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Paxlovid use is associated with lower risk of cardiovascular diseases in COVID-19 patients with autoimmune rheumatic diseases: a retrospective cohort study

BMC Medicine volume 22, Article number: 117 (2024) Cite this article

Abstract

Background

Paxlovid has been shown to be effective in reducing mortality and hospitalization rates in patients with coronavirus disease 2019 (COVID-19). It is not known whether Paxlovid can reduce the risk of cardiovascular diseases (CVD) in COVID-19-surviving patients with autoimmune rheumatic diseases (AIRDs).

Methods

TriNetX data from the US Collaborative Network were used in this study. A total of 5,671,395 patients with AIRDs were enrolled between January 1, 2010, and December 31, 2021. People diagnosed with COVID-19 were included in the cohort (n = 238,142) from January 1, 2022, to December 31, 2022. The Study population was divided into two groups based on Paxlovid use. Propensity score matching was used to generate groups with matched baseline characteristics. The hazard ratios (HRs) and 95% confidence intervals of cardiovascular outcomes, admission rate, mortality rate, and intensive care unit (ICU) admission rate were calculated between Paxlovid and non-Paxlovid groups. Subgroup analyses on sex, age, race, autoimmune diseases group, and sensitivity analyses for Paxlovid use within the first day or within 2–5 days of COVID-19 diagnosis were performed.

Results

Paxlovid use was associated with lower risks of cerebrovascular complications (HR = 0.65 [0.47–0.88]), arrhythmia outcomes (HR = 0.81 [0.68–0.94]), ischemic heart disease, other cardiac disorders (HR = 0.51 [0.35–0.74]) naming heart failure (HR = 0.41 [0.26–0.63]) and deep vein thrombosis (HR = 0.46 [0.24–0.87]) belonging to thrombotic disorders in AIRD patients with COVID-19. Compared with the Non-Paxlovid group, risks of major adverse cardiac events (HR = 0.56 [0.44–0.70]) and any cardiovascular outcome mentioned above (HR = 0.76 [0.66–0.86]) were lower in the Paxlovid group. Moreover, the mortality (HR = 0.21 [0.11–0.40]), admission (HR = 0.68 [0.60–0.76]), and ICU admission rates (HR = 0.52 [0.33–0.80]) were significantly lower in the Paxlovid group than in the non-Paxlovid group. Paxlovid appears to be more effective in male, older, and Black patients with AIRD. The risks of cardiovascular outcomes and severe conditions were reduced significantly with Paxlovid prescribed within the first day of COVID-19 diagnosis.

Conclusions

Paxlovid use is associated with a lower risk of CVDs and severe conditions in COVID-19-surviving patients with AIRD.

Peer Review reports

Background

The coronavirus disease 2019 (COVID-19) pandemic has led to more than 6 million confirmed deaths and 15 million estimated deaths and brought great challenges to more than 200 countries [1].

Monoclonal antibodies, multiple antivirals, immunomodulatory drugs, adjuvants, and Chinese herbal medicine have been suggested as drugs for COVID-19 [2, 3]. Paxlovid, an oral antiviral treatment, is composed of two compounds: PF-07,321,332, an oral covalent 3CL protease inhibitor of SARS-CoV-2, and ritonavir, an inhibitor of human immunodeficiency virus (HIV)-1 and HIV-2 protease [4]. Paxlovid has been shown to reduce the mortality and hospitalization rates in patients with COVID-19 [5, 6]. A cohort study using the largest database in Israel demonstrated that Paxlovid appears to be more effective in patients with cardiovascular disease (CVD) and immunosuppressed patients, particularly the elderly [6]. Moreover, observational studies also showed that antivirals such as Paxolid and Monupiravir reduced viral shedding time in COVID-19 patients and the latter effect seems stronger with COVID-19 vaccination [7, 8]. There is growing evidence that infection with COVID-19 is associated with the development of autoimmunity phenomena [9, 10]. A recent analysis reported that 90 reports (99 cases) of new-onset rheumatic autoimmune diseases during or after SARS-CoV-2 infection [11]. Both COVID-19 and autoimmune rheumatic diseases (AIRDs) present with various clinical symptoms involving different organs and systems, including the cardiovascular, renal, and neurological systems [12, 13]. COVID-19 infection may also lead to underlying flare of rheumatic disease. As patients with rheumatic diseases generally have an increased risk of infections and complications [14]. It is critical to control COVID-19 infection for AIRD patients at early time. In our previous studies, we have conducted retrospective cohort studies and noted that the risk of incidental CVDs and autoimmune diseases was substantially higher in the COVID-19 survivors [15, 16]. However, no large-scale study has assessed whether Paxlovid can reduce the risk of CVDs and severe conditions in COVID-19-surviving patients with AIRD. Therefore, the present study aimed to provide some evidence for Paxlovid use in COVID-19 patients with AIRD.

Methods

The study data were retrieved from the US Collaborative Network from 55 global healthcare organizations in the TriNetX Research Network. The largest worldwide COVID-19 dataset is presently housed in the TriNetX database, which is a global clinical research platform that collects real-time medical data. The database contains demographic details, diagnoses, procedures, medication information, laboratory tests, genomics, and healthcare utilization. The available data included in the database has been introduced in detail in our former research [16]. In the present study, the US Collaborative Network in TriNetX was used to build a cohort out of more than 92 million participants.

