the Netherlands. METHODS: A retrospective nationwide population-based observational study was conducted, using routinely collected administrative data concerning persons who died in 2017 in the Netherlands with underlying causes relevant for palliative care (N = 109,707). Data from four registries were linked for analysis. Scores on eight quality indicators concerning place of death and hospital care utilization were calculated, and compared across 31 healthcare insurance regions to establish relative benchmarks. RESULTS: On average, 36.4% of the study population died at home (range between regions 30.5%-42.6%) and 20.4% in hospital (range 16.6%-25.5%). Roughly half of the population who received hospital care at any time in the last year of life were found to (also) receive hospital care in the last month of life. In the last month, 32.0% of the study population were admitted to hospital (range 29.4-36.4%), 5.3% to an Intensive Care Unit (range 3.2-6.9%) and 23.9% visited an Emergency Department (range 21.0-27.4%). In the same time period, less than 1% of the study population was resuscitated in hospital or received tube or intravenous feeding in hospital. CONCLUSIONS: The variation between regions points towards opportunities for practice improvement. The best practice performance standards as set in this study serve as ambitious but attainable targets for those regions that currently do not meet the standards. Policymakers, healthcare providers and researchers can use the suggested performance standards to further analyze causes of variance between regions and develop and test interventions that can improve practice.

Antibiotic Prescribing in Dutch Daytime and Out-of-Hours General Practice during the COVID-19 Pandemic: A Retrospective Database Study.

COVID-19 restrictions have resulted in major changes in healthcare, including the prescribing of antibiotics. We aimed to monitor antibiotic prescribing trends during the COVID-19 pandemic in Dutch general practice, both during daytime and out-of-hours (OOH). Routine care data were used from 379 daytime general practices (DGP) and 28 OOH-services over the period 2019-2021. Per week, we analyzed prescription rates per 100,000 inhabitants, overall, for respiratory and urinary tract infections (RTIs and UTIs) specifically and within age categories. We assessed changes in antibiotic prescribing during different phases of the pandemic using interrupted time series analyses. Both at DGPs and OOH-services significantly fewer antibiotics were prescribed during the COVID-19 pandemic after government measures became effective. Furthermore, the number of contacts decreased in both settings. When restrictions were revoked in 2021 prescription rates increased both at DGP and OOH-services, returning to pre-pandemic levels at OOH-services, but not in DGP. Changes in antibiotic prescribing rates were prominent for RTIs and among children up to 11 years old, but not for UTIs. To conclude, while antibiotic prescribing decreased during the first year of the COVID-19 pandemic both in daytime and out-of-hours, the pandemic does not seem to have a lasting effect on antibiotic prescribing.

Prevalence of Health Problems and Health-Care Use in Partners of People with Dementia: Longitudinal Analysis with Routinely Recorded Health and Administrative Data.

INTRODUCTION: This study aims to provide insight into the prevalence of health problems and the frequency of general practitioner (GP) contacts in cohabiting partners of persons with dementia, during the year prior to the dementia diagnosis and up to 3 years after the diagnosis. METHODS: Partners of persons with dementia and a matched control group of partners of persons without dementia were identified in the routinely recorded electronic health records of 451 Dutch general practices in 2008-2015. These data were used to examine the prevalence of the partners' health problems. Differences between these partners and comparison partners in the prevalence of 16 groups of health problems (diagnostic chapters) and in the frequency of GP contacts were examined using generalized estimating equation models. RESULTS: 1,711 partners of persons with dementia and 6,201 comparison partners were included in the analyses. Social problems, more specifically problems related to the illness and/or the loss of the partner, were significantly more prevalent in partners than in comparison partners across the years (p < 0.01), as were musculoskeletal problems (p < 0.01). Respiratory and psychological problems increased over time in partners and remained stable in comparison partners. Across the years, partners contacted their GP more often than comparison partners (p < 0.01). DISCUSSION/CONCLUSION: Having a cohabiting partner with dementia has consequences for caregiver's physical and psychosocial health. The specific health problems found in this study and the increase in GP contacts might be relevant indicators of overburdening in partners of persons with dementia.

Adverse health effects after breast cancer up to 14 years after diagnosis.

