Introduction Cardiometabolic diseases (CMD) are the leading cause of death in high-income countries and are largely attributable to modifiable risk factors. Population health management (PHM) can... Show moreIntroduction Cardiometabolic diseases (CMD) are the leading cause of death in high-income countries and are largely attributable to modifiable risk factors. Population health management (PHM) can effectively identify patient subgroups at high risk of CMD and address missed opportunities for preventive disease management. Guided by the Reach, Efficacy, Adoption, Implementation and Maintenance (RE-AIM) framework, this scoping review of PHM interventions targeting patients in primary care at increased risk of CMD aims to describe the reported aspects for successful implementation.Methods A comprehensive search was conducted across 14 databases to identify papers published between 2000 and 2023, using Arksey and O'Malley's framework for conducting scoping reviews. The RE-AIM framework was used to assess the implementation, documentation, and the population health impact score of the PHM interventions.Results A total of 26 out of 1,100 studies were included, representing 21 unique PHM interventions. This review found insufficient reporting of most RE-AIM components. The RE-AIM evaluation showed that the included interventions could potentially reach a large audience and achieve their intended goals, but information on adoption and maintenance was often lacking. A population health impact score was calculated for six interventions ranging from 28 to 62%.Discussion This review showed the promise of PHM interventions that could reaching a substantial number of participants and reducing CMD risk factors. However, to better assess the generalizability and scalability of these interventions there is a need for an improved assessment of adoption, implementation processes, and sustainability. Show less
Background and ObjectivesFemale-specific factors and psychosocial factors may be important in the prediction of strokebut are not included in prediction models that are currently used. We... Show moreBackground and ObjectivesFemale-specific factors and psychosocial factors may be important in the prediction of strokebut are not included in prediction models that are currently used. We investigated whetheraddition of these factors would improve the performance of prediction models for the risk ofstroke in women younger than 50 years.MethodsWe used data from the Stichting Informatievoorziening voor Zorg en Onderzoek, population-based, primary care database of women aged 20–49 years without a history of cardiovasculardisease. Analyses were stratified by 10-year age intervals at cohort entry. Cox proportionalhazards models to predict stroke risk were developed, including traditional cardiovascularfactors, and compared with models that additionally included female-specific and psychosocialfactors. We compared the risk models using the c-statistic and slope of the calibration curve at afollow-up of 10 years. We developed an age-specific stroke risk prediction tool that may helpcommunicating the risk of stroke in clinical practice.ResultsWe included 409,026 women with a total of 3,990,185 person-years of follow-up. Strokeoccurred in 2,751 women (incidence rate 6.9 [95% CI 6.6–7.2] per 10,000 person-years).Models with only traditional cardiovascular factors performed poorly to moderately in all agegroups: 20–29 years: c-statistic: 0.617 (95% CI 0.592–0.639); 30–39 years: c-statistic: 0.615(95% CI 0.596–0.634); and 40–49 years: c-statistic: 0.585 (95% CI 0.573–0.597). After addingthe female-specific and psychosocial risk factors to the reference models, the model discrimi-nation increased moderately, especially in the age groups 30–39 (Dc-statistic: 0.019) and 40–49years (Dc-statistic: 0.029) compared with the reference models, respectively.DiscussionThe addition of female-specific factors and psychosocial risk factors improves the discrimina-tory performance of prediction models for stroke in women younger than 50 years. Show less
Background Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to... Show moreBackground Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first-ever cardiovascular event risk in men and women aged 30 to 49 years.Methods and Results We included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first-ever cardiovascular event. Our reference models were sex-specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C-index and calibration curve slopes at 10 years of follow-up. We stratified our analyses based on 30- to 39-year and 40- to 49-year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow-up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C-index, 0.648 [95% CI, 0.645-0.652]; men: C-index, 0.661 [95%CI, 0.658-0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C-index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model.Conclusions Sex-specific electronic health record-derived prediction models for first-ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data-driven predictor selection leads to identification of nontraditional cardiovascular predictors, which modestly increase performance of models. Show less
BackgroundPrediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to... Show moreBackgroundPrediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first‐ever cardiovascular event risk in men and women aged 30 to 49 years.Methods and ResultsWe included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first‐ever cardiovascular event. Our reference models were sex‐specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C‐index and calibration curve slopes at 10 years of follow‐up. We stratified our analyses based on 30‐ to 39‐year and 40‐ to 49‐year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow‐up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C‐index, 0.648 [95% CI, 0.645–0.652]; men: C‐index, 0.661 [95%CI, 0.658–0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C‐index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model.ConclusionsSex‐specific electronic health record‐derived prediction models for first‐ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data‐driven predictor selection leads to identification of nontraditional cardiovascular predictors, which modestly increase performance of models. Show less
Stroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics,... Show moreStroke is one of the leading causes of disability and death worldwide. Prevention of stroke is therefore essential. Effective prevention should be tailored to the clinical characteristics, lifestyle, and environment of the individual, among others. This is also known as precision prevention. An important example illustrating the need for precision prevention is the existence of sex differences in stroke occurrence. In practice, for predicting stroke risk, only traditional risk factors (such as smoking and hypertension) are included, and women-specific risk factors are not yet routinely included. As a result, women with an increased risk of stroke may be missed, which also prevents timely initiation of preventive treatments. In this thesis, I tried to lay the foundation for precision prevention of stroke in women.Part I discussed the pathophysiology underlying women-specific risk factors for stroke, and gender differences in the clinical presentation of stroke. I found that the mechanisms underlying the relationship between women-specific risk factors and stroke, in particular the relationship between migraine and cerebral infarctions, seem to be particularly significant in the childbearing phase of life.In Part II, I described how health data from the EHR can be used to develop prediction models for the risk of myocardial infarction or stroke specifically for women under 50 years of age, and found that women-specific risk factors can add value in the predictions. However, there is still a long way to go to actually implement these models in practice, such as testing them on new datasets, and complying with current laws and regulations for safe application. Show less
Background Socioeconomic status and ethnicity are not explicitly incorporated as risk factors in the four SCORE2 cardiovascular disease (CVD) risk models developed for country-wide implementation... Show moreBackground Socioeconomic status and ethnicity are not explicitly incorporated as risk factors in the four SCORE2 cardiovascular disease (CVD) risk models developed for country-wide implementation across Europe (low, moderate, high and very-high model). The aim of this study was to evaluate the performance of the four SCORE2 CVD risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands.Methods The SCORE2 CVD risk models were externally validated in socioeconomic and ethnic (by country of origin) subgroups, from a population-based cohort in the Netherlands, with GP, hospital and registry data. In total 155,000 individuals, between 40 and 70 years old in the study period from 2007 to 2020 and without previous CVD or diabetes were included. Variables (age, sex, smoking status, blood pressure, cholesterol) and outcome first CVD event (stroke, myocardial infarction, CVD death) were consistent with SCORE2. Findings 6966 CVD events were observed, versus 5495 events predicted by the CVD low-risk model (intended for use in the Netherlands). Relative underprediction was similar in men and women (observed/predicted (OE-ratio), 1.3 and 1.2 in men and women, respectively). Underprediction was larger in low socioeconomic subgroups of the overall study population (OE-ratio 1.5 and 1.6 in men and women, respectively), and