Participants

A total of 5,671,395 patients with AIRD were enrolled between January 1, 2010, and December 31, 2021, from 92,985,898 participants in the US Collaborative Network. People aged ≥ 18 years diagnosed with COVID-19, via either a COVID-19 positive test or ICD-10-CM = U07.1, were included in the cohort (n = 238,142) from January 1, 2022, and December 31, 2022. In addition, two groups of participants were selected: 16,396 patients who received Paxlovid within 5 days of COVID-19 diagnosis and 200,777 patients who received none of Paxlovid, molnupiravir, or remdesivir after COVID-19 diagnosis. The index date was defined as the date of the first administration of Paxlovid and the diagnosis of COVID-19, respectively. Patients diagnosed with CVD before the index date were excluded. In the Paxlovid group, patients who were treated with molnupiravir or remdesivir were also excluded. After exclusion, there were 8,805 patients in the Paxlovid group and 110,551 patients in the comparison group.

In the study cohort, propensity score matching (PSM) was used to stratify by age, sex, race, body mass index (BMI), socioeconomic status, comorbidities, medications, and medical utilization at a ratio of 1:1. After PSM, 8803 participants in the Paxlovid and 8803 comparisons in the non-Paxlovid groups were selected. Figure 1 shows the flowchart of the cohort.

Fig. 1
figure 1

Flow chart of cohort construction

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AIRDs included inflammatory arthritis, connective tissue diseases, autoimmune gastrointestinal diseases, and some endocrine diseases. The following AIRDs were included in the study.

  1. (1)

    Inflammatory arthritis: rheumatoid arthritis [ICD10 = M05 − M06] and ankylosing spondylitis [ICD10 = M45].

  2. (2)

    Connective tissue diseases: vasculitis [ICD10 = M30, M31, L95], atopic dermatitis [ICD10 = L20], psoriatic [ICD10 = L40], systemic lupus erythematosus [ICD10 = M32], dermatomyositis/polymyositis [ICD10 = M33], systemic sclerosis [ICD10 = M34], Sjogren’s syndrome [ICD10 = M35.0,], mixed connective tissue disease [ICD10 = M35.1], Bechet’s disease [ICD10 = M35.2], and polymyalgia rheumatica [ICD10 = M35.3].

  3. (3)

    Autoimmune gastrointestinal diseases: inflammatory bowel disease [ICD10 = K50 − K52], celiac disease [ICD10 = K90.0], and autoimmune hepatitis [ICD10 = K75.4].

  4. (4)

    Endocrine diseases: type 1 diabetes [ICD10 = E10] and autoimmune thyroiditis [ICD10 = E06.3].

In the study cohort, propensity score matching (PSM) was used to stratify by age, sex, race, body mass index (BMI), socioeconomic status (housing/economic circumstances problem [ICD-10-CM = Z59]; problems related to education and literacy [ICD-10-CM = Z55]; employment or unemployment problems [ICD-10-CM = Z56]; occupational exposure to risk factors [ICD-10-CM = Z57]), hypertension [ICD-10-CM = I10]; type 2 diabetes mellitus [ICD-10-CM = E11]; chronic kidney disease [ICD-10-CM = N18]; nicotine dependence [ICD-10-CM = F17]; overweight [ICD-10-CM = E66.3]; alcohol-related disorders [ICD-10-CM = F10], medications (etanercept, adalimumab, golimumab, rituximab, tocilizumab, abatacept, tofacitinib and corticosteroids), and medical utilization (ambulatory, emergency, and inpatient medical treatment) at a ratio of 1:1. These variables were collected within one year before the index date.

Outcomes

The incidence of CVDs and severe conditions in patients with COVID-19 was assessed from the index date to the end of follow-up (lasting 12 months). The cardiovascular complications and severe conditions in the study were defined as follows in Additional file 1: Table S1.

Statistical analyses

A built-in Propensity Score Matching (PSM) was employed to create groups with matched baseline characteristics using a greedy nearest neighbor matching approach, with a caliper set at 0.1 pooled standard deviations. The TriNetX was used to match the two groups at a fixed 1:1 ratio by age, sex, race, BMI, socioeconomic status, comorbidities, medications, and medical utilization. Standardized mean differences (SMD) were used to evaluate the balance of baseline characteristics in populations. In General, SMD < 0.1 is considered a small difference. After propensity score matching, a built-in Kaplan–Meier analysis was employed to assess the incidence of outcomes, and the log-rank test was utilized for significance testing. Additionally, a built-in Cox proportional hazard model was applied to estimate the hazard ratios between the Paxlovid and non-Paxlovid groups. The hazard ratio (HR) for cardiovascular outcomes and severe conditions was calculated for both the Paxlovid and Non-Paxlovid groups. Statistical significance was evaluated using the 95% confidence interval (95% CI).

Subgroup analyses were performed to investigate how the risks of cardiovascular outcomes and severe conditions in patients with AIRD differed with respect to sex, age, race, and autoimmune disease groups. In addition, considering possible differences between the early and late use of Paxlovid, a sensitivity analysis was performed for Paxlovid use within the first day and 2 to 5 days of COVID-19 diagnosis.