BACKGROUND: The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for which survivors visit their general practitioner up to 14 years after diagnosis. METHODS: We retrieved data on 11,671 women diagnosed with breast cancer in 2000-2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis). RESULTS: 229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection. CONCLUSIONS: Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects.

How Transitions in Dementia Care Trajectories Affect Health Problems in Partners: A Longitudinal Analysis With Linked Health and Administrative Data.

OBJECTIVES: To evaluate whether the prevalence of health problems in cohabiting partners of people with dementia differs between the year before and the year after 3 transitions: the diagnosis of dementia, institutionalization, and the death of the person with dementia. METHODS: Individuals with dementia and their partners were identified on the basis of data in the electronic health records (EHRs) of 451 Dutch general practices. EHRs were also the data source on their health problems, which were linked to the Dutch population registry and health administration data with demographic characteristics, date of institutionalization, and date of death. Differences in the prevalence of 16 groups of health problems and of specific health problems in partners during the year before and the year after the 3 transitions were examined using a generalized estimating equation. RESULTS: About 1,110 partners of persons with dementia were identified. Problems related to the illness and/or loss of the person with dementia were significantly more prevalent in the year after the dementia diagnosis (32% vs. 17%) and in the year after the death of the person with dementia (59% vs. 41%) than in the years before. Unspecified health problems were more prevalent in the year after the diagnosis than in the year before (30% vs. 22%). After institutionalization, an increase was found in digestive problems and urological problems (30% vs. 18% and 24% vs. 17%). DISCUSSION: Transitions during the dementia care trajectory, namely, the diagnosis, institutionalization, and death of the person with dementia, significantly affect the cohabiting partner's health.

Identifying multimorbid patients with high care needs - A study based on electronic medical record data.

BACKGROUND: Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these 'high need' patients are. Electronic medical records are a potential source to identify them. OBJECTIVES: To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value. METHODS: General practice electronic medical records of 245,065 patients with ≥2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict i) having at least 12 general practice contacts per year, ii) emergency department visit(s), and iii) unplanned hospitalisation(s). Predictors were patients' age, sex, morbidity, health services and medication use in the previous year. RESULTS: 11% of multimorbid patients had ≥12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%). CONCLUSION: Among multimorbid populations various 'high need' groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.

Applying machine learning on health record data from general practitioners to predict suicidality.

BACKGROUND: Suicidal behaviour is difficult to detect in the general practice. Machine learning (ML) algorithms using routinely collected data might support General Practitioners (GPs) in the detection of suicidal behaviour. In this paper, we applied machine learning techniques to support GPs recognizing suicidal behaviour in primary care patients using routinely collected general practice data. METHODS: This case-control study used data from a national representative primary care database including over 1.5 million patients (Nivel Primary Care Database). Patients with a suicide (attempt) in 2017 were selected as cases (N = 574) and an at risk control group (N = 207,308) was selected from patients with psychological vulnerability but without a suicide attempt in 2017. RandomForest was trained on a small subsample of the data (training set), and evaluated on unseen data (test set). RESULTS: Almost two-third (65%) of the cases visited their GP within the last 30 days before the suicide (attempt). RandomForest showed a positive predictive value (PPV) of 0.05 (0.04-0.06), with a sensitivity of 0.39 (0.32-0.47) and area under the curve (AUC) of 0.85 (0.81-0.88). Almost all controls were accurately labeled as controls (specificity = 0.98 (0.97-0.98)). Among a sample of 650 at-risk primary care patients, the algorithm would label 20 patients as high-risk. Of those, one would be an actual case and additionally, one case would be missed. CONCLUSION: In this study, we applied machine learning to predict suicidal behaviour using general practice data. Our results showed that these techniques can be used as a complementary step in the identification and stratification of patients at risk of suicidal behaviour. The results are encouraging and provide a first step to use automated screening directly in clinical practice. Additional data from different social domains, such as employment and education, might improve accuracy.

Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study.

