Islam Mehmet Muzaffer, Uygun Cemrenur, Delipoyraz Melike, Satici Merve Osoydan, Kurt Servan, Ademoglu Enis, Eroglu Serkan Emre
Department of Emergency Medicine, Umraniye Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Turk J Emerg Med. 2023 Jun 26;23(3):176-183. doi: 10.4103/tjem.tjem_33_23. eCollection 2023 Jul-Sep.
Hemorrhagic transformation (HT) is significantly related to poor neurological outcomes and mortality. Although variables and models that predict HT have been reported in the literature, the need for a model with high diagnostic performance continues. We aimed to propose a model that can accurately predict symptomatic HT within 7 days of acute ischemic stroke (AIS).
Patients with AIS admitted to the emergency department of a tertiary training and research hospital between November 07, 2021, and August 26, 2022, were included in this single-center retrospective study. For the model, binary logistics with the forced-entry method was used and the model was validated with 3-fold cross-validation. After the final model was created, the optimal cutoff point was determined with Youden's index. Another cut-off point was determined at which the sensitivity was the highest.
The mean age of the 423 patients included in the study was 70 (60-81) and 53.7% ( = 227) of the patients were male. Symptomatic HT was present in 31 (7.3%) patients. Mechanical thrombectomy, atrial fibrillation, and diabetes mellitus were the independent predictors ( < 0.001, = 0.003, = 0.006, respectively). The mean area under the curve of the receiver operating characteristics of the model was 0.916 (95% confidence interval [CI] = 0.876-0.957). The sensitivity for the optimal cut-off point was 90.3% (95% CI = 74.3%-97.9%) and specificity was 80.6% (95% CI = 76.4%-84.4%). For the second cutoff point where the sensitivity was 100%, the specificity was 60.5% (95% CI = 55.4%-65.3%).
The diagnostic performance of our model was satisfactory and it seems to be promising for symptomatic HT. External validation studies are required to implement our results into clinical use.
出血性转化（HT）与不良神经功能结局和死亡率显著相关。尽管文献中已报道了预测HT的变量和模型，但仍需要具有高诊断性能的模型。我们旨在提出一种能够准确预测急性缺血性卒中（AIS）7天内症状性HT的模型。
本单中心回顾性研究纳入了2021年11月7日至2022年8月26日期间在一家三级培训和研究医院急诊科收治的AIS患者。对于该模型，采用强制进入法的二元逻辑回归，并通过3折交叉验证对模型进行验证。在创建最终模型后，用约登指数确定最佳截断点。还确定了另一个截断点，此时灵敏度最高。
纳入研究的423例患者的平均年龄为70岁（60 - 81岁），53.7%（n = 227）的患者为男性。31例（7.3%）患者出现症状性HT。机械取栓、心房颤动和糖尿病是独立预测因素（分别为P < 0.001、P = 0.003、P = 0.006）。该模型的受试者操作特征曲线下平均面积为0.916（95%置信区间[CI] = 0.876 - 0.957）。最佳截断点的灵敏度为90.3%（95% CI = 74.3% - 97.9%），特异度为80.6%（95% CI = 76.4% - 84.4%）。对于灵敏度为100%的第二个截断点，特异度为60.5%（95% CI = 55.4% - 65.3%）。
我们模型的诊断性能令人满意，对于症状性HT似乎很有前景。需要进行外部验证研究以将我们的结果应用于临床。
