Critical Care Medicine

Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System Objectives: Assess patient outcomes in patients with suspected infection and the cost-effectiveness of implementing a quality improvement program. Design, Setting, and Participants: We conducted an observational single-center study of 13,877 adults with suspected infection between March 1, 2014, and July 31, 2017. The 18-month period before and after the effective date for mandated reporting of the sepsis bundle was examined. The Sequential Organ Failure Assessment score and culture and antibiotic orders were used to identify patients meeting Sepsis-3 criteria from the electronic health record. Interventions: The following interventions were performed as follows: 1) multidisciplinary sepsis committee with sepsis coordinator and data abstractor; 2) education campaign; 3) electronic health record tools; and 4) a Modified Early Warning System. Main Outcomes and Measures: Primary health outcomes were in-hospital death and length of stay. The incremental cost-effectiveness ratio was calculated and the empirical 95% CI for the incremental cost-effectiveness ratio was estimated from 5,000 bootstrap samples. Results: In multivariable analysis, the odds ratio for in-hospital death in the post- versus pre-implementation periods was 0.70 (95% CI, 0.57–0.86) in those with suspected infection, and the hazard ratio for time to discharge was 1.25 (95% CI, 1.20–1.29). Similarly, a decrease in the odds for in-hospital death and an increase in the speed to discharge was observed for the subset that met Sepsis-3 criteria. The program was cost saving in patients with suspected infection (–$272,645.7; 95% CI, –$757,970.3 to –$79,667.7). Cost savings were also observed in the Sepsis-3 group. Conclusions and Relevance: Our health system's program designed to adhere to the sepsis bundle metrics led to decreased mortality and length of stay in a cost-effective manner in a much larger catchment than just the cohort meeting the Centers for Medicare and Medicaid Services measures. Our single-center model of interventions may serve as a practice-based benchmark for hospitalized patients with suspected infection. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Dr. Afshar received funding from the National Institute of Health (NIH)/National Institute of Alcoholism and Alcohol Abuse (K23 AA024503). Dr. Churpek received funding from the NIH/National Institute of General Medical Sciences (R01 GM 123193), NIH/National Heart, Lung, and Blood Institute (K08 HL121080), American Thoracic Society Foundation: Recognition Award for Early Career Investigators, and from a patent pending (ARCD. P0535US.P2). He received support for article research from the NIH. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: Majid.afshar@lumc.edu Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

External Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium Assessment Objectives: To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Design: Prospective, multinational cohort study. Setting: Eleven ICUs from seven countries in three continents. Patients: Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed. Interventions: None. Measurements and Main Results: The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU–assessed patients and 892 Intensive Care Delirium Screening Checklist–assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71–0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64–0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66–0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74–0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72–0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67–0.75) using the Intensive Care Delirium Screening Checklist. Conclusions: Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist. This work was performed at the Radboud University Medical Center, Tufts Medical Center, The Canberra Hospital, Antwerp University Hospital, Erasmus Medical Center, Jeroen Bosch Ziekenhuis, Rigshospitalet, Mount Sinai Hospital, Medisch Spectrum Twente, Hospital Espírito Santo, and University Medical Centre Utrecht. Drs. Wassenaar, Schoonhoven, Donders, Pickkers, and van den Boogaard contributed to study concept and design. Drs. Wassenaar, Devlin, and van Haren, Slooter, Jorens, van der Jagt, Simons, Egerod, Burry, and Beishuizen, and Mr. Matos contributed to acquisition of data. Drs. Wassenaar, Donders, and van den Boogaard contributed to statistical analysis. Drs. Wassenaar, Schoonhoven, Donders, Pickkers, and van den Boogaard contributed to analysis and interpretation of data. Dr. Wassenaar contributed to drafting of the article. Drs. Schoonhoven, Devlin, and van Haren, Slooter, Jorens, van der Jagt, Simons, Egerod, Burry, and Beishuizen, Mr. Matos, and Drs. Donders, Pickkers, and van den Boogaard contributed to critical revision of the article for important intellectual content. Drs. Schoonhoven, Pickkers, and van den Boogaard contributed to study supervision. All authors read and approved the final article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Dr. Pickkers received funding from AM-Pharma, Adrenomed, Exponential Biotherapies, and Baxter Consultation (speaking fee). The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: mark.vandenboogaard@radboudumc.nl Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Declining Mortality of