Therapeutic Drug Monitoring of Nirmatrelvir/Ritonavirin Patients with COVID-19 

Therapeutic Drug Monitoring of Nirmatrelvir/Ritonavirin Patients with COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Therapeutic Drug Monitoring of Nirmatrelvir/Ritonavirin Patients with COVID-19 xu ping, zhang lijun, wu qingguo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5210990/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted 12 You are reading this latest preprint version Abstract Background The aim of this study is to retrospectively analyze the factors that lead to the drug concentration of Nirmatrelvir/Ritonavir (NMV/RTV) not reaching the standard. Methods In this study, the NMV/RTV drug concentration(Cnmv/rtv)data (n = 114) of COVID-19 patients over 18 years old were collected from May 2022 to October 2022, and the results of the patients were retrospectively compared. According to the analysis of the early study of NMV/RTV, combined with the research results at home and abroad, according to whether the measured drug concentration > 987ng/ml, the patients were divided into target group and non-target group ,The non-target group was defined as not reaching the trough concentration level. Results Serum NMV/RTV concentration in adult patients was correlated with prognostic nutritional index [PNI,(P < 0.05)], height (P < 0.05), weight (P < 0.05) and creatinine clearance [Crcl ,(P < 0.05)]. Multivariate analysis showed that height, weight, PNI, lymphocyte (LYM) and CrCl were independent influencing factors of NMV/RTV trough concentration. However, after the correction of BMI calculation, there was no correlation between NMV/RTV and BMI, so in the clinical medication plan, the drug was not adjusted according to the height and weight. Conclusions The serum NMV/RTV concentration of adult patients gradually decreased with the increase of CrCl. For patients with high and low CrCl, the trough concentration of NMV/RTV should be continuously monitored and the dosing regimen should be adjusted to achieve the target trough concentration in these patients to reduce the effect of CrCl. PNI is also a key factor affecting drug concentration. For poor nutritional status, drug concentration should be closely monitored and the dose should be adjusted. therapeutic drug monitoring Nirmatrelvir/Ritonavir CrCl PNI trough concentration Figures Figure 1 BACKGROUND In the field of antiviral therapy, pharmacological properties are more important for treatment selection, evaluation and optimization. Safe and effective drug treatment can prevent infection from progressing to more serious disease or even death, and is conducive to shortening the recovery time of patients and reducing the transmission rate. NMV/RTVcan also significantly reduce the risk of hospitalization and death associated with COVID-19 by blocking the major protease (Mpro) in the replication process of SARS-CoV-2 [ 1 ] . It has been shown that NMV/RTV retains consistent and potent antiviral activity in vitro in SARS-CoV-2 variants, including the Omicron variant. Such as SARS-CoV-2 Alpha, Beta, Gamma, Delta, Lambda, Mu and Omicron BA-1 variants, which are the advantages of NMV antiviral [ 2 ] . With the continuous mutation of the virus, NMV/RTV is not easy to fail, because the mutation mainly occurs in the spike protein [ 3 , 4 ] . Therefore, NMV/RTV is often used to treat COVID-19 in clinical practice. NMV has a short half-life and needs to be combined with RTV for treatment. The maximum inhibitory effect can be achieved at a dose of 100mg, which can significantly increase the blood concentration of NMV, a drug mainly metabolized by CYP3A, and maintain it at more than 90% of the effective concentration per day. The target drug concentration can ensure the efficacy of antiviral therapy. Therefore, we combined the current data at home and abroad from some people without underlying diseases to further integrate the population pharmacokinetic characteristics, which can help clinicians make better decisions. MATERIALS AND METHODS General material: A retrospective analysis was performed on 114 patients who were admitted to the COVID-19 department of Shanghai Public Health Clinical Center from May 2022 to July 2022 and had NMV/RTV drug concentration monitored. The variables involved included: gender, age, height, weight, blood drug concentration data, laboratory indicators, comorbidities, and disease severity classification.Inclusion criteria: (1)Age ≥ 18 years old, meeting the diagnostic criteria of 2019-ncov infection;(2) According to NMV/RTV (trade name: Paxlovid, manufacturer: Nirmatrelvir tablets company name: Pfizer Manufacturing Deutschland GmbH, Germany; Ritonavir Tablets Company Name: Hetero Labs Limited, India, Approval No. Nematavir 300 mg (150 mg x 2 tablets) combined with ritonavir 100 mg (100 mg x 1 tablet), oral administration every 12 hours for 5 days, may be used for nasal feeding in some critically ill patients. (3)after at least 1 day of administration of nemmatavir/ritonavir and monitoring of random concentrations after administration. Exclusion criteria: (1)no blood drug concentration was detected; (2)patients undergoing hemodialysis; (3)Pregnant and lactating patients; (4)Allergic reactions occurred during the use of drugs; (5) incomplete medication information records. This study met the requirements of medical ethics and was approved by the Ethics committee of our hospital (ethical approval number: 2021-S070-02). Groups: According to the inclusion and exclusion criteria, 114 patients were included in the final study. According to the existing research and product information [ 5 , 6 ] , the trough concentration of NMV/RTV was set to 987ng/ml, which was considered to be the minimum concentration of the drug. If the drug concentration did not reach this value, it was considered that the drug did not reach the standard. Therefore, according to whether the drug concentration reached 987ng/ml, the drug was divided into the standard group (group A,n = 74) and the failure group (group A,n = 40). According to the clinical diagnosis of COVID-19, the patients were divided into mild (n = 21), moderate (n = 53), severe (n = 29), and critical (n = 11) types. According to CURB-65 classification, the patients were divided into low risk group (n = 56), moderate risk group (n = 40) and high risk group (n = 18). Determination of plasma concentration of NMV/RTV: Plasma was precipitated by methanol and acetonitrile in proportion, and NMV/RTV and its internal standard were detected by multiple reaction monitoring (MRM) positive ion mode. Therapeutic drug monitoring (TDM) in patients with COVID-19 was performed using a validated UHPLC-MS/MS. Data Analysis: SPSS 26.0 software was used to analyze the data. The measurement data were tested for normality and expressed as mean ± standard deviation. The t test was used to analyze the difference between the two groups of normally distributed clinical data, and the nonparametric rank sum test was used to analyze the difference between the two groups of non-normally distributed clinical data. Count data were expressed as frequency (percentage), and the difference between groups was analyzed by chi-square analysis. The correlation between drug concentration and clinical data was explored. Spearman rank correlation analysis was used because the normal distribution was not satisfied. Multivariate logistic regression analysis was used to identify the risk factors for insufficient plasma concentration of nemmatavir/ritonavir. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of each risk factor for drug insufficiency in COVID-19 patients. P < 0.05 was considered statistically significant. RESULTS The data of the two groups were compared(Table.1): A total of 114 patients were included in the study, including 40 patients in the standard drug concentration group(group B) and 74 patients in the substandard drug concentration group༈groupA༉. The drug concentration of the standard group was 3842.84 ± 2658.61ng/ml, and the drug concentration of the substandard group was 443.1 ± 263.62ng/ml. There was a difference between the two groups. There were no significant differences in Cnmv/rtv, BMI, age, female, nucleic acid value, HGB, CRP, PT, cardiovascular disease, cancer, surgery and other indicators between the two groups. Correlation analysis between NMV concentration and clinical indicators(Table.2) According to the difference analysis between groups, bivariate analysis showed that the drug concentration was correlated with CrCl, diabetes, height and weight (P < 0.01). Multivariate regression analysis of drug concentration insufficiency(Table.3) According to the difference analysis between groups, multivariate regression analysis was performed on the indicators with differences between groups, and the results showed that except LYM, the other indicators were significant (P < 0.01). ROC prediction curve, predicting insufficient concentration(Table.4 Fig. 1 ) Areas under the curve (AUC) of Height、Weight、Crcl. Analysis and Discussion In a foreign study on the risk of progression to severe Covid-19 in 2246 patients within 28 days, NMV/RTV could reduce the risk by 89%. Patients who started treatment within 3 days and 5 days after symptom onset had relative risk reductions of 88.9% and 87.8%, respectively [ 7 ] . NMV/RTV has been shown to be a promising therapeutic option for treating patients with mild to moderate COVID-19 who are at risk of hospitalization or progression to severe disease [ 8 ] . Due to the high pharmacokinetic variability of NMV, insufficient concentrations increase the risk of treatment failure. Therefore, TDM monitoring is particularly important for clinical decision making. Considering the stability of the drug, this study screened the drug concentration for at least 1 day of treatment. Combined with the basic study results of 300/100 mg bid administration simulating NMV/RTV, it was shown that more than 90% of the subjects would achieve a target free Cmin above the in vitro EC90 after the first and subsequent doses. The interindividual variability in clearance ballooned to 60%. The predicted median Cmin at day 1 and steady-state was 987 ng/mL and 1800 ng/mL, respectively, approximately 3-and 6-fold higher than in vitro EC90. In this way, we believe that when the drug concentration does not reach the trough concentration after the first day, it is considered that the drug has not reached the effective concentration, so it is divided into the substandard group and the standard group. In the between-group analysis, the study found that there was a difference in the negative conversion time of nucleic acid between the substandard group and the standard group (P < 0.05). In order to ensure the accuracy of the results, we compared the basic characteristics of the patients between the two groups, and the nucleic acid viral load was double-well. There was no significant difference between the two groups (P = 0.2, P = 0.3). In order to exclude the influence of the severity of the disease on this phenomenon, we divided the disease into mild, moderate, severe and critical types according to the diagnosis and treatment of COVID-19. At the same time, we classified the severity of the disease according to CURB-65 score, and found that there was no difference between the groups. Therefore, the negative conversion time of nucleic acid after taking medicine represents the effect of drug treatment, and the negative conversion time of the substandard group is longer. We collected clinical data on drug concentration, and other laboratory indicators were the results of blood indicators within 3 days of concentration. We collected clinical data on drug concentration, and other laboratory indicators were the results of blood indicators within 3 days of concentration, including the following items, such as inflammatory indicators (LYM, high-sensitivity C-reactive protein, etc.), liver function (transaminase, bilirubin, protein, etc.), renal function (creatinine, urea, creatinine clearance rate, etc.). Myocardial markers (isoenzymes, BNP, etc.), nutritional indicators (prealbumin, PNI, etc.), basic characteristics (height, weight, age, gender, etc.), comorbidities such as cardiovascular disease, liver disease, kidney disease, surgery, diabetes, malignant tumors, etc The differences between the two groups were only in height, weight, PNI, LYM, CrCl, and diabetes (P < 0.05). After standardized by BMI, there was no difference in BMI between the two groups. Therefore, further correlation analysis and multivariate regression analysis showed that PNI and Crcl were parameters that we need to pay attention to. PNI is called prognostic nutritional index, which is mainly calculated by measuring serum albumin and peripheral blood lymphocytes. It can comprehensively reflect the nutritional status and immune status of patients. Several studies have shown that malnutrition can lead to changes in the immune system, suppress immune responses and increase susceptibility to infections such as COVID-19. In particular, elderly patients and those with underlying diseases are more likely to suffer from malnutrition [ 9 – 11 ] . Other studies have found that the proportion of malnutrition in critically ill patients can reach 70% and is closely related to death, length of hospital stay, and duration of mechanical ventilation [ 12 ] . Multiple systematic reviews and observational studies [ 11 , 13 , 14 ] have shown that PNI is a good evaluation index for predicting the severity and prognosis of the disease. Among 223 patients with severe COVID-19, the primary outcome indicator was the mortality during hospitalization. Through analysis, PNI and NLR were better than PLR and LMR in identifying the in-hospital mortality. Multivariate analysis showed that PNI(40.2,P = 0.009) was an independent predictor of mortality in patients with severe COVID-19, and the AUC value of PNI for predicting in-hospital mortality in patients with severe COVID-19 was 0.91. Secondly, in this study, we used Cockcroft-Gault (CG) common formula to calculate CrCl [ 15 ] , CrCl (mL/min) = [(140-age) × body weight (kg)] / [0.818 ×SCr (µmol/L)], if a