Search company, investor...

Castor EDC

Founded Year



Series B | Alive

Total Raised


Last Raised

$45M | 2 yrs ago

About Castor EDC

Castor EDC is a medical research data platform. It provides a decentralized clinical trial (DCT) solutions to control clinical trial design. It analyzes, manages, and organizes medical data collected from multiple electronic records. the company was founded in 2011 and is based in New York, New York.

Headquarters Location

175 Varick Street Ground Floor

New York, New York, 10014,

United States

Castor EDC's Product Videos

ESPs containing Castor EDC

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

Healthcare & Life Sciences / Drug R&D Tech

The randomization & trial supply management (RTSM) market provides a range of tools and software for managing clinical trials, including randomization and trial supply management. These systems can help ensure that clinical trials are conducted in a randomized and controlled manner, which is essential for producing reliable and unbiased results. RTSM systems can also help manage the distribution a…

Castor EDC named as Outperformer among 9 other companies, including Medrio, Clario, and MedNet Solutions.

Compete with Castor EDC?

Ensure that your company and products are accurately represented on our platform.

Castor EDC's Products & Differentiators


    Create an effortless experience for your patients with an all-in-one module to remotely recruit, screen and consent patients from the comfort of their home, onsite - or both.

Expert Collections containing Castor EDC

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Castor EDC is included in 6 Expert Collections, including Clinical Trials Tech.


Clinical Trials Tech

341 items

Companies developing products and services to streamline drug R&D, from drug discovery, pre-clinical testing, and clinical trials.


Digital Health

10,348 items

The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.



2,856 items

Companies developing, offering, or using electronic and telecommunication technologies to facilitate the delivery of health & wellness services from a distance. *Columns updated as regularly as possible; priority given to companies with the most and/or most recent funding.


Clinical Trials Tech Market Map

108 items

This CB Insights Tech Market Map highlights 100+ clinical trials tech companies that are addressing 8 distinct technology priorities that pharmaceutical companies and CROs face.


Digital Health 150

150 items

The winners of the third annual CB Insights Digital Health 150.


Biopharma Tech

326 items

Latest Castor EDC News

Optimal Dosing and Timing of High-Dose Corticosteroid Therapy in Hospitalized Patients With COVID-19: Study Protocol for a Retrospective Observational Multicenter Study (SELECT)

