@article{vandergun_external_2026,

title = {External Validation of a Machine Learning Model for the Early Prediction of Upper-Limb Recovery After Stroke},

author = {G. J. Gun and R. W. Selles and I. C. Brunner and C. Biering Lundquist},

url = {https://doi.org/10.1177/15459683261469091},

doi = {10.1177/15459683261469091},

issn = {1545-9683},

year  = {2026},

date = {2026-08-03},

urldate = {2026-08-03},

pages = {15459683261469091},

publisher = {SAGE Publications Inc STM},

abstract = {Background:Reliable prediction of upper-limb recovery after stroke can support rehabilitation planning, yet few prognostic models have been tested beyond their development cohorts. External validation is essential to establish the clinical reliability of these methods.Objective:To externally validate a previously developed machine learning model for predicting 6-month upper-limb capacity after stroke, measured by the Action Research Arm Test (ARAT).Methods:The model was developed on a multicentre Dutch cohort of first-ever ischaemic stroke patients and validated in an independent Danish prospective cohort, including both ischaemic and haemorrhagic strokes. Predictions were generated from baseline assessments at 2 weeks post-stroke. Model performance was evaluated using the median absolute error (MedAE) and calibration analysis.Results:The validation cohort comprised 80 patients assessed at 14 ± 4 days post-stroke. Overall prediction error was comparable between the validation and development cohorts (MedAE = 5.7 [IQR 1.9-15.1] vs 3.9 [IQR 1.1-13.0]; P = 0.12). Calibration was close to ideal for predicted ARAT scores above 40. In contrast, lower predicted ranges showed systematic underprediction, reflecting variable outcomes in severely impaired patients, a pattern similar to that observed in the development cohort.Conclusions:In an independent validation cohort, the machine learning model performed similarly to its development cohort but showed clinically relevant miscalibration in patients with low predicted ARAT scores. Inclusion of additional predictors is required to improve reliability in severely impaired patients before subsequent steps toward clinical implementation can be considered.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vandergun_accuracy_2026,

title = {Accuracy of Machine Learning to Predict Upper-Limb Outcome Within the First 72 Hours Poststroke},

author = {G. J. Gun and R. W. Selles and C. G. M. Meskers and E. E. H. Wegen and G. Kwakkel and ICAI Stroke Lab},

url = {https://www.ahajournals.org/doi/10.1161/STROKEAHA.125.054989},

doi = {10.1161/STROKEAHA.125.054989},

year  = {2026},

date = {2026-07-29},

volume = {0},

number = {0},

publisher = {American Heart Association},

abstract = {BACKGROUND:Timely and accurate prediction of poststroke motor outcome is important for efficient rehabilitation planning and resource allocation. Existing bedside models for predicting upper-limb outcome after stroke require further refinement to be effectively implemented in stroke units within the first 72 hours. This study aimed to develop and internally validate a machine learning model to predict the 6-month Action Research Arm Test score using simple clinical tests commonly assessed within the first 3 days poststroke.METHODS:In 296 first-ever ischemic stroke patients pooled from 4 prospective Dutch cohort studies across 44 centers (2000–2019), we compared the cross-validated prediction performance of multiple eXtreme Gradient Boosting models using different sets of bedside clinical tests to predict the 6-month Action Research Arm Test outcome (0–57). We then selected the model with the minimal predictor set that best balanced bedside feasibility and accuracy and validated it within 72 hours poststroke on a test data set (n=32) from the same cohort using median absolute error as the evaluation metric.RESULTS:A model incorporating Shoulder Abduction from the Motricity Index, voluntary finger extension, Fugl-Meyer Upper Extremity, and total National Institutes of Health Stroke Scale score as bedside tests, showed the best tradeoff between model simplicity and predictive accuracy (median absolute error, 5.9 on the 0–57 score; interquartile range, 2.9–12.9).CONCLUSIONS:Our model predicts the 6-month Action Research Arm Test score using a minimal set of bedside clinical tests collected within the first 3 days after stroke, achieving a median absolute error below the Action Research Arm Test minimal clinically important difference of 6 points.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vandergun_can_2025,

title = {Can machine learning improve on the early prediction of upper limb recovery after stroke?},

