Structured alcohol cessation support program versus current practice in acute alcoholic pancreatitis (PANDA): Study protocol for a multicentre cluster randomised controlled trial.

BACKGROUND/OBJECTIVES: The most important risk factor for recurrent pancreatitis after an episode of acute alcoholic pancreatitis is continuation of alcohol use. Current guidelines do not recommend any specific treatment strategy regarding alcohol cessation. The PANDA trial investigates whether implementation of a structured alcohol cessation support program prevents pancreatitis recurrence after a first episode of acute alcoholic pancreatitis. METHODS: PANDA is a nationwide cluster randomised superiority trial. Participating hospitals are randomised for the investigational management, consisting of a structured alcohol cessation support program, or current practice. Patients with a first episode of acute pancreatitis caused by harmful drinking (AUDIT score >7 and < 16 for men and >6 and < 14 for women) will be included. The primary endpoint is recurrence of acute pancreatitis. Secondary endpoints include cessation or reduction of alcohol use, other alcohol-related diseases, mortality, quality of life, quality-adjusted life years (QALYs) and costs. The follow-up period comprises one year after inclusion. DISCUSSION: This is the first multicentre trial with a cluster randomised trial design to investigate whether a structured alcohol cessation support program reduces recurrent acute pancreatitis in patients after a first episode of acute alcoholic pancreatitis, as compared with current practice. TRIAL REGISTRATION: Netherlands Trial Registry (NL8852). Prospectively registered.

Diagnostic properties of natriuretic peptides and opportunities for personalized thresholds for detecting heart failure in primary care.

OBJECTIVES: Heart failure (HF) is a prevalent syndrome with considerable disease burden, healthcare utilization and costs. Timely diagnosis is essential to improve outcomes. This study aimed to compare the diagnostic performance of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) in detecting HF in primary care. Our second aim was to explore if personalized thresholds (using age, sex, or other readily available parameters) would further improve diagnostic accuracy over universal thresholds. METHODS: A retrospective study was performed among patients without prior HF who underwent natriuretic peptide (NP) testing in the Amsterdam General Practice Network between January 2011 and December 2021. HF incidence was based on registration out to 90 days after NP testing. Diagnostic accuracy was evaluated with AUROC, sensitivity and specificity based on guideline-recommended thresholds (125 ng/L for NT-proBNP and 35 ng/L for BNP). We used inverse probability of treatment weighting to adjust for confounding. RESULTS: A total of 15,234 patients underwent NP testing, 6,870 with BNP (4.5 % had HF), and 8,364 with NT-proBNP (5.7 % had HF). NT-proBNP was more accurate than BNP, with an AUROC of 89.9 % (95 % CI: 88.4-91.2) vs. 85.9 % (95 % CI 83.5-88.2), with higher sensitivity (95.3 vs. 89.7 %) and specificity (59.1 vs. 58.0 %). Differentiating NP cut-off by clinical variables modestly improved diagnostic accuracy for BNP and NT-proBNP compared with a universal threshold. CONCLUSIONS: NT-proBNP outperforms BNP for detecting HF in primary care. Personalized instead of universal diagnostic thresholds led to modest improvement.

Early detection of colorectal cancer by leveraging Dutch primary care consultation notes with free text embeddings.

We aimed to assess the added predictive performance that free-text Dutch consultation notes provide in detecting colorectal cancer in primary care, in comparison to currently used models. We developed, evaluated and compared three prediction models for colorectal cancer (CRC) in a large primary care database with 60,641 patients. The prediction model with both known predictive features and free-text data (with TabTxt AUROC: 0.823) performs statistically significantly better (p < 0.05) than the other two models with only tabular (as used nowadays) and text data, respectively (AUROC Tab: 0.767; Txt: 0.797). The specificity of the two models that use demographics and known CRC features (with specificity Tab: 0.321; TabTxt: 0.335) are higher than that of the model with only free-text (specificity Txt: 0.234). The Txt and, to a lesser degree, TabTxt model are well calibrated, while the Tab model shows slight underprediction at both tails. As expected with an outcome prevalence below 0.01, all models show much uncalibrated predictions in the extreme upper tail (top 1%). Free-text consultation notes show promising results to improve the predictive performance over established prediction models that only use structured features. Clinical future implications for our CRC use case include that such improvement may help lowering the number of referrals for suspected CRC to medical specialists.