female patient, the result × 0.85. Finally, the relationship between CrCl and drug concentration was found to be negative. Not only that, other studies have shown that NMV has a molecular weight of 499.5D, about 35% is excreted by the kidney, and 70% is bound to protein. Ritonavir is mainly metabolized by the liver and is 99% bound to proteins. Therefore, NMV will accumulate with the decline of renal function [ 16 ] . We concluded that the trough concentration of NMV/RTV in adult patients gradually decreased with the decrease of PNI and the increase of CrCl. For such patients, we should pay attention to the trough concentration of NMV/RTV and adjust the dosing regimen to achieve the target trough concentration, so as to reduce the impact on clinical efficacy.Finally, there are some limitations in this study:1. This is a single-center study without multi-center study.2. Only single drug TDM monitoring is performed, and drug interactions are excluded [ 17 ] . Therefore, finally, for drugs affected by CYP3A4 enzyme, such as voriconazole, tacrolimus, digoxin, etc. clinical decision-making [ 18 – 20 ] needs to further consider drug interaction to evaluate the influencing factors of drug concentration substandard. Declarations Acknowledgements We sincerely thank the management and ethics committees of all participating hospitals for granting us data access for this study. Author contributions Zhang lijun and Wu qingguo designed the study. Xu ping collected the clinical data and analyzed the data. All authors contributed to the manuscript and approved the submitted version. All authors reviewed the manuscript. Funding This work was supported by the Fund for Shanghai Public Health Clinical Center, Hospital Project(KY-GW-2023-19) Data availability No datasets were generated or analysed during the current study. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval and consent to participate This study is a retrospective cohort study, but not a clinical trial, thus the clinical trial number is not applicable. Ethical approval was obtained from the Ethics Committee ofShanghai Public Health Clinical Center on July 26, 2022 (Granted number: 2021-S070-02), all procedures were in accordance with ethical standards, and informed consent was waived for this retrospective review. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Hung YP, Lee JC, Chiu CW et al. Oral Nirmatrelvir/Ritonavir Therapy for COVID-19: The Dawn in the Dark?[J]. Antibiot (Basel), 2022, 11(2). Gerhart J, Cox DS, Singh RSP, et al. A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir[J]. Clin Pharmacokinet. 2024;63(1):27–42. Chen SA, Arutyunova E, Lu J, et al. SARS-CoV-2 M(pro) Protease Variants of Concern Display Altered Viral Substrate and Cell Host Target Galectin-8 Processing but Retain Sensitivity toward Antivirals[J]. ACS Cent Sci. 2023;9(4):696–708. Candido KL, Eich CR, De Fariña LO, et al. Spike protein of SARS-CoV-2 variants: a brief review and practical implications[J]. Braz J Microbiol. 2022;53(3):1133–57. Singh RSP, Toussi SS, Hackman F, et al. Innovative Randomized Phase I Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID-19 Trial of Nirmatrelvir[J]. Clin Pharmacol Ther. 2022;112(1):101–11. Qu Y, Su C, Xiang Z, et al. Population pharmacokinetic modeling and simulation for nirmatrelvir exposure assessment in Chinese older patients with COVID-19 infection[J]. Eur J Pharm Sci. 2023;189:106535. Hammond J, Leister-Tebbe H, Gardner A, et al. Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19[J]. N Engl J Med. 2022;386(15):1397–408. Amani B, Amani B. Efficacy and safety of nirmatrelvir/ritonavir (Paxlovid) for COVID-19: A rapid review and meta-analysis[J]. J Med Virol. 2023;95(2):e28441. Zhang JJ, Dong X, Liu GH, et al. Risk and Protective Factors for COVID-19 Morbidity, Severity, and Mortality[J]. Clin Rev Allergy Immunol. 2023;64(1):90–107. Antwi J, Appiah B, Oluwakuse B, et al. The Nutrition-COVID-19 Interplay: a Review[J]. Curr Nutr Rep. 2021;10(4):364–74. Karimi A, Shobeiri P, Kulasinghe A, et al. Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis[J]. Front Immunol. 2021;12:741061. Abate SM, Chekole YA, Estifanos MB, et al. Prevalence and outcomes of malnutrition among hospitalized COVID-19 patients: A systematic review and meta-analysis[J]. Clin Nutr ESPEN. 2021;43:174–83. Wang ZH, Lin YW, Wei XB, et al. Predictive Value of Prognostic Nutritional Index on COVID-19 Severity[J]. Front Nutr. 2020;7:582736. Açıksarı G, Koçak M, Çağ Y, et al. Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study[J]. Biomark Insights. 2021;16:11772719211027022. Shahbaz H, Gupta M. Creatinine Clearance, StatPearls, Treasure Island (FL) ineligible companies. Disclosure: Mohit Gupta declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright © 2024. StatPearls Publishing LLC.; 2024. Hiremath S, Mcguinty M, Argyropoulos C, et al. Prescribing Nirmatrelvir/Ritonavir for COVID-19 in Advanced CKD[J]. Clin J Am Soc Nephrol. 2022;17(8):1247–50. Marzolini C, Kuritzkes DR, Marra F, et al. Recommendations for the Management of Drug-Drug Interactions Between the COVID-19 Antiviral Nirmatrelvir/Ritonavir (Paxlovid) and Comedications[J]. Clin Pharmacol Ther. 2022;112(6):1191–200. Abraham S, Nohria A, Neilan TG, et al. Cardiovascular Drug Interactions With Nirmatrelvir/Ritonavir in Patients With COVID-19: JACC Review Topic of the Week[J]. J Am Coll Cardiol. 2022;80(20):1912–24. Lemaitre F, Budde K, Van Gelder T, et al. Therapeutic Drug Monitoring and Dosage Adjustments of Immunosuppressive Drugs When Combined With Nirmatrelvir/Ritonavir in Patients With COVID-19[J]. Ther Drug Monit. 2023;45(2):191–9. Dewey KW, Yen B, Lazo J, et al. Nirmatrelvir/ritonavir Use With Tacrolimus in Lung Transplant Recipients: A Single-center Case Series[J]. Transplantation. 2023;107(5):1200–5. Tables Table.1 The data of the two groups were compared Parameters Group A(74) Group B(40) P Age(mean±SD) 81.5±11.8 78.38±12.97 0.20 Female(%) 19（26） 14（19） 0.153 Height(mean±SD) 1.61±0.05 1.64±0.06 <0.05 Weight(mean±SD) 57.51±3.6 59.58±4.72 <0.05 BMI(mean±SD) 22.19±1.59 22.32±2.22 0.73 Cnmv/rtv(mean±SD) 3842.84±2658.61 443.1±263.62 <0.05 CrCL(mean±SD) 54.78±25.94 66.89±31.04 <0.05 LYM(mean±SD) 1.44±0.75 1.14±0.65 <0.05 HGB(mean±SD) 109.47±22.64 111.2±22.41 0.70 CRP(mean±SD) 29.54±34.18 25.53±29.89 0.55 PNI(mean±SD) 43.13±5.69 40.17±6.58 <0.05 PT(mean±SD) 14.01±2.85 13.89±1.45 0.80 Viralload1 (mean±SD) 30.32±6.22 31.85±5.81 0.20 Viralload2(mean±SD) 32.51±6.08 33.75±5.83 0.30 Virus clearancetime 3.69±1.67 4.45±1.75 <0.05 Cardiovascular disease (%) 60（81） 28（38） 0.18 Malignant tumor (%) 7（9） 2（3） 0.93 Surgery (%) 9（12） 4（5） 0.73 Diabetes mellitus (%) 21（28） 5（7） <0.05 Cerebral disease（%） 26（35） 18（24） 0.18 liver disease (%) 3（4） 2（3） 0.71 Nephropathy (%) 8（11） 4（5） 0.73 Pulmonary diseases (%) 11 （15） 4 （5） 0.87 Other Diseases (%) 47 （64） 25 （34） 0.51 Table.2 The scores and diagnostic grades of the two groups were compared Indicators r P Cnmv/rtv vs PNI 0.086 0.363 Cnmv/rtv vs LYM 0.063 0.502 Cnmv/rtv vs CrCl -0.327 <0.001 Cnmv/rtv vs Diabetes mellitus 0.194 <0.001 