SAT0235 THE EFFECT OF ANTIPHOSPHOLIPID ANTIBODIES ON APTT WAVEFORM PATTERNS
Authors: Y. Suzuki, A. Mitsui, Y. Yamamoto, K. Noda, A. Nakajima
Journal: Annals of the Rheumatic Diseases (2020)

Background: Patients with antiphospholipid antibody (aPL) are said to be at increased risk for thrombosis, however it is difficult to predict whether they will develop thrombosis. In recent years, it has been revealed that the characteristics of the second derivative curve of APTT waveform with aPL positive patient is biphasic changes 1,2 . As first step in predicting the risk of thrombosis, we sought to understand the effect of aPL on APTT waveform patterns. Objectives: To analyze the characteristics of APTT waveforms according to the background diseases and the presence of aPL Methods: Patients who underwent coagulation function tests from 2017 to 2019 were analyzed. A coagulation waveform (Clot waveform: CW) was drawn using a fully automatic coagulation time measuring device manufactured by Instrumentation Laboratory From the APTT waveform, the 1st derivative curve (DC) indicating the coagulation speed and the 2nd DC indicating the coagulation acceleration were depicted to measure the 1st DC height, 2nd DC peak 1 time, and 2nd DC peak 1 height (Figure1 2 ). Patients were devided into CTD with aPL-negative patients (group A), aPL-positive patients with no prior thrombosis (group B), and antiphospholipid antibody syndrome (APS) (group C). Patients characteristics and aPL (anti-cardiolipin [CL] antibody IgM, anti-CL antibody IgG, anti-CLβ2GP1 complex antibody, LA-APTT, and LA- DRVVT) status were examind. A further analysis was performed according to the numbers of positive aPL. Comparison between the three groups were made by the one-way ANOVA method, with significant differences set as p-values &lt;0.1. Factors with significant differences were analyzed by Steel-Dwass test. APTT waveforms was analyzed according to the numbers of positive aPL by least squares methods. Furthermore, to determine the cut off value of APTT, 1st DC height, 2nd DC peak 1 time, and 2nd DC peak 1 height for each case with 2 or more positive aPLs and 3 positive aPLs, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity and specificity were caliculated. Figure 1. Results: The APTT waveform was analyzed in 61 patients (51 women, 83.6%) with average age of 54.1 ± 17.1 years. Group A was 26 cases, Group B was 18 cases, and Group C was 17 cases. APTT, 2nd DC peak1 time, 2nd DC peak1 height, 1st DC peak time were significantly different among A, B, and C groups (p &lt;0.01). APTT, 1st DC peak height, 2nd DC peak 1 time, and 2nd DC peak 1 height differed among the number of aPL (p &lt; 0.01, respectively). APTT and 2nd DC peak1 time prolonged by 9.43 (seconds) and 16.3 (seconds) respectively according to the number of aPLs increased, and 1st DC peak height (mabs/s) and 2nd DC peak1 height (mabs/s 2 ) decreased by 56.4 (mabs/s) and 223.9 (mabs/s 2 ) respectively according to the number of aPLs decreased (Table 1). APTT&gt; 35.2 (seconds) (sensitivity 80%, specificity 80.4%), 2nd DC peak1 height&gt; 302 (mabs/s 2 ) (sensitivity 80%, specificity 91.3%) were relevant to the presence of two or more aPLs and APTT&gt; 35.2 (seconds) (sensitivity 100%, specificity 80%), 2nd DC peak1 height&gt; 302 (mabs/s 2 ) (sensitivity 100%, specificity 90%) were relevant to the presence of three aPLs. Table 1. The number of positive aPL 0 1 2 3 p value The number of cases 27 19 4 11 APTT (seconds) 28.9 [26.8, 31.4] 30.9 [29.1, 38.2] 31.1 [27.3, 54.2] 60.7 [45.9, 73.7] 0.0001 2nd DC peak time (seconds) 29.2 [26.8, 30.7] 33.7 [31, 41.7] 36.1 [31.5, 99] 75.8 [50.5, 102.4] 0.0001 2nd DC peak height (mabs/s 2 ) 839.9 [666.1, 962.2] 669.6 [346.4, 946] 608.4 [137.8, 956.7] 119.3 [30.6, 196] 0.0001 1st DC peak height (mabs/s) 309.6 [260.6, 355.1] 271.5 [168, 353] 241.8 [96.3, 364.0] 135.6 [76.8, 163] 0.0001 Conclusion: The presence of aPL was more related to the 2nd DC peak1 height of APTT waveform than APTT. A detailed review of the APTT waveform may further predict future thrombosis risk. References: [1]Tokunaga N, et al. Blood Coagul Fibrinolysis. 2016;27:474-476. [2]Matsumoto T, et al. Int J Hematol. 2017;105:174-183. Disclosure of Interests: YASUO SUZUKI: None declared, Asako Mitsui: None declared, Yoshiki Yamamoto: None declared, Kentaro Noda: None declared, Ayako Nakajima Grant/research support from: AN has received research grants from Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Consultant of: AN has consultant fee from Nippon Kayaku Co. Ltd., Speakers bureau: AN has received speaker’s fee from AbbVie Japan GK, Actelion Pharmaceuticals Japan LTD., Asahi Kasei Pharma Co., Astellas Pharma Inc., Ayumi Pharmaceutical Co., Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., GlaxoSmithKline K.K., Hisamitsu Pharmaceutical Co. Inc., Kyorin Pharmaceutical Co. Ltd., Mitsubishi Tanabe Pharma Co., Otsuka Pharmaceutical Co. Ltd., Pfizer Japan Inc., and Teijin Pharma Ltd.