Cardiovascular Outcomes in COVID-19 Patients Treated with Paxlovid: A Multicenter Retrospective Study

Abstract

Background

The Coronavirus disease 2019 (COVID-19) pandemic necessitated rapid advances in treatment, with Paxlovid emerging as an effective oral antiviral. Despite its efficacy in reducing hospitalizations and mortality among high-risk patients, the impact of Paxlovid on cardiovascular outcomes remains unclear, especially given the increased cardiovascular risks associated with COVID-19.

Methods

We conducted a retrospective cohort study using data from the Chang Gung Memorial Hospital System in Taiwan of patients admitted with COVID-19 from January 1, 2022 to December 31, 2022. Propensity score matching was used to create comparable cohorts of patients treated with Paxlovid and those not treated with Paxlovid. The primary outcomes were cardiovascular events and all-cause mortality within a 12-month follow-up period.

Results

The study analyzed 606 patients treated with Paxlovid and 1,809 matched patients who were not. Paxlovid significantly reduced all-cause mortality at 3 months (relative risk [RR] 0.75, p = 0.0216) and 6 months (RR 0.81, p = 0.0492), but this effect was not sustained at 12 months (p = 0.2069). Notably, venous thromboembolism rates were significantly higher in the Paxlovid group at 6 months (RR 4.78, p = 0.0057) and 12 months (RR 2.65, p = 0.0477).

Conclusions

While Paxlovid treatment resulted in significant short-term survival improvements among COVID-19 patients, it was also associated with a higher incidence of venous thromboembolic complications. These findings highlight the need for careful patient selection and monitoring, particularly for those with preexisting cardiovascular conditions.

Abbreviations

CGMF, Chang Gung Medical Foundation

CGMH, Chang Gung Memorial Hospital

CGRD, Chang Gung Research Database

CI, Confidence interval

COVID-19, Coronavirus disease 2019

DDI, Drug-drug interaction

ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification

ICD-10-CM, International Classification of Diseases, Tenth Revision, Clinical Modification

IRB, Institutional Review Board

RR, Relative risk

SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2

VTE, Venous thromboembolism

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has led to unprecedented challenges for the healthcare industry and driven rapid advances in treatment. Among these treatments, Paxlovid has been shown to be an effective oral antiviral against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Paxlovidis comprised of nirmatrelvir and ritonavir, and administration early in the course of the disease has been linked to decreases in hospitalizations and mortality among high-risk patients, making it a key element in the fight against COVID-19.¹ Studies have shown that Paxlovid can prolong oxygen support-free survival and decrease the risk of hospitalization or death related to COVID-19 by 89% when initiated within three days of the onset of symptoms in adults at high risk.^2,3

Despite these benefits, the impact of Paxlovid on cardiovascular outcomes remains underexplored. This is particularly important given the increased cardiovascular risks associated with COVID-19, including cerebrovascular disorders, dysrhythmias, inflammatory heart diseases, ischemic heart diseases, and thromboembolic disorders.^4-6 Furthermore, the propensity of the virus to trigger inflammatory cascades and hypercoagulable states raises concerns in patients with existing cardiovascular comorbidities.

Paxlovid has also been associated with interactions with other drugs that require detailed evaluation, especially in patients receiving treatment for cardiovascular conditions. Taken together, these findings underscore the need for a thorough investigation into the safety and enduring cardiovascular effects of Paxlovid therapy, aspects that have yet to be fully clarified. Therefore, this study aims to shed light on the cardiovascular consequences of Paxlovid use, and to enhance the understanding of its risk-benefit profile in diverse clinical contexts.

METHODS

Data source

In this retrospective cohort study, patient data were obtained from the largest health-care provider in Taiwan, the Chang Gung Memorial Hospital (CGMH) system, comprising three tertiary-care medical centers and four major teaching hospitals.^6-10 The CGMH system includes more than 10,000 beds and admits more than 280,000 patients a year, servicing approximately one-tenth of the Taiwanese population annually. The hospital identification number of each patient was encrypted and de-identified to protect their privacy, and, therefore, the need for informed consent was waived for this study. Diagnosis and laboratory data could be linked and continuously monitored using consistent data encryption. The Institutional Review Board (IRB) of Chang Gung Medical Foundation (CGMF) approved the study protocol (IRB No: 202300205B0).