Jun 2, 2023

April 14, 2023 . Optimal Dosing and Timing of High-Dose Corticosteroid Therapy in Hospitalized Patients With COVID-19: Study Protocol for a Retrospective Observational Multicenter Study (SELECT) Optimal Dosing and Timing of High-Dose Corticosteroid Therapy in Hospitalized Patients With COVID-19: Study Protocol for a Retrospective Observational Multicenter Study (SELECT) Authors of this article: 2Department of Viroscience, Erasmus University Medical Center, Rotterdam, Netherlands 3Department of Infectious Diseases, Public Health Service of Amsterdam, Amsterdam, Netherlands 4HIV Monitoring Foundation, Amsterdam, Netherlands 5Infectious Diseases, Amsterdam University Medical Centers, location University of Amsterdam, Amsterdam, Netherlands 6Department of Intensive Care, Amsterdam University Medical Centers, location Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands 7Laboratory of Experimental Intensive Care and Anesthesiology, Amsterdam University Medical Centers, location Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands 8Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Centers, location University of Amsterdam, Amsterdam, Netherlands 9Department of Internal Medicine and Intensive Care, St Antonius Hospital, Nieuwegein, Netherlands 10Department of Intensive Care, Maasstad Ziekenhuis, Rotterdam, Netherlands 11Department of Intensive Care, Onze Lieve Vrouwe Gasthuis Hospital, Amsterdam, Netherlands 12Laboratory of Translational Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands 13Department of Pulmonology, Amphia Hospital, Breda, Netherlands 14Department of Intensive Care, Amphia Hospital, Breda, Netherlands 15Department of Anesthesiology and Intensive Care, Isala Clinics, Zwolle, Netherlands 16Department of Intensive Care, University Medical Center Groningen, University of Groningen, Groningen, Netherlands 17Department of Pathology and Medical Biology, Groningen Research Institute for Asthma and Chronic Obstructive Pulmonary Disease, University Medical Center Groningen, University of Groningen, Groningen, Netherlands 18Department of Anesthesiology, Amsterdam University Medical Centers, location Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands 19Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands 20Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands 21Division of Allergy & Clinical Immunology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands 22Department of Internal Medicine, Erasmus University Medical Center, Erasmus, Netherlands 23See Acknowledgements Abstract Background: In hospitalized patients with COVID-19, the dosing and timing of corticosteroids vary widely. Low-dose dexamethasone therapy reduces mortality in patients requiring respiratory support, but it remains unclear how to treat patients when this therapy fails. In critically ill patients, high-dose corticosteroids are often administered as salvage late in the disease course, whereas earlier administration may be more beneficial in preventing disease progression. Previous research has revealed that increased levels of various biomarkers are associated with mortality, and whole blood transcriptome sequencing has the ability to identify host factors predisposing to critical illness in patients with COVID-19. Objective: Our goal is to determine the most optimal dosing and timing of corticosteroid therapy and to provide a basis for personalized corticosteroid treatment regimens to reduce morbidity and mortality in hospitalized patients with COVID-19. Methods: This is a retrospective, observational, multicenter study that includes adult patients who were hospitalized due to COVID-19 in the Netherlands. We will use the differences in therapeutic strategies between hospitals (per protocol high-dose corticosteroids or not) over time to determine whether high-dose corticosteroids have an effect on the following outcome measures: mechanical ventilation or high-flow nasal cannula therapy, in-hospital mortality, and 28-day survival. We will also explore biomarker profiles in serum and bronchoalveolar lavage fluid and use whole blood transcriptome analysis to determine factors that influence the relationship between high-dose corticosteroids and outcome. Existing databases that contain routinely collected electronic data during ward and intensive care admissions, as well as existing biobanks, will be used. We will apply longitudinal modeling appropriate for each data structure to answer the research questions at hand. Results: As of April 2023, data have been collected for a total of 1500 patients, with data collection anticipated to be completed by December 2023. We expect the first results to be available in early 2024. Conclusions: This study protocol presents a strategy to investigate the effect of high-dose corticosteroids throughout the entire clinical course of hospitalized patients with COVID-19, from hospital admission to the ward or intensive care unit until hospital discharge. Moreover, our exploration of biomarker and gene expression profiles for targeted corticosteroid therapy represents a first step towards personalized COVID-19 corticosteroid treatment. Trial Registration: NCT05403359; International Registered Report Identifier (IRRID): DERR1-10.2196/48183 JMIR Res