Dr Yên-Lan Nguyen
email: yen-lan.nguyen@aphp.fr

Gilles Hejblum

Bertrand Guidet

CUB-Réa, ECCRN, EDUSepsis, GiVITI, InFACT group, NICE, OUTCOMERéa, PROSAFE, REVA, SCCTG, SEPNET, USCIITG Bricnet

Structural features shared by ICUs belonging to research networks An international survey. "Critical care research network survey"

Keywords: critical care, health services research, organizations The critical care research network survey

Structural features shared by ICUs belonging to research networks an international survey. "Critical care research network survey"

Yên-Lan Nguyen, Gilles Hejblum, Bertrand Guidet

Members of the Health Services and Research Section of the European Society of ntensive Care Medicine (ES CM) and the "Critical care research network survey" collaboration Group are listed in the acknowledgement section.

Background

Thirty years ago, critical care research was dominated by industry supported or individual researcher led studies. Following the oncology or cardiology research networks models, the Canadian Critical Care Trial Group (CCCTG), created in 1989, has gathered academic researchers physically separated, whose aim was to implement collegially a research agenda driven by the interests and needs of clinicians. The originality of CCCTG was to follow a programmatic model of clinical research that includes both integrated series of investigations to determine best practices, the development of guidelines, translational research strategies and new research questions [START_REF] Marshall | Investigator-led clinical research consortia: the Canadian Critical Care Trials Group[END_REF]. The successes of CCCTG in terms of funding, studies completion and publications may have prompted the creation of other critical care research networks (2)(3).

The main advantages of conducting a trial within a research network are to benefit from a professional development opportunity, a mentorship by experienced researchers, to enhance funding opportunities and to speed up recruitment. Nowadays, most of the large observational or experimental acute care studies are conducted through critical care research networks (CCRN). The results of these studies contribute widely to the construction and update of international critical care guidelines or recommendations. But could we apply these results to all acute care settings? There is a unanimous agreement that current guidelines may not apply to hard-to-reach acute care settings in low and middle income countries. Then, a better knowledge of structural patterns of CCRN participating ICUs may help researchers, policy makers and funders, to better define future research studies at the era of global health.

The purposes of the present study were to describe CCRN and the structural and managerial patterns of participating ICUs and their research activities. Our study gives both insights into internal organizations of current CCRN and a detailed description of structural and managerial patterns of participating ICUs and their research activities.

Methods

The study protocol was approved by the Review board of the French Society of Critical Care Medicine in March 2013. This study was endorsed by the International Forum of Acute Care Trialists (InFACT) and by the European CCRN of the European Society of Intensive Care Medicine. A scientific advisory board gathered international experts on the field and the study was supported by the Health Services and Research Outcome section of the European Society of Intensive Care Medicine. Some of the results of this study have been previously reported in the form of abstracts.

Study's design

We conducted a cross-sectional survey. Directors of research networks were contacted through emails or directly during international meetings about critical care medicine. We used a broad definition of network including groups of ICUs mainly associated for composing a data registry.

The network and the ICU questionnaires

A network questionnaire and an ICU questionnaire were created for the study. The first versions of these questionnaires were written by YLN and BG, and were reviewed by the scientific advisory board of the study (composed of critical care experts on critical care organization and management). Composed of 22 open questions, the network questionnaire was sent to critical care network directors by email. The network questionnaire investigated network creation date, existence of a document stating the objectives and organization of the network, ownership, funding, partnerships, memberships (total number, pre-requisites, incentives, advantages), research activities (registry, studies settings, number of studies conducted, number of patients recruited, collection of biological samples, collaboration with other networks/public health authorities and number of publications).

Composed of 41 questions, the ICU questionnaire was created and administered using SurveyMonkey platform (SurveyMonkey Inc., Palo Alto, CA) under the form an equestionnaire to be self-completed by responders via the Internet. An email with a link to the e-questionnaire was sent to each member of the participating networks by the network referent between May and December 2013. Questions were on hospital organization (teaching status, ownership, size, number of ICUs, facilities available in the ICU, presence of an intermediate care unit/medical emergency team); ICU organization (case-mix, physician staffing model, medical night-staffing, medical working hours); ICU admissions (beds number, annual admissions number (total, those with invasive mechanical ventilation, with renal support therapy); ICU resources (computerized physician order entry, electronic medical record system, medical error-reporting system, telemedicine); nurses (nurse to patients ratio, working patterns); allied health professionals (physician-assistants, respiratory therapists, nurse practitioners, nurse assistants, physiotherapists, pharmacists, dieticians, social workers, microbiologists); communication (daily multidisciplinary ICU round); quality improvement measures (nosocomial infections, written clinical protocols, patient-family satisfaction evaluation, quality improvement projects, benchmarking); and individual critical care network involvement (motivations for being part of the network, participation to studies, projects submitted to the research network, percentage of patients enrolled in studies, barriers to enrolling a patient in a study of the research network, resources provided by the network).