Results

Baseline characteristics of the participants

The demographic characteristics, socioeconomic status, co-morbidities, medications, and medical utilization of the Paxlovid and non-Paxlovid groups before and after PSM are presented in Table 1. The mean age of the participants in the Paxlovid group was about 54 years after matching. Approximately 70.3% of the patients were female and the major race was White (82%). The two groups were well-matched concerning socioeconomic status, comorbidities, medications, and medical utilization (SMD < 0.1).

Table 1 Demographic characteristics of Paxlovid and Non-Paxlovid
Full size table

Incidence of cardiovascular complications in the two groups

The risks of cardiovascular outcomes in the Paxlovid and non-Paxlovid groups were assessed (Fig. 2 and Additional file 1: Table S2). The 12-month follow-up of the patients showed that Paxlovid reduced the risk of CVDs and severe conditions in COVID-19-surviving patients with AIRD.

Fig. 2
figure 2

Forest plot of the risk of outcomes among those exposed to Paxlovid compared to non-Paxlovid

Full size image

Paxlovid was associated with lower risks of cerebrovascular complications (HR [95% CI] = 0.65 [0.47–0.88]) such as stroke (HR = 0.66 [0.47–0.92]). Moreover, the risks of arrhythmia outcomes (HR = 0.81 [0.68–0.94]) such as atrial fibrillation and flutter (HR = 0.46 [0.30–0.68]) and ventricular arrhythmia (HR = 0.74 [0.57–0.95]) were reduced. Ischemic heart disease (HR = 0.56 [0.35–0.86]) such as angina (HR = 0.50 [0.25–0.97]) also exhibited lower risks in the Paxlovid group. There were decreased risks of other cardiac disorders (HR = 0.51 [0.35–0.74]) naming heart failure (HR = 0.41 [0.26–0.63]). Deep vein thrombosis (HR = 0.46 [0.24–0.87]) belonging to thrombotic disorders also exhibited significantly lower risks in the Paxlovid group.

Compared with the Non-Paxlovid group, there were decreased risks of MACE (major adverse cardiac events) (HR = 0.56 [0.44–0.70]) and any of the above-mentioned cardiovascular outcomes (HR = 0.76 [0.66–0.86]).

Moreover, the mortality rate in the Paxlovid group was significantly lower than that in the non-Paxlovid group (HR = 0.21 [0.11–0.40]). Finally, the use of Paxlovid reduced the admission (HR = 0.68 [0.60–0.76]) and the ICU admission rates (HR = 0.52 [0.33–0.80]) in the Paxlovid group.

The Kaplan–Meier curve of incidence of the cardiovascular outcomes also indicated a difference of probability between the two groups in Fig. 3 (Log-rank test, P < 0.001).

Fig. 3
figure 3

Kaplan–Meier plot for risk of outcomes

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Subgroup analyses

The risks of CVDs in subgroups were evaluated based on sex, age, and race. Both male and female patients in the Paxlovid group exhibited a significant reduction in the risks of MACE and any cardiovascular outcome, compared with those in the non-Paxlovid group. Compared to the female group, Paxlovid seems to have had a stronger effect in the male group with lower risks of MACE and any cardiovascular outcome. Moreover, the Paxlovid group had significantly lower risks of mortality, admission, and ICU rates in both male and female subgroups. The female subgroup in the Paxlovid group had significantly lower risks of arrhythmia (HR = 0.76 [0.61–0.94]), thrombotic disorders (HR = 0.60[0.35–0.99]), and other cardiac disorders (HR = 0.55 [0.32–0.93]). By contrast, the male subgroup in the Paxlovid group had lower risks of cerebrovascular complications (HR = 0.47 [0.25–0.86]), arrhythmia (HR = 0.56 [0.39–0.78]), THD (HR = 0.33 [0.15–0.72]), other cardiac disorders (HR = 0.36 [0.19–0.67]) and thrombotic disorders (HR = 0.40 [0.17–0.94]) than the male subjects in the Non-Paxlovid group (Additional file 1: Table S3 and Fig. 4).

Fig. 4
figure 4

Forest plot of the risk of outcomes for stratification of sex

Full size image

The middle-aged (aged 45–64 years) and elderly (aged ≥ 65 years) subgroups in the Paxlovid group had a significantly reduced risk of MACE and any cardiovascular outcome. Moreover, the middle-aged subgroups in the Paxlovid group exhibited significantly lower risks of arrhythmia (HR = 0.61 [0.43–0.86]) and thrombotic disorders (HR = 0.44 [0.20–0.94]). The elderly group in the Paxlovid group had lower risks of cerebrovascular complications (HR = 0.55 [0.35–0.85]), arrhythmia (HR = 0.68 [0.50–0.90]), and other cardiac disorders (HR = 0.34 [0.18–0.62]) than the elderly in the non-Paxlovid group. The risk of mortality rate was lower in the middle-aged and elderly subgroups of the Paxlovid group. In addition, the younger (aged 20–44 years) and elderly patients in the Paxlovid group had a significantly reduced risk of admission rate.

Finally, the elderly patients in the Paxlovid group had a significantly lower risk of ICU rate (HR = 0.33 [0.14–0.75]) than those in the Non-Paxlovid group (Additional file 1: Table S4 and Fig. 5).