Protoc 2023;12:e48183 >6 aThis table shows the equivalent corticosteroids and dosages. The equivalent dose is derived from Farmacotherapeutisch Kompas [ 18 ]. Study Design and Setting The SELECT study is a retrospective cohort study with data from 22 hospitals in the Netherlands, including 4 academic and 18 nonacademic hospitals ( Figure 2 ). Other hospitals may be included as the study progresses and we will use routinely collected electronic data from the participating hospitals. The data sets will contain data on hospitalized patients with COVID-19 from the date the first patient was admitted to the hospital in the Netherlands (March 1, 2020) until data transfer (expected May-December 2023). We will use the heterogeneity in therapeutic regimens in time (ie, first, second, and third epidemiological wave), centers (per protocol high-dose corticosteroids or not), and individual patients in the initial databases to address our aims. In a subpopulation of patients, we will use biobank material collected within the Collaboration on Identification, Understanding and Improved Management of patients with infectious diseases study of the Erasmus MC or collected at the Amsterdam University Medical Center (UMC) within the MERMAIDS-ARI, VIS cohort study or the Amsterdam UMC COVID-19 biobank. ‎ Figure 2. Participating hospitals of the Steroids in Hospitalized Patients with COVID-19 in The Netherlands (SELECT) study. UMC: University Medical Center. MC: Medical Center; OLVG: Onze Lieve Vrouwe Gasthuis. Patient Selection and Inclusion Criteria Adult patients (≥18 years) hospitalized with polymerase chain reaction–confirmed COVID-19 or high clinical suspicion of COVID-19 will be included ( Figure 3 ). Pregnant patients and patients receiving corticosteroids for other conditions during or prior to hospital admission are eligible, although a prespecified sensitivity analysis excluding these patients will be performed. Patients who die within 48 hours after admission or patients who object to participate will be excluded from the study. In WP1 and WP4, we will include patients from all SARS-CoV-2 epidemic waves, and in WP2-3, we will include patients from the first and second waves only. Specific inclusion and exclusion criteria for each work package will be applied. In WP1A, patients will be included who are admitted to the ward with a World Health Organization clinical progression scale class of 4-5 [ 19 ] (ie, no oxygen or oxygen therapy via a cannula or [non-rebreather] mask). WP1B includes patients who have progressed to the World Health Organization clinical progression scale class of 5-6 (ie, noninvasive oxygen therapy, including conventional oxygen via cannula [non-rebreather] mask, high-flow nasal cannula, noninvasive continuous positive airway pressure, and noninvasive bilevel positive airway pressure) after in-hospital treatment with 10 days of 6 mg per day dexamethasone. The study population for WP2-4 consists of patients admitted to the ICU for a period exceeding 48 hours, who received invasive mechanical ventilation via an endotracheal tube, a tracheostomy, or extracorporeal membrane oxygenation during their ICU stay. ‎ Figure 3. Patient inclusion. World Health Organization (WHO) classification: 0=uninfected, 1=asymptomatic; viral RNA detected, 2=symptomatic; independent, 3=symptomatic; assistance needed, 4=hospitalized; oxygen by mask or nasal prongs, 5=hospitalized; oxygen by mask or nasal prongs, 6=hospitalized; oxygen by noninvasive ventilation or high flow, 7=intubation and mechanical ventilation partial pressure of oxygen in arterial blood–fraction of inspiratory oxygen concentration (PaO2/FiO2) ratio of ≥150, 8=mechanical ventilation PaO2/FiO2 ratio of <150 or vasopressors, 9=mechanical ventilation PaO2/FiO2 ratio of <150 and vasopressors, dialysis or extracorporeal membrane oxygenation, 10=dead. PCR: polymerase chain reaction; WP: work package. Ethics Approval and Consent to Participate The study will be conducted according to the principles of the Declaration of Helsinki (64th World Medical Association General Assembly, Fortaleza, Brazil, October 2013) and in accordance with Erasmus MC Research code, Netherlands Code of Conduct for Research Integrity, General Data Protection Regulation, Code of Conduct for Health Research (not subjected to the Medical Research Involving Human Subjects Act [non-WMO]), Code of Conduct for Responsible Use of Human Tissue (Non-WMO), and Medical Treatment Contracts Act. The study was approved by the Medical Research Ethics Committee from Erasmus University Medical Center (MEC-2022-0297) and the local review boards of all other participating centers. Regarding informed consent procedure: within the Erasmus MC, a consent waiver was provided by the Medical Research Ethics Committee for research including patients with COVID-19 due to the pandemic (exception due to emergency situation). Therefore, an opt-out informed consent procedure will be used in the participating centers, and subjects with registered objections to participate will be excluded from the study. This opt-out registration method was approved by the Medical Research Ethics Committee from Erasmus University Medical Center (MEC-2022-0297). Each center is responsible for appropriate informed