Cnmv/rtv vs Height -0.246 <0.01 Cnmv/rtv vs weight -0.364 <0.001 Table.3 Multivariate regression analysis of drug concentration insufficiency Factor-s Regression coefficient Standard error Wald χ2 OR 95%CI P Height 3.484 0.649 28.859 32.602 9.144～116.239 <0.001 Weight 0.107 0.01 114.358 1.113 1.092～1.136 <0.001 CrCL 0.015 0.001 135.827 1.015 1.013～1.018 <0.001 LYM -0.096 0.073 1.745 0.908 0.788～1.048 0.187 PNI -0.085 0.009 90.966 0.919 0.903～0.935 <0.001 Diabetes mellitus -1.178 0.086 189.554 0.308 0.26～0.364 <0.001 Constant term -9.528 1.121 72.202 <0.001 Table.4 ROC prediction curve, predicting insufficient concentration Test result variable cutoffvalue AUC P 95%CI Sensitivity （%） Specificity （%） Height 1.61 0.637 <0.05 0.531 ~ 0.742 0.525 0.662 Weight 59.5 0.636 <0.05 0.529 ~ 0.743 0.575 0.649 Crcl 75.69 0.617 <0.05 0.502 ~ 0.731 0.4 0.851 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 23 Oct, 2024 Reviews received at journal 22 Oct, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviews received at journal 22 Oct, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviews received at journal 14 Oct, 2024 Reviewers agreed at journal 09 Oct, 2024 Reviewers agreed at journal 09 Oct, 2024 Reviewers invited by journal 09 Oct, 2024 Editor assigned by journal 09 Oct, 2024 Submission checks completed at journal 08 Oct, 2024 First submitted to journal 05 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5210990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369393576,"identity":"7b8cf88d-37c0-49c5-81ea-d90e75b52d6e","order_by":0,"name":"xu ping","email":"","orcid":"","institution":"Shanghai Public Health Clinical Center","correspondingAuthor":false,"prefix":"","firstName":"xu","middleName":"","lastName":"ping","suffix":""},{"id":369393577,"identity":"6c867970-bce6-4485-889d-167aabbed88f","order_by":1,"name":"zhang lijun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCRBRIMHMwMB8AEgwJBCpxQCkhS2BJC0ggseAOC3ys5ufPfxiYMFucLvn8+eCmjt5/A3Mxz5+waOFcc4xc2MZoMMM7pzdJj3j2LNiiQNsybNl8Ghhlkgwk5YAabmRu42Zh+1w4gYGHmNmCTxa2CTSv0G15Dz+zPOPCC08Ejlmkh8gWhikedsgWhg/4NEiIZFTJg0KZMkbaWbSvH3PEmccZktmxqODQX5G+jbJHxV1yXw3koEO+3Ynsb+9+TDjD3x6gICZh4EhGco+AORCRPACkJl2CC1QkVEwCkbBKBgFMAAAC49IB7L0uBsAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Public Health Clinical Center","correspondingAuthor":true,"prefix":"","firstName":"zhang","middleName":"","lastName":"lijun","suffix":""},{"id":369393578,"identity":"6908a4f1-bca8-4850-9da9-13ca50c1b358","order_by":2,"name":"wu qingguo","email":"","orcid":"","institution":"Shanghai Public Health Clinical Center","correspondingAuthor":false,"prefix":"","firstName":"wu","middleName":"","lastName":"qingguo","suffix":""}],"badges":[],"createdAt":"2024-10-06 03:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5210990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5210990/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-024-10291-6","type":"published","date":"2024-12-18T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69352122,"identity":"5e39263d-6a63-4400-9e75-1ff2b939f8e9","added_by":"auto","created_at":"2024-11-19 13:09:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45422,"visible":true,"origin":"","legend":"\u003cp\u003eROC prediction curve, predicting insufficient concentration\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5210990/v1/74c0347c9a28f41739dc8d84.png"},{"id":72202805,"identity":"b125ece9-59cd-4e13-b8be-717854b7b7fc","added_by":"auto","created_at":"2024-12-23 16:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":553002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5210990/v1/5f334975-276c-40c4-859d-26e485a95321.pdf"},{"id":69352121,"identity":"d652ffd0-0656-4cc6-95f3-22f15db865ac","added_by":"auto","created_at":"2024-11-19 13:09:13","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5210990/v1/f43c2f6f6adaf7d0e8f55b2f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Therapeutic Drug Monitoring of Nirmatrelvir/Ritonavirin Patients with COVID-19 ","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eIn the field of antiviral therapy, pharmacological properties are more important for treatment selection, evaluation and optimization. Safe and effective drug treatment can prevent infection from progressing to more serious disease or even death, and is conducive to shortening the recovery time of patients and reducing the transmission rate. NMV/RTVcan also significantly reduce the risk of hospitalization and death associated with COVID-19 by blocking the major protease (Mpro) in the replication process of SARS-CoV-2 \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It has been shown that NMV/RTV retains consistent and potent antiviral activity in vitro in SARS-CoV-2 variants, including the Omicron variant. Such as SARS-CoV-2 Alpha, Beta, Gamma, Delta, Lambda, Mu and Omicron BA-1 variants, which are the advantages of NMV antiviral \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the continuous mutation of the virus, NMV/RTV is not easy to fail, because the mutation mainly occurs in the spike protein \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, NMV/RTV is often used to treat COVID-19 in clinical practice. NMV has a short half-life and needs to be combined with RTV for treatment. The maximum inhibitory effect can be achieved at a dose of 100mg, which can significantly increase the blood concentration of NMV, a drug mainly metabolized by CYP3A, and maintain it at more than 90% of the effective concentration per day. The target drug concentration can ensure the efficacy of antiviral therapy. Therefore, we combined the current data at home and abroad from some people without underlying diseases to further integrate the population pharmacokinetic characteristics, which can help clinicians make better decisions.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneral material:\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed on 114 patients who were admitted to the COVID-19 department of Shanghai Public Health Clinical Center from May 2022 to July 2022 and had NMV/RTV drug concentration monitored. The variables involved included: gender, age, height, weight, blood drug concentration data, laboratory indicators, comorbidities, and disease severity classification.Inclusion criteria: (1)Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old, meeting the diagnostic criteria of 2019-ncov infection;(2) According to NMV/RTV (trade name: Paxlovid, manufacturer: Nirmatrelvir tablets company name: Pfizer Manufacturing Deutschland GmbH, Germany; Ritonavir Tablets Company Name: Hetero Labs Limited, India, Approval No. Nematavir 300 mg (150 mg x 2 tablets) combined with ritonavir 100 mg (100 mg x 1 tablet), oral administration every 12 hours for 5 days, may be used for nasal feeding in some critically ill patients. (3)after at least 1 day of administration of nemmatavir/ritonavir and monitoring of random concentrations after administration. Exclusion criteria: (1)no blood drug concentration was detected; (2)patients undergoing hemodialysis; (3)Pregnant and lactating patients; (4)Allergic reactions occurred during the use of drugs; (5) incomplete medication information records. This study met the requirements of medical ethics and was approved by the Ethics committee of our hospital (ethical approval number: 2021-S070-02).