Fig. 5
figure 5

Forest plot of the risk of outcomes for stratification of age

Full size image

With respect to race, the Black group in the Paxlovid group had significantly lower risks of MACE (HR = 0.07 [0.01–0.54]) and any cardiovascular outcome (HR = 0.32 [0.10–0.61]) than that in the non-Paxlovid group. Moreover, the Black subgroup in the Paxlovid group had significantly lower risks of arrhythmia (HR = 0.38 [0.18–0.81]) and thrombotic disorders (HR = 0.13 [0.01–1.05]). The Asian subgroup in the Paxlovid group had a significantly lower risk of arrhythmia (HR = 0.29 [0.03–2.68]) than that in the non-Paxlovid group. The risk of admission rate was significantly reduced in the White and Asian subgroups of the Paxlovid group. In addition, the Asian subgroup in the Paxlovid group had a significantly lower risk of mortality rate (HR = 0.51 [0.05–5.58]) than that in the non-Paxlovid group (Additional file 1: Table S5).

The risks of CVDs on different autoimmune diseases were evaluated in Additional file 1:Tables S6 and S7. The ankylosing spondylitis subgroup in the Paxlovid group had a significantly reduced risk of MACE (HR = 0.28 [0.13–0.59]) and any cardiovascular outcome (HR = 0.69 [0.48–0.99]). Moreover, the same subgroup in the Paxlovid group had significantly lower risks of cerebrovascular complications (HR = 0.30 [0.11–0.80]), IHD (HR = 0.11 [0.02–0.87]) and other cardiac disorders (HR = 0.17 [0.05–0.55]) than in the non-Paxlovid group. The psoriasis subgroup in the Paxlovid group had a significantly reduced risk of any cardiovascular outcome (HR = 0.65 [0.40–0.98]) whereas rheumatoid arthritis and systemic lupus erythematosus subgroups in the Paxlovid group had a significantly lower risk of admission rate (HR = 0.64 [0.44–0.90] and HR = 0.46 [0.23–0.90]).

Sensitivity analyses

Paxlovid is prescribed for five consecutive days to patients with mild to moderate COVID-19 disease. Therefore, Paxlovid use within the first day and within 2–5 days of diagnosis was next assessed to evaluate whether the patients received the medication on time. The risks of cardiovascular outcomes were reduced significantly when Paxlovid was used within the first day of COVID-19 diagnosis. In addition, the mortality (HR = 0.20 [0.09–0.45]), admission (HR = 0.70 [0.61–0.79]), and ICU admission rates (HR = 0.41 [0.23–0.71]) were also reduced significantly when Paxlovid was used within the first day. However, when Paxlovid was used within days 2–5 days of COVID-19 diagnosis, only arrhythmia (HR = 0.46 [0.23–0.91]), MACE (HR = 0.34 [0.13–0.85]) and any cardiovascular outcome (HR = 0.44 [0.26–0.74]) had lower risks compared with the non-Paxlovid group (Table 2). Moreover, the risks of the outcomes from the second day to the fifth day were also evaluated (Additional file 1: Tables S8 and S9). Since the sample for each day was rather small, the risks of the outcomes on each day were not significantly reduced.

Table 2 Risk of outcomes exposed to Paxlovid compared to non-Paxlovid
Full size table

Discussion

The present study showed that treatment with Paxlovid, particularly within the first day of COVID-19 diagnosis could significantly reduce the risks of cardiovascular complications including cerebrovascular complications, arrhythmia, IHD, and thromboembolic disorders in COVID-19-surviving patients with AIRD. The risks of MACE and any cardiovascular complications were also reduced after Paxlovid use. Moreover, Paxlovid reduced the mortality, admission, and ICU admission rates in COVID-19-surviving patients with AIRD which was consistent with other studies on usual participants [17,18,19]. Additionally, we also performed a side-by-side analysis in patients without autoimmune diseases and found that Paxlovid decreased the risk of MACE (HR = 0.75 [0.69–0.81]) and any cardiovascular outcome mentioned above (HR = 0.85 [0.80–0.88]) (Additional file 1: Table S10, S11). Kaplan–Meier analysis demonstrated the same result (log-rank, P < 0.001) (Additional file 1: Fig. S1). However, the effect of Paxlovid seemed stronger with more CVDs in patients with autoimmune diseases. A recent real-life study in 35 Chinese patients with SARS-CoV-2 infection also demonstrated that early treatment of Paxlovid with patients who are immunocompromised (including seven with autoimmune rheumatic conditions) got satisfactory results [20]. However, another multicenter randomized controlled study illustrated that Paxlovid showed no significant reduction in the risk of all-cause mortality for severe adult patients with COVID-19 on day 28 which may be owing to a small number of patients recruited [21].