Study design and patients

This retrospective cohort study used data from the COVID-19 database of CGMH and the Chang Gung Research Database (CGRD), and included patients admitted from January 1, 2022 to December 31, 2022 (Figure 1). The study population comprised individuals diagnosed with COVID-19. The exclusion criteria were patients with a history of coronary artery disease, heart failure, atrial fibrillation, venous thromboembolism (VTE), ischemic stroke, peripheral artery disease, those under 18 years of age, and those treated with Remdesivir during their hospitalization. Following initial screening, 4,660 patients met the inclusion criteria, of whom 606 had received Paxlovid therapy. To facilitate a balanced comparison, 1:3 propensity score matching was employed, adjusting for age, gender, comorbid conditions, concurrent medications, and laboratory data. This resulted in a well-matched cohort for analysis, comprising 606 patients in the Paxlovid group and 1,809 patients in the non-treated group.

Figure 1.

Study design and screening criteria for including Coronavirus disease 2019 (COVID-19) patients treated with and without Paxlovid.

Study outcomes and covariates

Diseases were identified using International Classification of Diseases, 9^th and 10^th Revision, Clinical Modification (ICD-9-CM, ICD-10-CM) codes. Outcomes of primary interest were the following eight events: acute coronary syndrome, heart failure, atrial fibrillation, VTE, ischemic stroke, peripheral artery disease, cardiovascular death, and all-cause mortality. Data on cardiovascular death and all-cause death were retrieved from the national death registry dataset. Each patient was followed until the day of outcome occurrence, date of death, or up to one year of follow-up, whichever came first. The ICD-9-CM and ICD-10-CM disease codes are provided in the Supplementary Table 1.

Covariates included age, gender, comorbidities, medications, and laboratory data. Comorbidities included diabetes mellitus, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, liver cirrhosis, chronic kidney disease, autoimmune disease, and cancer. The presence of a comorbidity was defined as having either two outpatient diagnoses or one inpatient diagnosis. To address the potential underrepresentation of COVID-19 patients who may not have engaged with the healthcare system prior to the pandemic, we expanded the data collection period beyond the initially considered span of the preceding year and encompassing health records from as far back as 2001 to ensure a comprehensive analysis of comorbidities. Most diagnostic codes of these comorbidities have been validated in previous studies.^11,12

Statistical analysis

There were substantial differences in the clinical characteristics between the COVID-19 patients treated with Paxlovid and those not treated with Paxlovid (left panel in Table 1) which may have led to bias and affected comparisons of the two groups. To reduce this bias, we performed propensity score matching to make the two groups comparable. The quality of matching was assessed using the absolute standardized mean difference between the two groups after matching, where a value less than 0.1 was considered to indicate a negligible difference.

Table 1. Baseline characteristics of study patients before or after propensity score matching.