comparable in Dutch and the combined "other ethnicities" low socioeconomic subgroups. Underprediction in the Surinamese subgroup was largest (OE-ratio 1.9, in men and women), particularly in the low socioeconomic Surinamese subgroups (OE-ratio 2.5 and 2.1 in men and women). In the subgroups with underprediction in the low-risk model, the intermediate or high-risk SCORE2 models showed improved OE-ratios. Discrimination showed moderate performance in all subgroups and the four SCORE2 models, with C-statistics between 0.65 and 0.72, similar to the SCORE2 model development study.Interpretation The SCORE 2 CVD risk model for low-risk countries (as the Netherlands are) was found to underpredict CVD risk, particularly in low socioeconomic and Surinamese ethnic subgroups. Including socioeconomic status and ethnicity as predictors in CVD risk models and implementing CVD risk adjustment within countries is desirable for adequate CVD risk prediction and counselling. Show less
Objective: Structural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare... Show moreObjective: Structural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare Authority expanded regulations, creating novel opportunities to reimburse RPM. Despite these regulations, barriers to the reim-bursement of RPM remain. This study aimed to identify the barriers and facilitators of structural reimbursement of RPM in hospital care in the Netherlands and to propose actionable recommendations. Methods: This is an exploratory qualitative study with relevant stakeholders in the Dutch purchasing market: the Dutch Healthcare Authority, health insurers, and healthcare providers. Semi-structured interviews were held between October and December of 2020. All interviews were conducted using a digital medium, transcribed verbatim, and thematically analyzed. Results: Multiple perceived barriers were mentioned: wrong pocket problems (i.e. the entity that bears the costs of implementation does not receive the benefits), no uniform quality and outcome indicators, lack of willingness to redesign care pathways by providers, and difficulties implementing cross-sector models. Perceived facilitators included interdisciplinary cooperation and transparency, the use of alternative payment models, increase in the total number of patients per RPM project, and the optional reimbursement scheme. Conclusion: Our interviews found barriers and facilitators concerning structural reimbursement of RPM in hos-pital settings in the Netherlands. Our results emphasize that the successful integration of structural reimburse-ment requires: 1) understanding the improvement potential of RPM by creating business cases, 2) co-creation (redesigning care paths) from the outset of an RPM project, 3) and allocating financial risk by providers and insurers. Public Interest Summary: The COVID-19 pandemic has demonstrated the strong potential of consultation and monitoring patients at a distance. Remote patient monitoring -the use of information technologies for moni-toring patients at a distance -is seen as a potential solution to urgent challenges in the healthcare system. Nevertheless, embedding remote patient monitoring innovations into routine healthcare is often challenging, partly due to difficulties in reimbursing these initiatives. Barriers to reimbursing remote patient monitoring included organizational factors, no uniform quality and outcome indicators, and difficulties using different payment models. Perceived facilitators included an increase in the total number of patients per project, better interdisciplinary cooperation and transparency, and help from the Dutch Healthcare Authority. Introducing these insights into healthcare policy dialogues could support reimbursement of remote patient monitoring and stim-ulate the collaboration of healthcare stakeholders responsible for implementing and scaling up remote patient monitoring projects. Show less
ObjectiveStructural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare... Show moreObjectiveStructural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare Authority expanded regulations, creating novel opportunities to reimburse RPM. Despite these regulations, barriers to the reimbursement of RPM remain. This study aimed to identify the barriers and facilitators of structural reimbursement of RPM in hospital care in the Netherlands and to propose actionable recommendations.MethodsThis is an exploratory qualitative study with relevant stakeholders in the Dutch purchasing market: the Dutch Healthcare Authority, health insurers, and healthcare providers. Semi-structured interviews were held between October and December of 2020. All interviews were conducted using a digital medium, transcribed verbatim, and thematically analyzed.ResultsMultiple perceived barriers were mentioned: wrong pocket