Most of the AIRD patients (25.1% in the Paxlovid group and 23.8% in the non-Paxlovid group) received corticosteroid treatment. It has been reported that Prednisolone was safe to coadminister NMVr. Additionally, Methylprednisone and Prednisone had potential interaction with NMVr requiring dose adjustment or temporary discontinuation of the drug. Therefore, the ani-inflammatory drug interactions with Paxlovid were not as strong as some CVD medications [22]. Sex-specific risk factors have been identified in autoimmune diseases and CVDs [23, 24]. Younger females are usually protected from CVDs compared with age-matched. However, females tend to develop CVDs following menopause [25]. Systemic lupus erythematosus, rheumatoid arthritis, and Sjogren’s syndrome are more common in women than men [26,27,28]. By contrast, male patients easily suffered from ankylosing spondylitis [29]. In the present study, Paxlovid significantly tended to protect male patients with AIRD from cardiovascular risks compared with women. Moreover, Paxlovid use reduced risks of CVD more evidently in patients with ankylosing spondylitis rather than in female-dominated autoimmune diseases, including Systemic lupus erythematosus, rheumatoid arthritis, and Sjogren’s syndrome. A large amount of evidence indicated that elderly people infected with SARS-CoV-2 experience severe COVID-19 and have a higher mortality than young people [30, 31].In the present study, Paxlovid reduced CVD risks, severe conditions, and mortality rates in the elderly patients(age ≥ 65 years) with AIRD more significantly than the age-matched non-Paxlovid subjects in comparison with younger patients with COVID-19. Overall, these results indicated that Paxlovid could produce stronger therapeutic effects in moderate and severe COVID-19 patients with AIRDs. Mulnupiravir, controversially banned by EMA, may also have a similar efficacy to Paxlovid against cardiovascular events, based upon both animal studies and observational ones [7, 8, 32]. Large cohort studies regarding cardiovascular outcomes may also need to be performed.

Nearly 23% of reported COVID-19 deaths were those of Black people, even though they account for roughly 13% of the US population [33]. Racial disparities in COVID-19 have been observed and documented across geographical regions [34,35,36]. Many studies have revealed that Black patients experience more severe COVID-19 outcomes than White patients [37,38,39]. Of note, in the present study, Paxlovid reduced the risks of cardiovascular complications more significantly, particularly of MACE (HR = 0.07 [0.009–0.54]) in Black people than in White and Asian people.

Paxlovid is usually prescribed for five consecutive days to patients with mild or moderate COVID-19. In the present study, most of the patients (n = 7190) received Paxlovid within the first day of COVID-19 diagnosis. Moreover, Paxlovid use within the first day of COVID-19 diagnosis could reduce the risks of CVD, mortality, and severe conditions rather than Paxlovid use within days 2–5 of COVID-19 diagnosis. Although the risks of Paxlovid use from the second day to the fifth day were also assessed, the results were not significant owing to the small number of available patients for a particular day. These results suggest that Paxlovid should be recommended as soon as COVID-19 diagnosis is confirmed. Vaccination remains the most cost-effective tool against COVID-19 mortality, especially for high-risk patients, to be preferred to anti-virals considering not only the cost involved, but also the risk of developing drug resistance with widespread use of antivirals [7]. Therefore, vaccination before the onset of COVID-19 and Paxlovid treatment in early time could have great effects to reduce the risks.

Our study has several limitations. First, although autoimmune disease stratification was implemented for the subgroup analysis, the information on the disease activity of the autoimmune diseases could not be retrieved from TriNetX. Secondly, even though the treatment start time of Paxlovid was evaluated, Paxlovid dose data could not be obtained from the database. Due to the limitations of the TrinetX platform, we could not match the same index date between both groups. It would raise the potential for immortal time bias. To validate the robustness of our study, we performed a sensitivity analysis where both group's dates of index are COVID-19(Additional file 1: TableS12). The number of Asian people in our study was rather small, which may have produced a racial bias in the results. Health insurance coverage had a significant impact on the utilization of healthcare services during the COVID-19 pandemic [40]. However, healthcare insurance status could not be obtained from TrinetX which may produce potential confounding.

In addition, PSM was performed to avoid bias, but misclassification bias and residual confounding could not be completely avoided because of certain disadvantages of an electronic health record database. Furthermore, COVID-19 infection itself increases the risk of cardiovascular events, especially in unvaccinated patients. Although the study showed a reduction in cardiovascular events after the use of Paxlovid, it is not clear whether the reduction is due to the drug itself or the indirect effect of effective control of the viral infection. Finally, some immunosuppressants and biologic treatments may have adverse effects on the cardiovascular risks of patients with AIRD and some drugs may have complex drug-drug interactions with Paxlovid [5].

Conclusions

Taken together, in this retrospective cohort study, Paxlovid use was associated with lower risks of CVDs and severe conditions in COVID-19-surviving patients with AIRD.

Availability of data and materials

The data that support the findings of this study are available from the TriNetX Analytics Network. https://trinetx.com.

Abbreviations

AIRD:

Autoimmune rheumatic disease

BMI:

Body mass index

COVID-19:

Coronavirus disease 2019

CVD:

Cardiovascular disease

HIV:

Human immunodeficiency virus

HR:

Hazard ratios

ICU:

Intensive care unit

PSM:

Propensity score matching

SMD:

Standardized mean differences

References

  1. COVID-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. Lancet. 2022;399:1513–36. https://0-doi-org.brum.beds.ac.uk/10.1016/s0140-6736(21)02796-3.

