Population health interventions for cardiometabolic diseases in primary care: a scoping review and RE-AIM evaluation of current practices

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 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.

Sex differences in onset to hospital arrival time, prestroke disability, and clinical symptoms in patients with a large vessel occlusion: a MR CLEAN Registry substudy

BACKGROUND

Women 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.

METHODS

We 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).

RESULTS

We 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.

CONCLUSION

Women 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.

Added Predictive Value of Female-Specific Factors and Psychosocial Factors for the Risk of Stroke in Women Under 50

BACKGROUND AND OBJECTIVES

Female-specific factors and psychosocial factors may be important in the prediction of stroke but are not included in prediction models that are currently used. We investigated whether addition of these factors would improve the performance of prediction models for the risk of stroke in women younger than 50 years.

METHODS

We 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 cardiovascular disease. Analyses were stratified by 10-year age intervals at cohort entry. Cox proportional hazards models to predict stroke risk were developed, including traditional cardiovascular factors, and compared with models that additionally included female-specific and psychosocial factors. We compared the risk models using the c-statistic and slope of the calibration curve at a follow-up of 10 years. We developed an age-specific stroke risk prediction tool that may help communicating the risk of stroke in clinical practice.

RESULTS

We included 409,026 women with a total of 3,990,185 person-years of follow-up. Stroke occurred 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 age groups: 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 adding the female-specific and psychosocial risk factors to the reference models, the model discrimination increased moderately, especially in the age groups 30-39 (Δc-statistic: 0.019) and 40-49 years (Δc-statistic: 0.029) compared with the reference models, respectively.

DISCUSSION

The addition of female-specific factors and psychosocial risk factors improves the discriminatory performance of prediction models for stroke in women younger than 50 years.

Cardiovascular Risk Prediction in Men and Women Aged Under 50 Years Using Routine Care Data

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 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.

Developing Clinical Prediction Models Using Primary Care Electronic Health Record Data: The Impact of Data Preparation Choices on Model Performance

Objective

To quantify prediction model performance in relation to data preparation choices when using electronic health records (EHR).

Study Design and Setting

Cox proportional hazards models were developed for predicting the first-ever main adverse cardiovascular events using Dutch primary care EHR data. The reference model was based on a 1-year run-in period, cardiovascular events were defined based on both EHR diagnosis and medication codes, and missing values were multiply imputed. We compared data preparation choices based on (i) length of the run-in period (2- or 3-year run-in); (ii) outcome definition (EHR diagnosis codes or medication codes only); and (iii) methods addressing missing values (mean imputation or complete case analysis) by making variations on the derivation set and testing their impact in a validation set.

Results

We included 89,491 patients in whom 6,736 first-ever main adverse cardiovascular events occurred during a median follow-up of 8 years. Outcome definition based only on diagnosis codes led to a systematic underestimation of risk (calibration curve intercept: 0.84; 95% CI: 0.83-0.84), while complete case analysis led to overestimation (calibration curve intercept: -0.52; 95% CI: -0.53 to -0.51). Differences in the length of the run-in period showed no relevant impact on calibration and discrimination.

Conclusion

Data preparation choices regarding outcome definition or methods to address missing values can have a substantial impact on the calibration of predictions, hampering reliable clinical decision support. This study further illustrates the urgency of transparent reporting of modeling choices in an EHR data setting.

Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage in Young Patients With a History of Migraine

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 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.

Clarifying responsibility: professional digital health in the doctor-patient relationship, recommendations for physicians based on a multi-stakeholder dialogue in the Netherlands

Background

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07316-0.

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

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 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.

Sex Differences in Presentation of Stroke: A Systematic Review and Meta-Analysis

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 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]; I²=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]; I²=95.0%; 17 studies) and coma/stupor (OR, 1.39 [95% CI, 1.25-1.55]; I²=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]; I²=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]; I²=48.6%; 11 studies) and vertigo (OR, 1.23 [95% CI, 1.13-1.34]; I²=44.0%; 8 studies) occurred more frequently, whereas symptoms of paresis/hemiparesis (OR, 0.73 [95% CI, 0.54-0.97]; I²=72.6%; 7 studies) and focal visual disturbances (OR, 0.83 [95% CI, 0.70-0.99]; I²=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.