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGroups:\u003c/h3\u003e\n\u003cp\u003eAccording to the inclusion and exclusion criteria, 114 patients were included in the final study. According to the existing research and product information \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, the trough concentration of NMV/RTV was set to 987ng/ml, which was considered to be the minimum concentration of the drug. If the drug concentration did not reach this value, it was considered that the drug did not reach the standard. Therefore, according to whether the drug concentration reached 987ng/ml, the drug was divided into the standard group (group A,n\u0026thinsp;=\u0026thinsp;74) and the failure group (group A,n\u0026thinsp;=\u0026thinsp;40). According to the clinical diagnosis of COVID-19, the patients were divided into mild (n\u0026thinsp;=\u0026thinsp;21), moderate (n\u0026thinsp;=\u0026thinsp;53), severe (n\u0026thinsp;=\u0026thinsp;29), and critical (n\u0026thinsp;=\u0026thinsp;11) types. According to CURB-65 classification, the patients were divided into low risk group (n\u0026thinsp;=\u0026thinsp;56), moderate risk group (n\u0026thinsp;=\u0026thinsp;40) and high risk group (n\u0026thinsp;=\u0026thinsp;18).\u003c/p\u003e\n\u003ch3\u003eDetermination of plasma concentration of NMV/RTV:\u003c/h3\u003e\n\u003cp\u003ePlasma was precipitated by methanol and acetonitrile in proportion, and NMV/RTV and its internal standard were detected by multiple reaction monitoring (MRM) positive ion mode. Therapeutic drug monitoring (TDM) in patients with COVID-19 was performed using a validated UHPLC-MS/MS.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis:\u003c/h2\u003e \u003cp\u003eSPSS 26.0 software was used to analyze the data. The measurement data were tested for normality and expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The t test was used to analyze the difference between the two groups of normally distributed clinical data, and the nonparametric rank sum test was used to analyze the difference between the two groups of non-normally distributed clinical data. Count data were expressed as frequency (percentage), and the difference between groups was analyzed by chi-square analysis. The correlation between drug concentration and clinical data was explored. Spearman rank correlation analysis was used because the normal distribution was not satisfied. Multivariate logistic regression analysis was used to identify the risk factors for insufficient plasma concentration of nemmatavir/ritonavir. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of each risk factor for drug insufficiency in COVID-19 patients. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe data of the two groups were compared(Table.1):\u003c/h2\u003e \u003cp\u003eA total of 114 patients were included in the study, including 40 patients in the standard drug concentration group(group B) and 74 patients in the substandard drug concentration group༈groupA༉. The drug concentration of the standard group was 3842.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2658.61ng/ml, and the drug concentration of the substandard group was 443.1\u0026thinsp;\u0026plusmn;\u0026thinsp;263.62ng/ml. There was a difference between the two groups. There were no significant differences in Cnmv/rtv, BMI, age, female, nucleic acid value, HGB, CRP, PT, cardiovascular disease, cancer, surgery and other indicators between the two groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis between NMV concentration and clinical indicators(Table.2)\u003c/h3\u003e\n\u003cp\u003eAccording to the difference analysis between groups, bivariate analysis showed that the drug concentration was correlated with CrCl, diabetes, height and weight (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003ch3\u003eMultivariate regression analysis of drug concentration insufficiency(Table.3)\u003c/h3\u003e\n\u003cp\u003eAccording to the difference analysis between groups, multivariate regression analysis was performed on the indicators with differences between groups, and the results showed that except LYM, the other indicators were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cb\u003eROC prediction curve, predicting insufficient concentration(Table.4\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003eAreas under the curve (AUC) of Height、Weight、Crcl.\u003c/p\u003e "},{"header":"Analysis and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003eIn a foreign study on the risk of progression to severe Covid-19 in 2246 patients within 28 days, NMV/RTV could reduce the risk by 89%. Patients who started treatment within 3 days and 5 days after symptom onset had relative risk reductions of 88.9% and 87.8%, respectively \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. NMV/RTV has been shown to be a promising therapeutic option for treating patients with mild to moderate COVID-19 who are at risk of hospitalization or progression to severe disease \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Due to the high pharmacokinetic variability of NMV, insufficient concentrations increase the risk of treatment failure. Therefore, TDM monitoring is particularly important for clinical decision making. Considering the stability of the drug, this study screened the drug concentration for at least 1 day of treatment. Combined with the basic study results of 300/100 mg bid administration simulating NMV/RTV, it was shown that more than 90% of the subjects would achieve a target free Cmin above the in vitro EC90 after the first and subsequent doses. The interindividual variability in clearance ballooned to 60%. The predicted median Cmin at day 1 and steady-state was 987 ng/mL and 1800 ng/mL, respectively, approximately 3-and 6-fold higher than in vitro EC90. In this way, we believe that when the drug concentration does not reach the trough concentration after the first day, it is considered that the drug has not reached the effective concentration, so it is divided into the substandard group and the standard group. In the between-group analysis, the study found that there was a difference in the negative conversion time of nucleic acid between the substandard group and the standard group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In order to ensure the accuracy of the results, we compared the basic characteristics of the patients between the two groups, and the nucleic acid viral load was double-well. There was no significant difference between the two groups (P\u0026thinsp;=\u0026thinsp;0.2, P\u0026thinsp;=\u0026thinsp;0.3). In order to exclude the influence of the severity of the disease on this phenomenon, we divided the disease into mild, moderate, severe and critical types according to the diagnosis and treatment of COVID-19. At the same time, we classified the severity of the disease