Cirrhotic Variceal Bleeding Requiring Admission to Intensive Care: A Binational Cohort Study Objectives: We aimed to describe changes over time in admissions and outcomes, including length of stay, discharge destinations, and mortality of cirrhotic patients admitted to the ICU for variceal bleeding, and to compare it to the outcomes of those with other causes of ICU admissions. Design: Retrospective analysis of data captured prospectively in the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Adult Patient Database. Settings: One hundred eighty-three ICUs in Australia and New Zealand. Patients: Consecutive admissions to these ICUs for upper gastrointestinal bleeding related to varices in patients with cirrhosis between January 1, 2005, and December 31, 2016. Interventions: None. Measurements and Main Results: ICU admissions for variceal bleeding in cirrhotic patients accounted for 4,003 (0.6%) of all 720,425 nonelective ICU admissions. The proportion of ICU admissions for variceal bleeding fell significantly from 0.8% (83/42,567) in 2005 to 0.4% (53/80,388) in 2016 (p < 0.001). Hospital mortality rate was significantly higher within admissions for variceal bleeding compared with nonelective ICU admissions (20.0% vs 15.7%; p < 0.0001), but decreased significantly over time, from 24.6% in 2005 to 15.8% in 2016 (annual decline odds ratio, 0.93; 95% CI, 0.90–0.96). There was no difference in the reduction in mortality from variceal bleeding over time between liver transplant and nontransplant centers (p = 0.26). Conclusions: Admission rate to ICU and mortality of cirrhotic patients with variceal bleeding has declined significantly over time compared with other causes of ICU admissions with the outcomes comparable between liver transplant and nontransplant centers. Dr. Majeed helped with drafting of the article, interpretation of the data, and study concept. Dr. Majumdar helped with preparation and critical review of the article. Dr. Bailey helped with data acquisition, statistical analysis, and critical review of the article. Drs. Kemp and Bellomo helped with preparation and critical review of the article. Mr. Pilcher helped with data acquisition, review of the article, interpretation of the data, and study concept. Dr. Roberts helped with preparation and critical review of the article, and study concept. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grants from the Alfred Hospital Department of Gastroenterology and the Australian and New Zealand Intensive Care Research Centre. The authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: a.majeed@alfred.org.au Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Antimicrobial Disposition During Pediatric Continuous Renal Replacement Therapy Using an Ex Vivo Model Objectives: Little is known on the impact of continuous renal replacement therapy on antimicrobial dose requirements in children. In this study, we evaluated the pharmacokinetics of commonly administered antimicrobials in an ex vivo continuous renal replacement therapy model. Design: An ex vivo continuous renal replacement therapy circuit was used to evaluate drug-circuit interactions and determine the disposition of five commonly used antimicrobials (meropenem, piperacillin, liposomal amphotericin B, caspofungin, and voriconazole). Setting: University research laboratory. Patients: None. Interventions: Antimicrobials were administered into a reservoir containing whole human blood. The reservoir was connected to a pediatric continuous renal replacement therapy circuit programmed for a 10 kg child. Continuous renal replacement therapy was performed in the hemodiafiltration mode and in three phases correlating with three different continuous renal replacement therapy clearance rates: 1) no clearance (0 mL/kg/hr, to measure adsorption), 2) low clearance (20 mL/kg/hr), and 3) high clearance (40 mL/kg/hr). Blood samples were drawn directly from the reservoir at baseline and at 5, 20, 60, and 180 minutes during each phase. Five independent continuous renal replacement therapy runs were performed to assess inter-run variability. Antimicrobial concentrations were measured using validated liquid chromatography-mass spectrometry assays. A closed-loop, flow-through pharmacokinetic model was developed to analyze concentration-time profiles for each drug. Measurements and Main Results: Circuit adsorption of antimicrobials ranged between 13% and 27%. Meropenem, piperacillin, and voriconazole were cleared by the continuous renal replacement therapy circuit and clearance increased with increasing continuous renal replacement therapy clearance rates (7.66 mL/min, 4.97 mL/min, and 2.67 mL/min, respectively, for high continuous renal replacement therapy clearance). Amphotericin B and caspofungin had minimal circuit clearance and did not change with increasing continuous renal replacement therapy clearance rates. Conclusions: Careful consideration of drug-circuit interactions during continuous renal replacement therapy is essential for appropriate drug dosing in critically ill children. Antimicrobials have unique adsorption and clearance profiles during continuous renal replacement therapy, and this knowledge is important to optimize antimicrobial therapy. Drs. Purohit and Elkomy shared equally in the first authorship. Drs. Purohit and Elkomy contributed equally to be the first author on this article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grant