Sex Differences in Risk Profile, Stroke Cause and Outcome in Ischemic Stroke Patients With and Without Migraine

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 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.

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

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 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.

Telemonitoring for Patients With COVID-19: Recommendations for Design and Implementation

Despite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity to carefully monitor patients with a confirmed or suspected case of COVID-19 from home and allows for the timely identification of worsening symptoms. Additionally, it may decrease the number of hospital visits and admissions, thereby reducing the use of scarce resources, optimizing health care capacity, and minimizing the risk of viral transmission. In this paper, we present a COVID-19 telemonitoring care pathway developed at a tertiary care hospital in the Netherlands, which combined the monitoring of vital parameters with video consultations for adequate clinical assessment. Additionally, we report a series of medical, scientific, organizational, and ethical recommendations that may be used as a guide for the design and implementation of telemonitoring pathways for COVID-19 and other diseases worldwide.

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.

Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed.

Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).

Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

Migraine and Cerebrovascular Atherosclerosis in Patients With Ischemic Stroke

BACKGROUND AND PURPOSE

Migraine is a well-established risk factor for ischemic stroke, but migraine is also related to other vascular diseases. This study aims to investigate the association between migraine and cerebrovascular atherosclerosis in patients with acute ischemic stroke.

METHODS

We retrieved data on patients with ischemic stroke from the DUST (Dutch Acute Stroke Study). Migraine history was assessed with a migraine screener and confirmed by telephone interview based on the ICHD criteria (International Classification of Headache Disorders). We assessed intra- and extracranial atherosclerotic changes and quantified intracranial internal carotid artery calcifications as measure of atherosclerotic burden on noncontrast computed tomography and computed tomographic angiography. We calculated risk ratios with adjustments for possible confounders with multivariable Poisson regression analyses.

RESULTS

We included 656 patients, aged 18 to 99 years, of whom 53 had a history of migraine (29 with aura). Patients with migraine did not have more frequent atherosclerotic changes in intracranial (51% versus 74%; adjusted risk ratio, 0.82; 95% confidence interval, 0.64-1.05) or extracranial vessels (62% versus 79%; adjusted risk ratio, 0.93; 95% confidence interval, 0.77-1.12) than patients without migraine and had comparable internal carotid artery calcification volumes (largest versus medium and smallest volume tertile, 23% versus 35%; adjusted risk ratio, 0.93; 95% confidence interval, 0.57-1.52).

CONCLUSIONS

Migraine is not associated with excess atherosclerosis in large vessels in patients with acute ischemic stroke. Our findings suggest that the biological mechanisms by which migraine results in ischemic stroke are not related to macrovascular cerebral atherosclerosis.

Role of atherosclerosis, clot extent, and penumbra volume in headache during ischemic stroke

OBJECTIVE

To investigate the role of large vessel atherosclerosis, blood clot extent, and penumbra volume in relation to headache in ischemic stroke patients.

METHODS

In this cross-sectional study, we performed noncontrast CT, CT angiography (CTA), and CT perfusion (CTP) in 284 participants from the Dutch Acute Stroke Study and Leiden Stroke Cohort within 9 hours after ischemic stroke onset. We collected headache characteristics prospectively using a semi-structured questionnaire. Atherosclerosis was assessed by evaluating presence of plaques in extracranial and intracranial vessels and by quantifying intracranial carotid artery calcifications. Clot extent was estimated by the clot burden score on CTA and penumbra volume by CTP. We calculated risk ratios (RRs) with adjustments (aRR) for possible confounders using multivariable Poisson regression.

RESULTS

Headache during stroke was reported in 109/284 (38%) participants. Headache was less prevalent in patients with than in patients without atherosclerosis in the extracranial anterior circulation (35% vs 48%; RR 0.72; 95% confidence interval [CI] 0.54-0.97). Atherosclerosis in the intracranial arteries was also associated with less headache, but this association was not statistically significant. Penumbra volume (aRR 1.08; 95% CI 0.63-1.85) and clot extent (aRR 1.02; 95% CI 0.86-1.20) were not related with headache.

CONCLUSIONS

Headache in the early phase of ischemic stroke tends to occur less often in patients with atherosclerosis than in patients without atherosclerosis in the large cerebral arteries. This finding lends support to the hypothesis that vessel wall elasticity is a necessary contributing factor in the occurrence of headache during acute ischemic stroke.