according to CURB-65 score, and found that there was no difference between the groups. Therefore, the negative conversion time of nucleic acid after taking medicine represents the effect of drug treatment, and the negative conversion time of the substandard group is longer. We collected clinical data on drug concentration, and other laboratory indicators were the results of blood indicators within 3 days of concentration. We collected clinical data on drug concentration, and other laboratory indicators were the results of blood indicators within 3 days of concentration, including the following items, such as inflammatory indicators (LYM, high-sensitivity C-reactive protein, etc.), liver function (transaminase, bilirubin, protein, etc.), renal function (creatinine, urea, creatinine clearance rate, etc.). Myocardial markers (isoenzymes, BNP, etc.), nutritional indicators (prealbumin, PNI, etc.), basic characteristics (height, weight, age, gender, etc.), comorbidities such as cardiovascular disease, liver disease, kidney disease, surgery, diabetes, malignant tumors, etc The differences between the two groups were only in height, weight, PNI, LYM, CrCl, and diabetes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After standardized by BMI, there was no difference in BMI between the two groups. Therefore, further correlation analysis and multivariate regression analysis showed that PNI and Crcl were parameters that we need to pay attention to.\u003c/p\u003e \u003cp\u003ePNI is called prognostic nutritional index, which is mainly calculated by measuring serum albumin and peripheral blood lymphocytes. It can comprehensively reflect the nutritional status and immune status of patients. Several studies have shown that malnutrition can lead to changes in the immune system, suppress immune responses and increase susceptibility to infections such as COVID-19. In particular, elderly patients and those with underlying diseases are more likely to suffer from malnutrition \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Other studies have found that the proportion of malnutrition in critically ill patients can reach 70% and is closely related to death, length of hospital stay, and duration of mechanical ventilation \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Multiple systematic reviews and observational studies \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e have shown that PNI is a good evaluation index for predicting the severity and prognosis of the disease. Among 223 patients with severe COVID-19, the primary outcome indicator was the mortality during hospitalization. Through analysis, PNI and NLR were better than PLR and LMR in identifying the in-hospital mortality.\u003c/p\u003e \u003cp\u003eMultivariate analysis showed that PNI(40.2,P\u0026thinsp;=\u0026thinsp;0.009) was an independent predictor of mortality in patients with severe COVID-19, and the AUC value of PNI for predicting in-hospital mortality in patients with severe COVID-19 was 0.91. Secondly, in this study, we used Cockcroft-Gault (CG) common formula to calculate CrCl\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, CrCl (mL/min) = [(140-age) \u0026times; body weight (kg)] / [0.818 \u0026times;SCr (\u0026micro;mol/L)], if a female patient, the result \u0026times; 0.85. Finally, the relationship between CrCl and drug concentration was found to be negative. Not only that, other studies have shown that NMV has a molecular weight of 499.5D, about 35% is excreted by the kidney, and 70% is bound to protein. Ritonavir is mainly metabolized by the liver and is 99% bound to proteins. Therefore, NMV will accumulate with the decline of renal function \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe concluded that the trough concentration of NMV/RTV in adult patients gradually decreased with the decrease of PNI and the increase of CrCl. For such patients, we should pay attention to the trough concentration of NMV/RTV and adjust the dosing regimen to achieve the target trough concentration, so as to reduce the impact on clinical efficacy.Finally, there are some limitations in this study:1. This is a single-center study without multi-center study.2. Only single drug TDM monitoring is performed, and drug interactions are excluded \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, finally, for drugs affected by CYP3A4 enzyme, such as voriconazole, tacrolimus, digoxin, etc. clinical decision-making \u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e needs to further consider drug interaction to evaluate the influencing factors of drug concentration substandard.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the management and ethics committees of all participating hospitals for granting us data access for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Zhang lijun and Wu qingguo designed the study. Xu ping collected the clinical data and analyzed the data. All authors contributed to the manuscript and approved the submitted version. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fund for Shanghai Public Health Clinical Center, Hospital Project(KY-GW-2023-19)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective cohort study, but not a clinical trial, thus the clinical trial number is not applicable.\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Committee ofShanghai Public Health Clinical Center on July 26, 2022 (Granted number: 2021-S070-02), all procedures were in accordance with ethical standards, and informed consent was waived for this retrospective review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHung YP, Lee JC, Chiu CW et al. Oral Nirmatrelvir/Ritonavir Therapy for COVID-19: The Dawn in the Dark?[J]. Antibiot (Basel), 2022, 11(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerhart J, Cox DS, Singh RSP, et al. A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir[J]. Clin Pharmacokinet. 2024;63(1):27\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen SA, Arutyunova E, Lu J, et al. SARS-CoV-2 M(pro) Protease Variants of Concern Display Altered Viral Substrate and Cell Host Target Galectin-8 Processing but Retain Sensitivity toward Antivirals[J]. ACS Cent Sci. 2023;9(4):696\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCandido KL, Eich CR, De Fari\u0026ntilde;a LO, et al. Spike protein of SARS-CoV-2 variants: a brief review and practical implications[J]. 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Clin Nutr ESPEN. 2021;43:174\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang ZH, Lin YW, Wei XB, et al. Predictive Value of Prognostic Nutritional Index on COVID-19 Severity[J]. Front Nutr. 2020;7:582736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA\u0026ccedil;ıksarı G, Ko\u0026ccedil;ak M, \u0026Ccedil;ağ Y, et al. Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study[J]. Biomark Insights. 