Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms

But if you don't tell us the protocols used to recover from a mild stroke. Useless. 

Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms

Xinping Lin^1,2†, Shiteng Lin^1,2†, XiaoLi Cui^3†, Daizun Zou^1,2, FuPing Jiang⁴, JunShan Zhou⁵, NiHong Chen⁵, Zhihong Zhao^6*, Juan Zhang^3* and Jianjun Zou^2,7*

• ¹School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
• ²Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ³Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁴Department of Geriatrics, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁵Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁶Department of Neurology, The First Affiliated Hospital (People's Hospital of Hunan Province), Hunan Normal University, Changsha, China
• ⁷Department of Clinical Pharmacology, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China

Background and Purpose: Treatment for mild stroke remains an open question.(Well then, solve that problem by creating protocols. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who would be at high risk of post-stroke disability (PSD) if they only received medical therapy and, more importantly, to aid neurologists in making individual clinical decisions in emergency contexts.

Methods: Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020. The exclusion criteria were patients who received thrombolytic therapy, age <18 years, lack of 3-month modified Rankin Scale (mRS), disabled before the index stroke, with an admission National Institute of Health stroke scale (NIHSS) > 5. The primary outcome was PSD, corresponding to 3-month mRS ≥ 2. We developed five ML models and assessed the area under curve (AUC) of receiver operating characteristic, calibration curve, and decision curve analysis. The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. Finally, rapid-DAMS (R-DAMS) was constructed for a more urgent situation based on DAMS.

Results: A total of 1,905 mild stroke patients were enrolled in this study, and patients with PSD accounted for 23.4% (447). There was no difference in AUCs between the five models (ranged from 0.691 to 0.823). Although there was similar discriminative performance between ML models, the support vector machine model exhibited higher net benefit and better calibration (Brier score, 0.159, calibration slope, 0.935, calibration intercept, 0.035). Therefore, this model was selected for DAMS. In addition, SHAP approach showed that the most crucial feature was NIHSS on admission. Finally, R-DAMS was constructed and there was similar discriminative performance between R-DAMS and DAMS, but the former performed worse on calibration.

Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.

Introduction

Around half of patients with ischemic stroke have mild neurological symptoms (1), usually with the expectation that such patients will come back to their pre-stroke activities regardless of the treatment. However, over one-third of mild stroke patients present with some degree of post-stroke disability (PSD) (2–4), which may be the result of inadequate acute treatments, early stroke recurrence, serious complications, or other reasons (1, 5). For the acute treatment of mild stroke patients, the guidelines from the American Heart Association/American Stroke Association (AHA/ASA) (6) distinguish disabling from non-disabling stroke and recommend intravenous (IV) alteplase only for the former. Nonetheless, the more certain, but not definitive, concept of “disabling stroke” is subjective and requires interpretation by individual neurologists. On the other hand, there is a trade-off between the benefits of IV alteplase and the risk of symptomatic intracranial hemorrhage (sICH). Therefore, decisions on how to treat mild stroke patients should be made on an individual basis.

3-month modified Rankin Scale (mRS), a valuable instrument for testing therapeutic interventions (7, 8), was used to assess the levels of PSD (5, 8). For mild stroke patients who only received medical therapy but had PSD, such therapy is not enough. Therefore, mild stroke patients who would be at high risk of PSD if they only received medical therapy should be early identified in emergency contexts, and some aggressive treatments, such as IV alteplase or close monitoring preventing worsening, should be taken in time. Unexpectedly, neurologists' overall accuracy for identifying those patients was staggeringly low (16.9%) (9). Each day that such a problem continues to exist means that uncounted mild stroke patients are being left with preventable disability.

However, none of the previously published risk models which were developed to predict the function outcome after stroke are fit to solve this problem. For example, the Totaled Health Risks in Vascular Events (THRIVE) score and the Houston Intra-Arterial Therapy (HIAT) score assign 0 points for National Institute of Health stroke scale (NIHSS) ≤ 5, losing the predictive power of NIHSS in mild stroke patients (10, 11). NIHSS on admission has been proven to be a strong predictor of PSD (5). Thus, despite convenient clinical applicability, these models cannot accurately identify mild stroke patients at high risk of PSD. Such models remain inadequate.

With the increased clinical data gathered for each patient, modern medical decision-making demands accurate, novel, and prediction-driven decision support. Machine learning (ML) algorithm, as a burgeoning statistical approach, is well-suited for that mission. Numerous studies with a considerable number of patients have shown great potential for ML approaches to predict recurrence (12), swallowing recovery (13), or aphasia (14) in patients with stroke. However, a model based on ML algorithms, focusing on the more debatable area of treating MS, has not yet been established.

Here, our goal was to develop and validate a prediction-driven decision support tool based on ML algorithms, called DAMS (Disability After Mild Stroke), to early identify mild stroke patients who would be at high risk of PSD if they only received medical therapy, and more importantly, to assist neurologists to make individual clinical decisions for mild stroke patients.