Characteristics	Before propensity score matching			After propensity score matching
	Paxlovid (n = 606)	Non Paxlovid (n = 4054)	ASMD	Paxlovid (n = 606)	Non Paxlovid (n = 1809)	ASMD
Demographics
Age (years)	62.20 ± 17.24	57.00 ± 19.74	0.2806	62.20 ± 17.24	63.28 ± 17.64	0.0619
Gender
Male	325 (53.63)	1899 (46.84)	0.1361	325 (53.63)	980 (54.17)	0.0108
Female	281 (46.37)	2155 (53.16)	0.1361	281 (46.37)	829 (45.83)	0.0108
Comorbidities
Diabetes mellitus	6 (0.99)	23 (0.57)	0.0478	6 (0.99)	18 (1.00)	0.0010
Hypertension	232 (38.28)	1481 (36.53)	0.0362	232 (38.28)	729 (40.30)	0.0414
Hyperlipidemia	115 (18.98)	632 (15.59)	0.0897	115 (18.98)	334 (18.46)	0.0133
COPD	51 (8.42)	298 (7.35)	0.0397	51 (8.42)	152 (8.40)	0.0007
Liver cirrhosis	43 (7.10)	278 (6.86)	0.0094	43 (7.10)	144 (7.96)	0.0326
Peptic ulcer disease	135 (22.28)	903 (22.27)	0.0002	135 (22.28)	413 (22.83)	0.0132
Chronic kidney disease	81 (13.37)	822 (20.28)	0.1855	81 (13.37)	260 (14.37)	0.0289
Autoimmune disease	13 (2.15)	102 (2.52)	0.0245	13 (2.15)	43 (2.38)	0.0155
Cancer	281 (46.37)	1106 (27.28)	0.4038	281 (46.37)	812 (44.89)	0.0297
Medication
NSAID	188 (31.02)	1134 (27.97)	0.0669	188 (31.02)	529 (29.24)	0.0388
Antiplatelet (Aspirin/Clopidogrel/Ticagrelor)	41 (6.77)	211 (5.20)	0.0662	41 (6.77)	107 (5.91)	0.0353
COX-2 inhibitor	51 (8.42)	217 (5.35)	0.1215	51 (8.42)	142 (7.85)	0.0209
Anticoagulant agents (Warfarin/NOAC)	0 (0.00)	70 (1.73)	0.1876	0 (0.00)	0 (0.00)	-
Other OHAs	73 (12.05)	454 (11.20)	0.0265	73 (12.05)	221 (12.22)	0.0052
Insulin	86 (14.19)	587 (14.48)	0.0083	86 (14.19)	261 (14.43)	0.0069
ACEi/ARB/ARNI	80 (13.20)	540 (13.32)	0.0035	80 (13.20)	252 (13.93)	0.0213
Beta-blocker	37 (6.11)	314 (7.75)	0.0646	37 (6.11)	111 (6.14)	0.0013
dCCB	100 (16.50)	727 (17.93)	0.0379	100 (16.50)	318 (17.58)	0.0287
MRA	30 (4.95)	177 (4.37)	0.0275	30 (4.95)	98 (5.42)	0.0212
Loop diuretic	83 (13.70)	581 (14.33)	0.0181	83 (13.70)	250 (13.82)	0.0035
Thiazide	3 (0.50)	19 (0.47)	0.0043	3 (0.50)	11 (0.61)	0.0148
Statin	64 (10.56)	313 (7.72)	0.0987	64 (10.56)	175 (9.67)	0.0295
Fibrate	10 (1.65)	35 (0.86)	0.0710	10 (1.65)	22 (1.22)	0.0362
Laboratory
ALT, U/L	34.52 ± 42.74	45.81 ± 177.24	0.0876	34.52 ± 42.74	35.55 ± 52.88	0.0214
Creatinine, mg/dL	0.81 ± 0.36	1.31 ± 1.97	0.3531	0.81 ± 0.36	0.82 ± 0.35	0.0282
eGFR, mL/min*1.73 m2	109.54 ± 55.75	101.35 ± 65.65	0.1345	109.54 ± 55.75	107.06 ± 49.67	0.0470
Sodium, mEq/L	135.81 ± 5.09	136.22 ± 5.57	0.0768	135.81 ± 5.09	135.87 ± 5.97	0.0108
Potassium, mEq/L	3.77 ± 0.46	3.81 ± 0.55	0.0789	3.77 ± 0.46	3.78 ± 0.52	0.0204
Hemoglobin, g/dL	11.64 ± 2.29	11.59 ± 2.29	0.0218	11.64 ± 2.29	11.67 ± 2.20	0.0134
Whole blood cell, 103/μL	8.84 ± 16.09	9.30 ± 6.68	0.0373	8.84 ± 16.09	8.60 ± 7.55	0.0191
Neutrophil, 103/μL	6.35 ± 6.69	7.11 ± 4.97	0.1290	6.35 ± 6.69	6.39 ± 4.16	0.0072
Platelet, 103/μL	212.83 ± 105.33	233.77 ± 109.54	0.1949	212.83 ± 105.33	215.42 ± 101.81	0.0250
Lymphocyte, 103/μL	1.22 ± 2.23	1.31 ± 0.90	0.0529	1.22 ± 2.23	1.21 ± 0.84	0.0059
Neutrophil-to-lymphocyte ratio (N/L ratio)	8.31 ± 11.00	8.69 ± 13.14	0.0314	8.31 ± 11.00	8.27 ± 11.56	0.0035
Platelet lymphocyte ratio (P/L ratio)	275.16 ± 279.17	252.81 ± 271.42	0.0812	275.16 ± 279.17	260.02 ± 301.32	0.0521

Mean ± standard deviation, N (%).

ACEi, angiotensin-converting enzyme inhibitor; ALT, alanine aminotransferase; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; ASMD, acid sphingomyelinase deficiency; COPD, chronic obstructive pulmonary disease; COX-2 inhibitor, cyclooxygenase-2 inhibitor; dCCB, dihydropyridine calcium channel blocker; eGFR, estimated glomerular filtration rate; MRA, mineralocorticoid receptor antagonist; NOAC, non-vitamin K antagonist oral anticoagulant; NSAID, nonsteroidal anti-inflammatory drug; OHA, oral hypoglycemic agent.