Participants

The critical care research network directors submitted the study protocol to their scientific board of the network prior to participation in the study.

Phrasing of the questions was discussed and revised several times by the scientific advisory board. The e-questionnaire was translated in Spanish and Italian. We used special functions for avoiding missing data (e.g. mandatory answers in order to get to next question). In order to enhance response rate, three reminders were sent to critical care research members during the study period, and several emails with the participation rate to the e-survey were sent to research network directors. We planned to directly contact the directors of the participating ICUs through email, in the case of missing data. Sample size was not an issue in this study aiming first at the description of CCRNs' or anizations.

Statistical methods

We conducted descriptive analyses for documenting ICU characteristics across CCRNs.

Questionnaires with more than 50% missing answers or duplicate completions were excluded from the analysis. Qualitative variables are presented as percentages, while quantitative variables are shown either as means together with the corresponding 95% confidence interval, or as medians together with the interquartile range (IQR). The exploration of the association between ICU volume and structural patterns was limited to patterns previously reported for being associated with improved outcomes (teaching status, closed ICU model, 24/7 intensivist coverage, daily multi-disciplinary round, cumulative presence of written procedures, presence of an intermediate care unit, presence of a medical emergency team). Volume admission handled as an ordinal variable characterizing the number of admissions per year was investigated for its association with structural patterns using a generalized linear model (GLM). In a second step, a multivariable GLM analysis was performed for all variables with a p val e ≤ 0.20 in the ivariate analyses. Because we anticipated that potential missing data would be difficult to appropriately handle with imputation techniques (suspecting that the rationale for missing data in this survey would not be any kind of random-like process), we a priori decided to remove any record with missing data encountered in a given analysis. All analyses were performed with R statistical freeware version 3.5.0. Considering the initial 399 ICUs questionnaires finally documented, 33 (8%) were excluded because of too poor a completion content (n=5) or duplicate completion (n=28), finally resulting in 366 responses analyzed. National CCRN were considered the main network of belonging so we did not evaluate EECRN individually. The 2 ICUs belonging to AZURéa also belonged to another larger national network so we did not evaluate AZURéa separately. 1 and2)

Results

We

Networks characteristics (Tables

The median [IQR] participation rate of the networks was 21% [12%-38%]. Responder ICUs originated from 15 countries with Europe predominantly represented (n=228, 62%); the five most frequent countries being the Netherlands (n=57, 16%), Spain (n=49, 13%), Brazil (n=44, 12%), Italy (n=44, 12%), and France (n=30, 8%). Membership belonging to networks concerned either ICUs (n=10; 59%), either individuals (n=5; 29%) or both (n=2; 12%). Network funding was often mixed (association of public, private funding and membership fees), and ranged from 0.1 to hundreds of millions of dollars. The number of studies conducted, patients included and publications varied a lot across CCRNs. Some CCRNs (n=4, 24%) conducted studies involving emergency departments, operating rooms, post-anesthesia care unit and ambulances services. Many CCRNs had a registry data (n=12, 70%), and 5 (28%) collect biological samples. The collaboration with other research networks or health authorities was very frequent (n=13, 76%).

ICU participation to CCRN (Table 3)

Most ICU directors agreed or strongly agreed that belonging to a CCRN is an important feature contributing to being recognized at the academic/social level (n=233; 70%), to collaborate with experts in the field (n=284; 85%), to improve practices (n=286; 86%) leads to changes in their local practices (n=243; 73%). Majority agreed or strongly agreed that contributing in a CCRN is time consuming (n=243; 73%) and had hired a research assistant or data manager (n=172; 52%). During the last 3 years, ICUs participated in 2 [START_REF] Marshall | Investigator-led clinical research consortia: the Canadian Critical Care Trials Group[END_REF][START_REF] Cook | Multicenter clinical research in adult critical care[END_REF][START_REF] Jones | Advancing intensive care research in Australia and New Zealand: development of the binational ANZIC Research Centre[END_REF] observational and 1 [0-2] randomized controlled studies. Majority of ICUs did not submit any research proposal (n=179; 54%). Most ICU directors (n=229; 75%) recognize having enrolled less than 10% of their patients in CCRN studies. The major barriers to enrolling a patient were the lack of manpower (n=156; 47%), the consent not obtained (n=147; 44%), a protocol too complex or time consuming (n=142; 43%) and the non-adhesion of other clinicians (n=11; 33%). Regarding network support, most common resources provided were statistical (n=159; 44%) or logistical support (n=146; 45%) and money (n=67; 20%).

