Publications by authors named "Benjamin Skov Kaas-Hansen"

12 Publications

  • Page 1 of 1

Patient-important outcomes other than mortality in recent ICU trials: protocol for a scoping review.

Acta Anaesthesiol Scand 2021 Jun 5. Epub 2021 Jun 5.

Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.

Background: Randomised clinical trials (RCTs) conducted in intensive care units (ICUs) frequently focus on all-cause mortality, but other patient-important outcomes are increasingly used and recommended. Their use, however, is not straightforward: choices and definitions, operationalisation of death, handling of missing data, choice of effect measures, and statistical analyses for these outcomes vary greatly.

Methods: We will conduct a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. We will search 10 selected general and speciality journals for RCTs conducted in adult ICU patients from 2018 and onwards reporting at least one patient-important outcome other than mortality (including days alive without life support/days alive and out of hospital-type outcomes, health-related quality of life, functional/cognitive/neurological outcomes and other general patient-important outcomes). We will summarise data on outcome measures and definitions, assessment time points, proportions and handling of death, proportions and handling of missing data, and effect measures and statistical methods used for analysis.

Discussion: The outlined scoping review will provide an overview of choices, definitions and handling of patient-important outcomes other than mortality in contemporary RCTs conducted in adult ICU patients. This may guide discussions with patients and relatives, the design of future RCTs, and research on optimal outcome choices and handling.
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http://dx.doi.org/10.1111/aas.13937DOI Listing
June 2021

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

JMIR Med Inform 2021 Apr 5;9(4):e21547. Epub 2021 Apr 5.

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.

Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.

Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.

Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.

Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.

Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
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http://dx.doi.org/10.2196/21547DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023380PMC
April 2021

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Sci Rep 2021 02 5;11(1):3246. Epub 2021 Feb 5.

Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.
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http://dx.doi.org/10.1038/s41598-021-81844-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864944PMC
February 2021

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Sci Rep 2021 02 5;11(1):3246. Epub 2021 Feb 5.

Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.
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http://dx.doi.org/10.1038/s41598-021-81844-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864944PMC
February 2021

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.

Lancet Digit Health 2020 04 12;2(4):e179-e191. Epub 2020 Mar 12.

Department of Intensive Care, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.

Background: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data improved mortality prognostication for patients in the ICU by providing real-time predictions of 90-day mortality. In addition, we examined to what extent such a dynamic model could be made interpretable by quantifying and visualising the features that drive the predictions at different timepoints.

Methods: Based on the Simplified Acute Physiology Score (SAPS) III variables, we trained a machine learning model on longitudinal data from patients admitted to four ICUs in the Capital Region, Denmark, between 2011 and 2016. We included all patients older than 16 years of age, with an ICU stay lasting more than 1 h, and who had a Danish civil registration number to enable 90-day follow-up. We leveraged static data and physiological time-series data from electronic health records and the Danish National Patient Registry. A recurrent neural network was trained with a temporal resolution of 1 h. The model was internally validated using the holdout method with 20% of the training dataset and externally validated using previously unseen data from a fifth hospital in Denmark. Its performance was assessed with the Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (AUROC) as metrics, using bootstrapping with 1000 samples with replacement to construct 95% CIs. A Shapley additive explanations algorithm was applied to the prediction model to obtain explanations of the features that drive patient-specific predictions, and the contributions of each of the 44 features in the model were analysed and compared with the variables in the original SAPS III model.

Findings: From a dataset containing 15 615 ICU admissions of 12 616 patients, we included 14 190 admissions of 11 492 patients in our analysis. Overall, 90-day mortality was 33·1% (3802 patients). The deep learning model showed a predictive performance on the holdout testing dataset that improved over the timecourse of an ICU stay: MCC 0·29 (95% CI 0·25-0·33) and AUROC 0·73 (0·71-0·74) at admission, 0·43 (0·40-0·47) and 0·82 (0·80-0·84) after 24 h, 0·50 (0·46-0·53) and 0·85 (0·84-0·87) after 72 h, and 0·57 (0·54-0·60) and 0·88 (0·87-0·89) at the time of discharge. The model exhibited good calibration properties. These results were validated in an external validation cohort of 5827 patients with 6748 admissions: MCC 0·29 (95% CI 0·27-0·32) and AUROC 0·75 (0·73-0·76) at admission, 0·41 (0·39-0·44) and 0·80 (0·79-0·81) after 24 h, 0·46 (0·43-0·48) and 0·82 (0·81-0·83) after 72 h, and 0·47 (0·44-0·49) and 0·83 (0·82-0·84) at the time of discharge.