Characteristics of heart failure in the Amsterdam metropolitan area (AMSTERDAM-HF): Data from a dynamic general practice cohort (2011-2021).

BACKGROUND: Heart failure (HF) is a common cardiac syndrome with a high disease burden and poor prognosis in our aging populations. Understanding the characteristics of patients with newly diagnosed HF is essential for improving care and outcomes. The AMSTERDAM-HF study is aimed to examine the population characteristics of patients with incident HF. METHODS: We performed a retrospective dynamic cohort study in the Amsterdam general practice network consisting of 904,557 individuals. Incidence HF rates, geographical demographics, patient characteristics, risk factors, symptoms prior to HF diagnosis, and prognosis were reported. RESULTS: The study identified 10,067 new cases of HF over 6,816,099 person-years. The median age of patients was 77 years (25th-75th percentile: 66-85), and 48% were male. The incidence rate of HF was 213.44 per 100,000 patient-years, and was higher in male versus female patients (incidence rate ratio: 1.08, 95%-CI:1.04-1.13). Hypertension (men 46.3% and women 55.8%), coronary artery disease (men 36% and women 25%) and diabetes mellitus (men 30.5% and women 26.8%) were the most common risk factors. Dyspnoea and oedema were key reported symptoms prior to HF diagnosis. Survival rates at 10-year follow-up were poor, particularly in men (36.4%) compared to women (39.7%). Incidence rates, comorbidity burden and prognosis were worse in city districts with high ethnic diversity and low socio-economic position. CONCLUSION: Our study provides insights into incident HF in a contemporary Western European, multi-ethnic, urban population. It highlights notable sex, age, and geographical differences in incidence rates, risk factors, symptoms and prognosis.

Cost-effectiveness of general practitioner- versus surgeon-led colon cancer survivorship care: an economic evaluation alongside a randomised controlled trial.

PURPOSE: The aim of this study is to assess cost-effectiveness of general practitioner (GP) versus surgeon-led colon cancer survivorship care from a societal perspective. METHODS: We performed an economic evaluation alongside the I CARE study, which included 303 cancer patients (stages I-III) who were randomised to survivorship care by a GP or surgeon. Questionnaires were administered at baseline, 3-, 6-, 12-, 24- and 36-months. Costs included healthcare costs (measured by iMTA MCQ) and lost productivity costs (SF-HLQ). Disease-specific quality of life (QoL) was measured using EORTC QLQ-C30 summary score and general QoL using EQ-5D-3L quality-adjusted life years (QALYs). Missing data were imputed. Incremental cost-effectiveness ratios (ICERs) were calculated to relate costs to effects on QoL. Statistical uncertainty was estimated using bootstrapping. RESULTS: Total societal costs of GP-led care were significantly lower compared to surgeon-led care (mean difference of - €3895; 95% CI - €6113; - €1712). Lost productivity was the main contributor to the difference in societal costs (- €3305; 95% CI - €5028; - €1739). The difference in QLQ-C30 summary score over time between groups was 1.33 (95% CI - 0.049; 3.15). The ICER for QLQ-C30 was - 2073, indicating that GP-led care is dominant over surgeon-led care. The difference in QALYs was - 0.021 (95% CI - 0.083; 0.040) resulting in an ICER of 129,164. CONCLUSIONS: GP-led care is likely to be cost-effective for disease-specific QoL, but not for general QoL. IMPLICATIONS FOR CANCER SURVIVORS: With a growing number of cancer survivors, GP-led survivorship care could help to alleviate some of the burden on more expensive secondary healthcare services.

Detection of colon cancer recurrences during follow-up care by general practitioners vs surgeons.