BACKGROUND: A proactive person-centred care process is advocated for people with multimorbidity. To that aim, general practitioners may benefit from support in the identification of high-need patients, i.e. patients who are high or suboptimal users of health services and/or have a poor quality of life. To develop such support, we examined whether knowledge about patients' illness perceptions and personal resources to manage their health and care is useful to identify high-need patients among multimorbid general practice populations. METHODS: Survey data, collected in 2016 and 2017, of 601 patients with two or more chronic diseases (e.g. COPD, diabetes, Parkinson's disease) registered with 40 general practices in the Netherlands were analysed by logistic regression analysis to predict frequent contact with the general practice, contact with general practice out-of-office services, unplanned hospitalisations and poor health related quality of life. Patients' illness perceptions and personal resources (education, health literacy, mastery, mental health status, financial resources, social support) were included as predictors. RESULTS: The four outcomes were only weakly associated among themselves (Phi .07-.19). Patients' illness perceptions and personal resources were of limited value to predict potentially suboptimal health service use, but they were important predictors of health related quality of life. Patients with a poor health related quality of life could be identified by their previously reported illness perceptions (attributing many symptoms to their chronic conditions (B = 1.479, P < .001), a high level of concern (B = 0.844, P = .002) and little perceived control over their illness (B = -0.728, P = .006)) combined with an experienced lack of social support (B = -0.527, P = .042) and a poor mental health status (B = -0.966, P = .001) (sensitivity 80.7%; specificity 68.1%). CONCLUSIONS: Multimorbid patients who frequently contact the general practice, use general practice out-of-office services, have unplanned hospitalisations or a poor health related quality of life are largely distinct high-need subgroups. Multimorbid patients at risk of developing a poor quality of life can be identified from specific illness beliefs, a poor mental health status and unmet social needs.

Availability and effectiveness of decision aids for supporting shared decision making in patients with advanced colorectal and lung cancer: Results from a systematic review.

INTRODUCTION: Shared decision making is not always commonplace in advanced colorectal or lung cancer care. Decision aids (DAs) might be helpful. This review aimed (a) to provide an overview of DAs for patients with advanced colorectal or lung cancer and assess their availability; and (b) to assess their effectiveness if possible. METHODS: A systematic literature search (PubMed/EMBASE/PsycINFO/CINAHL) and Internet and expert searches were carried out to identify relevant DAs. Data from the DAs included were extracted and the quality of studies, evidence (Grading of Recommendations Assessment, Development and Evaluation) and effectiveness (International Patient Decision Aid Standards) of DAs were determined. RESULTS: Ten of the 12 DAs included (four colorectal cancer, four lung cancer and four generic) are still available. Most (9/12) were applicable throughout the disease pathway and usable for all decisions, or to the decision for supportive care with/without anti-cancer therapy. Seven studies tested effectiveness. Effects on patient outcomes varied, but were generally weakly positive (e.g., DAs improved patient satisfaction) with low evidence. Study quality was fair to good. CONCLUSION: There is a lack of readily available DAs that have been demonstrated to be effective in advanced colorectal or lung cancer. Rigorous testing of the effects of currently available and future DAs, to improve patient outcomes, is urgently needed.

The involvement of primary care physicians in care for childhood cancer survivors.

BACKGROUND: Childhood cancer survivors (CCS) are at risk of developing long-term morbidity, which is likely to be presented to a primary care physician (PCP). Therefore, insight into CCS's PCP-based health care use is needed. We investigated the volume and underlying health problems of PCP-based health care use and the determinants for PCP-based health care use in CCS. PROCEDURE: Data from a Dutch cohort of 6018 eligible five-year CCS were linked to the Nivel Primary Care database, which contains detailed data from a representative sample of 10% of all Dutch PCPs. Per CCS, two matched controls were selected. Negative binomial regression was performed to compare the annual number of contacts between CCS and controls, and to identify determinants for PCP-based care use among CCS. RESULTS: This study included 602 CCS and 1204 controls. CCS were 1.3 times more likely to contact their PCP than controls (95% CI, 1.2-1.5), up to 1.5 times at attained age over 40 years (95% CI, 1.2-1.8). CCS were 4.9 times more likely to contact their PCP for new malignancies, 3.1 for hematological conditions, and 2.8 for endocrine conditions. Female sex, higher attained age, and treatment with radiotherapy were determinants for having more PCP contacts. CONCLUSIONS: PCPs play an important role in care for CCS. CCS use more PCP-based care than matched controls, mainly for severe conditions such as malignancies, hematological, and endocrine conditions. Our results emphasize the importance of disseminating the current knowledge on long-term morbidity in CCS and on their optimal follow-up care among PCPs.