Hospital organization (Additional file 1)

Most ICUs were located in teaching hospitals (n=293; 80%). The median number of staffed ICU beds was 15 [START_REF] Arabi | Structure, Organization, and Delivery of Critical Care in Asian ICUs[END_REF][START_REF] Sakr | The impact of hospital and ICU organizational factors on outcome in critically ill patients: results from the Extended Prevalence of Infection in Intensive Care study[END_REF][START_REF] Estenssoro | Organizational Issues, Structure, and Processes of Care in 257 ICUs in Latin America: A Study From the Latin America Intensive Care Network[END_REF][START_REF] Capuzzo | Hospital mortality of adults admitted to Intensive Care Units in hospitals with and without Intermediate Care Units: a multicentre European cohort study[END_REF][START_REF] Maharaj | Rapid response systems: a systematic review and meta-analysis[END_REF][START_REF] Wilcox | Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses[END_REF][START_REF] Wallace | Nighttime intensivist staffing and mortality among critically ill patients[END_REF][START_REF] Vukoja | Critical care outcomes in resource-limited settings[END_REF][START_REF] Diaz | Global Critical Care: Moving Forward in Resource-Limited Settings[END_REF][START_REF] Tse-Hua | Comparing Response Rates from Web and Mail Surveys: A Meta-Analysis[END_REF][START_REF]InFACT Global H1N1 Collaboration. InFACT: a global critical care research response to H1N1[END_REF][START_REF] Simonsen | Clinical research networks are key to accurate and timely assessment of pandemic clinical severity[END_REF][21][22] and the annual volume of admissions was 968 . Majority of ICUs worked with an intermediate care unit (n=196; 54%) and with an in-hospital medical emergency team (n=261; 71%). An increasing ICU annual volume of admissions was associated with teaching status (p=8 x 10 -14 ) and the presence of a medical emergency team (p=7 x 10 -3 ).

ICU organization and resources (Additional file 2)

Most ICUs had an adult mixed patient case-mix (n=275; 74%), a closed model (n=322; 88%), a 24/7 intensivist coverage (n=254; 69%) and a daily multi-disciplinary round (n=268; 76%) High-volume ICUs were more likely to have a closed model (p=0.03) and established written protocols (p<0.01). The majority of ICUs had more nurses than a regular ward (n=344; 94%), and single patients rooms (n=237; 65%). Approximately two-thirds of ICUs had a computerized physician order entry (n=221; 61%), an electronic medical record system (n=236; 66%) and a medical error reporting system (n=234; 65%).

Working patterns of physicians, nurses and allied health care workers (Additionnal file 3)

The majority of the respondents declared that junior physicians work less than 50 h per week (n=190; 52%) and that senior physicians work between 51 and 70 h per week (n=192; 53%).

The occurrence of extended night shifts (>16h) was common (n=218; 60%).

The most frequent working patterns of nurses were 3 x 8 h (n=173; 48%). The presence of a physician assistant (n=179; 51%), a nurse-practitioner (n=221; 62%), a nurse-assistant (n=244; 69%), a physiotherapist (n=288; 81%), a pharmacist (n=197; 56%) and a dietician (n=205; 58%) was frequent. Less than half of ICUs had access to a social worker (n=167; 47%) or a microbiologist (n=138; 39%).

Quality improvement measures (Additional file 4)

The majority of ICUs declared measuring their rate of nosocomial infections (n=331; 94%), implementing quality improvement measures to reduce them (n=306; 87%) and conducting routine benchmarking with other ICUs (n=283; 80%). Most ICUs declared having established protocols for weaning from mechanical ventilation (n=224; 64%), sedation management (n=266; 76%), protective ventilation for acute lung injury (n=228; 65%), early goaled therapy for sepsis (n=226; 64%) and glucose management (n=305; 87%). High-volume ICUs were more likely to have established written protocols (p<0.01).