    Article  CAS  Google Scholar 

  2. Drożdżal S, Rosik J, Lechowicz K, Machaj F, Szostak B, Przybyciński J, et al. An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment. Drug Resist Updat. 2021;59:100794. https://0-doi-org.brum.beds.ac.uk/10.1016/j.drup.2021.100794.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Huang K, Zhang P, Zhang Z, Youn JY, Wang C, Zhang H, et al. Traditional Chinese Medicine (TCM) in the treatment of COVID-19 and other viral infections: efficacies and mechanisms. Pharmacol Ther. 2021;225:107843. https://0-doi-org.brum.beds.ac.uk/10.1016/j.pharmthera.2021.107843.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mahase E. Covid-19: Pfizer’s paxlovid is 89% effective in patients at risk of serious illness, company reports. Bmj. 2021;375:n2713. https://0-doi-org.brum.beds.ac.uk/10.1136/bmj.n2713.

    Article  PubMed  Google Scholar 

  5. Wen W, Chen C, Tang J, Wang C, Zhou M, Cheng Y, et al. Efficacy and safety of three new oral antiviral treatment (molnupiravir, fluvoxamine and Paxlovid) for COVID-19: a meta-analysis. Ann Med. 2022;54:516–23. https://0-doi-org.brum.beds.ac.uk/10.1080/07853890.2022.2034936.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Najjar-Debbiny R, Gronich N, Weber G, Khoury J, Amar M, Stein N, et al. Effectiveness of Paxlovid in Reducing Severe Coronavirus Disease 2019 and Mortality in High-Risk Patients. Clin Infect Dis. 2023;76:e342–9.

    Article  CAS  PubMed  Google Scholar 

  7. L. Cegolon, R. Pol, O. Simonetti, F. Larese Filon, R. Luzzati. Molnupiravir, Nirmatrelvir/Ritonavir, or Sotrovimab for High-Risk COVID-19 patients infected by the omicron variant: hospitalization, mortality, and time until negative swab test in real life. Pharmaceuticals (Basel), 2023;16.https://0-doi-org.brum.beds.ac.uk/10.3390/ph16050721

  8. De Vito A, Moi G, Saderi L, Puci MV, Colpani A, Firino L, et al. Vaccination and antiviral treatment reduce the time to negative SARS-CoV-2 swab: a real-life study. Viruses. 2023;15:2180. https://0-doi-org.brum.beds.ac.uk/10.3390/v15112180.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gazzaruso C, Carlo Stella N, Mariani G, Nai C, Coppola A, Naldani D, et al. High prevalence of antinuclear antibodies and lupus anticoagulant in patients hospitalized for SARS-CoV2 pneumonia. Clin Rheumatol. 2020;39:2095–7. https://0-doi-org.brum.beds.ac.uk/10.1007/s10067-020-05180-7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bastard P, Rosen LB, Zhang Q, Michailidis E, Hoffmann HH, Zhang Y, et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science. 2020;370:eabd4585. https://0-doi-org.brum.beds.ac.uk/10.1126/science.abd4585.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gracia-Ramos AE, Martin-Nares E, Hernández-Molina G. New onset of autoimmune diseases following COVID-19 diagnosis. Cells. 2021;10:3592. https://0-doi-org.brum.beds.ac.uk/10.3390/cells10123592.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liu Y, Sawalha AH, Lu Q. COVID-19 and autoimmune diseases. Curr Opin Rheumatol. 2021;33:155–62. https://0-doi-org.brum.beds.ac.uk/10.1097/bor.0000000000000776.

    Article  PubMed  Google Scholar 

  13. Tang KT, Hsu BC, Chen DY. Autoimmune and rheumatic manifestations associated with COVID-19 in adults: an updated systematic review. Front Immunol. 2021;12:645013. https://0-doi-org.brum.beds.ac.uk/10.3389/fimmu.2021.645013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tariq S, Van Eeden C, Tervaert JWC, Osman MS. COVID-19, rheumatic diseases and immune dysregulation-a perspective. Clin Rheumatol. 2021;40:433–42. https://0-doi-org.brum.beds.ac.uk/10.1007/s10067-020-05529-y.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Chang R, Yen-Ting Chen T, Wang SI, Hung YM, Chen HY, Wei CJ. Risk of autoimmune diseases in patients with COVID-19: a retrospective cohort study. EClinicalMedicine. 2023;56:101783. https://0-doi-org.brum.beds.ac.uk/10.1016/j.eclinm.2022.101783.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wang W, Wang CY, Wang SI, Wei JC. Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: a retrospective cohort study from the TriNetX US collaborative networks. EClinicalMedicine. 2022;53:101619. https://0-doi-org.brum.beds.ac.uk/10.1016/j.eclinm.2022.101619.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Yip TC, Lui GC, Lai MS, Wong VW, Tse YK, Ma BH, et al. Impact of the use of oral antiviral agents on the risk of hospitalization in community coronavirus disease 2019 patients (COVID-19). Clin Infect Dis. 2023;76:e26–33. https://0-doi-org.brum.beds.ac.uk/10.1093/cid/ciac687.

    Article  CAS  PubMed  Google Scholar 

  18. Ma BH, Yip TC, Lui GC, Lai MS, Hui E, Wong VW, et al. Clinical outcomes following treatment for COVID-19 With Nirmatrelvir/Ritonavir and molnupiravir among patients living in nursing homes. JAMA Netw Open. 2023;6:e2310887. https://0-doi-org.brum.beds.ac.uk/10.1001/jamanetworkopen.2023.10887.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tian F, Chen Z, Feng Q. Nirmatrelvir-ritonavir compared with other antiviral drugs for the treatment of COVID-19 patients: a systematic review and meta-analysis. J Med Virol. 2023;95:e28732. https://0-doi-org.brum.beds.ac.uk/10.1002/jmv.28732.