Healthy elderly and influenza vaccination.

In many countries, those at risk for complications due to influenza are invited for influenza vaccination, to prevent serious consequences for themselves and those around them. However, vaccination rates are decreasing. The first invitation for vaccination may provide an opportunity to convey ample information about the (dis)advantages of vaccination. We aimed to identify subgroups less likely to be vaccinated after their first invitation. Using data from 87 general practices participating in NIVEL Primary Care Database, we selected persons invited for vaccination for the first time because of their 60th birthday. Of 3.238 included persons, 78% were not vaccinated after their first invitation and in the vast majority (84%) this decision remained consistent over the next years. Men and those with fewer GP contacts were less likely to be vaccinated. This latter group is not easily reached by the GP, so maybe other ways should be considered to convey information about influenza vaccination.

Palliative care for patients with cancer: do patients receive the care they consider important? A survey study.

BACKGROUND: In many countries, GPs and home care nurses are involved in care for patients with advanced cancer. Given the varied and complex needs of these patients, providing satisfactory care is a major challenge for them. We therefore aimed to study which aspects of care patients, GPs and home care nurses consider important and whether patients receive these aspects. METHODS: Seventy-two Dutch patients with advanced cancer, 87 GPs and 26 home care nurses rated the importance of support when experiencing symptoms, respect for patients' autonomy and information provision. Patients also rated whether they received these aspects. Questionnaires were based on the CQ index palliative care. RESULTS: Almost all patients rated information provision and respect for their autonomy as important. The majority also rated support when suffering from specific symptoms as important, especially support when in pain. In general, patients received the care they considered important. However, 49% of those who considered it important to receive support when suffering from fatigue and 23% of those who wanted to receive information on the expected course of their illness did not receive this or only did so sometimes. CONCLUSION: For most patients with advanced cancer, the palliative care that they receive matches what they consider important. Support for patients experiencing fatigue may need more attention. When symptoms are difficult to control, GPs and nurses may still provide emotional support and practical advice. Furthermore, we recommend that GPs discuss patients' need for information about the expected course of their illness.

Are the effects of cognitive behavior therapy for severe fatigue in cancer survivors sustained up to 14 years after therapy?

PURPOSE: Cognitive behavior therapy (CBT) reduces cancer-related fatigue (CRF) in cancer survivors in the short term. We examined fatigue levels up to 14 years after CBT. METHODS: Eligible participants of two randomized controlled trials who had completed CBT for CRF and a post-treatment assessment were contacted (n = 81). Fatigue was assessed with the subscale "fatigue severity" of the Checklist Individual Strength (CIS-fatigue). The course of fatigue over time was examined with linear mixed model analyses. Fatigue levels of participants were compared to matched population controls at long-term follow-up. We tested with multiple regression analysis if fatigue at follow-up was predicted by the patients' fatigue level and fatigue-perpetuating factors directly after CBT (post-CBT). RESULTS: Seventy-eight persons completed a follow-up assessment (response rate = 96%, mean time after CBT = 10 years). The mean level of fatigue increased from 23.7 (SD = 11.1) at post-CBT to 34.4 (SD = 12.4) at follow-up (p < 0.001). Population controls (M = 23,9, SD = 11.4) reported lower fatigue levels than participants. Half of the patients (52%) who were recovered from severe fatigue at post-CBT (CIS-fatigue < 35) were still recovered at long-term follow-up. Patients with lower fatigue levels at post-CBT were less likely to show relapse. CONCLUSION: Despite initial improvement after CBT, levels of fatigue deteriorated over time. Half of the patients who were recovered from severe fatigue after CBT still scored within normal ranges of fatigue at long-term follow-up. IMPLICATIONS FOR CANCER SURVIVORS: It should be explored how to help patients with a relapse of severe fatigue following an initially successful CBT. They may profit from CBT again, or another evidence-based intervention for fatigue (like mindfulness or exercise therapy). Future research to gain insight into reasons for relapse is warranted.

Causality on longitudinal data: Stable specification search in constrained structural equation modeling.

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.