BACKGROUND: In the I CARE study, colon cancer patients were randomly assigned to receive follow-up care from either a general practitioner (GP) or a surgeon. Here, we address a secondary outcome, namely, detection of recurrences and effect on time to detection of transferring care from surgeon to GP. METHODS: Pattern, stage, and treatment of recurrences were described after 3 years. Time to event was defined as date of surgery, until date of recurrence or last follow-up, with death as competing event. Effects on time to recurrence and death were estimated as hazard ratios (HRs) using Cox regression. Restricted mean survival times were estimated. RESULTS: Of 303 patients, 141 were randomly assigned to the GP and 162 to the surgeon. Patients were male (67%) with a mean age of 68.0 (8.4) years. During follow-up, 46 recurrences were detected; 18 (13%) in the GP vs 28 (17%) in the surgeon group. Most recurrences were detected via abnormal follow-up tests (74%) and treated with curative intent (59%). Hazard ratio for recurrence was 0.75 (95% confidence interval [CI] = 0.41 to 1.36) in GP vs surgeon group. Patients in the GP group remained in the disease-free state slightly longer (2.76 vs 2.71 years). Of the patients, 38 died during follow-up; 15 (11%) in the GP vs 23 (14%) in the surgeon group. Of these, 21 (55%) deaths were related to colon cancer. There were no differences in overall deaths between the groups (HR = 0.76, 95% CI = 0.39 to 1.46). CONCLUSION: Follow-up provided by GPs vs surgeons leads to similar detection of recurrences. Also, no differences in mortality were found.

TARGET-HF: developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data.

BACKGROUND: Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data. METHODS: Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor. RESULTS: Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n = 25,524) and validation (n = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P < 0.001). CONCLUSION: The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation.

Reasons for No Colonoscopy After an Unfavorable Screening Result in Dutch Colorectal Cancer Screening: A Nationwide Questionnaire.

PURPOSE: We aimed to assess participant-reported factors associated with non-follow-up with colonoscopy in colorectal cancer (CRC) screening. METHODS: In May 2019, we distributed a nationwide cross-sectional questionnaire (n = 4,009) to participants in the Dutch CRC screening program who received a positive fecal immunochemical test (FIT). Among respondents who reported no colonoscopy, we assessed the presence of a contraindication, and those without were compared with those who reported colonoscopy by logistic regression analysis. RESULTS: Of 2,225 respondents (56% response rate), 730 (33%) reported no colonoscopy. A contraindication was reported by 55% (n = 404). Decisional difficulties (odds ratio [OR] = 0.29; 95% CI, 0.18-0.47), lacking the opportunity to discuss the FIT outcome (OR = 0.45; 95% CI, 0.28-0.72), and a low estimated risk of CRC (OR = 0.45; 95% CI, 0.26-0.76) were negatively associated with follow-up. Knowledge items negatively associated with follow-up included having an alternative explanation for the positive FIT (OR = 0.3; 95% CI, 0.21-0.43), having trust in the ability to self-detect CRC (OR = 0.42; 95% CI, 0.27-0.65), and thinking that polyp removal is ineffective (OR = 0.59; 95% CI, 0.43-0.82). The belief that the family physician would support colonoscopy showed the strongest positive association with follow-up (OR = 2.84; 95% CI, 2.01-4.02) CONCLUSIONS: Because decisional difficulties and certain convictions regarding CRC and screening are associated with non-follow-up, personalized screening counseling might be an intervention worth exploring as a means of improving follow-up in the Dutch CRC screening program. Involving family physicians might also prove beneficial.

Addressing colon cancer patients' needs during follow-up consultations at the outpatient clinic: a multicenter qualitative observational study.