    Article  CAS  PubMed  Google Scholar 

  20. Sun F, Lin Y, Wang X, Gao Y, Ye S. Paxlovid in patients who are immunocompromised and hospitalised with SARS-CoV-2 infection. Lancet Infect Dis. 2022;22:1279. https://0-doi-org.brum.beds.ac.uk/10.1016/s1473-3099(22)00430-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu J, Pan X, Zhang S, Li M, Ma K, Fan C, et al. Efficacy and safety of Paxlovid in severe adult patients with SARS-Cov-2 infection: a multicenter randomized controlled study. Lancet Reg Health West Pac. 2023;33:100694. https://0-doi-org.brum.beds.ac.uk/10.1016/j.lanwpc.2023.100694.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Abraham S, Nohria A, Neilan TG, Asnani A, Saji AM, Shah J, et al. Cardiovascular drug interactions with Nirmatrelvir/Ritonavir in patients with COVID-19: JACC review topic of the week. J Am Coll Cardiol. 2022;80:1912–24. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jacc.2022.08.800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ishimori ML, Martin R, Berman DS, Goykhman P, Shaw LJ, Shufelt C, et al. Myocardial ischemia in the absence of obstructive coronary artery disease in systemic lupus erythematosus. JACC Cardiovasc Imaging. 2011;4:27–33. https://doi.org/10.1016/j.jcmg.2010.09.019.

    Article  PubMed  Google Scholar 

  24. Li H, Konja D, Wang L, Wang Y. Sex differences in adiposity and cardiovascular diseases. Int J Mol Sci. 2022;23:9338. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23169338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Medina D, Mehay D, Arnold AC. Sex differences in cardiovascular actions of the renin-angiotensin system. Clin Auton Res. 2020;30:393–408. https://0-doi-org.brum.beds.ac.uk/10.1007/s10286-020-00720-2.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Nusbaum JS, Mirza I, Shum J, Freilich RW, Cohen RE, Pillinger MH, et al. Sex differences in systemic lupus erythematosus: epidemiology, clinical considerations, and disease pathogenesis. Mayo Clin Proc. 2020;95:384–94. https://0-doi-org.brum.beds.ac.uk/10.1016/j.mayocp.2019.09.012.

    Article  CAS  PubMed  Google Scholar 

  27. Favalli EG, Biggioggero M, Crotti C, Becciolini A, Raimondo MG, Meroni PL. Sex and management of rheumatoid arthritis. Clin Rev Allergy Immunol. 2019;56:333–45. https://0-doi-org.brum.beds.ac.uk/10.1007/s12016-018-8672-5.

    Article  PubMed  Google Scholar 

  28. Konttinen YT, Fuellen G, Bing Y, Porola P, Stegaev V, Trokovic N, et al. Sex steroids in Sjögren’s syndrome. J Autoimmun. 2012;39:49–56. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jaut.2012.01.004.

    Article  CAS  PubMed  Google Scholar 

  29. Sieper J, Braun J, Rudwaleit M, Boonen A, Zink A. Ankylosing spondylitis: an overview. Ann Rheum Dis. 2002;61 Suppl 3:iii8-18. https://0-doi-org.brum.beds.ac.uk/10.1136/ard.61.suppl_3.iii8.

    Article  CAS  PubMed  Google Scholar 

  30. Vahia IV, Jeste DV, Reynolds CF 3rd. Older adults and the mental health effects of COVID-19. Jama. 2020;324:2253–4. https://0-doi-org.brum.beds.ac.uk/10.1001/jama.2020.21753.

    Article  CAS  PubMed  Google Scholar 

  31. Hendren NS, de Lemos JA, Ayers C, Das SR, Rao A, Carter S, et al. Association of body mass index and age with morbidity and mortality in patients hospitalized with COVID-19: results from the american heart association covid-19 cardiovascular disease registry. Circulation. 2021;143:135–44. https://0-doi-org.brum.beds.ac.uk/10.1161/circulationaha.120.051936.

    Article  CAS  PubMed  Google Scholar 

  32. Rasmussen HB, Hansen PR. Molnupiravir Revisited-Critical Assessment of Studies in Animal Models of COVID-19. Viruses. 2023;15:2151. https://0-doi-org.brum.beds.ac.uk/10.3390/v15112151.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hawkins RB, Charles EJ, Mehaffey JH. Socio-economic status and COVID-19-related cases and fatalities. Public Health. 2020;189:129–34. https://0-doi-org.brum.beds.ac.uk/10.1016/j.puhe.2020.09.016.

    Article  CAS  PubMed  Google Scholar 

  34. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382:2534–43. https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMsa2011686.

    Article  CAS  PubMed  Google Scholar 

  35. Okoh AK, Sossou C, Dangayach NS, Meledathu S, Phillips O, Raczek C, et al. Coronavirus disease 19 in minority populations of Newark, New Jersey. Int J Equity Health. 2020;19:93. https://0-doi-org.brum.beds.ac.uk/10.1186/s12939-020-01208-1.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Imam Z, Odish F, Gill I, O’Connor D, Armstrong J, Vanood A, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan United States. J Intern Med. 2020;288(469):76. https://0-doi-org.brum.beds.ac.uk/10.1111/joim.13119.