consent procedures, which are covered in the data transfer agreements. Regarding the biobank data, only biobank material collected within the Collaboration on Identification, Understanding and Improved Management of patients with infectious diseases study of the Erasmus MC (METC number primary application: 2017-0417 and amendment 2020-0222) and samples collected at the Amsterdam UMC within the MERMAIDS-ARI (NL54834.018.15), VIS cohort study (NL73759.018.20), and the Amsterdam UMC COVID-19 biobank will be used. Data Collection In WP1, variation in corticosteroid treatment protocols on the ward between centers is evaluated via questionnaires and extraction of data from the electronic patient record. The focus lies on collecting data from centers that use high-dose corticosteroids on the ward directly after admission and subsequently collecting data from centers with low-dose regimens as a comparison. For the data collection of ICU patients (WP2-3), a collaboration with 2 large COVID-19 study groups is started: the Practice of Ventilation in COVID-19 study (ie, PROVENT-COVID) study and the “Practice of Adjunctive Treatments in Intensive Care Unit Patients with Coronavirus Disease 2019” (PROACT-COVID) study [ 20 , 21 ]. The data of interest will be collected in Castor EDC [ 22 ], and it will be imported from each center via a secured data transfer system. Corticosteroid data will be collected on a daily basis including type, dose, and administration route. Regarding biobank WP4, a number of serum biomarker levels were already determined during standard COVID-19 care. In case of missing measurements, the stored samples (Tempus tubes) in the biobank can be used. The BALF was collected during routine COVID-19 care in the Amsterdam University Medical Centers and saved in aliquots at −80 °C for further analysis. SARS-CoV-2 viral load will be measured in BALF by using an in-house reverse transcription polymerase chain reaction. Subsequently, other commercially and generally available biomarkers might be determined in serum and BALF later by using a multiplex assay, such as Luminex. For the whole genome transcriptomes analysis, material was collected and saved in blood RNA Tempus tubes. Interventions Patients were administered with >6 mg dexamethasone or an equivalent corticosteroid for a minimum of 3 consecutive days ( Table 1 ). Our study permits the use of other general concurrent interventions, such as venous thromboembolism prophylaxis, fever and glucose control, fluid management, antibiotics, and antiviral drugs. The use of other immunomodulatory agents such as baricitinib, JAK inhibitors, and interleukin 6 pathway inhibitors, as well as antiviral drugs such as remdesivir, is allowed as part of a clinical trial or hospital policy. However, a sensitivity analysis will be performed excluding these patients. Outcomes Primary Outcomes Several outcome parameters will be studied for the different work packages based on the available data. In WP1A, the primary outcome of interest is hospital mortality in patients who do not receive high-flow nasal canule or invasive mechanical ventilation due to restrictions in care, either on medical grounds or advance directives of the patient. In patients who do not have restrictions in care, the need for high-flow nasal canule or invasive mechanical ventilation will be the primary outcome of interest (WHO severity 6-9). In WP1B, WP2, and WP3, 28-day survival and 28-day need for invasive mechanical ventilation will be studied as primary outcomes. In WP4, we will study the response to corticosteroids using the same outcomes. Secondary Outcomes Secondary outcomes for the ICU work packages include ICU mortality; in-hospital mortality; hospital length of stay (the number of days from the date of ICU admission to date of ICU discharge or death); mechanical ventilation duration (total duration of mechanical ventilation in days); and ventilator-free days, and alive at day 28. In all 4 work packages, we will evaluate the incidence of general systemic complications during the hospital stay as a secondary outcome, including myocardial infarction, deep venous thrombosis, pulmonary embolus, hyperglycemia, acute kidney injury, and sepsis. The definition of the various general systemic complications is shown in Table S1 in Multimedia Appendix 1 . Statistical Analysis Data will be analyzed on a convenience sample. No sample size calculations are considered for the study nor will they be provided post hoc [ 23 ]. The use of high-dose corticosteroids will be defined as a dichotomous variable (yes or no) in primary analysis and as a continuous variable in secondary analysis. To study between-center variation in corticosteroid dosing strategy, the proportion of individuals who received high-dose corticosteroids will be compared across hospitals. Determinants of receiving high-dose corticosteroids will be evaluated using a multivariable logistic regression model with study center forced in the model. Whether the effects of significant determinants are different across centers will be tested through a determinant × center interaction term added for each determinant, separately. In WP1, WP2, and WP3, several statistical analysis approaches will be considered to determine the association