The primary endpoint was the occurrence of acute coronary syndrome, heart failure, atrial fibrillation, VTE, ischemic stroke, peripheral artery disease, cardiovascular death, and all-cause mortality assessed over a 12-month follow-up period. Given the nature of the study, competing risks were not accounted for in the analysis. Therefore, relative risk (RR) was used as the primary measure of association between Paxlovid treatment and the outcomes of interest. This metric provided an estimate of the magnitude of the association, which was calculated alongside 95% confidence intervals (CIs) to assess the precision of the estimates. Statistical significance was set at a p value of less than 0.05. Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA), with the RR offering insights into the risk of outcomes between the Paxlovid-treated and non-Paxlovid-treated groups.

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki and the Declaration of Taipei on ethical considerations regarding health databases by the World Medical Association. The study protocol was approved, and informed consent was exempted by the IRB of CGMF (IRB approval No: 202300205B0) as all data in the CGRD are encrypted, deidentified, and tightly regulated by CGMH and CGMF.

RESULTS

Study population

Table 1 shows the demographic and clinical characteristics of the patients before and after propensity score matching. Initially, 13,047 patients were considered. After applying the exclusion criteria, 4,660 patients were eligible for analysis. Paxlovid was administered to 606 patients, and 4,054 patients did not receive Paxlovid. After matching, the two groups were comparable in terms of age and gender distribution, with no significant differences observed in comorbidities such as diabetes mellitus, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, liver cirrhosis, peptic ulcer disease, and autoimmune diseases. Medication use, including non-steroidal anti-inflammatory drugs, anti-platelets, and cyclooxygenase-2 inhibitors, was also closely matched, ensuring baseline characteristic uniformity for subsequent outcome analyses.

According to government policy in Taiwan at the time, there was no mandatory requirement for COVID-19 patients to undergo polymerase chain reaction testing to confirm a negative status upon discharge. Therefore, we defined recovery from COVID-19 based on whether the patients were discharged. In the Paxlovid group of 606 patients, 585 (96.53%) were discharged, indicating recovery, while 21 (3.47%) died in the hospital. In addition, two patients (0.33%) died during the treatment period and did not complete the therapy. The remaining 99.67% completed the 5-day treatment course.

Cardiovascular events and all-cause death during follow-up

The analysis of cardiovascular outcomes and mortality during follow-up after propensity score matching revealed varying effects across different outcomes and time points, as shown in Table 2 and Figure 2. At 3 months, there were no significant differences between the two groups in terms of acute coronary syndrome, heart failure, atrial fibrillation, VTE, ischemic stroke, peripheral artery disease, and cardiovascular death. However, there was a significant reduction in all-cause mortality among the Paxlovid users (10.89% vs. 14.59%, p = 0.0216), with a RR of 0.75 and 95% CI of 0.58 to 0.96.

Table 2. Cardiovascular outcomes and mortality during follow-up.

Outcomes	After propensity score matching		p value	RR [95% Cl]
	Paxlovid (n = 606)	Non Paxlovid (n = 1,809)
3 months
Acute coronary syndrome	1 (0.17)	1 (0.06)	0.4390	2.99 [0.19-47.65]
Heart failure	3 (0.50)	8 (0.44)	> 0.9999	1.12 [0.30-4.21]
Atrial fibrillation	1 (0.17)	2 (0.11)	> 0.9999	1.49 [0.14-16.43]
Venous thromboembolism	4 (0.66)	4 (0.22)	0.1147	2.99 [0.75-11.90]
Ischemic stroke	2 (0.33)	14 (0.77)	0.3853	0.43 [0.10-1.87]
Peripheral artery disease	0 (0.00)	3 (0.17)	0.5773	-
Cardiovascular death	3 (0.50)	24 (1.33)	0.0919*	0.37 [0.11-1.23]
All-cause mortality	66 (10.89)	264 (14.59)	0.0216*	0.75 [0.58-0.96]
6 months
Acute coronary syndrome	1 (0.17)	4 (0.22)	> 0.9999	0.75 [0.08-6.66]
Heart failure	3 (0.50)	8 (0.44)	> 0.9999	1.12 [0.30-4.21]
Atrial fibrillation	1 (0.17)	3 (0.17)	> 0.9999	1.00 [0.10-9.55]
Venous thromboembolism	8 (1.32)	5 (0.28)	0.0057	4.78 [1.57-14.54]
Ischemic stroke	5 (0.83)	17 (0.94)	0.7971*	0.88 [0.33-2.37]
Peripheral artery disease	0 (0.00)	3 (0.17)	0.5773	-
Cardiovascular death	6 (0.99)	30 (1.66)	0.2400*	0.60 [0.25-1.43]
All-cause mortality	94 (15.51)	345 (19.07)	0.0492*	0.81 [0.66-1.00]
12 months
Acute coronary syndrome	1 (0.17)	6 (0.33)	0.6877	0.50 [0.06-4.12]
Heart failure	5 (0.83)	11 (0.61)	0.5669	1.36 [0.47-3.89]
Atrial fibrillation	2 (0.33)	3 (0.17)	0.6047	1.99 [0.33-11.88]
Venous thromboembolism	8 (1.32)	9 (0.50)	0.0477	2.65 [1.03-6.85]
Ischemic stroke	7 (1.16)	20 (1.11)	0.9201*	1.04 [0.44-2.46]
Peripheral artery disease	1 (0.17)	3 (0.17)	> 0.9999	1.00 [0.10-9.55]
Cardiovascular death	6 (0.99)	35 (1.93)	0.1192*	0.51 [0.22-1.21]
All-cause mortality	117 (19.31)	393 (21.72)	0.2069*	0.89 [0.74-1.07]