problems (i.e. the entity that bears the costs of implementation does not receive the benefits), no uniform quality and outcome indicators, lack of willingness to redesign care pathways by providers, and difficulties implementing cross-sector models. Perceived facilitators included interdisciplinary cooperation and transparency, the use of alternative payment models, increase in the total number of patients per RPM project, and the optional reimbursement scheme.ConclusionOur interviews found barriers and facilitators concerning structural reimbursement of RPM in hospital settings in the Netherlands. Our results emphasize that the successful integration of structural reimbursement requires: 1) understanding the improvement potential of RPM by creating business cases, 2) co-creation (redesigning care paths) from the outset of an RPM project, 3) and allocating financial risk by providers and insurers. Show less
BackgroundWe observed subarachnoid cerebrospinal fluid (CSF) hyperintensities at non-contrast 7-tesla (T) fluid-attenuated inversion recovery (FLAIR) MRI, frequently topographically associated with... Show moreBackgroundWe observed subarachnoid cerebrospinal fluid (CSF) hyperintensities at non-contrast 7-tesla (T) fluid-attenuated inversion recovery (FLAIR) MRI, frequently topographically associated with cortical superficial siderosis (cSS), in participants with cerebral amyloid angiopathy (CAA). To systemically evaluate these CSF hyperintensities we investigated their frequency and anatomical and temporal relationship with cSS on 7T and 3T MRI in hereditary Dutch-type CAA (D-CAA), sporadic CAA (sCAA), and non-CAA controls.MethodsCAA participants were included from two prospective natural history studies and non-CAA controls from a 7T study in healthy females and females with ischemic stroke. CSF hyperintensities were scored by two independent observers.ResultsWe included 38 sCAA participants (mean age 72y), 50 D-CAA participants (mean age 50y) and 44 non-CAA controls (mean age 53y, 15 with stroke). In total 27/38 (71 %, 95 %CI 56–84) sCAA and 23/50 (46 %, 95 %CI 33–60) D-CAA participants had subarachnoid CSF hyperintensities at baseline 7T. Most (96 %) of those had cSS, in 54 % there was complete topographical overlap with cSS. The remaining 46 % had ≥1 sulcus with CSF hyperintensities without co-localizing cSS. None of the healthy controls and 2/15 (13 %, 95 %CI 2–41, 100 % cSS overlap) of the stroke controls had CSF hyperintensities. In 85 % of the CAA participants CSF hyperintensities could retrospectively be identified at 3T. Of the 35 CAA participants with follow-up 7T after two years, 17/35 (49 %) showed increase and 6/35 (17 %) decrease of regional CSF hyperintensities. In 2/11 (18 %) of participants with follow-up who had baseline CSF hyperintensities without overlapping cSS, new cSS developed at those locations.ConclusionsSubarachnoid CSF hyperintensities at 7T FLAIR MRI occur frequently in CAA and are associated with cSS, although without complete overlap. We hypothesize that the phenomenon could be a sign of subtle plasma protein or blood product leakage into the CSF, resulting in CSF T1-shortening. Show less
Ali, M.; Meij, A. van der; Os, H.J.A. van; Zwet, E.W. van; Spaander, F.H.M.; Hofmeijer, J.; ... ; MR CLEAN Registry Investigators 2022
BackgroundWomen have been reported to have worse outcomes after endovascular treatment (EVT), despite a similar treatment effect in non-clinical trial populations. We aimed to assess sex... Show moreBackgroundWomen have been reported to have worse outcomes after endovascular treatment (EVT), despite a similar treatment effect in non-clinical trial populations. We aimed to assess sex differences at hospital presentation with respect to workflow metrics, prestroke disability, and presenting clinical symptoms. MethodsWe included consecutive patients from the Multicentre Randomised Controlled Trial of Endovascular Treatment for Acute Ischaemic Stroke in The Netherlands (MR CLEAN) Registry (2014-2018) who received EVT for anterior circulation large vessel occlusion (LVO). We assessed sex differences in workflow metrics, prestroke disability (modified Rankin Scale (mRS) score >= 1), and stroke severity and symptoms according to the National Institutes of Health Stroke Scale (NIHSS) score on hospital admission with logistic and linear regression analyses and calculated the adjusted OR (aOR). ResultsWe included 4872 patients (47.6% women). Compared with men, women were older (median age 76 vs 70 years) and less often achieved good functional outcome at 90 days (mRS <= 2: 35.2% vs 46.4%, aOR 0.70, 95% CI 0.60 to 0.82). Mean onset-to-door time was longer in women (2 hours 16 min vs 2 hours 7 min, adjusted delay 9 min, 95% CI 4 to 13). This delay contributed to longer onset-to-groin times (3 hours 26 min in women vs 3 hours 13 min in men, adjusted delay 13 min, 95% CI 9 to 17). Women more often had prestroke disability (mRS >= 1: 41.1% vs 29.1%, aOR 1.57, 95% CI 1.36 to 1.82). NIHSS on admission was essentially similar in men and women (mean 15 +/- 6 vs 15 +/- 6, NIHSS <10 vs >= 10, aOR 0.91, 95% CI 0.78 to 1.06). There were no clear sex differences in the occurrence of specific stroke symptoms. ConclusionWomen with LVO had longer onset-to-door times and more often prestroke disability than men. Raising awareness of these differences at hospital presentation and investigating underlying causes may help to improve outcome after EVT in women. Show less
