Research Title: Evaluation of coagulation status using clot waveform analysis in severe COVID-19 patients
Presenter: Dr Lakshmy Nair A/P Hari Kumar
Department of Haematology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan
I have no personal or financial interests to declare.
I have no financial support from an industry source at the current presentation.
21st ASM CPathAMM 2026
NO Disclosure
Name of Author: Dr Lakshmy Nair A/P Hari Kumar
Co-authors: Dr Salfarina Iberahim/ Dr Zefarina Zulkafli
Picture/screenshot taking is NOT ALLOWED during my presentation (including presented slides)
COVID-19 infection
risk of thromboembolism
HYPERCOAGULABLE STATE
COAGULOPATHY
✓ PT/INR/aPTT
✓ D-dimer assay
✓ Fibrinogen assay
✓ CBC
Role of clot waveform analysis (CWA)?
Since the onset of the COVID-19 pandemic in 2020, only a limited number of studies
(approximately 10–15 globally) have evaluated clot waveform analysis parameters,
with even fewer focusing specifically on severe or critically ill patients.
There is currently little-to-no published Malaysian study specifically evaluating CWA
parameters in COVID-19 patients.
√ CWA as an investigative tool of coagulopathy remains underexplored
√ May have potential utility in predicting thromboembolic events
Research Questions…
What is the role of clot waveform analysis (CWA) as a tool for evaluating coagulation in COVID-19 patients, particularly those with severe disease?
Do clot waveform analysis (CWA) parameters differ between healthy individuals and severe COVID-19 patients presenting with acute venous thromboembolism?
Main study objective:
To evaluate the coagulation status using Clot Waveform Analysis (CWA) amongst patients with severe Covid-19.
To compare CWA parameters between healthy individuals and severe COVID-19 patients.
To compare CWA parameters between healthy individuals and severe COVID-19 patients with an acute venous thromboembolic event.
Methodology
Comparative cross-sectional study
14 patients with severe COVID-19 (Cat 4&5) admitted to Medical SARI ward between Jan 2021 till Dec 2024 with a coagulation profile (PT/aPTT) prior to administration of anticoagulant.
15 healthy controls free from chronic disease
❌ on long-term anticoagulation prior to diagnosis of COVID-19
❌ pregnant individuals
❌ children/ young adults <15 years of age
Severe COVID-19 patients
Healthy controls
Demographic data
Diagnosis during admission period
Overall disease progression
Thromboembolic complications
Physical records - BHT (Unit Recod HPUSM)
Electronic recods- Discharge summary system
Haematological parameters (including CWA parameters)
PT: APTT:
- Peak time velocity - Peak time velocity�- Peak height velocity - Peak height velocity�- Delta change - Peak time acceleration �- Baseline length - Baseline length�- Endpoint - Endpoint� - Delta change
ACL TOP 300 CTS (Werfen:Benford,USA) coagulation analyzer
Data analysis
Comparisons between severe COVID-19 patients and healthy controls
Independent samples T-test (normally distributed variables with homogeneity of variance satisfied)
Welch T-test when assumption of equal variances was violated
Man-Whitney U test for variables not normally distributed
Ethical approval was given by the Human Research Ethics Committee of Universiti Sains Malaysia (JEPeM code: USM/JEPeM/KK/24100896).
Normal PT clot signature
Normal aPPT clot signature
Figures adapted from Iberahim et al.,
1st derivative curve: reflects thrombin generation activity (velocity)
2nd derivative curve: reflects generation of prothrombinase complex (acceleration)
Coagulation analysers
ACL TOP vs Sysmex
Light transmittance methodology | Light absorbance methodology |
Nomenclature used: Min1, Min2 and Max2 represent maximum velocity for thrombin generation, maximum acceleration and maximum deceleration of clot formation. | Nomenclature used: peak height velocity or Max1, peak time acceleration or Max2 and Min2 for maximum velocity, acceleration and deceleration, respectively. |
Tan JY et al., (2026)Decoding Clot Waveform Analysis: Toward Better Understanding and Harmonization.