PURPOSE: To describe colon cancer patients' needs and how healthcare providers respond to these needs during routine follow-up consultations in hospital. METHODS: A multicenter qualitative observational study, consisting of follow-up consultations by surgeons and specialized oncology nurses. Consultations were analyzed according to Verona Coding Definitions of Emotional Sequences. Patients' questions, cues, and concerns were derived from the data and categorized into supportive care domains. Responses of healthcare providers were defined as providing or reducing space for disclosure. Patient satisfaction with care was measured with a short questionnaire. RESULTS: Consultations with 30 patients were observed. Questions typically centered around the health system and information domain (i.e., follow-up schedule and test results; 92%). Cues and concerns were mostly associated with the physical and daily living domain (i.e., experiencing symptoms and difficulties resuming daily routine; 43%), followed by health system and information (i.e., miscommunication or lack of clarity about follow-up; 28%), and psychological domain (i.e., fear of recurrence and complications; 28%). Problems in the sexuality domain hardly ever arose (0%). Healthcare providers provided space to talk about half of the cues and concerns (54%). Responses to cancer-related versus unrelated problems were similar. Overall, the patients were satisfied with the information and communication received. CONCLUSIONS: Colon cancer patients express various needs during consultations. Healthcare providers respond to different types of needs in a similar fashion. We encourage clinicians to discuss all supportive care domains, including sexuality, and provide space for further disclosure. General practitioners are trained to provide holistic care and could play a greater role.

Detection of atrial fibrillation in primary care with radial pulse palpation, electronic blood pressure measurement and handheld single-lead electrocardiography: a diagnostic accuracy study.

OBJECTIVE: To determine the diagnostic accuracy of three tests-radial pulse palpation, an electronic blood pressure monitor and a handheld single-lead ECG device-for opportunistic screening for unknown atrial fibrillation (AF). DESIGN: We performed a diagnostic accuracy study in the intention-to-screen arm of a cluster randomised controlled trial aimed at opportunistic screening for AF in general practice. We performed radial pulse palpation, followed by electronic blood pressure measurement (WatchBP Home A) and handheld ECG (MyDiagnostick) in random order. If one or more index tests were positive, we performed a 12-lead ECG at shortest notice. Similarly, to limit verification bias, a random sample of patients with three negative index tests received this reference test. Additionally, we analysed the dataset using multiple imputation. We present pooled diagnostic parameters. SETTING: 47 general practices participated between September 2015 and August 2018. PARTICIPANTS: In the electronic medical record system of the participating general practices (n=47), we randomly marked 200 patients of ≥65 years without AF. When they visited the practice for any reason, we invited them to participate. Exclusion criteria were terminal illness, inability to give informed consent or visit the practice or having a pacemaker or an implantable cardioverter-defibrillator. OUTCOMES: Diagnostic accuracy of individual tests and test combinations to detect unknown AF. RESULTS: We included 4339 patients; 0.8% showed new AF. Sensitivity and specificity were 62.8% (range 43.1%-69.7%) and 91.8% (91.7%-91.8%) for radial pulse palpation, 70.0% (49.0%-80.6%) and 96.5% (96.3%-96.7%) for electronic blood pressure measurement and 90.1% (60.8%-100%) and 97.9% (97.8%-97.9%) for handheld ECG, respectively. Positive predictive values were 5.8% (5.3%-6.1%), 13.8% (12.2%-14.8%) and 25.2% (24.2%-25.8%), respectively. All negative predictive values were ≥99.7%. CONCLUSION: In detecting AF, electronic blood pressure measurement (WatchBP Home A), but especially handheld ECG (MyDiagnostick) showed better diagnostic accuracy than radial pulse palpation. TRIAL REGISTRATION NUMBER: Netherlands Trial Register No. NL4776 (old NTR4914).

Symptoms and seeking supportive care and associations with quality of life after treatment for colon cancer: Results from the I CARE cohort study.