Background: Young patients with aneurysmal subarachnoid hemorrhage (aSAH) and a history of migraine may have an increased risk of delayed cerebral ischemia. We investigated this potential... Show moreBackground: Young patients with aneurysmal subarachnoid hemorrhage (aSAH) and a history of migraine may have an increased risk of delayed cerebral ischemia. We investigated this potential association in a prospective cohort of aSAH patients under 50 years of age. Methods: In our prospective cohort study, we included patients with aSAH under 50 years of age from 3 hospitals in the Netherlands. We assessed lifetime migraine history with a short screener. Delayed cerebral ischemia was defined as neurological deterioration lasting >1 hour not attributable to other causes by diagnostic workup. Adjustments were made for possible confounders in multivariable Cox regression analyses, and adjusted hazard ratios were calculated. Results: We included 236 young aSAH patients (mean age, 41 years; 64% women) of whom 44 (19%) had a history of migraine (16 with aura). Patients with aSAH and a history of migraine were not at increased risk of developing delayed cerebral ischemia compared with patients without migraine (25% versus 20%; adjusted hazard ratio, 1.16 [95% CI, 0.57-2.35]). Additionally, no increased risk was found in migraine patients with aura (adjusted hazard ratio, 0.85 [95% CI, 0.30-2.44]) or in women (adjusted hazard ratio, 1.24 [95% CI, 0.58-2.68]). Conclusions: Patients with aSAH under the age of 50 years with a history of migraine are not at increased risk of delayed cerebral ischemia. Show less
Ali, M.; Os, H.J.A. van; Weerd, N. van der; Schoones, J.W.; Heymans, M.W.; Kruyt, N.D.; ... ; Wermer, M.J.H. 2022
Background and Purpose: Women have worse outcomes than men after stroke. Differences in presentation may lead to misdiagnosis and, in part, explain these disparities. We investigated whether there... Show moreBackground and Purpose: Women have worse outcomes than men after stroke. Differences in presentation may lead to misdiagnosis and, in part, explain these disparities. We investigated whether there are sex differences in clinical presentation of acute stroke or transient ischemic attack. Methods: We conducted a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Inclusion criteria were (1) cohort, cross-sectional, case-control, or randomized controlled trial design; (2) admission for (suspicion of) ischemic or hemorrhagic stroke or transient ischemic attack; and (3) comparisons possible between sexes in >= 1 nonfocal or focal acute stroke symptom(s). A random-effects model was used for our analyses. We performed sensitivity and subanalyses to help explain heterogeneity and used the Newcastle-Ottawa Scale to assess bias. Results: We included 60 studies (n=582 844; 50% women). In women, headache (pooled odds ratio [OR], 1.24 [95% CI, 1.11-1.39]; I-2=75.2%; 30 studies) occurred more frequently than in men with any type of stroke, as well as changes in consciousness/mental status (OR, 1.38 [95% CI, 1.19-1.61]; I-2=95.0%; 17 studies) and coma/stupor (OR, 1.39 [95% CI, 1.25-1.55]; I-2=27.0%; 13 studies). Aspecific or other neurological symptoms (nonrotatory dizziness and non-neurological symptoms) occurred less frequently in women (OR, 0.96 [95% CI, 0.94-0.97]; I-2=0.1%; 5 studies). Overall, the presence of focal symptoms was not associated with sex (pooled OR, 1.03) although dysarthria (OR, 1.14 [95% CI, 1.04-1.24]; I-2=48.6%; 11 studies) and vertigo (OR, 1.23 [95% CI, 1.13-1.34]; I-2=44.0%; 8 studies) occurred more frequently, whereas symptoms of paresis/hemiparesis (OR, 0.73 [95% CI, 0.54-0.97]; I-2=72.6%; 7 studies) and focal visual disturbances (OR, 0.83 [95% CI, 0.70-0.99]; I-2=62.8%; 16 studies) occurred less frequently in women compared with men with any type of stroke. Most studies contained possible sources of bias. Conclusions: There may be substantive differences in nonfocal and focal stroke symptoms between men and women presenting with acute stroke or transient ischemic attack, but sufficiently high-quality studies are lacking. More studies are needed to address this because sex differences in presentation may lead to misdiagnosis and undertreatment. Show less