author = {G. J. Gun and C. G. M. Meskers and E. R. Andrinopoulou and E. Grauwmeijer and L. Hoogendam and E. E. H. Wegen and D. Bos and S. Cornelissen and E. Hu and P. Hulst and X. Li and H. Lingsma and F. Nijenhuis and B. Roozenbeek and D. Ruijters and M. Silkens and R. Su and S. Sülz and T. Walsum and G. Kwakkel and R. W. Selles and ICAI Stroke Lab},

url = {https://doi.org/10.1186/s12984-025-01743-4},

doi = {10.1186/s12984-025-01743-4},

issn = {1743-0003},

year  = {2025},

date = {2025-10-27},

urldate = {2025-10-27},

volume = {22},

number = {1},

pages = {223},

abstract = {Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predict arm-hand capacity at six months post-stroke using clinical variables from the first week; (2) compare its performance to a mixed-effects model; and (3) co-design a user-friendly output visualisation with clinician input.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{langerak_prognostic_2025,

title = {Prognostic Targeting Improves Statistical Power and Efficiency in Randomized Controlled Trials in Upper Extremity Stroke Rehabilitation},

author = {A. J. Langerak and G. J. Gun and C. G. M. Meskers and J. B. J. Bussmann and E. E. H. Wegen and G. Kwakkel and R. W. Selles},

url = {https://doi.org/10.1177/15459683251369467},

doi = {10.1177/15459683251369467},

issn = {1545-9683},

year  = {2025},

date = {2025-09-22},

urldate = {2025-09-22},

pages = {15459683251369467},

publisher = {SAGE Publications Inc STM},

abstract = {Introduction: Randomized Controlled Trials (RCTs) are essential to underpin the superiority of novel interventions affecting upper extremity capacity post-stroke. However, many RCTs are underpowered, due to heterogeneity in recovery. Prognostic targeting may help reduce sample sizes while maintaining sufficient power.Objective: This study investigates the effects of prognostic targeting on the required sample size to achieve 70% to 90% power in early post-stroke RCTs with upper extremity capacity measured with the Action Research Arm Test (ARAT) as the outcome.Patients and methods: Serial data from 4 prospective cohort studies (N?=?372 stroke patients) were pooled, with assessments from week 1 to 6 months post-stroke. Using this dataset, we generated synthetic 6-month ARAT outcomes and analyzed data cross-sectionally and longitudinally, with and without prognostic targeting based on a pre-existing prognostic model predicting 6-month outcome. We then calculated power for different sample sizes and assessed trial efficiency, determined by the estimated sample size and inclusion rate.Results: Prognostic targeting within 3?weeks post-stroke theoretically reduced the required sample size by up to 56% and improved trial efficiency by 40 to 45% for detecting a 6-point ARAT difference at 6 months. The targeted trials needed 220, 270, and 360 patients vs. 470, 560, and 820 in non-targeted trials for 70% to 90% power. Benefits persisted in longitudinal analyses.Conclusion: This study demonstrates the benefits of prognostic targeting for improving power and efficiency in early post-stroke upper extremity trials using ARAT as outcome. We strongly recommend its use in future stroke rehabilitation and recovery studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{li_hypertension_2025,

title = {Hypertension, intracranial arteriosclerosis, and structural brain changes in patients with TIA or ischemic stroke},

author = {X. Li and B. P. Berghout and G. Rooijen and M. K. Ikram and B. Roozenbeek and D. Bos},

doi = {10.1177/23969873241307099},

issn = {2396-9881},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {European Stroke Journal},

volume = {10},

number = {3},

pages = {804–812},

abstract = {INTRODUCTION: Hypertension is a major risk factor of structural brain changes, including atrophy and cerebral small vessel disease. Intracranial arteriosclerosis could be an underlying mechanism between hypertension and structural brain changes. This study investigated whether intracranial carotid artery calcification (ICAC), as a proxy for intracranial arteriosclerosis, explains the association between hypertension and structural brain changes in patients with TIA or ischemic stroke. 

PATIENTS AND METHODS: About 968 patients (mean age 62.7 years) with TIA or ischemic stroke from a registry who underwent non-contrast CT (NCCT) and CT-angiography (CTA) were included in this study. Presence and volume (mm3) of ICAC were assessed on CTA. Subtypes of ICAC were assessed on NCCT, where ICAC was categorized into intimal and internal elastic lamina (IEL) type calcification. Structural brain changes, indicated by atrophy, periventricular and deep white matter lesions (WML), and lacunes were assessed on NCCT. Mediation analysis was performed using ICAC, ICAC volume, and ICAC subtypes as mediators. 