OBJECTIVE: Patients treated for colon cancer report many symptoms that affect quality of life (QoL). Survivorship care aims at QoL improvement. In this study, we assess associations between symptoms and seeking supportive care and lower QoL and QoL changes overtime during survivorship care. METHODS: A prospective cohort of colon cancer survivors. Questionnaires are administered at inclusion and 6 months later to evaluate symptoms, functioning and seeking supportive care including associations with QoL, using the EORTC QLQ-C30. RESULTS: The mean QoL score at the first questionnaire was 82 (scale 1-100), which improved over time. Pain, bowel symptoms and problems in physical, role, cognitive or social functioning are associated with lower QoL at inclusion but are not associated with QoL changes over time. Seeking support for lower bowel symptoms, physical functioning or fatigue is associated with lower QoL. After 6 months, seeking support for upper bowel symptoms or physical functioning is associated with a tendency towards less QoL improvement. CONCLUSION: QoL of colon cancer survivors improves over 6 months, but seeking support for specific symptoms barely contribute to this improvement. IMPLICATIONS: This study confirms the importance of addressing symptoms, problems related to functioning and seeking supportive care during survivorship care.

Delivering colon cancer survivorship care in primary care; a qualitative study on the experiences of general practitioners.

BACKGROUND: With more patients in need of oncological care, there is a growing interest to transfer survivorship care from specialist to general practitioner (GP). The ongoing I CARE study was initiated in 2015 in the Netherlands to compare (usual) surgeon- to GP-led survivorship care, with or without access to a supporting eHealth application (Oncokompas). METHODS: Semi-structured interviews were held at two separate points in time (i.e. after 1- and 5-years of care) to explore GPs' experiences with delivering this survivorship care intervention, and study its implementation into daily practice. Purposive sampling was used to recruit 17 GPs. Normalisation Process Theory (NPT) was used as a conceptual framework. RESULTS: Overall, delivering survivorship care was not deemed difficult and dealing with cancer repercussions was already considered part of a GPs' work. Though GPs readily identified advantages for patients, caregivers and society, differences were seen in GPs' commitment to the intervention and whether it felt right for them to be involved. Patients' initiative with respect to planning, absence of symptoms and regular check-ups due to other chronic care were considered to facilitate the delivery of care. Prominent barriers included GPs' lack of experience and routine, but also lack of clarity regarding roles and responsibilities for organising care. Need for a monitoring system was often mentioned to reduce the risk of non-compliance. GPs were reticent about a possible future transfer of survivorship care towards primary care due to increases in workload and financial constraints. GPs were not aware of their patients' use of eHealth. CONCLUSIONS: GPs' opinions and beliefs about a possible future role in colon cancer survivorship care vary. Though GPs recognize potential benefit, there is no consensus about transferring survivorship care to primary care on a permanent basis. Barriers and facilitators to implementation highlight the importance of both personal and system level factors. Conditions are put forth relating to time, reorganisation of infrastructure, extra personnel and financial compensation. TRIAL REGISTRATION: Netherlands Trial Register; NTR4860 . Registered on the 2nd of October 2014.

Higher chances of survival to hospital admission after out-of-hospital cardiac arrest in patients with previously diagnosed heart disease.

AIM: This study aimed to determine whether patients suffering from out-of-hospital cardiac arrest (OHCA) with a pre-OHCA diagnosis of heart disease have higher survival chances than patients without such a diagnosis and to explore possible underlying mechanisms. METHODS: A retrospective cohort study in 3760 OHCA patients from the Netherlands (2010-2016) was performed. Information from emergency medical services, treating hospitals, general practitioner, resuscitation ECGs and civil registry was used to assess medical histories and the presence of pre-OHCA diagnosis of heart disease. We used multivariable regression analysis to calculate associations with survival to hospital admission or discharge, immediate causes of OHCA (acute myocardial infarction (AMI) vs non-AMI) and initial recorded rhythm. RESULTS: Overall, 48.1% of OHCA patients had pre-OHCA heart disease. These patients had higher odds to survive to hospital admission than patients without pre-OHCA heart disease (OR 1.25 (95%CI 1.05 to 1.47)), despite being older and more often having cardiovascular risk factors and some non-cardiac comorbidities. These patients also had higher odds of shockable initial rhythm (SIR) (OR 1.60 (1. 36 to 1.89)) and a lower odds of AMI as immediate cause of OHCA (OR 0.33 (0.25 to 0.42)). Their chances of survival to hospital discharge were not significantly larger (OR 1.16 (0.95 to 1.42)). CONCLUSION: Having pre-OHCA diagnosed heart disease is associated with better odds to survive to hospital admission, but not to hospital discharge. This is associated with higher odds of a SIR and in a subgroup with available diagnosis a lower proportion of AMI as immediate cause of OHCA.