between high-dose corticosteroids and outcome, and statistical analyses will depend on the structure of the available data. A multivariable logistic regression model will be used with high-dose corticosteroids as exposure and survival at day 28 as an outcome. Covariates used for adjustment will include age, gender, BMI, smoking status, prior administration of corticosteroids during admission, comorbidity, sequential organ failure assessment score, oxygen saturation, partial pressure of oxygen in arterial blood–fraction of inspiratory oxygen concentration ratio, C-reactive protein, procalcitonin, ferritin, interleukin 6, D-dimer, lactate dehydrogenase, platelet count, lymphopenia, and level of respiratory support. Covariates can be added or removed based on insights from the data set or from the literature. A model might be constructed in which patients are matched on a propensity score based on their probability to receive high-dose corticosteroid treatment. Propensity scores will be determined using a logistic regression model with high-dose corticosteroids as a dependent variable and determinants of high-dose corticosteroids as independent variables. After matching, survival and mortality at day 28 and other outcome parameters will be compared between matched groups using logistic regression. A Cox proportional hazards model might be performed to determine the effect of high-dose corticosteroids on time to death while taking into account the days of administration using time-dependent covariates. Other approaches involving censoring weights for differential loss to follow-up (eg, marginal structural models) or competing risks (eg, joint models) might be explored. Furthermore, we will use clinical and biological data to perform latent class analysis to search for subphenotypes, which respond more favorably to corticosteroid treatment. This heterogeneity of treatment effect will be evaluated by including the latent class analysis-derived subphenotype, corticosteroid exposure, and an interaction term of the 2 as c-variate in the above-described survival models. Heterogeneity of treatment effect is considered to be present if the interaction term has a P value of <.05. Regarding WP4, patients are randomly selected and cases will be patients treated with high-dose corticosteroids, whereas controls will be patients who were not treated with high-dose corticosteroids. In WP4a, inverse probability treatment weighting and inverse probability of censoring weights will be used together in a marginal structural model. After we have analyzed the effect of high-dose corticosteroids on mortality and the other outcome measures, we will use this model to determine if the listed biomarkers modify the effect of high-dose corticosteroids on mortality. In WP4b, we will identify transcriptome features that are differentially altered between cases and controls. Mean transcriptome levels will be compared using an unpaired t-test and P values will be adjusted for false-discovery rate using the Benjamini-Hochberg procedure. The log2 mean fold change of levels between cases and controls will also be calculated for each transcriptome. We will define transcriptome features as those with an adjusted P value of <.05 and a >2.0 or <–2.0 log2 mean fold change in level. Results The study was funded in December 2021. As of April 2023, data have been collected for a total of 1500 patients, with data collection anticipated to be completed by December 2023. We expect the first results to be available in early 2024. Discussion The SELECT study is a retrospective multicenter observational cohort study that aims to determine the most optimal dosing and timing of high-dose corticosteroid therapy in hospitalized patients with COVID-19 and to provide a basis for personalized treatment regimens using biomarkers and whole transcriptome analyses. Increasing insight in the pathophysiological pathways that underlie SARS-CoV-2 infection and the efficacy of high-dose corticosteroid therapy could lead to more precise targeting of high-dose corticosteroid therapy, and as a result, could improve patient outcomes. The necessity of exploring this matter is underlined by the fact that there is currently no consensus regarding the administration of high-dose corticosteroid therapy in patients with COVID-19. This study design has several strengths. First, no studies have been performed that evaluate the effect of high-dose corticosteroids throughout the entire clinical course of hospitalized patients with COVID-19. Our study will evaluate the effect of high-dose corticosteroids from hospital admission to the ward or ICU, until hospital discharge. Furthermore, our exploration of biomarker and gene expression profiles for targeted corticosteroid therapy represents a first step toward personalized corticosteroid therapy in COVID-19. By using existing research databases and biobanks, successful data collection is assured, and we will be able to include a large number of patients, which increases the generalizability of the results. Due to the retrospective study design, some methodological considerations require further discussion. A major issue for any observational study is the fact that treatment modalities are not randomly assigned, but rather