Data presented as number (percentage). Relative risk reference: Non-Paxlovid.

* Denotes Chi-square test, the rest Fisher’s exact test.

CI, confidence interval; RR, relative risk.

Figure 2.

Forest plot of relative risks for cardiovascular outcomes and all-cause mortality at 3, 6, and 12 months. CI, confidence interval.

At 6 months, VTE showed a significantly higher risk in the Paxlovid group compared to the non-Paxlovid group (1.32% vs. 0.28%, p = 0.0057), with an RR of 4.78 (95% CI: 1.57 to 14.54). Meanwhile, acute coronary syndrome, heart failure, atrial fibrillation, ischemic stroke, peripheral artery disease, and cardiovascular death did not show significant differences. All-cause mortality was again lower in the Paxlovid group (15.51% vs. 19.07%, p = 0.0492), suggesting a protective effect with an RR of 0.81 (95% CI: 0.66 to 1.00).

By 12 months, the pattern of no significant difference in most cardiovascular outcomes continued, except for VTE, which remained significantly higher in the Paxlovid group (1.32% vs. 0.50%, p = 0.0477) with an RR of 2.65 (95% CI: 1.03 to 6.85). The difference in all-cause mortality between the groups decreased over time and was not significant at 12 months (19.31% vs. 21.72%, p = 0.2069).

We further analyzed the causes of death in both groups, as shown in Supplementary Table 2. Among the total 510 deaths, malignant neoplasms (cancer) accounted for the majority (52.7%), followed by pneumonia (11.0%) and cardiovascular disease (8.0%). The distribution of causes was generally similar between the two groups, although the Paxlovid group had a slightly higher proportion of deaths due to cancer (59.8% vs. 50.6%) and a lower proportion due to cardiovascular disease (5.1% vs. 8.9%). Other causes included sepsis, chronic liver disease, and indeterminate causes, each comprising smaller proportions.

DISCUSSION

This is the first large-scale population study of cardiovascular outcomes associated with Paxlovid use in patients with COVID-19. The findings of this study warrant a thorough and careful analysis, particularly in the context of the interactions of Paxlovid with prevalent cardiovascular medications. Paxlovid has been heralded as an impressive antiviral medication, demonstrating substantial short-term efficacy in the first 3 and 6 months of follow-up and playing a pivotal role in mitigating the risks associated with COVID-19. The initial follow-up periods are particularly critical as they mark the phase of acute recovery and potential onset of post-infection sequelae, illustrating the clinical value of Paxlovid.

A review by the American College of Cardiology showed that Paxlovid can prevent severe COVID-19 progression but that it interacts with various cardiovascular drugs, necessitating awareness and adjustments to avoid adverse effects.¹³ Specifically, certain antiarrhythmics such as Quinidine, Amiodarone (Cordarone®), Dronedarone (Multaq®), Propafenone (Rytmonorm®), and Flecainide (Tambocor®), anticoagulants such as Rivaroxaban (Xarelto®), antiplatelets such as Ticagrelor (Brilinta®) and Clopidogrel (Plavix®), lipid-lowering agents such as Lovastatin (Mevacor®), Simvastatin (Zocor®), Atorvastatin (Lipitor®), and Rosuvastatin (Crestor®), heart failure medications such as Eplerenone (Inspra®) and Ivabradine (Coralan®), and pulmonary hypertension treatments such as Tadalafil (Cialis®), Vardenafil (Levitra®), and Sildenafil (Viagra®, Revatio®), have been identified as potentially having significant drug-drug interactions (DDIs) with Paxlovid.¹³ These interactions could lead to increased plasma levels, enhanced bleeding risk, or other complications requiring dose adjustments or alternative treatments.

