Diagnosing heparin-induced thrombocytopenia: The need for accuracy and speed

Anti-PF4 antibodies and their relationship with COVID infection

Detecting anti-PF4 antibodies remains the golden diagnostic method for heparin-induced thrombocytopenia (HIT) diagnosis with high sensitivity and specificity. Various lab tests detect anti-PF4 antibodies, including immunoassays and functional assays. Even with positive detection of the anti-PF4 antibody, several factors are involved in the result. The concept of anti-PF4 disorders was recently brought to light during the COVID pandemic since the development of vaccine-induced thrombotic thrombocytopenia (VITT) with the adenovirus-vectored-DNA vaccine during the pandemic. Circumstances that detect anti-PF4 antibodies are classified as anti-PF4 disorders, including VITT, autoimmune HIT and spontaneous HIT. Some studies showed a higher percentage of anti-PF4 antibody detection among the population infected by COVID-19 without heparin exposure and some supported the theory that the anti-PF4 antibodies were related to the disease severity. In this review article, we provide a brief review of anti-PF4 disorders and summarize the current studies of anti-PF4 antibodies and COVID-19 infection.

[Thromboembolic diseases from a haemostaseologic point of view]

Venous thromboembolism is one of the most common vascular diseases. Increased thrombin formation together with reduced blood flow create a hypercoagulable environment that induces thrombus formation. Anticoagulants play a pivotal role in the treatment and secondary prophylaxis of venous thromboembolism because they effectively interrupt this hypercoagulability. A personalized assessment of the thrombotic risk is essential for planning the duration and intensity of secondary prophylaxis. The occurrence of thrombosis outside a typical risk situation, an atypical localization and a family history of thrombosis indicate a thrombophilic state. In these cases, thrombophilia diagnostics are useful for extended risk assessment. If anti-phospholipid antibodies are detected, the risk of recurrence is particularly increased.

A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: A prospective, multicenter, observational study

BACKGROUND

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.

METHODS

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.

FINDINGS

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.

INTERPRETATION

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.

FUNDING

Swiss National Science Foundation (SNSF), and International Society on Thrombosis and Haemostasis (ISTH).

Antibodies against Platelet Factor 4 and Their Associated Pathologies: From HIT/HITT to Spontaneous HIT-Like Syndrome, to COVID-19, to VITT/TTS

Antibodies against platelet factor 4 (PF4), a protein released from alpha-granules of activated platelets, may cause a number of pathophysiological conditions. The most commonly known is heparin-induced thrombocytopenia (HIT), which develops in a small proportion of people treated with the anticoagulant drug heparin. Notably, PF4 binds with high affinity to heparin, and in HIT, complexes of PF4/H may, in a small proportion of susceptible patients, trigger the development of anti-PF4 antibodies and subsequent platelet activation and aggregation, ultimately leading to the development of pathological thrombosis at sites of vessel occlusion. Of more modern interest, antibodies against PF4 may also arise in patients with COVID-19 (Coronavirus Disease 2019) or in patients who have been vaccinated against COVID-19, especially in recipients of adenovirus-based vaccines. For this latter group of patients, the terms VITT (vaccine-induced [immune] thrombotic thrombocytopenia) and TTS (thrombotic thrombocytopenia syndrome) have been coined. Another category associated with this pathophysiology comprises those in whom a precipitating event is not clear; this category is referred to as ‘spontaneous HIT-like syndrome’. Despite its name, it arises as an HIT-mimicking disorder but without antecedent heparin exposure. In this narrative review, we describe the development of antibodies against PF4, and associated pathophysiology, in such conditions.