RESULTS: ICAC was prevalent in 67.8% of patients, with 52.6% of them exhibiting intimal calcification, and 26.5% exhibiting IEL calcification. Atrophy, periventricular WML, deep WML, and lacunes were present in 48.1%, 56.4%, 43.0% and 17.1% of patients respectively. The presence of ICAC explained 7.1% of the association of hypertension with periventricular WML, 3.6% with deep WML, and 17.6% with lacunes. Hypertension was associated with increased atrophy through ICAC (OR: 1.02, 95% CI: 1.00-1.05). In subgroup analyses, IEL calcification partly explained the association between hypertension and periventricular WML (16.8%), and atrophy (OR: 1.12, 95% CI: 1.02-1.27). Intimal calcification did not explain any association. 

CONCLUSION: ICAC partially explained the association between hypertension and atrophy, periventricular and deep WML, and lacunes. Although intimal calcification was more prevalent in ischemic stroke patients, IEL calcification takes the leading role in explaining the association between hypertension and structural brain changes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{van_hulst_decision-analytic_2025,

title = {A Decision-Analytic Model to Evaluate Cost-Effectiveness of Regional Implementation of a Mobile Stroke Unit},

author = {P. L. Hulst and R. M. Wijdeven and E. Venema and F. M. E. Pinckaers and M. G. M. Hunink and A. Lugt and D. W. J. Dippel and H. F. Lingsma and D. Bos and B. Roozenbeek},

doi = {10.1212/WNL.0000000000213834},

issn = {1526-632X},

year  = {2025},

date = {2025-08-01},

urldate = {2025-08-01},

journal = {Neurology},

volume = {105},

number = {3},

pages = {e213834},

abstract = {BACKGROUND AND OBJECTIVES: Mobile stroke units (MSUs) have the potential to improve functional outcome of ischemic stroke patients, through shortening onset-to-treatment times. Previous cost-effectiveness studies have limited generalizability to nonmetropolitan settings and did not evaluate cost-effectiveness over a lifetime horizon. We aimed to develop a regionally adaptable decision-analytic model, to evaluate cost-effectiveness of MSU implementation and to identify the optimal dispatch scenario. 

METHODS: We developed a generalizable state-transition microsimulation model with modifiable region-specific parameters and dispatch characteristics to evaluate the lifetime cost-effectiveness from a health care perspective of 1-year MSU implementation. We used the southwest of the Netherlands (1,770,000 inhabitants, 1,592 km2, 7 primary stroke centers, 2 thrombectomy-capable stroke centers) as an example. Region-specific input parameters for the model, such as population density, age distribution, and driving times, were obtained at the level of postal codes. We developed a virtual cohort of suspected stroke patients based on age-dependent stroke risks and the number of inhabitants per postal code. We compared the combined dispatch of an MSU and emergency medical services (EMS) with dispatch of EMS alone for patients with onset-to-alarm time <6 hours, living within the catchment area of the MSU. In the base case analysis, the MSU could be dispatched to all postal codes in the study region between 7.00 am and 11.00 pm from a central dispatch site. We assessed the long-term cost-effectiveness through incremental net monetary benefits (iNMBs). Discount rates were 1.5% for effects and 4.0% for costs. 

RESULTS: In the base case scenario, the MSU was dispatched to 2,080 of 3,628 patients (57.3%) with a suspected stroke and onset-to-alarm time <6 hours, resulting in a lifetime gain of 399 (95% CI 384-414) additional quality-adjusted life years, €3.9 million (95% CI €3.5 million-€4.3 million) cost savings, and an iNMB of €23.9 million (95% CI €22.8 million-€24.9 million). A smaller catchment area for MSU dispatch was associated with increased cost-effectiveness. 

DISCUSSION: Adding an MSU to the dispatch strategy for suspected stroke patients is expected to be cost-effective in our region. Our model facilitates evaluation of the cost-effectiveness of MSU implementation in different regions, settings, and scenarios with varying characteristics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}