The inflammatory response triggered by COVID-19 infection significantly contributes to greater susceptibility to VTE, emphasizing a critical aspect of disease management.¹⁴ This response is characterized by endothelial damage, hypercoagulability, and vascular inflammation.¹⁵ In a study from the Netherlands involving 184 patients admitted to the intensive care unit, the cumulative incidence of composite outcomes was 31% (95% CI 20-41). Of these, computed tomography pulmonary angiography and/or ultrasonography confirmed VTE in 27% (95% CI 17-37%), while arterial thrombotic events were observed in 3.7% (95% CI 0-8.2%).¹⁶ While the administration of Paxlovid is pivotal in reducing severe disease progression and viral load, careful consideration is necessary due to the potential interactions with cardiovascular medications, particularly anticoagulants and antiplatelet agents.¹⁷ This interplay highlights the need for individualized clinical evaluation that align the therapeutic benefits of Paxlovid with the imperative to minimize thrombotic complications.

The implications of these interactions are profound, particularly for patients with existing cardiovascular conditions, who represent a significant portion of the high-risk population benefiting from Paxlovid. Despite the effectiveness of Paxlovid in reducing hospitalization and mortality among COVID-19 patients, the possibility of significant pharmacological interactions reinforce the importance of clinicians being vigilant and informed about managing these risks. This awareness is crucial for ensuring the safe and effective use of Paxlovid, especially in patients at high risk for both COVID-19 and cardiovascular events.

Our study showed a mortality benefit at 3 and 6 months, but this was not sustained at 12 months, suggesting that the current 5-day regimen may have limitations. Unlike the typical 7 to 14-day antibiotic courses for systemic infections, the shorter duration of Paxlovid may not fully suppress the SARS-CoV-2 virus, especially in patients with comorbidities or those at higher risk. The potential of the virus for rapid mutation or rebound after initial treatment could explain the lack of sustained benefits. In addition, changes in treatment protocols and patient management strategies over time may have influenced long-term outcomes. Further research is necessary to determine whether extending Paxlovid therapy could provide more lasting protection against mortality, particularly in vulnerable populations. This research could lead to refined treatment protocols that better address the complexities of long-term COVID-19 management and improve patient outcomes.

While Paxlovid did not significantly impact short-term VTE risk (at 3 months), it was associated with a notable increase in mid- to long-term VTE risk at 6 and 12 months in our study. The exact mechanism behind this delayed effect remains uncertain. COVID-19 is known to promote a state that favors clotting; however, the precise pathophysiological mechanisms are not fully understood. It has been suggested that the thrombotic aspect of the disease may stem from reactions of the immune system, leading to damage to the vascular endothelium, provoking inflammation, triggering the coagulation cascade, and impairing fibrinolysis.¹⁸ Therapeutic interventions targeting these specific processes could be key in enhancing patient recovery and reducing thrombosis-related deaths. Building on this, our findings revealed an elevated long-term risk of VTE when using Paxlovid. Possible mechanisms include rebound hypercoagulability following discontinuation of antiviral treatment, interactions with concomitant medications, and a prolonged state of inflammation or endothelial dysfunction triggered by COVID-19 and influenced by Paxlovid. This highlights the importance of a holistic approach to patient care, considering the immediate efficacy of Paxlovid against COVID-19 while also considering the prolonged cardiovascular risks stemming from its use with other medications. Consequently, there is an imperative for future research to investigate these aspects further, aiming to refine treatment protocols. The goal is to ensure the advantages of Paxlovid are harnessed effectively, minimizing cardiovascular risks for those with existing conditions.

In summary, Paxlovid demonstrates significant short-term benefits in reducing mortality and hospitalizations among high-risk patients. Despite its proven efficacy, its use may remain suboptimal due to concerns about cost, the ‘rebound’ phenomenon, and lack of familiarity among clinicians.¹⁹ Addressing these barriers to access and increasing awareness could enhance its prescription. Moreover, the potential cardiovascular risks associated with Paxlovid warrant prudent patient selection and monitoring. Future research should continue to evaluate the long-term effects of Paxlovid, including its impact on cardiovascular outcomes and its role in mitigating long COVID.