Interpretation: The prediction of 90-day mortality improved with 1-h sampling intervals during the ICU stay. The dynamic risk prediction can also be explained for an individual patient, visualising the features contributing to the prediction at any point in time. This explanation allows the clinician to determine whether there are elements in the current patient state and care that are potentially actionable, thus making the model suitable for further validation as a clinical tool.

Funding: Novo Nordisk Foundation and the Innovation Fund Denmark.
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http://dx.doi.org/10.1016/S2589-7500(20)30018-2DOI Listing
April 2020

Effect of Routine Cytochrome P450 2D6 and 2C19 Genotyping on Antipsychotic Drug Persistence in Patients With Schizophrenia: A Randomized Clinical Trial.

JAMA Netw Open 2020 12 1;3(12):e2027909. Epub 2020 Dec 1.

Copenhagen Research Center for Mental Health-CORE, Roskilde, Denmark.

Importance: Genetic polymorphism of genes encoding the drug metabolizing enzymes, cytochrome P450 2D6 and 2C19 (CYP2D6 and CYP2C19), is associated with treatment failure of and adverse reactions to psychotropic drugs. The clinical utility of routine CYP2D6 and CYP2C19 genotyping (CYP testing) is unclear.

Objective: To estimate whether routine CYP testing effects the persistence of antipsychotic drug treatment.

Design, Setting, And Participants: This single-masked, 3-group randomized clinical trial included patients aged 18 years or older who had been diagnosed within the schizophrenic spectrum (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes, F20-F29) and not previously genotyped. A total of 669 of 1406 potentially eligible patients from 12 psychiatric outpatient clinics in Denmark were approached between July 2008 and December 2009. Overall, 528 patients were genotyped and randomly allocated to 1 of 3 study groups or exclusion in a sequence of 1:1:1:3 using a predictive enrichment design, aiming to double the proportion of poor or ultrarapid metabolizers for CYP2D6 or CYP2C19. Outcome measurements were recorded at baseline and 1-year follow-up. Data analysis was performed in December 2012 and updated March 2019.

Interventions: The trial included 2 intervention groups, where antipsychotic drug treatment was guided by either CYP test (CYP test-guided [CTG]) or structured clinical monitoring (SCM), in which adverse effects and factors influencing compliance were systematically recorded at least once quarterly, and 1 control group.

Main Outcomes And Measures: Primary outcome was antipsychotic drug persistence, ie, days to first modification of the initial treatment. Secondary outcomes were number of drug and dose changes, adverse effects, and psychotic symptoms, ie, hallucinations and delusions.

Results: A total of 528 participants were genotyped, and 311 (median [interquartile range {IQR} age, 41 [30-50] years; 139 [45%] women; median [IQR] duration of illness, 6 [3-13] years) were randomly allocated to 1 of 3 study groups. Overall, 61 participants (20%) were extreme metabolizers. There was no difference in antipsychotic drug persistence between the CTG group and the control group (hazard ratio [HR], 1.02; 95% CI, 0.71-1.45) or SCM and the control group (HR, 0.88; 95% CI, 0.61-1.26). Subanalyses among extreme metabolizers showed similar results (CTG: HR, 0.99; 95% CI, 0.48-2.03; SCM: HR, 0.93; 95% CI, 0.44-1.96).

Conclusions And Relevance: The results of this randomized clinical trial do not support routine CYP testing in patients with schizophrenia.

Trial Registration: ClinicalTrials.gov Identifier: NCT00707382.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.27909DOI Listing
December 2020

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.

Nat Commun 2020 10 6;11(1):5009. Epub 2020 Oct 6.

Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.
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http://dx.doi.org/10.1038/s41467-020-18849-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538555PMC
October 2020

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study.

Lancet Rheumatol 2020 Nov 21;2(11):e698-e711. Epub 2020 Aug 21.