Silven, A.V.; Peet, P.G. van; Boers, S.N.; Tabak, M.; Groot, A. de; Hendriks, D.; ... ; Villalobos-Quesada, M.J. 2022
Background Implementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal... Show moreBackground Implementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas. This study aimed to identify challenges regarding responsibility and liability when prescribing digital health in clinical practice. This was part of an overarching project aiming to explore the most pressing ethical and legal obstacles regarding the implementation and adoption of digital health in the Netherlands, and to propose actionable solutions. Methods A series of multidisciplinary focus groups with stakeholders who have relevant digital health expertise were analysed through thematic analysis. Results The emerging general theme was 'uncertainty regarding responsibilities' when adopting digital health. Key dilemmas take place in clinical settings and within the doctor-patient relationship ('professional digital health'). This context is particularly challenging because different stakeholders interact. In the absence of appropriate legal frameworks and codes of conduct tailored to digital health, physicians' responsibility is to be found in their general duty of care. In other words: to do what is best for patients (not causing harm and doing good). Professional organisations could take a leading role to provide more clarity with respect to physicians' responsibility, by developing guidance describing physicians' duty of care in the context of digital health, and to address the resulting responsibilities. Conclusions Although legal frameworks governing medical practice describe core ethical principles, rights and obligations of physicians, they do not suffice to clarify their responsibilities in the setting of professional digital health. Here we present a series of recommendations to provide more clarity in this respect, offering the opportunity to improve quality of care and patients' health. The recommendations can be used as a starting point to develop professional guidance and have the potential to be adapted to other healthcare professionals and systems. Show less
Silven, A.V.; Peet, P.G. van; Boers, S.N.; Tabak, M.; Groot, A. de; Hendriks, D.; ... ; Villalobos-Quesada, M. 2022
BackgroundImplementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas... Show moreBackgroundImplementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas. This study aimed to identify challenges regarding responsibility and liability when prescribing digital health in clinical practice. This was part of an overarching project aiming to explore the most pressing ethical and legal obstacles regarding the implementation and adoption of digital health in the Netherlands, and to propose actionable solutions.MethodsA series of multidisciplinary focus groups with stakeholders who have relevant digital health expertise were analysed through thematic analysis.ResultsThe emerging general theme was ‘uncertainty regarding responsibilities’ when adopting digital health. Key dilemmas take place in clinical settings and within the doctor-patient relationship (‘professional digital health’). This context is particularly challenging because different stakeholders interact. In the absence of appropriate legal frameworks and codes of conduct tailored to digital health, physicians’ responsibility is to be found in their general duty of care. In other words: to do what is best for patients (not causing harm and doing good). Professional organisations could take a leading role to provide more clarity with respect to physicians’ responsibility, by developing guidance describing physicians’ duty of care in the context of digital health, and to address the resulting responsibilities.ConclusionsAlthough legal frameworks governing medical practice describe core ethical principles, rights and obligations of physicians, they do not suffice to clarify their responsibilities in the setting of professional digital health. Here we present a series of recommendations to provide more clarity in this respect, offering the opportunity to improve quality of care and patients’ health. The recommendations can be used as a starting point to develop professional guidance and have the potential to be adapted to other healthcare professionals and systems. Show less
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied... Show moreWhile the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance. Show less
Weerd, N. van der; Os, H.J.A. van; Ali, M.; Schoones, J.W.; Maagdenberg, A.M.J.M. van den; Kruyt, N.D.; ... ; Wermer, M.J.H. 2021