Limitations

Our study provides valuable insights into the cardiovascular outcomes of Paxlovid use in patients with COVID-19. However, several limitations should be acknowledged. A significant limitation of our study is the absence of vaccination status data, which we could not include due to the lack of linkage to patient records. Given that vaccination coverage was notably high in Taiwan, with 94% of the population receiving the first dose and 88.9% the second as of January 10, 2024, it is likely that a substantial portion of our study population was vaccinated. This could have impacted the observed associations between Paxlovid use and cardiovascular outcomes, potentially introducing bias if the protective effects of vaccination were unevenly distributed among cohorts.

In addition, our observational study design precludes the establishment of causality. The retrospective approach may lead to selection bias, given that the patients who received Paxlovid may have differed from those who did not in ways our study could not detect. Furthermore, reliance on administrative data introduces the possibility of errors in coding and documentation.

Another concern is the possibility of residual confounding due to factors such as socioeconomic status, comorbidities, or disparities in healthcare access, which we were unable to measure. Moreover, while the potential for DDIs was a significant focus of our analysis, the lack of detailed patient medication histories limits our ability to fully ascertain the influence of DDIs on the observed outcomes.

Our study is based on a single healthcare system in Taiwan, with a nearly homogenous patient population. This may limit the applicability of our results to other healthcare settings or demographic groups. Therefore, it is crucial to conduct future studies that include more diverse populations to enhance external validity. We believe that such research will be essential in determining whether the cardiovascular outcomes associated with Paxlovid use observed in our study are consistent across different populations globally.

In light of these limitations, our findings highlight the critical need to consider individual patient characteristics, including vaccination status, in assessing the benefits and risks of Paxlovid in treating COVID-19. These results should be interpreted with caution and regarded as a starting point for more comprehensive studies. Future research should also aim to include vaccination data, offering a more comprehensive understanding of the relationship between Paxlovid use, vaccination status, and cardiovascular outcomes in patients with COVID-19.

CONCLUSIONS

Our study emphasizes the balance between the benefits of Paxlovid for patients with COVID-19 and its risks for those with cardiovascular issues. While the drug shows promise in lowering short-term mortality, this benefit must be carefully balanced against the increased risk of complications such as VTE. Thus, prescribing Paxlovid requires a personalized approach, focusing on patient-specific management and increased clinician awareness to avoid adverse outcomes. Future research is needed to develop safer protocols for patients with cardiovascular risks.

DECLARATION OF CONFLICT OF INTEREST

All the authors declare no conflict of interest.

SUPPLEMENTARY MATERIALS

Supplementary Table 1. Disease codes utilized in this study.