2021;16:11772719211027022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaz H, Gupta M. Creatinine Clearance, StatPearls, Treasure Island (FL) ineligible companies. Disclosure: Mohit Gupta declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright \u0026copy; 2024. StatPearls Publishing LLC.; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiremath S, Mcguinty M, Argyropoulos C, et al. Prescribing Nirmatrelvir/Ritonavir for COVID-19 in Advanced CKD[J]. Clin J Am Soc Nephrol. 2022;17(8):1247\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarzolini C, Kuritzkes DR, Marra F, et al. Recommendations for the Management of Drug-Drug Interactions Between the COVID-19 Antiviral Nirmatrelvir/Ritonavir (Paxlovid) and Comedications[J]. Clin Pharmacol Ther. 2022;112(6):1191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham S, Nohria A, Neilan TG, et al. Cardiovascular Drug Interactions With Nirmatrelvir/Ritonavir in Patients With COVID-19: JACC Review Topic of the Week[J]. J Am Coll Cardiol. 2022;80(20):1912\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemaitre F, Budde K, Van Gelder T, et al. Therapeutic Drug Monitoring and Dosage Adjustments of Immunosuppressive Drugs When Combined With Nirmatrelvir/Ritonavir in Patients With COVID-19[J]. Ther Drug Monit. 2023;45(2):191\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewey KW, Yen B, Lazo J, et al. Nirmatrelvir/ritonavir Use With Tacrolimus in Lung Transplant Recipients: A Single-center Case Series[J]. Transplantation. 2023;107(5):1200\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable.1 The data of the two groups were compared\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eGroup A(74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eGroup B(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eAge(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e81.5\u0026plusmn;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e78.38\u0026plusmn;12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eFemale(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e19（26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e14（19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eHeight(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e1.61\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e1.64\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eWeight(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e57.51\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e59.58\u0026plusmn;4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eBMI(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e22.19\u0026plusmn;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e22.32\u0026plusmn;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eCnmv/rtv(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e3842.84\u0026plusmn;2658.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e443.1\u0026plusmn;263.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eCrCL(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e54.78\u0026plusmn;25.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e66.89\u0026plusmn;31.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eLYM(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e1.44\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eHGB(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e109.47\u0026plusmn;22.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e111.2\u0026plusmn;22.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eCRP(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e29.54\u0026plusmn;34.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e25.53\u0026plusmn;29.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003ePNI(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e43.13\u0026plusmn;5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e40.17\u0026plusmn;6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003ePT(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e14.01\u0026plusmn;2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e13.89\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eViralload1\u0026nbsp;(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e30.32\u0026plusmn;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e31.85\u0026plusmn;5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eViralload2(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e32.51\u0026plusmn;6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e33.75\u0026plusmn;5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eVirus clearancetime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e3.69\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e4.45\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eCardiovascular disease\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e60（81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e28（38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eMalignant tumor\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e7（9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e2（3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eSurgery\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e9（12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e4（5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e21（28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e5（7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eCerebral disease（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e26（35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e18（24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eliver disease\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e3（4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e2（3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eNephropathy\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e8（11）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e4（5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003ePulmonary diseases\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e（15）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e（5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7551%;\"\u003e\n \u003cp\u003eOther Diseases\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47\u003c/strong\u003e（64）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e（34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable.2 The scores and diagnostic grades of the two groups were compared\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7658%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7297%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs PNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs LYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs CrCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e-0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs\u0026nbsp;Diabetes\u0026nbsp;mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs\u0026nbsp;Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e-0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54.5045%;\"\u003e\n \u003cp\u003eCnmv/rtv\u0026nbsp;vs\u0026nbsp;weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7658%;\"\u003e\n \u003cp\u003e-0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.7297%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable.3 Multivariate regression analysis of drug concentration insufficiency\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eFactor-s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5605%;\"\u003e\n \u003cp\u003eRegression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.535%;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eWald \u0026chi;2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.07643%;\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.87261%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e3.