Screening for paroxysmal atrial fibrillation in primary care using Holter monitoring and intermittent, ambulatory single-lead electrocardiography.

BACKGROUND: Timely detection of atrial fibrillation (AF) is important because of its increased risk of thrombo-embolic events. Single time point screening interventions fall short in detection of paroxysmal AF, which requires prolonged electrocardiographic monitoring, usually using a Holter. However, traditional 24-48 h Holter monitoring is less appropriate for screening purposes because of its low diagnostic yield. Intermittent, ambulatory screening using a single-lead electrocardiogram (1 L-ECG) device can offer a more efficient alternative. METHODS: Primary care patients of ≥65 years participated in an opportunistic screening study for AF. We invited patients with a negative 12 L-ECG to wear a Holter monitor for two weeks and to use a MyDiagnostick 1 L-ECG device thrice daily. We report the yield of paroxysmal AF found by Holter monitoring and calculate the diagnostic accuracy of the 1 L-ECG device's built-in AF detection algorithm with the Holter monitor as reference standard. RESULTS: We included 270 patients, of whom four had AF in a median of 8.0 days of Holter monitoring, a diagnostic yield of 1.5% (95%-CI: 0.4-3.8%). In 205 patients we performed simultaneous 1 L-ECG screening. For diagnosing AF based on the 1 L-ECG device's AF detection algorithm, sensitivity was 66.7% (95%-CI: 9.4-99.2%), specificity 68.8% (95%-CI: 61.9-75.1%), positive predictive value 3.1% (95%-CI: 1.4-6.8%) and negative predictive value 99.3% (95%-CI: 96.6-99.9%). CONCLUSION: We found a low diagnostic yield of paroxysmal AF using Holter monitoring in elderly primary care patients with a negative 12 L-ECG. The diagnostic accuracy of an intermittently, ambulatory used MyDiagnostick 1 L-ECG device as interpreted by its built-in AF detection algorithm is limited.

Recruitment challenges to the I CARE study: a randomised trial on general practitioner-led colon cancer survivorship care.

OBJECTIVES: The I CARE study (Improving Care After colon canceR treatment in the Netherlands) aims to compare surgeon-led to general practitioner (GP)-led colon cancer survivorship care. Recruitment to the trial took longer than expected. In this descriptive study, recruitment is critically reviewed. SETTING: Patients were recruited from eight Dutch medical centres. PARTICIPANTS: Patients treated with curative intent for stages I-III colon cancer. Target patient sample size was calculated at 300. INTERVENTIONS: Patients were randomised to surgeon-led (usual) versus GP-led care, with or without access to an eHealth application (Oncokompas). OUTCOME MEASURES: Baseline characteristics of (non-)participants, reasons for non-participation and strategies to improve recruitment were reviewed. RESULTS: Out of 1238 eligible patients, 353 patients were included. Of these, 50 patients dropped out shortly after randomisation and before start of the intervention, resulting in a participation rate of 25%. Participants were on average slightly younger (68.1 years vs 69.3 years) and more often male (67% vs 50%) in comparison to non-participants. A total of 806 patients declined participation for reasons most often relating to research (57%), including the wish to remain in specialist care (31%) and too much effort to participate (12%). Some patients mentioned health (9%) and confrontation with the disease (5%) as a reason. In 43 cases, GPs declined participation, often related to the study objective, need for financial compensation and time restraints. The generally low participation rate led to concerns about reaching the target sample size. Methods to overcome recruitment challenges included changes to the original recruitment procedure and the addition of new study centres. CONCLUSIONS: Challenges were faced in the recruitment to a randomised trial on GP-led colon cancer survivorship care. Research on the transition of care requires sufficient time, funding and support base among patients and healthcare professionals. These findings will help inform researchers and policy-makers on the development of future practices. TRIAL REGISTRATION NUMBER: NTR4860.