Disease	ICD-9-CM	ICD-10-CM
Covid-19		U071, Z8616
Coronary artery disease	410-414	I20-I25
Myocardial infarction	410, 412	I21, I22, I252
Heart failure	428	I50
Stroke	430, 431, 436, 434.1, 434.9, 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.00, 434.01, 434.10, 434.11, 434.90, 434.91	I60-I64
Peripheral vascular disease	440.0, 440.2, 440.3, 440.8, 440.9, 443, 444.0, 444,22, 444.8, 447.8, 447.9	I70.0, I70.2, I73, I74.09, I77.8
Atrial fibrillation	427.31	I480-I482, I4891
Systemic embolism	444, 445	I74
Pulmonary embolism	4151	I26
Venous thromboembolism	4534, 4535, 45372, 45373, 45374, 45375, 45382, 45383, 45384, 45385 4151, 4162	I801, I802, I803, I808, I809, I824, I825, I82A, I82B, I8260, I8262, I8270, I8272, I26, I2782
Diabetes mellitus	250.x1, 250.x3	E10
Hypertension	401.xx, 402.xx, 403.xx, 404.xx, 405.xx	I10-I13, I15, I16
Hyperlipidemia	272	E78
COPD	491.xx, 492.xx, 496.xx	J41, J42, J43
Liver cirrhosis	571.2, 571.5, 571.6	K70.30, K74.0, K74.60, K74.69, K74.3, K74.4, K74.5
Peptic ulcer disease	531-534	K25-K28
Chronic kidney disease	016.0x, 095.4x, 283.11, 236.9x, 250.4x, 274.1x, 403.xx-404.xx, 440.1x, 442.1x, 446.21, 447.3x, 572.4x, 580.xx-589.xx, 642.1x, 646.2x, 753.1x	A18.11, D59.3, E10.2, E11.2, E13.2, I12, I13, K76.7, M10.3, M31.0, N00, N01, N02, N03, N04, N05, N06, N07, N08, N14, N15.0, N15.8, N15.9, N16, N17.1, N17.2, N18, N19, N20.0, N25, N26.1, N26.9, N27, Q61
Autoimmune disease	136.1, 446.0, 446.2, 446.4, 446.5, 443.1, 446.7, 555, 556, 710.0, 710.1, 710.2, 710.4, 710.3, 714.0, 694.4, 710.2	I73.1, L10.0, L10.1, L10.2, L10.4, L10.9, K50, K51, M06.9, M30.0, M31.30, M31.6, M32.10, M33.20, M33.03, M33.13, M33.90, M33.93, M34.0, M34.1, M34.9, M35.00, M35.01
Cancer	140-208	Cxx
Acute coronary syndrome		I200, I24, I21, I23
Ischemic stroke		I66, I65.1, I65.0, I65.8, I65.9, I63.6, I63.8, I63.9, G45.0, G45.8, G45.1, G45.2, G46.0, G46.1, G46.2, G45.9, G45.4, G46.3, G46.4, G46.5, G46.6, G46.7, G46.8, I67.0, I67.1, I67.2, I67.4, I67.5, I67.6, I67.7, I67.9, I68.0, I68.2, I68.8
Peripheral artery disease		I65.2, I77.9

COPD, chronic obstructive pulmonary disease; COVID-19, Coronavirus Disease 2019; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; ICD-10-CM, International Classification of Diseases, Tenth Revision, Clinical Modification.

Supplementary Table 2. Cause of death for Paxlovid and non-Paxlovid groups.

	Total	Paxlovid	Non-Paxlovid
All-cause mortality	510	117	393
Cancer	269 (52.7)	70 (59.8)	199 (50.6)
Pneumonia	56 (11.0)	10 (8.5)	46 (11.7)
Cardiovascular disease	41 (8.0)	6 (5.1)	35 (8.9)
Sepsis	14 (2.7)	5 (4.3)	9 (2.3)
Chronic liver disease or cirrhosis	9 (1.8)	1 (0.9)	8 (2.0)
Diabetes mellitus	4 (0.8)	0 (0)	4 (1.0)
Accidental injury	3 (0.6)	1 (0.9)	2 (0.5)
Parkinson’s disease	2 (0.4)	1 (0.9)	1 (0.3)
Kidney failure	3 (0.6)	1 (0.9)	2 (0.5)
Hypertensive disease	2 (0.4)	0 (0)	2 (0.5)
Pulmonary disease due to aspiration	2 (0.4)	1 (0.9)	1 (0.3)
Intentional self-harm (suicide)	2 (0.4)	0 (0)	2 (0.5)
Gastric and duodenal ulcers	1 (0.2)	0 (0)	1 (0.3)
Senility (old age)	1 (0.2)	1 (0.9)	0 (0)
Musculoskeletal or connective tissue disease	1 (0.2)	0 (0)	1 (0.3)
Meningitis	1 (0.2)	0 (0)	1 (0.3)
Indeterminate*	99 (19.4)	20 (17.1)	79 (20.1)

Data presented as number (percentage).

* Includes cases with unclear, unspecified, or undocumented causes of death, as well as patients discharged against medical advice in critical condition, for whom the exact cause of death could not be determined.

CONSENT FOR PUBLICATION

All authors give consents for the publication of identifiable details, which can include photographs and/or videos and/or case history and/or details within the text to be published.

AVAILABILITY OF DATA AND MATERIALS

Data are available with from the corresponding author upon reasonable request.

FUNDING

This work was supported by grants from Chang Gung Memorial Hospital, Linkou. The funding for this study was provided through grant numbers CGRPVVM0011, CMRPG3N0431, and CIRPG3L0023, with Pao-Hsien Chu, M.D. being the recipient of the latter two grants.

AUTHOR CONTRIBUTIONS

Study conception and design: WLC, VCW, PHC, YTH. Acquisition of data: CLW, YCW, YTH. Analysis and interpretation of data: CHH, CHC, SWC, SHC. Drafting of manuscript: WLC, VCW. Critical revision: CHC, PHC.