    Article  CAS  Google Scholar 

  37. Fu J, Reid SA, French B, Hennessy C, Hwang C, Gatson NT, et al. Racial disparities in COVID-19 outcomes among black and white patients with cancer. JAMA Netw Open. 2022;5:e224304. https://0-doi-org.brum.beds.ac.uk/10.1001/jamanetworkopen.2022.4304.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Khanijahani A, Iezadi S, Gholipour K, Azami-Aghdash S, Naghibi D. A systematic review of racial/ethnic and socioeconomic disparities in COVID-19. Int J Equity Health. 2021;20:248. https://0-doi-org.brum.beds.ac.uk/10.1186/s12939-021-01582-4.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, et al. Racial and ethnic disparities in COVID-19-related infections, hospitalizations, and deaths : a systematic review. Ann Intern Med. 2021;174:362–73. https://0-doi-org.brum.beds.ac.uk/10.7326/m20-6306.

    Article  PubMed  Google Scholar 

  40. Krishnamoorthy Y, Kuberan D, Krishnan M, Sinha I, Kanth K, Samuel G. Impact of health insurance coverage on health care utilization during COVID-19 pandemic: A propensity score matched survey analysis in a target region in India. Int J Health Plann Manage. 2023;38:723–34. https://0-doi-org.brum.beds.ac.uk/10.1002/hpm.3620.

    Article  PubMed  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China [grant number: 82004238], Natural Science Foundation of Zhejiang Province [grant numbers: LBY21H270001], China Postdoctoral Fund [grant number: 2019M660952] and Young Elite Scientists Sponsorship Program by CACM [grant number: CACM2021-QNRC2-B01].

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Authors and Affiliations

  1. Department of Rheumatology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China

    Weijie Wang

  2. Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Science, Beijing, China

    Weijie Wang

  3. Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan

    Yu-Hsun Wang

  4. Department of Allergy, Immunology & Rheumatology, Chung Shan Medical University Hospital, Taichung, Taiwan

    Ching-Hua Huang & James Cheng-Chung Wei

  5. Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan

    Tsung-Hsueh Hsieh & James Cheng-Chung Wei

  6. Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan

    James Cheng-Chung Wei

  7. Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan

    James Cheng-Chung Wei

  8. Department of Pharmacy, Chung Shan Medical University Hospital, Taichung, Taiwan

    Ching-Hua Huang

  9. Senior TriNetX Healthcare Partnership Manager EMEA, TriNetX, Cambridge, USA

    Gema Hernández Ibarburu

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  1. Weijie Wang
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  2. Yu-Hsun Wang
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Contributions

WJ-W wrote the draft of the manuscript; YH-W and TH-H performed data analysis; CH-H and CY-W revised the manuscript critically. GH-I checked the TriNetX data and revised the manuscript critically. James CC-W designed and supervised the study. All authors contributed to the manuscript revision and read and approved the submitted version.

Corresponding authors

Correspondence to Ching-Hua Huang or James Cheng-Chung Wei.

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The use of TriNetX for the present study was approved by the authority of the Institutional Review Board of Chung Shan Medical University Hospital (No: CS2-21176).

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

Table S1. Outcome Definitions of cardiovascular complications and severe conditions. Table S2. Risk of outcomes exposed to Paxlovid compared to Non-Paxlovid. Table S3. Sex stratification of risk of outcomes exposed to Paxlovid compared to Non-Paxlovid. Table S4. Age stratification of risk of outcomes exposed to Paxlovid compared to Non-Paxlovid. Table S5. Race stratification of risk of outcomes exposed to Paxlovid compared to non-Paxlovid. Table S6. Autoimmune diseases stratification of risk of outcomes exposed to Paxlovid compared to Non-Paxlovid. Table S7. Autoimmune diseases stratification of risk of outcomes exposed to Paxlovid compared to Non-Paxlovid. Table S8. Risk of outcomes exposed to Paxlovid compared to non-Paxlovid based on different time. Table S9. Risk of outcomes exposed to Paxlovid compared to Non-Paxlovid based on different time. Table S10. Demographic characteristics of Paxlovid and Non-Paxlovid in non-autoimmune population. Table S11. Risk of outcomes exposed to Paxlovid compared to Non-Paxlovid in non-autoimmune population. Table S12. Sensitivity analysis for risk of outcomes exposed to Paxlovid compared to Non-Paxlovid indexed to COVID-19 onset. Table S13. Sensitivity analysis for risk of outcomes exposed to Paxlovid compared to Non-Paxlovid with a five-day washout period. Fig.S1. Kaplan-Meier plot for risk of cardiovascular diseases in non-autoimmune population.

Additional file 2:

Table S1. STROBE Statement—Checklist of items that should be included in reports of cohort studies.

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Wang, W., Wang, YH., Huang, CH. et al. Paxlovid use is associated with lower risk of cardiovascular diseases in COVID-19 patients with autoimmune rheumatic diseases: a retrospective cohort study. BMC Med 22, 117 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12916-024-03331-0

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