Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model

 This prediction does absolutely no good unless you have created protocols that reduce this risk to zero. Do you tell your patients: Oh you have this % chance of having a hemorrhagic stroke but we have NOTHING FOR IT.  We are just letting you have two strokes in a row; deal with it. And by the way we have NO PROTOCOLS IN REHAB that will get you anywhere close to 100% recovery.

Or do you just lie by omission by telling your patients nothing. Mine did.

Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model

Junfeng Liu^1†, Yanan Wang^1†, Yuxi Jin², Wen Guo¹, Quhong Song¹, Chenchen Wei^1,3, Jing Li⁴, Shanshan Zhang⁵ and Ming Liu^1*

• ¹Department of Neurology, Center of Cerebrovascular Diseases, West China Hospital, Sichuan University, Chengdu, China
• ²West China School of Medicine, Sichuan University, Chengdu, China
• ³Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
• ⁴Department of Neurology, The First People’s Hospital of Ziyang, Ziyang, China
• ⁵Department of Neurology, Mianyang Central Hospital, Mianyang, China

Objectives: We aimed to develop and validate a novel multi-biomarker model for predicting hemorrhagic transformation (HT) risk after acute ischemic stroke (AIS).

Methods: We prospectively included patients with AIS admitted within 24 h of stroke from January 1st 2016 to January 31st 2019. A panel of 17 circulating biomarkers was measured and analyzed in this cohort. We assessed the ability of individual circulating biomarkers and the combination of multiple biomarkers to predict any HT, symptomatic HT (sHT) and parenchymal hematoma (PH) after AIS. The strategy of multiple biomarkers in combination was then externally validated in an independent cohort of 288 Chinese patients.

Results: A total of 1207 patients with AIS (727 males; mean age, 67.2 ± 13.9 years) were included as a derivation cohort, of whom 179 patients (14.8%) developed HT. The final multi-biomarker model included three biomarkers [platelets, neutrophil-to-lymphocyte ratios (NLR), and high-density lipoprotein (HDL)] from different pathways, showing a good performance for predicting HT in both the derivation cohort (c statistic = 0·64, 95% CI 0·60–0·69), and validation cohort (c statistic = 0·70, 95% CI 0·58–0·82). Adding these three biomarkers simultaneously to the basic model with conventional risk factors improved the ability of HT reclassification [net reclassification improvement (NRI) 65.6%, P < 0.001], PH (NRI 64.7%, P < 0.001), and sHT (NRI 71.3%, P < 0.001).

Conclusion: This easily applied multi-biomarker model had a good performance for predicting HT in both the derivation and external validation cohorts. Incorporation of biomarkers into clinical decision making may help to identify patients at high risk of HT after AIS and warrants further consideration.

Introduction

Hemorrhagic transformation (HT) is not only a part of the natural history of ischemic stroke, but also a major complication of reperfusion therapy, and is associated with increased morbidity and mortality (Álvarez-Sabín et al., 2013). A recent meta-analysis (Sadeh-Gonik et al., 2018) reported a HT and symptomatic HT (sHT) rates of 24% and 8%, respectively, when patients treated with thrombectomy. In a survey of emergency physicians about thrombolysis, Brown et al. (2005) reported about two thirds of physicians may overestimate the risk of HT, and so they inappropriately avoid thrombolysis. In high-income countries, only 20% of ischemic stroke patients received thrombolysis (Langhorne et al., 2018), and less than 3% of patients in China (Wu et al., 2019). In addition, our previous review (Wu et al., 2019) suggested that a concern over HT risk also hampers the widespread adoption of anticoagulation for secondary prevention in patients with atrial fibrillation (AF) in Asia, especially in China. Therefore, early and quickly identification of patients at high risk of HT to help individualized treatment decisions in patients with ischemic stroke is a top priority of research on stroke.

At present, evaluating the risk of HT after ischemic stroke mainly depends on clinical factors and neuroimaging (Álvarez-Sabín et al., 2013). By combining those clinical and neuroimaging markers, several risk models (Cucchiara et al., 2008; Lou et al., 2008; Menon et al., 2012; Strbian et al., 2012; Saposnik et al., 2013) have been developed to stratify the risk of HT after thrombolysis. However, existing models have only a modest capability in predicting those at “high risk” for HT after stroke (Sung et al., 2013), irrespective of the use of thrombolysis. In addition, some neuroimaging markers used in these models may not be routinely available in all ischemic stroke patients due to contraindications and lack of availability. Hence, there has been increasing interest to find new approaches which can achieve better accuracy and be easier to use in predicting HT. Circulating biomarkers may be a promising approach.

A growing number of studies reported that individual novel biomarkers, such as matrix metalloproteinase 9, S100B, and tight-junction proteins, were associated with HT after ischemic stroke (Montaner et al., 2003; Foerch et al., 2007; Kelly et al., 2008; Kazmierski et al., 2012; Yuan et al., 2018). However, there are multiple practical challenges before these novel biomarkers can be used in the clinical practice, including the need for high sensitivity and specificity, and fast and easy-to-assess availability of the results. Furthermore, few studies investigated the relationship between multiple biomarkers and HT systematically, and the clinical significance of multiple biomarkers when used in combination for predicting HT is also unknown. Therefore, we aimed to develop and externally validate a novel multi-biomarker model for predicting HT after ischemic stroke that can be calculated from readily available routine clinical circulation biomarkers.