Demographic data
Variables | n (%) |
Age (years) | 65.00 (16.08)a |
Gender | |
Male | 6 (42.9) |
Female | 8 (57.1) |
Diabetes | |
No | 8 (57.1) |
Yes | 6 (42.9) |
Hypertension | |
No | 5 (35.7) |
Yes | 9 (64.3) |
Variables | n (%) |
Hypercholesterolemia | |
No | 10 (71.4) |
Yes | 4 (28.6) |
Ischaemic heart disease | |
No | 12 (85.7) |
Yes | 2 (14.3) |
CKD | |
No | 12 (85.7) |
Yes | 2 (14.3) |
Thromboembolic complications | |
No | 11 (78.6) |
Yes | 3 (21.4) |
(a) mean (sd)
Comparison of CWA parameters between severe COVID-19 and healthy controls
CWA Parameters Prothrombin time | Healthy Controls Median (IQR) [n=15] | Severe COVID-19 Median (IQR) [n=14] | p value |
PT(s) | 10.13 (0.27) a | 12.61 (1.80) a | <0.001 b |
Baseline length (s) | 2.70 (0.78) a | 5.75 (2.00) a | <0.001 c |
Endpoint (s) | 77.00 (2.00) | 70.00 (7.00) | < 0.001 d |
Peak time velocity (s) | 10.00 (0.50) | 12.25 (1.80) | < 0.001 d |
Peak height velocity (mAbs) | 119.00 (37.00) | 179.00 (159.50) | 0.310 d |
Delta change (mAbs) | 104.00 (31.50) | 150.00 (168.60) | 0.451 d |
a Mean (SD) b Welch t-test c Independent t-test d Mann Whitney U test
Comparison of CWA parameters between severe COVID-19 and healthy controls
CWA Parameters activated thromboplastin time | Healthy Controls Median (IQR) [n=15] | Severe COVID-19 Median (IQR) [n=14] | p value |
aPPT(s) | 33.70 (6.20) | 33.00 (9.80) | 0.400 |
Baseline length (s) | 14.00 (5.50) | 14.00 (7.50) | 0.949d |
Endpoint (s) | 65.50 (6.50) | 60.50 (19.10) | 0.747d |
Peak time velocity (s) | 36.00 (7.00) | 34.00 (9.60) | 0.310d |
Peak height velocity (mAbs) | 221.07 (46.07) a | 272.89 (133.36) | 0.187b |
Peak time acceleration (s) | 33.30 (6.00) | 34.12 (9.50) | 0.331d |
Delta change (mAbs) | 199.40 (41.41)a | 249.54 (135.00)a | 0.202b |
a Mean (SD) b Welch t-test c Independent t-test d Mann Whitney U test
Comparison of CWA parameters between Severe COVID-19 patients with thromboembolic complications and healthy controls
CWA parameters | Healthy Controls (n=15)�Median (min-max) | Severe COVID-19 with Thrombosis (n=3)�Median (min-max) |
Prothrombin time | | |
PT (s) | 10.13 (9.50-10.50) | 13.10 (13.00-15.90) |
Baseline length (s) | 2.50 (2.00-4.00) | 7.00 (5.50-8.50) |
Endpoint (s) | 77.00 (76.00-81.00) | 70.00 (64.00-70.00) |
Delta change (mAbs) | 104.00 (88.00-141.00) | 232.00 (88.00-250.00) |
Peak time velocity (s) | 10.00 (9.50-10.50) | 13.00 (13.00-16.00) |
Peak height velocity (mAbs) | 119.00 (93.50-187.50) | 253.50 (107.50-273.00) |
CWA parameters | Healthy Controls (n=15)�Median (min-max) | Severe COVID-19 with Thrombosis (n=3)�Median (min-max) |
Activated thromboplastin time | | |
aPTT (s) | 33.70 (29.00-46.70) | 33.60 (32.40-37.40) |
Baseline length (s) | 14.00 (11.50-23.00) | 14.00 (14.00-17.00) |
Endpoint (s) | 65.50 (45.00-69.00) | 57.00 (52.00-60.00) |
Delta change (mAbs) | 198.30 (146.00-272.50) | 368.00 (133.00-397.00) |
Peak time velocity (mAbs) | 36.00 (31.00-50.00) | 35.00 (33.00-39.00) |
Peak height velocity (mAbs) | 210.00 (170.00-322.00) | 379.50 (157.50-411.00) |
Peak time acceleration (s) | 33.50 (29.00-46.50) | 32.00 (32.00-37.00) |
Discussion
Our study observed statistically significant increase (p<0.001) in CWA-PT parameters for peak time velocity in
the severe COVID-19 patients compared to the healthy controls; however, there was no statistical significance noted between
the two groups with regards to peak height velocity and delta change. There was significantly prolonged baseline length in
the severe COVID-19 group compared to healthy controls.