Janssen Research and Development, Titusville, NJ, USA.

Background: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the value was less than 0·4.

Findings: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation: Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.
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http://dx.doi.org/10.1016/S2665-9913(20)30276-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442425PMC
November 2020

An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza.

medRxiv 2020 Apr 25. Epub 2020 Apr 25.

Science Policy and Research, National Institute for Health and Care Excellence, UK.

Background: To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza.

Methods: We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014-19.

Results: 6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use.

Conclusions: Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.
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http://dx.doi.org/10.1101/2020.04.22.20074336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239064PMC
April 2020

Heterogeneity of treatment effect of prophylactic pantoprazole in adult ICU patients: a post hoc analysis of the SUP-ICU trial.

Intensive Care Med 2020 04 14;46(4):717-726. Epub 2020 Jan 14.

Department of Intensive Care 4131, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

Purpose: The Stress Ulcer Prophylaxis in the Intensive Care Unit (SUP-ICU) trial compared prophylactic pantoprazole with placebo in 3291 adult ICU patients at risk of clinically important gastrointestinal bleeding (CIB). As a predefined subgroup analysis suggested increased 90-day mortality with pantoprazole in the most severely ill patients, we aimed to further explore whether heterogenous treatment effects (HTE) were present.

Methods: We assessed HTE in subgroups defined according to illness severity by SAPS II quintiles and the total number of risk factors for CIB using Bayesian hierarchical models, and on the continuous scale using Bayesian logistic regression models with interactions. Estimates were presented as posterior probability distributions of odds ratios (ORs), probabilities of different effect sizes, and marginal effects plots.

Results: We observed potential HTE for 90-day mortality according to illness severity (median subgroup OR range 0.90-1.09) with higher risk in the most severely ill, but not with different numbers of risk factors (1.00-1.02). We observed potential HTE of pantoprazole for clinically important events (0.86-1.18) and infectious adverse events (0.88-1.27) with higher risk in patients with greater illness severity and in those with more risk factors for CIB. Pantoprazole substantially and consistently reduced the risk of CIB with no indications of HTE (0.53-0.63).

Conclusions: In this post hoc analysis of the SUP-ICU trial, we found indications of HTE with increased risks of serious adverse events in patients with greater illness severity or more risk factors for CIB allocated to pantoprazole. These findings are hypothesis-generating and warrant further prospective investigation. CLINICALTRIALS.

Gov Identifier: NCT02467621.
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http://dx.doi.org/10.1007/s00134-019-05903-8DOI Listing
April 2020

Different Original and Biosimilar TNF Inhibitors Similarly Reduce Joint Destruction in Rheumatoid Arthritis-A Network Meta-Analysis of 36 Randomized Controlled Trials.

Int J Mol Sci 2019 Sep 5;20(18). Epub 2019 Sep 5.

Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Munkesøvej 18, 4000 Roskilde, Denmark.

The effect of five approved tumour necrosis factor inhibitors (TNFi: infliximab, etanercept, adalimumab, certolizumab, and golimumab) on joint destruction in rheumatoid arthritis (RA) have been compared versus methotrexate (MTX) in randomized controlled trials (RCTs) but have not been compared directly to each other or to an otherwise untreated placebo control. The present analysis compares effects of standard doses, high doses, and low doses of TNFis on radiographic joint destruction in RA and relate these effects to MTX and placebo by means of a Bayesian network meta-analysis. We identified 31 RCTs of the effect of TNFis on joint destruction and 5 RCTs with controls, which indirectly could link otherwise untreated placebo controls to the TNFi treatments in the network. The previously untested comparison with placebo was performed to estimate not only the effect relative to another drug, but also the absolute attainable effect. Compared to placebo there was a highly significant inhibitory effect on joint destruction of infliximab, etanercept, adalimumab, certolizumab, and golimumab, which was about 0.9% per year as monotherapy and about 1.2% per year when combined with MTX. Although significantly better than MTX and placebo, golimumab seemed inferior to the remaining TNFis. There was no difference between original reference drugs (Remicade, Enbrel) and the almost identical copy drugs (biosimilars).
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http://dx.doi.org/10.3390/ijms20184350DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770755PMC
September 2019