未经证实:在床边诊断肝素诱导的血小板减少症(HIT)仍然具有挑战性,暴露大量有延迟诊断或过度治疗风险的患者。我们假设机器学习算法可用于开发更准确和用户友好的诊断工具,该工具集成了各种临床和实验室信息,并考虑了复杂的相互作用。
UNASSIGNED:我们进行了一项前瞻性队列研究,包括2018年至2021年来自10个研究中心的1393例疑似HIT患者。收集详细的临床信息和实验室数据,并进行了各种免疫测定。洗涤的血小板肝素诱导的血小板活化测定(HIPA)用作参考标准。
未经证实:HIPA在119例患者中诊断为HIT(患病率8.5%)。训练数据集中的特征选择过程(75%的患者)产生了以下预测变量:(1)免疫测定测试结果,(2)血小板最低点,(3)普通肝素的使用,(4)CRP,(5)血小板减少的时机,(6)其他原因引起的血小板减少。在化学发光免疫测定(CLIA)和ELISA的情况下,性能最好的模型是支持向量机,以及颗粒凝胶免疫测定(PaGIA)中的梯度增强机。在验证数据集中(25%的患者),所有模型的AUROC为0.99(95%CI:0.97,1.00).与目前推荐的诊断算法(4Ts评分,免疫测定),假阴性患者的数量从12例减少到6例(-50.0%;ELISA),9比3(-66.7%,PaGIA)和14至5(-64.3%;CLIA)。假阳性个体的数量从87个减少到61个(-29.8%;ELISA),200增加到63(-68.5%;PaGIA),CLIA从50增加到63(+29.0%)。
UNASSIGNED:我们用于HIT诊断的用户友好的机器学习算法(https://toradi-hit.org)比当前推荐的诊断算法更准确。它有可能减少临床实践中的延迟诊断和过度治疗。未来的研究将在更广泛的环境中验证该模型。
UNASSIGNED:瑞士国家科学基金会(SNSF),和国际血栓形成和止血协会(ISTH)。
UNASSIGNED: Diagnosing heparin-induced thrombocytopenia (HIT) at the bedside remains challenging, exposing a significant number of patients at risk of delayed diagnosis or overtreatment. We hypothesized that machine-learning algorithms could be utilized to develop a more accurate and user-friendly diagnostic tool that integrates diverse clinical and laboratory information and accounts for complex interactions.
UNASSIGNED: We conducted a prospective cohort study including 1393 patients with suspected HIT between 2018 and 2021 from 10 study centers. Detailed clinical information and laboratory data were collected, and various immunoassays were conducted. The washed platelet heparin-induced platelet activation assay (HIPA) served as the reference standard.
UNASSIGNED: HIPA diagnosed HIT in 119 patients (prevalence 8.5%). The feature selection process in the training dataset (75% of patients) yielded the following predictor variables: (1) immunoassay test result, (2) platelet nadir, (3) unfractionated heparin use, (4) CRP, (5) timing of thrombocytopenia, and (6) other causes of thrombocytopenia. The best performing models were a support vector machine in case of the chemiluminescent immunoassay (CLIA) and the ELISA, as well as a gradient boosting machine in particle-gel immunoassay (PaGIA). In the validation dataset (25% of patients), the AUROC of all models was 0.99 (95% CI: 0.97, 1.00). Compared to the currently recommended diagnostic algorithm (4Ts score, immunoassay), the numbers of false-negative patients were reduced from 12 to 6 (-50.0%; ELISA), 9 to 3 (-66.7%, PaGIA) and 14 to 5 (-64.3%; CLIA). The numbers of false-positive individuals were reduced from 87 to 61 (-29.8%; ELISA), 200 to 63 (-68.5%; PaGIA) and increased from 50 to 63 (+29.0%) for the CLIA.
UNASSIGNED: Our user-friendly machine-learning algorithm for the diagnosis of HIT (https://toradi-hit.org) was substantially more accurate than the currently recommended diagnostic algorithm. It has the potential to reduce delayed diagnosis and overtreatment in clinical practice. Future studies shall validate this model in wider settings.
UNASSIGNED: Swiss National Science Foundation (SNSF), and International Society on Thrombosis and Haemostasis (ISTH).