Effect of general practitioner-led versus surgeon-led colon cancer survivorship care, with or without eHealth support, on quality of life (I CARE): an interim analysis of 1-year results of a randomised, controlled trial.

BACKGROUND: Colon cancer is associated with an increased risk of physical and psychosocial morbidity, even after treatment. General practitioner (GP) care could be beneficial to help to reduce this morbidity. We aimed to assess quality of life (QOL) in patients who received GP-led survivorship care after treatment for colon cancer compared with those who received surgeon-led care. Furthermore, the effect of an eHealth app (Oncokompas) on QOL was assessed in both patient groups. METHODS: We did a pragmatic two-by-two factorial, open-label, randomised, controlled trial at eight hospitals in the Netherlands. Eligible patients were receiving primary surgical treatment for stage I-III colon cancer or rectosigmoid carcinoma and qualified for routine follow-up according to Dutch national guidelines. Patients were randomly assigned (1:1:1:1)-via computer-generated variable block randomisation stratified by age and tumour stage-to survivorship care overseen by a surgeon, survivorship care overseen by a surgeon with access to Oncokompas, survivorship care overseen by a GP, or survivorship care overseen by a GP with access to Oncokompas. Blinding of the trial was not possible. The primary endpoint of the trial was QOL at 5 years, as measured by the change from baseline in the European Organistion for Research and Treatment of Cancer QLQ-C30 summary score. Here, we report an unplanned interim analysis of QOL at the 12-month follow-up. Grouped comparisons were done (ie, both GP-led care groups were compared with both surgeon-led groups, and both Oncokompas groups were compared with both no Oncokompas groups). Differences in change of QOL between trial groups were estimated with linear mixed-effects models. A change of ten units was considered clinically meaningful. Analysis was by intention to treat. This trial is registered with the Netherlands Trial Register, NTR4860. FINDINGS: Between March 26, 2015, and Nov 21, 2018, 353 patients were enrolled and randomly assigned. There were 50 early withdrawals (27 patient decisions and 23 GP withdrawals). Of the remaining 303 participants, 79 were assigned to surgeon-led care, 83 to surgeon-led care with Oncokompas, 73 to GP-led care, and 68 to GP-led care with Oncokompas. Median follow-up was 12·2 months (IQR 12·0-13·0) in all groups. At baseline, QOL was high in all trial groups. At 12 months, there was no clinically meaningful difference in change from baseline in QOL between the GP-led care groups and the surgeon-led care groups (difference in summary score -2·3 [95% CI -5·0 to 0·4]) or between the Oncokompas and no Oncokompas groups (-0·1 [-2·8 to 2·6]). INTERPRETATION: In terms of QOL, GP-led survivorship care can be considered as an alternative to surgeon-led care within the first year after colon cancer treatment. Other outcomes, including patient and physician preferences, will be important for decisions about the type of survivorship care. FUNDING: Dutch Cancer Society (KWF).

Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project.

INTRODUCTION: Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information. AIM: To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA. METHODS: The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA. CONCLUSION: The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.

CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening.

AIMS: To validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF's potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data. METHODS: We included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients. RESULTS: Among 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF's C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%-5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts. CONCLUSION: In patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

Evaluation of general practitioners' single-lead electrocardiogram interpretation skills: a case-vignette study.