Discussion

To our knowledge, this study is the first describing major CCRN and structural patterns of participating ICUs. We found that CCRNs have different organizational patterns and resources, do not limit strictly their research to the ICU setting and collaborate frequently with each other.

Most ICUs are located in large teaching hospitals and share similar structural and managerial patterns. Regarding their participation to CCRN, the benefits reported are the recognition at the academic level, working with experts and enhancing their own clinical practice. The average rate of patients included in CCRN studies remained low.

Critical care research networks are organized differently but share similar vision

The differences between CCRNs may be partly related to their age, to different critical care delivery organizations and the lack of professional society's reco endations. It is not surprising that the oldest CCRNs (CCCTG, ARDSNet and ANZICS) had higher levels of funding and a higher number of conducted studies or publications. The different types of memberships (individuals versus ICUs) observed in our study might be related to the different roles of ICU directors (mainly administrative or managerial) across health care systems. The frequent collaboration observed between CCRNs and between CCRNs and health authorities, is partly related to belonging to the InFACT group that promotes this vision of promoting international collaboration (4).

Participating ICUs share the same motivations and difficulties

The underlying motivations of belonging to a CCRN appear laudable and our results enlighten us in the potentially positive impact of being part of a CCRN in terms of the quality of care delivered. In the lack of quality improvement through regional outreach, as a way to improve quality of care, health care authorities might encourage ICUs to be part of a CCRN [START_REF] Nguyen | Reorganizing adult critical care delivery: the role of regionalization, telemedicine, and community outreach[END_REF]. But, given the lack of international evaluation of CCRN (unlike HIV/AIDS research), the use of CCRN participation as a surrogate of quality of care, appears to be too premature [START_REF] Kagan | Developing a conceptual framework for an evaluation system for the NIAID HIV/AIDS clinical trials networks[END_REF].

As previously mentioned, the rate of patient's enroll ent re ained low within ICUs(1). The barriers reported are related to the lack of time and difficulties regarding the consent process. [START_REF] Diaz | Global Critical Care: Moving Forward in Resource-Limited Settings[END_REF].

Study limitations

Our study has several limitations. As any survey, our study may suffer from a selection bias.

Nevertheless and interestingly, the median participation rate of the networks observed in this study was not significantly different (p=0.46) from that issued from the participation rates to online surveys reported in a meta-analysis exploring such rates [START_REF] Tse-Hua | Comparing Response Rates from Web and Mail Surveys: A Meta-Analysis[END_REF]. Despite having excluded all ICUs with repeated or missing data and having contacted ICU directors directly, some data might be misleading or not reflect clinical practice at bedside (e.g. compliance to written protocols). Our data is 6 years old and it is possi le that so e CUs' feat res ay have changed. Indeed, in the era of global health, the InFACT group has broadened its collaborations since 2013 and is currently developing programs for pandemic preparedness, education, mentorship and research in geographic areas that were previously underrepresented [START_REF]InFACT Global H1N1 Collaboration. InFACT: a global critical care research response to H1N1[END_REF] [START_REF] Simonsen | Clinical research networks are key to accurate and timely assessment of pandemic clinical severity[END_REF].

Conclusions

This study indicates that despite having different organizational patterns, CCRN share similar ventures, especially the value of improving quality of critical care delivery which appears to be one of the main motivations of the participating centers. CCRN participating ICUs share several structural and managerial patterns which have been reported as being associated with improved clinical outcomes. These observations enlighten the importance of CCRN to enhance quality of critical care delivery and the benefits of conducting interventional trials within CCRN.

  sent 21 invitations to participate to our study in October 2011 and 19 CCRNs (AMIBNet (Associacão de Medicine Intensive Brasileira Network), ANZICS (Australian and New Zealand Intensive Care Society Trials group), ARDSNet (Acute Respiratory Distress Syndrom Network), ASDI (Austrian Network), AZURéa (French Network focused on perioperative care), CCCTG (Canadian Critical Care Trial Group), CCCTG (Chinese Critical Care Trial Group), BRICNet (Brazilian network), CUB-Réa (French Network), ECCRN (European Critical Care Research Network), EDUSepsis (Spanish Network focused on Sepsis), GiVITI (Italian Network), NICE (Dutch Network), OUTCOMERéa (French Network), PROSAFE (Polish, Hungarian, Slovenian, Greek and Cyprian Network), RéVA (European network focused on mechanical ventilation), SCCTG (Scandinavian Critical Care Trial Group), SEPNET (German Network focused on sepsis), USCIITG (United States Critical Illness and Injury Trial group) ) accepted to participate (the 2 that could not participate were from France and from the United Kingdom). The two questionnaires were sent in May 2013. Data was collected between May and December 2013.