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e28.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e32.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e9.144～116.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e114.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e1.092～1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eCrCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e135.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e1.013～1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eLYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e1.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e0.788～1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003ePNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e90.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e0.903～0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003emellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e-1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e189.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e0.26～0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9682%;\"\u003e\n \u003cp\u003eConstant term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5605%;\"\u003e\n \u003cp\u003e-9.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.535%;\"\u003e\n \u003cp\u003e1.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9682%;\"\u003e\n \u003cp\u003e72.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.07643%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.0191%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.87261%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable.4 ROC prediction curve, predicting insufficient concentration\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eTest\u0026nbsp;result\u0026nbsp;variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003ecutoffvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 98px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003cp\u003e（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.531 ~ 0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.529 ~ 0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCrcl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e75.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.502 ~ 0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"therapeutic drug monitoring, Nirmatrelvir/Ritonavir, CrCl, PNI, trough concentration","lastPublishedDoi":"10.21203/rs.3.rs-5210990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5210990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of this study is to retrospectively analyze the factors that lead to the drug concentration of Nirmatrelvir/Ritonavir (NMV/RTV) not reaching the standard.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, the NMV/RTV drug concentration(Cnmv/rtv)data (n\u0026thinsp;=\u0026thinsp;114) of COVID-19 patients over 18 years old were collected from May 2022 to October 2022, and the results of the patients were retrospectively compared. According to the analysis of the early study of NMV/RTV, combined with the research results at home and abroad, according to whether the measured drug concentration\u0026thinsp;\u0026gt;\u0026thinsp;987ng/ml, the patients were divided into target group and non-target group ,The non-target group was defined as not reaching the trough concentration level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum NMV/RTV concentration in adult patients was correlated with prognostic nutritional index [PNI,(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)], height (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), weight (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and creatinine clearance [Crcl ,(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)]. Multivariate analysis showed that height, weight, PNI, lymphocyte (LYM) and CrCl were independent influencing factors of NMV/RTV trough concentration. However, after the correction of BMI calculation, there was no correlation between NMV/RTV and BMI, so in the clinical medication plan, the drug was not adjusted according to the height and weight.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe serum NMV/RTV concentration of adult patients gradually decreased with the increase of CrCl. For patients with high and low CrCl, the trough concentration of NMV/RTV should be continuously monitored and the dosing regimen should be adjusted to achieve the target trough concentration in these patients to reduce the effect of CrCl. PNI is also a key factor affecting drug concentration. For poor nutritional status, drug concentration should be closely monitored and the dose should be adjusted.\u003c/p\u003e","manuscriptTitle":"Therapeutic Drug Monitoring of Nirmatrelvir/Ritonavirin Patients with COVID-19 ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 13:08:40","doi":"10.21203/rs.3.rs-5210990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-23T07:14:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-22T18:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160238786474864677963718272520138166708","date":"2024-10-22T16:24:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-22T07:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202819764113737526478757660954218626073","date":"2024-10-20T15:21:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T16:33:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130408659012435187276847952380995144590","date":"2024-10-09T21:03:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241987750998264254952816793212975950301","date":"2024-10-09T17:29:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-09T14:49:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-09T05:17:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-08T23:45:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-10-06T03:10:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39f1d934-5006-43ae-a72c-a42a8ca4e1c9","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:13:43+00:00","versionOfRecord":{"articleIdentity":"rs-5210990","link":"https://doi.org/10.1186/s12879-024-10291-6","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2024-12-18 15:57:11","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-11-19 13:08:40","video":"","vorDoi":"10.1186/s12879-024-10291-6","vorDoiUrl":"https://doi.org/10.1186/s12879-024-10291-6","workflowStages":[]},"version":"v1","identity":"rs-5210990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5210990","identity":"rs-5210990","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}