likely given based on specific patient characteristics or within-center protocols. This nonrandomized assignment is prone to bias by indication. To correct this bias, our initial analysis step will be the identification of factors that contribute to the allocation of corticosteroid treatment. Subsequently, we will examine several statistical approaches, such as the use of propensity scores at the patient level to minimize treatment indication bias. The findings of this study could support COVID-19 guidelines and hospital protocols regarding the management of hospitalized patients with COVID-19. Thus far, the translation of predictive biomarkers and whole transcriptome analysis into an applicable bedside tool is highly challenging. We expect to provide valuable tools to guide the dosing and timing of high-dose corticosteroid therapy. Furthermore, the answers to our research questions might be extrapolated to patients with non–COVID-19 ARDS, as the use of high-dose corticosteroids is still a matter of debate in this group of patients. The mortality rate of non–COVID-19 ARDS is even higher than that of COVID-19 ARDS [ 24 ], emphasizing the need for improved treatment strategies. Nonetheless, more efforts are required to determine similarities and differences between patients with COVID-19 ARDS and those with non–COVID-19 ARDS to determine whether extrapolation from one group to the other is possible. In conclusion, our study protocol presents a strategy to gain a better understanding of the optimal dosing and timing of high-dose corticosteroid therapy in hospitalized patients with COVID-19 and thereby improve patient outcomes. Acknowledgments We gratefully acknowledge all site investigators of the participating centers. PROVENT-COVID Collaborators (in alphabetic order): S Ahuja; JP van Akkeren; AG Algera; CK Algoe; RB van Amstel; P van de Berg; DC Bergmans; DI van den Bersselaar; FA Bertens; AJ Bindels; JS Breel; CL Bruna; MM de Boer; S den Boer; LS Boers; M Bogerd; LD Bos; M Botta; OL Baur; H de Bruin; LA Buiteman-Kruizinga; O Cremer; RM Determann; W Dieperink; Jv Dijk; DA Dongelmans; MJ de Graaff; MS Galekaldridge; LA Hagens; JJ Haringman; ST van der Heide; PL van der Heiden; LL Hoeijmakers; L Hol; MW Hollmann; J Horn; R van der Horst; EL Ie; D Ivanov; NP Juffermans; E Kho; ES de Klerk; AW Koopman; M Koopmans; S Kucukcelebi; MA Kuiper; DW de Lange; I Martin-Loeches; G Mazzinari; DM van Meenen; N van Mourik; SG Nijbroek; EA Oostdijk; F Paulus; CJ Pennartz; J Pillay; IM Purmer; TC Rettig; O Roca; JP Roozeman; MJ Schultz; A Serpa Neto; C Sivakorn; GS Shrestha; ME Sleeswijk; PE Spronk; AC Strang; W Stilma; P Swart; A Tri; AM Tsonas; CMA Valk; AP Vlaar; LI Veldhuis; WH van der Ven; P van Velzen; P van Vliet; P van der Voort; L van Welie; B van Wijk; T Winters; WY Wong; AR van Zanten. PROACT-COVID Collaborators (in alphabetic order): E Aydeniz; P van de Berg; DC Bergmans; M Bevers; S den Boer; LS Boers; LD Bos; M Botta; LA Buiteman-Kruizinga; W Coene; M Delmte; Vincenzo Di Leo; DA Dongelmans; TP Dormans; LM Elting; AA Esmeijer; M Gama de Abreu; AR Girbes; MJ de Graaff; DM Go; RL Goossen; HJ Hansen; JJ Haringman; L Hol; MW Hollmann; PL van der Heiden; J Horn; LE van Ingen; NP Juffermans; MA Kuiper; LJ Kuipers; E Koornstra; A Lokhorst; SG Nijbroek; I Martin–Loeches; G Mazzinari; S Myatra; F Paulus; M Offermans; T Pisters; A Prins; P van Oosten; J Pillay; IM Purmer; AS Rezaee; TCD Rettig; O Roca; NM Rosenberg; N Schavemaker; AA Sciascera; MJ Schultz; A Serpa Neto; G Shrestha; ME Sleeswijk; W Stilma; AC Strang; PE Spronk; PR Tuinman; AM Tsonas; CMA Valk; M Verboom; AP Vlaar; FL van der Ven; WH van der Ven; P van Velzen; EJ Verhoef; TD Vermeulen; P van Vliet; JJ Voorham; PH van der Voort; M van der Woude; Weiner; N Yaali; JM Zandvliet; AR van Zanten; TZ van Zijl; and SA Zonneveld. The SELECT study received an unrestricted research grant from ZonMw (grant number 0430102110010). The funders of this study did not play any role in the design of the study, nor in the writing of this manuscript or the decision to publish. Data Availability The data sets used and analyzed during the current study are available from the corresponding author upon reasonable request. Authors' Contributions HE, JS, LDJB, and AB designed the study and arranged funding. AB outlined the statistical analysis. KD, JAH, and VASHD drafted the manuscript. All authors critically revised and approved this final manuscript. Conflicts of Interest WHO coronavirus (COVID-19) dashboard. World Health Organization. URL: [accessed 2023-03-16] Lamontagne F, Agarwal A, Rochwerg B, Siemieniuk RA, Agoritsas T, Askie L, et al. A living WHO guideline on drugs for covid-19. BMJ 2020;370:m3379 [ CrossRef ] [ Medline ] De Vries HJ, Endeman H, van der Hoeven JG, Heunks LMA. Lung-protective mechanical ventilation in patients with COVID-19. Neth J Crit Care 2020;28(3):120-124 [ ] Leisman DE, Ronner L, Pinotti R, Taylor MD, Sinha P, Calfee CS, et al. Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir Med 2020;8(12):1233-1244 [ ] [ CrossRef ] [ Medline ] Nossent EJ, Schuurman AR, Reijnders TDY, Saris A, Jongerius I, Blok SG, et al. Pulmonary procoagulant and innate immune responses in critically ill COVID-19 patients. Front Immunol 2021;12:664209 [ ] [ CrossRef ] [ Medline ] ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA 2012;307(23):2526-2533 [ CrossRef ] [ Medline ] Figliozzi S, Masci PG, Ahmadi N, Tondi L, Koutli E, Aimo A, et al. Predictors of adverse prognosis in COVID-19: a systematic review and meta-analysis. Eur J Clin Invest 2020;50(10):e13362 [ CrossRef ] [ Medline ] Tong-Minh K, van der Does Y, Engelen S, de Jong E, Ramakers C, Gommers D, et al. High procalcitonin levels associated with increased intensive care unit admission and mortality in patients with a COVID-19 infection in the emergency department. BMC Infect Dis 2022;22(1):165 [ ] [ CrossRef ] [ Medline ] Annane D, Pastores SM, Rochwerg B, Arlt W, Balk RA, Beishuizen A, et al. Guidelines for the diagnosis and management of critical illness-related corticosteroid insufficiency (CIRCI) in critically ill patients (Part I): Society of Critical Care Medicine (SCCM) and European Society of Intensive Care Medicine (ESICM) 2017. Intensive Care Med 2017;43(12):1751-1763 [ CrossRef ] [ Medline ] RECOVERY Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, et al. Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med 2021;384(8):693-704 [ ] [ CrossRef ] [ Medline ] Azoulay E, de Waele J, Ferrer R, Staudinger T, Borkowska M, Povoa P, et al. International variation in the management of severe COVID-19 patients. Crit Care 2020;24(1):486 [ ] [ CrossRef ] [ Medline ] Granholm A, Munch MW, Myatra SN, Vijayaraghavan BKT, Cronhjort M, Wahlin RR, et al. Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia: a pre-planned, secondary Bayesian analysis of the COVID STEROID 2 trial. Intensive Care Med 2022;48(1):45-55 [ ] [ CrossRef ] [ Medline ] Bouadma L, Mekontso-Dessap A, Burdet C, Merdji H, Poissy J, Dupuis C, COVIDICUS Study Group. High-dose dexamethasone and oxygen support strategies in intensive care unit patients with severe COVID-19 acute hypoxemic respiratory failure: the COVIDICUS randomized clinical trial. JAMA Intern Med 2022;182(9):906-916 [ CrossRef ] [ Medline ] Pinzón MA, Ortiz S, Holguín H, Betancur JF, Cardona Arango D, Laniado H, et al. Dexamethasone vs methylprednisolone high dose for Covid-19 pneumonia. PLoS One 2021;16(5):e0252057 [ ] [ CrossRef ] [ Medline ] COVID STEROID 2 Trial Group, Munch MW, Myatra SN, Vijayaraghavan BKT, Saseedharan S, Benfield T, et al. Effect of 12 mg vs 6 mg of Dexamethasone on the number of days alive without life support in adults With COVID-19 and severe hypoxemia: the COVID STEROID 2 randomized trial. JAMA 2021;326(18):1807-1817 [ ] [ CrossRef ] [ Medline ] Tan RSJ, Ng KT, Xin CE, Atan R, Yunos NM, Hasan MS. High-dose versus low-dose corticosteroids in COVID-19 patients: a systematic review and meta-analysis. J Cardiothorac Vasc Anesth 2022;36(9):3576-3586 [ ] [ CrossRef ] [ Medline ] Meduri GU, Headley S, Tolley E, Shelby M, Stentz F, Postlethwaite A. Plasma and BAL cytokine response to corticosteroid rescue treatment in late ARDS. Chest 1995;108(5):1315-1325 [ CrossRef ] [ Medline ] Farmacotherapeutisch Kompas. Zorginstituut Nederland. URL: [accessed 2023-05-11] WHO Working Group on the Clinical Characterisation Management of COVID-19 infection. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis 2020;20(8):e192-e197 [ ] [ CrossRef ] [ Medline ] Botta M, Tsonas AM, Pillay J, Boers LS, Algera AG, Bos LDJ, PRoVENT-COVID Collaborative Group. Ventilation management and clinical outcomes in invasively ventilated patients with COVID-19 (PRoVENT-COVID): a national, multicentre, observational cohort study. Lancet Respir Med 2021;9(2):139-148 [ ] [ CrossRef ] [ Medline ] Valk CMA, Swart P, Boers LS, Botta M, Bos LDJ, Gama de Abreu M, et al. Practice of adjunctive treatments in critically ill COVID-19 patients-rational for the multicenter observational PRoAcT-COVID study in The Netherlands. Ann Transl Med 2021;9(9):813 [ ] [ CrossRef ] [ Medline ] Castor electronic data capture. Castor EDC. 2019. URL: [accessed 2023-04-28] Levine M, Ensom MH. Post hoc power analysis: an idea whose time has passed? Pharmacotherapy 2001;21(4):405-409 [ CrossRef ] [ Medline ] Villar J, Blanco J, Añón JM, Santos-Bouza A, Blanch L, Ambrós A, ALIEN Network. The ALIEN study: incidence and outcome of acute respiratory distress syndrome in the era of lung protective ventilation. Intensive Care Med 2011;37(12):1932-1941 [ CrossRef ] [ Medline ] ‎ UMC: University Medical Center WP: work package Edited by T Leung; The proposal for this study was peer reviewed by the COVID-19 Program - The Netherlands Organisation for Health Research and Development / ZorgOnderzoek Nederland (ZON) and the area Medical Sciences (MW) (ZonMw, Netherlands). See the Multimedia Appendix for the peer-review report; submitted 14.04.23; accepted 24.04.23; published 02.06.23 ©Katrijn Daenen, Jilske A Huijben, Anders Boyd, Lieuwe D J Bos, Sara C M Stoof, Hugo van Willigen, Diederik A M P J Gommers, Hazra S Moeniralam, Corstiaan A den Uil, Nicole P Juffermans, Merijn Kant, Abraham J Valkenburg, Janesh Pillay, David M P van Meenen, Frederique Paulus, Marcus J Schultz, Virgil A S H Dalm, Eric C M van Gorp, Janke Schinkel, Henrik Endeman, PRoVENT- and PRoAcT-COVID Collaborative Group. Originally published in JMIR Research Protocols (, 02.06.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.