  Despite the absence of a consensual definition of critical care and a wide variation of critical care delivery across health care systems, participating ICUs share several organizational and managerial patterns, some of them being associated with improved outcomes (9)(10)(11)(12)(13)(14)[START_REF] Wallace | Nighttime intensivist staffing and mortality among critically ill patients[END_REF]. Whether sharing similar structural patterns are related to the participation to a CCRN or to national recommendations remains unknown. These results highlight that current published evidence is mainly based on data of patients hospitalized in ICUs located in large teaching hospitals with a lot of manpower. An evaluation of the benefits of current recommendations in ICUs located in small community hospitals or with less manpower, might seem justified. Such evaluation is already conducted in ICUs located in hard-to-reach settings in low and middle income countries, that have sicker patients and lower resources[START_REF] Vukoja | Critical care outcomes in resource-limited settings[END_REF]

	experimental and observational) but may lead to a plausible co-enrolment effect (7). To
	facilitate the reporting of co-enrolment, recent guidelines suggest the use of the SPICE-8
	criteria (8).
	Participating ICUs to CCRN share similar structural and managerial patterns

As s ested y Cook et al., potential strate ies to increase patient's enroll ent efficiency are more participating center, weekends or afterhours enrolment or co-enrolment with another trial

[START_REF] Cook | Enrollment of intensive care unit patients into clinical st dies: a trinational s rvey of researchers' experiences, beliefs, and practices[END_REF]

. Co-enrolment seems appealing particularly in a variety of paired study designs (e.g.

AP-HP, Hôpital Cochin, Service d'Anesthésie-Réanimation Chirurgicale, Sorbonne Université, INSERM UMR S 1138, nstit t Pierre o is d' pidé iolo ie et de Santé Publique, Paris, France (yen-lan.nguyen@aphp.fr)

Sorbonne Université, INSERM UMR S 1136, nstit t Pierre o is d' pidé iolo ie et de Santé P li e, Paris, France (gilles.hejblum@inserm.fr)

AP-HP, Hôpital Saint-Antoine, Service de Réanimation Médicale, Sorbonne Université, INSERM UMR S 1136, nstit t Pierre o is d' pidé iolo ie et de Santé Publique, Paris, France (bertrand.guidet@aphp.fr)

Acknowledgements:

We warmly thank ESICM and the INFACT group for their endorsement of this study. We gratefully acknowledge all the contributors to this study, the research network coordinators and the members of the HSRO section of the ESICM. Maurizia Capuzzo, Kathy Rowan, Hans Flaaten, Andreas Valentin, Jeremy M Kahn, R Ferrer,

grant from funding agencies in the public, commercial, or not-for-profit sectors.

Andrew Rhodes, Rui Moreno (ESICM) John Marshall, Robert Fowler (CCCTG) Mario Tavola (GIVITI) Michael O' eary, Steve We (ANZ CS) Anders Perner (SCCTG) Taylor Thomspon, Timothy Walsh, Bernard Gordon (ARDSNetwork) Ricard Ferrer, Antonio Artigas (EduSEPSIS) Jonathan Sevransky, Greg Martin (USCIITG) Bertrand Guidet (CUB-Réa) Laurent Brochard, Jean-Christophe Richard, Alain Mercat (RéVA) Akos Csomos (PROSAFE) Peter Van de Voort (NICE) Fernando Bozza, Jorge Salluh (BRICNET) Du Bin (CCCCTG) Dimitri Gusmão (AMIBNET) Jean-Yves Lefrant, Bernard Allaouchiche, Jean-Michel Constantin, Marc Leone, Samir Jaber (AZURéa) Timsit Jean-Francois (OUTCOMEREA) Hermann Wrigge, Konrad Reinhart (SEPNet) John Marshall (INFACT) The critical care research network survey May 2019

Availability of data and material

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

List of abbreviations

ICU: intensive care unit CCRN: critical care research network PPY: person per year GLM: generalized linear model

Authors' contributions

The study concept was created by YLN, GH and BG. YLN and GH had full access to all the data and took the responsibility of the integrity of the data and the accuracy of data analysis.

Statistical analyses were carried out by GH and YLN. The manuscript was written by YLN and GH and revised by BG. Final approval of the version to be submitted was given by all coauthors. All authors read and approved the final manuscript.