No statistical significant changes seen between both groups for CWA-aPTT parameters.
Fan et al., (2020)� | COVID-19 associated coagulopathy in critically ill patients: A hypercoagulable state demonstrated by parameters of haemostasisand clot waveform analysis | This study demonstrated increased in Min1, Min 2 and Max 2 (clot velocity, acceleration and deceleration) and delta change for both PT and aPTT for 12 critically ill COVID-19 patients in ICU care. |
Tan CW et al., (2020) | Critically ill COVID‐19 infected patients exhibit increased clot�waveform analysis parameters consistent with hypercoagulability | This study demonstrated an increasing and sustained trend of CWA-aPTT parameters in 3 critically ill COVID-19 patients admitted to ICU, especially in Min1 parameter. |
Tan CW et al., (2021) | Clinical and laboratory features of hypercoagulability in COVID‑19 and other respiratory viral infections amongst predominantly younger adults with few comorbidities | This study demonstrated COVID-19 subjects needing supplementary oxygen support had elevated CWA parameters including min1 and min2. |
Ichikawa J et al., (2022) | Evaluation of coagulation status using clot waveform analysis in general ward patients with COVID-19. | This study demonstrated that CWA results showed significantly higher, min1, min2, max2, and median delta change in COVID-19 patients and were more pronounced in critically ill patients. |
PT /aPTT ( / - ) | Does not reflect true hemostatic condition of patients. Various studies have demonstrated variable PT/aPTT results in COVID-19 patients |
Baseline length (CWA-PT) | Hypothesize the prolonged baseline length reflects a delay in clot initiation |
Peak time velocity (CWA-PT) Peak height velocity (CWA-PT) (-) Delta change (CWA-PT) (-) | PTV: rapid thrombin burst with hyperactivation of the coagulation cascade (changes in early kinetics of clot formation). PHV: despite lack of statistical significance→ markedly wider IQR in the severe COVID-19 patients compared to normal controls. 7/14 patients reported higher PHV compared to normal range. |
Endpoint (CWA-PT) | Hypothesize that the lower endpoint value suggests rapidity of clotting (initiation to fibrin formation) amongst the severe COVID-19 patients. |
There is dysregulation of the normal coagulation process in patients with severe COVID-19 and this is characterized by a delayed initiation but accelerated propagation clot formation. | |
CWA-aPTT (all parameters showed no statistical significant differences between both groups) | Possible causes for this results:
We noted 6/14 patients had increased DC and PHV compared to normal range, reflecting possible increase clot propagation in this subgroup of patients.
|
Patient 1
69 years old Man with underlying Hypertension & hyperlipidaemia
Severe COVID-19 (Cat4)
Developed acute pulmonary embolism (Day 14 of disease)
Patient 2
63 years old Lady with underlying diabetes mellitus type II, Hypertension and Ischaemic heart disease
Severe COVID-19 (Cat5)
Developed acute pulmonary embolism (Day 19 of disease)
Patient 3
82 years old Man with no known medical illness
Severe COVID19-(Cat5)
Developed posterior circulation infarct and obstructive hydrocephalus (1 month of disease)
International publications Helms et al., (2021) and Klok et al., (2020)
✔ high incidence of thromboembolic complications in COVID-19 patients with ICU admission
✔ pulmonary embolism most common complications
Local publication Mansor et al., (2025)
✔ 44.9% of critically ill COVID-19 patients developed a thromboembolic complication� ✔ VTE most prominent complication followed by pulmonary embolism
✔ Higher incidence of thromboembolic complications in older patients, those with underlying comorbidities� and prolonged ICU stay
PT-CWA | Discussion |
Baseline length Delta change Peak time velocity Peak height velocity Endpoint | The increased PT-CWA parameters in the patients with a thromboembolic complications suggests that there is coagulation dysregulation in these patients, with accelerated clot propagation and strength with shortened clotting time. No large cohort studies with published data on changes seen in CWA parameters in the critically-ill COVID-19 patients with an acute thromboembolic complication. Tan CW et al., (2019) : Reported higher Min1, Min2, Max2 and delta change for aPTT-CWA parameters in patients with a VTE compared to controls. |
aPTT-CWA | |
Baseline length Peak time velocity Peak time acceleration Delta change Peak Height velocity Endpoint |
Limitations & Recommendations
A large multi-center cohort study is needed to validate these
findings
sampling time with concern to disease progression
Serial PT,aPTT and CWA data at fixed points during the disease will reflect
hemostatic change seen at various stages of the disease (better overview of the
real-time coagulation status)
Automated system would overcome this bias
Conclusion
Our study successfully demonstrated that severe COVID-19 patients had dysregulated coagulation compared with healthy controls.
