假阳性悖论
假阴性反应
医学
免疫分析
假阳性率
贝叶斯概率
人工智能
内科学
计算机科学
抗体
免疫学
作者
Thomas Steinauer,Elena Matthey‐Guirao,Francisco J. Gómez,Luana Rittener-Ruf,Matteo Marchetti,Matthew Goodyer,Mitja Nabergoj,Stefano Barelli,Francesco Grandoni,Maxime G. Zermatten,Lorenzo Alberio
出处
期刊:Blood
[Elsevier BV]
日期:2025-05-22
卷期号:146 (7): 887-896
被引量:1
标识
DOI:10.1182/blood.2024027517
摘要
Early recognition and treatment of heparin-induced thrombocytopenia (HIT) are crucial to prevent severe complications. While immunoassays offer rapid diagnosis, their sensitivity and specificity are suboptimal. Sequential combinations of quantitative immunoassay results improve their diagnostic accuracy. We aimed to validate two Bayesian approaches combining two rapid immunoassays and to compare their diagnostic performance with two other diagnostic approaches ("Hamilton", "TORADI-HIT" algorithms). We included 1194 patients with suspected HIT, of whom 6.0% had confirmed HIT. HemosIL Acustar HIT-IgG (CLIA) and HemosIL HIT-Ab (LIA) (Instrumentation Laboratory, Munich, Germany) were performed. Definite HIT confirmation or exclusion was made using heparin-induced platelet activation (HIPA) test and PF4-enhanced HIPA (PIPA). Our sequential approaches (CLIA first and LIA for unsolved cases or vice versa) correctly excluded HIT in 95.6% and 96.4%, predicted HIT in 95.8% and 97.2%, with 3.3% and 2.3% of cases remaining undetermined. There were 13 respectively 15 false positive and no false negative predictions. The modified version of the "Hamilton algorithm" correctly excluded HIT in 92.1% and predicted HIT in 97.2% with 88 false positive and two false negative results. The "TORADI-HIT algorithm" correctly excluded HIT in 97.9% and predicted HIT in 93.8% (10 false positives, three false negatives). In conclusion, a Bayesian approach sequentially employing two immunoassays is accurate for HIT diagnosis. Performing immunoassays simultaneously without considering clinical pre-test probability misses HIT cases. The "TORADI-HIT algorithm" offers better HIT exclusion with a 6% false-negative rate. Using our Bayesian approach, HIT exclusion or recognition can be achieved in ≥97% of cases within <1 hour.
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