BACKGROUND: Handheld single-lead electrocardiograms (1L-ECG) present a welcome addition to the diagnostic arsenal of general practitioners (GPs). However, little is known about GPs' 1L-ECG interpretation skills, and thus its reliability in real-world practice. OBJECTIVE: To determine the diagnostic accuracy of GPs in diagnosing atrial fibrillation or flutter (AF/Afl) based on 1L-ECGs, with and without the aid of automatic algorithm interpretation, as well as other relevant ECG abnormalities. METHODS: We invited 2239 Dutch GPs for an online case-vignette study. GPs were asked to interpret four 1L-ECGs, randomly drawn from a pool of 80 case-vignettes. These vignettes were obtained from a primary care study that used smartphone-operated 1L-ECG recordings using the AliveCor KardiaMobile. Interpretation of all 1L-ECGs by a panel of cardiologists was used as reference standard. RESULTS: A total of 457 (20.4%) GPs responded and interpreted a total of 1613 1L-ECGs. Sensitivity and specificity for AF/Afl (prevalence 13%) were 92.5% (95% CI: 82.5-97.0%) and 89.8% (95% CI: 85.5-92.9%), respectively. PPV and NPV for AF/Afl were 45.7% (95% CI: 22.4-70.9%) and 98.8% (95% CI: 97.1-99.5%), respectively. GP interpretation skills did not improve in case-vignettes where the outcome of automatic AF-detection algorithm was provided. In detecting any relevant ECG abnormality (prevalence 22%), sensitivity, specificity, PPV and NPV were 96.3% (95% CI: 92.8-98.2%), 68.8% (95% CI: 62.4-74.6%), 43.9% (95% CI: 27.7-61.5%) and 97.9% (95% CI: 94.9-99.1%), respectively. CONCLUSIONS: GPs can safely rule out cardiac arrhythmias with 1L-ECGs. However, whenever an abnormality is suspected, confirmation by an expert-reader is warranted.

Opportunistic screening versus usual care for detection of atrial fibrillation in primary care: cluster randomised controlled trial.

OBJECTIVE: To investigate whether opportunistic screening in primary care increases the detection of atrial fibrillation compared with usual care. DESIGN: Cluster randomised controlled trial. SETTING: 47 intention-to-screen and 49 usual care primary care practices in the Netherlands, not blinded for allocation; the study was carried out from September 2015 to August 2018. PARTICIPANTS: In each practice, a fixed sample of 200 eligible patients, aged 65 or older, with no known history of atrial fibrillation in the electronic medical record system, were randomly selected. In the intention-to-screen group, 9218 patients eligible for screening were included, 55.0% women, mean age 75.2 years. In the usual care group, 9526 patients were eligible for screening, 54.3% women, mean age 75.0 years. INTERVENTIONS: Opportunistic screening (that is, screening in patients visiting their general practice) consisted of three index tests: pulse palpation, electronic blood pressure measurement with an atrial fibrillation algorithm, and electrocardiography (ECG) with a handheld single lead electrocardiographic device. The reference standard was 12 lead ECG, performed in patients with at least one positive index test and in a sample of patients (10%) with three negative tests. If 12 lead ECG showed no atrial fibrillation, patients were invited for more screening by continuous monitoring with a Holter electrocardiograph for two weeks. MAIN OUTCOME MEASURES: Difference in the detection rate of newly diagnosed atrial fibrillation over one year in intention-to-screen versus usual care practices. RESULTS: Follow-up was complete for 8874 patients in the intention-to-screen practices and for 9102 patients in the usual care practices. 144 (1.62%) new diagnoses of atrial fibrillation in the intention-to-screen group versus 139 (1.53%) in the usual care group were found (adjusted odds ratio 1.06 (95% confidence interval 0.84 to 1.35)). Of 9218 eligible patients in the intention-to-screen group, 4106 (44.5%) participated in the screening protocol. In these patients, 12 lead ECG detected newly diagnosed atrial fibrillation in 26 patients (0.63%). In the 266 patients who continued with Holter monitoring, four more diagnoses of atrial fibrillation were found. CONCLUSIONS: Opportunistic screening for atrial fibrillation in primary care patients, aged 65 and over, did not increase the detection rate of atrial fibrillation, which implies that opportunistic screening for atrial fibrillation is not useful in this setting. TRIAL REGISTRATION: Netherlands Trial Register No NL4776 (old NTR4914).