Castor EDC Frequently Asked Questions (FAQ)

  • When was Castor EDC founded?

    Castor EDC was founded in 2011.

  • Where is Castor EDC's headquarters?

    Castor EDC's headquarters is located at 175 Varick Street Ground Floor, New York.

  • What is Castor EDC's latest funding round?

    Castor EDC's latest funding round is Series B.

  • How much did Castor EDC raise?

    Castor EDC raised a total of $64.38M.

  • Who are the investors of Castor EDC?

    Investors of Castor EDC include INKEF Capital, Two Sigma Ventures, F-Prime Capital, Eight Roads Ventures, Hambrecht Ducera Growth Ventures and 3 more.

  • Who are Castor EDC's competitors?

    Competitors of Castor EDC include Lindus Health, BEKHealth, OpenClinica, Curebase, Reify Health, Medable, Saama Technologies, TrialSpark, Hawthorne Effect, TrialScope and 14 more.

  • What products does Castor EDC offer?

    Castor EDC's products include eConsent and 2 more.

Compare Castor EDC to Competitors

Medable Logo

Medable operates as a global platform for decentralized clinical trials. It solves the systemic challenges inherent in modern clinical trials including access, interoperability between systems, and inefficient technology experiences. It was formerly known as Dermatrap. It was founded in 2012 and is based in Palo Alto, California.

Curebase Logo

Curebase provides decentralized clinical research software solutions. It reduces recruitment times, automates manual steps, and lets drug companies distribute their trials to clinics. It was founded in 2017 and is based in San Francisco, California.


ObvioHealth develops ClaimIt, a proprietary digital platform that utilizes a 100% online and mobile app interface to execute clinical trials. ClaimIt removes the costly overhead of physical site visits and brings the trial directly to the mobile device of each subject.


Bloqcube develops cloud-based software enabling distributed and remote clinical trials with storage and real-time reporting of clinical data. It focuses on accelerating clinical trials using modern technology and built integrated clinical trials management and finance systems that enable life science companies to automate trials, monitor in real-time, and deliver immutable data, and more. The company was founded in 2017 and is based in Piscataway, New Jersey.

Evidation Logo

Evidation operates as a multichannel health tracking platform. It enables users to connect with multiple appications to track everyday activity and earn rewards for steps, sleep, surveys, and more. The company was founded in 2012 and is based in San Mateo, California.


THREAD is a virtual research platform used by biopharma, CROs, non-profit researchers, and life science organizations to capture global clinical study data in between, as well as during and instead of clinic visits.

Discover the right solution for your team

The CB Insights tech market intelligence platform analyzes millions of data points on vendors, products, partnerships, and patents to help your team find their next technology solution.

Request a demo

CBI websites generally use certain cookies to enable better interactions with our sites and services. Use of these cookies, which may be stored on your device, permits us to improve and customize your experience. You can read more about your cookie choices at our privacy policy here. By continuing to use this site you are consenting to these choices.