Background: Women are more affected by stroke than men. This might, in part, be explained by sex differences in stroke pathophysiology. The hemostasis system is influenced by sex hormones and... Show moreBackground: Women are more affected by stroke than men. This might, in part, be explained by sex differences in stroke pathophysiology. The hemostasis system is influenced by sex hormones and associated with female risk factors for stroke, such as migraine.Aim: To systematically review possible sex differences in hemostatic related factors in patients with ischemic stroke in general, and the influence of migraine on these factors in women with ischemic stroke.Results: We included 24 studies with data on sex differences of hemostatic factors in 7247 patients with ischemic stroke (mean age 57-72 years, 27-57% women) and 25 hemostatic related factors. Levels of several factors were higher in women compared with men; FVII:C (116% +/- 30% vs. 104% +/- 30%), FXI (0.14 UI/mL higher in women), PAI-1 (125.35 +/- 49.37 vs. 96.67 +/- 38.90 ng/mL), D-dimer (1.25 +/- 0.31 vs. 0.95 +/- 0.24 mg/mL), and aPS (18.7% vs. 12.0% positive). In contrast, protein-S (86.2% +/- 23.0% vs. 104.7% +/- 19.8% antigen) and P-selectin (48.9 +/- 14.4 vs. 79.1 +/- 66.7 pg/mL) were higher in men. Most factors were investigated in single studies, at different time points after stroke, and in different stroke subtypes. Only one small study reported data on migraine and hemostatic factors in women with ischemic stroke. No differences in fibrinogen, D-dimer, t-PA, and PAI-1 levels were found between women with and without migraine.Conclusion: Our systematic review suggests that sex differences exist in the activation of the hemostatic system in ischemic stroke. Women seem to lean more toward increased levels of procoagulant factors whereas men exhibit increased levels of coagulation inhibitors. To obtain better insight in sex-related differences in hemostatic factors, additional studies are needed to confirm these findings with special attention for different stroke phases, stroke subtypes, and not in the least women specific risk factors, such as migraine. Show less
Background: An increased risk of stroke in patients with migraine has been primarily found for women. The sex-dependent mechanisms underlying the migraine-stroke association, however, remain... Show moreBackground: An increased risk of stroke in patients with migraine has been primarily found for women. The sex-dependent mechanisms underlying the migraine-stroke association, however, remain unknown. This study aims to explore these sex differences to improve our understanding of pathophysiological mechanisms behind the migraine-stroke association.Methods: We included 2,492 patients with ischemic stroke from the prospective multicenter Dutch Parelsnoer Institute Initiative study, 425 (17%) of whom had a history of migraine. Cardiovascular risk profile, stroke cause (TOAST classification), and outcome [modified Rankin scale (mRS) at 3 months] were compared with both sexes between patients with and without migraine.Results: A history of migraine was not associated with sex differences in the prevalence of conventional cardiovascular risk factors. Women with migraine had an increased risk of stroke at young age (onset < 50 years) compared with women without migraine (RR: 1.7; 95% CI: 1.3-2.3). Men with migraine tended to have more often stroke in the TOAST category other determined etiology (RR: 1.7; 95% CI: 1.0-2.7) in comparison with men without migraine, whereas this increase was not found in women with migraine. Stroke outcome was similar for women with or without migraine (mRS >= 3 RR 1.1; 95% CI 0.7-1.5), whereas men seemed to have a higher risk of poor outcome compared with their counterparts without migraine (mRS >= 3 RR: 1.5; 95% CI: 1.0-2.1).Conclusion: Our results indicate possible sex differences in the pathophysiology underlying the migraine-stroke association, which are unrelated to conventional cardiovascular risk factors. Further research in larger cohorts is needed to validate these findings. Show less
Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These... Show moreDespite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (+/- 7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (+/- 2) compared to 26 (+/- 1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients. Show less
Ramos, L.A.; Kappelhof, M.; Os, H.J.A. van; Chalos, V.; Kranendonk, K. van; Kruyt, N.D.; ... ; Marquering, H.A. 2020
Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could... Show moreBackground: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score >= 5, despite EVT treatment.Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives.Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0-4 patients, 27-61 (3-6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99-163 (21-34%) were correctly identified by the models.Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice. Show less