Increased CWA parameters reported especially with prothrombin time correlate with previous publications highlighting the hypercoagulable state associated with these group of patients.
The overall increased CWA parameters observed in the 3 critically ill patients with an acute thromboembolic complication compared to the normal control suggests there is probable use of CWA as a prognostic investigative tool for this disease.
Referrences
Klok F, Kruip M, van der Meer NJM, Arbous MS, Gommers DAMPJ, Kant KM, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res 2020 . 2020 Jul.
Sutanto H, Soegiarto G. Risk of Thrombosis during and after a SARS-CoV-2 Infection: Pathogenesis, Diagnostic Approach, and Management. Hematol Rep. 2023 Jun 1;15(2):225. doi:10.3390/HEMATOLREP15020024 PubMed PMID: 37092518.
Fan BE, Ng J, Chan SSW, Christopher D, Tso ACY, Ling LM, et al. COVID-19 associated coagulopathy in critically ill patients: A hypercoagulable state demonstrated by parameters of haemostasis and clot waveform analysis. J Thromb Thrombolysis. 2021;4.
Tan CW, Low JGH, Wong WH, Chua YY, Goh SL, Ng HJ. Critically ill COVID-19 infected patients exhibit increased clot waveform analysis parameters consistent with hypercoagulability. Epub . 2020 May 4.
Tan CW, Tan JY, Wong WH, Cheong MA, Ng IM, Conceicao EP, et al. Clinical and laboratory features of hypercoagulability in COVID-19 and other respiratory viral infections amongst predominantly younger adults with few comorbidities. Sci Rep. 2021 Jan 19.
Jing Yuan Tan, Marvin Raden Torres De Guzman, Wan Hui Wong, Chi Kiat Yeo, Guan Hao Goh, Heng Joo Ng, et al. Decoding Clot Waveform Analysis: Toward Better Understanding and Harmonization. Seminars in Thrombosis and Haemostasis [Internet]. 2026 [cited 2026 Apr 12]. Available from: https://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-2778-9810.pdf
Iberahim S, Yusoff RM, Noor NHM, Hassan R, Ramli NN, Bahar R, et al. Coagulation Status Using Clot Wave Analysis in Patients With Prolonged Immobilization. Cureus. 2024 Jan 2;16(1):e51483. doi:10.7759/cureus.51483 PubMed PMID: 38304638.
Ichikawa J, Okazaki R, Fukuda T, Ono T, Ishikawa M, Komori M. Evaluation of coagulation status using clot waveform analysis in general ward patients with COVID-19. J Thromb Thrombolysis. 2021 Jan 1;53(1):118. doi:10.1007/S11239-021-02499-Z PubMed PMID: 34263423.
Helms J, Severac F, Merdji H, Schenck M, Clere-Jehl R, Baldacini M, et al. Higher anticoagulation targets and risk of thrombotic events in severe COVID-19 patients: bi-center cohort study. Ann Intensive Care. 2021 Dec 1;11(1):14. doi:10.1186/s13613-021-00809-5 PubMed PMID: 33492445.
Mansor NF, Abdul Halim Zaki I, Kiok LC, Seng EK, Ravi T, Pathmanathan M, et al. The prevalence of thromboembolic events among COVID-19 patients admitted to a single centre intensive care unit (ICU): an epidemiological study from a Malaysian population. J Pharm Policy Pract. 2025;18(1):2449044. doi:10.1080/20523211.2024.2449044 PubMed PMID: 39917475.
Tan CW, Cheen MHH., Wong WH, Wu IQ, Chua BLW., Ahamedulla SH, et al. Elevated activated partial thromboplastin time-based clot waveform analysis markers have strong positive association with acute venous thromboembolism. . Biochem Med (Zagreb). 2019 Jun 15.