Macleod Brandi M, Wilkins Pamela A, McCoy Annette M, Bishop Rebecca C
Department of Veterinary Clinical Medicine, University of Illinois, Urbana, Illinois, USA.
Equine Vet J. 2025 Apr 24. doi: 10.1111/evj.14517.
BACKGROUND: Viscoelastic coagulation testing (VCT) identifies subclinical disruption of coagulation homeostasis and may improve prognostication, particularly for patients with severe systemic inflammation or shock. Machine learning (ML) algorithms may capture complex relationships between clinical variables better than linear regression (GLM). OBJECTIVE: To evaluate the utility of ML models incorporating VCT and clinical data to predict survival outcomes in horses with acute abdominal pain. STUDY DESIGN: Retrospective observational cohort study. METHODS: VCT (VCM Vet™) was performed on 57 horses with acute abdominal pain at admission, with clinical data collected retrospectively. Coagulopathy was defined as ≥2 abnormal VCT parameters. GLM and random forest (RF) classification models were developed to predict short-term survival. A training cohort of 40 horses was used for model development, and model performance was determined using the remaining 17 horses. RF models were implemented in a web-based application to demonstrate clinical application. RESULTS: There were 31 survivors and 26 non-survivors. The majority of cases were colitis (47.7%), with smaller proportions of impactions, strangulating obstructions and other causes of colic. Coagulopathy diagnosis alone performed poorly for survival prediction (sensitivity 81% [95% CI 64-94], specificity 31% [95% CI 15-50], AUC = 0.515). Final GLM included SIRS score (OR 0.37 [95% CI 0.071-1.68]; p = 0.2), L-lactate (OR 0.51 [0.25-0.82]; p = 0.02), clot time (CT; OR 1.0 [0.99-1.0], p = 0.39), and clot amplitude at 10 min (A10; OR 0.89 [0.74-1.02], p = 0.2). Final RF model included heart rate, PCV, L-lactate, white blood cell count, neutrophil count, clot amplitude at 20 min (A20) and CT. RF models improved sensitivity (RF 91% [95% CI 60-100]; RF 83% [95% CI 42-99]) and specificity (both 83% [95% CI 42-99]) compared to GLM (sensitivity 65% [95% CI 47-79], specificity 42% [95% CI 26-61]). MAIN LIMITATIONS: Small number of horses, convenience sampling. Model validation with an independent population is needed to support clinical applicability. CONCLUSIONS: L-lactate remains a key predictor of survival in horses with colic. The integration of VCT with clinical data in machine learning models may enhance prognostication.
背景：粘弹性凝血检测（VCT）可识别凝血稳态的亚临床破坏，可能改善预后，特别是对于患有严重全身炎症或休克的患者。机器学习（ML）算法可能比线性回归（GLM）更好地捕捉临床变量之间的复杂关系。 目的：评估结合VCT和临床数据的ML模型在预测急性腹痛马匹生存结局中的效用。 研究设计：回顾性观察队列研究。 方法：对57匹入院时患有急性腹痛的马匹进行VCT（VCM Vet™）检测，并回顾性收集临床数据。凝血障碍定义为≥2个VCT参数异常。开发GLM和随机森林（RF）分类模型以预测短期生存。40匹马的训练队列用于模型开发，并使用其余17匹马确定模型性能。RF模型在基于网络的应用程序中实施以展示临床应用。 结果：有31例幸存者和26例非幸存者。大多数病例为结肠炎（47.7%），肠梗阻、绞窄性肠梗阻和其他腹痛原因的比例较小。仅凝血障碍诊断对生存预测的表现较差（敏感性81%[95%CI 64-94]，特异性31%[95%CI 15-50]，AUC = 0.515）。最终的GLM包括全身炎症反应综合征（SIRS）评分（OR 0.37[95%CI 0.071-1.68]；p = 0.2）、L-乳酸（OR 0.51[0.25-0.82]；p = 0.02）、凝血时间（CT；OR 1.0[0.99-1.0]，p = 0.39）和10分钟时的血凝块振幅（A10；OR 0.89[0.74-1.02]，p = 0.2）。最终的RF模型包括心率、红细胞压积、L-乳酸、白细胞计数、中性粒细胞计数、20分钟时的血凝块振幅（A20）和CT。与GLM（敏感性65%[95%CI 47-79]，特异性42%[95%CI 26-61]）相比，RF模型提高了敏感性（RF 91%[95%CI 60-100]；RF 83%[95%CI 42-99]）和特异性（均为83%[95%CI 42-99]）。 主要局限性：马匹数量少，方便抽样。需要用独立群体进行模型验证以支持临床适用性。 结论：L-乳酸仍然是腹痛马匹生存的关键预测指标。在机器学习模型中将VCT与临床数据相结合可能会增强预后评估。
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