from the Child Health Research Institute, Lucile Packard Foundation for Children's Health and the Stanford Clinical and Translational Science Award UL1 TR001085. Dr. Purohit disclosed that this work was supported by the National Institutes of Health and the Child Health Research Institute, Lucile Packard Foundation for Children's Health and the Stanford Clinical and Translational Science Award UL1 TR001085. Dr. Drover received funding from Masimo. The remaining authors have disclosed that they do not have any potential conflicts of interest. This work was performed at Stanford University School of Medicine. For information regarding this article, E-mail: drpurohit22@gmail.com Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Implementation of a Bundled Consent Process in the ICU: A Single-Center Experience Objectives: A bundled consent process, where patients or surrogates provide consent for all commonly performed procedures on a single form at the time of ICU admission, has been advocated as a method for improving both rates of documented consent and patient/family satisfaction, but there has been little published literature about the use of bundled consent. We sought to determine how residents in an academic medical center with a required bundled consent process actually obtain consent and how they perceive the overall value, efficacy, and effects on families of this approach. Design: Single-center survey study. Setting: Medical ICUs in an urban academic medical center. Subjects: Internal medicine residents. Interventions: We administered an online survey about bundled consent use to all residents. Quantitative and qualitative data were analyzed. Measurements and Main Results: One-hundred two of 164 internal medicine residents (62%) completed the survey. A majority of residents (55%) reported grouping procedures and discussing general risks and benefits; 11% reported conducting a complete informed consent discussion for each procedure. Respondents were divided in their perception of the value of bundled consent, but most (78%) felt it scared or stressed families. A minority (26%) felt confident that they obtained valid informed consent for critical care procedures with the use of bundled consent. An additional theme that emerged from qualitative data was concern regarding the validity of anticipatory consent. Conclusions: Resident physicians experienced with the use of bundled consent in the ICU held variable perceptions of its value but raised concerns about the effect on families and the validity of consent obtained with this strategy. Further studies are necessary to further explore what constitutes best practice for informed consent in critical care. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Dr. Stevens is supported by Agency for Healthcare Research and Quality Grant 5K08HS024288 and a Clinical Scientist Development Award from the Doris Duke Charitable Foundation. The remaining authors have disclosed that they do not have any conflicts of interest. For information regarding this article, E-mail: aanandai@bidmc.harvard.edu Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Agreement With Consensus Statements on End-of-Life Care: A Description of Variability at the Level of the Provider, Hospital, and Country Objectives: To develop an enhanced understanding of factors that influence providers' views about end-of-life care, we examined the contributions of provider, hospital, and country to variability in agreement with consensus statements about end-of-life care. Design and Setting: Data were drawn from a survey of providers' views on principles of end-of-life care obtained during the consensus process for the Worldwide End-of-Life Practice for Patients in ICUs study. Subjects: Participants in Worldwide End-of-Life Practice for Patients in ICUs included physicians, nurses, and other providers. Our sample included 1,068 providers from 178 hospitals and 31 countries. Interventions: None. Measurements and Main Results: We examined views on cardiopulmonary resuscitation and withholding/withdrawing life-sustaining treatments, using a three-level linear mixed model of responses from providers within hospitals within countries. Of 1,068 providers from 178 hospitals and 31 countries, 1% strongly disagreed, 7% disagreed, 11% were neutral, 44% agreed, and 36% strongly agreed with declining to offer cardiopulmonary resuscitation when not indicated. Of the total variability in those responses, 98%, 0%, and 2% were explained by differences among providers, hospitals, and countries, respectively. After accounting for provider characteristics and hospital size, the variance partition was similar. Results were similar for withholding/withdrawing life-sustaining treatments. Conclusions: Variability in agreement with consensus statements about end-of-life care is related primarily to differences among providers. Acknowledging the primary source of variability may facilitate efforts to achieve consensus and improve decision-making for critically ill patients and their family members at the end of life. Worldwide End-of-Life Practice for Patients in ICUs (WELPICUS) Investigators Steering Committee are as follows: Charles L. Sprung (chairman), Elie Azoulay, J. Randall Curtis, Jozef Kesecioglu, Paulo Maia, Andrej Michalsen, Moshe Sonnenblick, and Robert Truog. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Dr. De Robertis received funding from Masimo and Aguettant. Dr. Kross received funding from the National Institutes of Health. Dr. Michalsen received funding from lectures from ViDia Hospital, Stuttgart Hospital, and Konstanz Hospital. Dr. Sprung's institution received funding from Asahi Kasei Pharma America (data monitoring committee) and LeukoDx (consultant and principal investigator of study). The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: along11@uw.edu Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Effect of Prone Positioning on Intraocular Pressure in Patients With Acute Respiratory Distress Syndrome Objectives: To evaluate the effect of prolonged duration of prone position (with head laterally rotated) on intraocular pressure in acute respiratory distress syndrome patients. Design: Prospective observational study. Setting: University hospital ICU. Patients: Twenty-five acute respiratory distress syndrome patients, age 60 years (51–67 yr), Sequential Organ Failure Assessment score 10 (10–12), PaO2/FIO2 ratio of 90 (65–120), and all in septic shock. Interventions: None. Measurements and Main Results: Intraocular pressure (in mm Hg) measured by hand-held applanation tonometer, at different time points. Before prone (in both eyes): at 30–45° head-end elevation position (THE pre-prone), in supine position just before turning prone (Tsupine pre-prone); during prone (in nondependent eye): at 10 minutes (T10 prone), 30 minutes (T30 prone), and at just before end of prone session (Tend-prone). After end of prone session (both eyes): at 5 minutes (T5 supine post-prone), 10 minutes (T10 HE post-prone), 15 minutes (T15 HE post-prone), and 30 minutes (T30 HE post-prone). Median duration of prone position was 14 hours (12–18 hr). Median intraocular pressure increased significantly (p ≤ 0.001) in both eyes. In dependent eye, from 15 (12–19) at THE pre-prone to 24, 21, 19, and 16 at T5 supine post-prone, T10 HE post-prone, T15 HE post-prone, and T30 HE post-prone respectively, whereas in nondependent eye from 14 (12–18.5) at THE pre-prone to 23, 25, 32, 25, 22, 20, and 17 at T10 prone, T30 prone, Tend-prone, T5 supine post-prone, T10 HE post-prone, T15 HE post-prone, and T30 HE post-prone respectively. Bland-Altman plot analysis showed significant linear relationship (r = 0.789; p ≤ 0.001) with good agreement between rise in mean intraocular pressure of the both eyes (dependent eye and nondependent eye) with their paired differences after the end of different duration of prone session (T5 supine post-prone). Conclusions: There is significant increase in intraocular pressure due to prone positioning among acute respiratory distress syndrome patients. Intraocular pressure increases as early as 10 minutes after proning, with increasing trend during prone position, which persisted even at 30 minutes after the end of post prone session although with decreasing trend. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Presented, in part, as an abstract at the ESICM LIVES (September 27, 2017, Vienna, Austria) and ISCCM CRITICARE (March 8, 2018, Varanasi, India) meetings. Dr. Gurjar received funding from Intramural and Extramural Research Grant (unrelated to submitted work), and he received funding from Jaypee Medical Publishers, New Delhi, India (royalties). The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: m.gurjar@rediffmail.com Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Why My Steroid Trials in Septic Shock Were "Positive"? No abstract available

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy Objectives: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. Design: Retrospective. Setting: ICUs at four academic medical centers in the United States. Patients: Comatose patients with acute hypoxic-ischemic encephalopathy. Interventions: None. Measurements and Main Results: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. Conclusions: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance. Drs. Ghassemi and Amorim contributed equally as co-first authors of this work. The Critical Care Electroencephalogram Monitoring Research Consortium Board consists of: Chair: Brandon M. Westover, MD, PhD; Vice-Chair: Emily Gilmore, MD; Secretary: Aaron Struck, MD; Member-at-Large: Nicholas Gaspard, MD, PhD; Immediate Past Chair: Jong Woo Lee, MD, PhD; and Past Chair: Nicholas S. Abend, MD, MSCE. Drs. Ghassemi, Amorim, Lee, Cash, Brown, Mark, and Westover contributed to conception and design of the study. Drs. Ghassemi, Amorim, and Westover contributed to analysis of data. Drs. Ghassemi, Amorim, and Westover contributed to preparing the figures. Drs. Ghassemi and Amorim, Mr. Al Hanai, Drs. Lee, Herman, Sivaraju, and Gaspard, Mr. Biswal, Mr. Moura Junior, and Dr. Westover contributed to data acquisition. Drs. Ghassemi and Amorim, Mr. Al Hanai, Drs. Lee, Herman, Sivaraju, and Gaspard, Mr. Biswal, Mr. Moura Junior, and Drs. Cash, Brown, Mark, and Westover contributed to drafting the text. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grants from National Institutes of Health (NIH) 1R01NS102190, 1R01NS102574, and 1R01NS107291 (to Dr. Westover); R01GM104987 (to Dr. Mark); T32HL007901, T90DA22759, and T32EB001680 (to Dr. Ghassemi); National Institute of Neurological Disorders and Stroke 1K23NS090900 (to Dr. Westover); Salerno foundation (M.G.M.); Neurocritical Care Society research training fellowship and American Heart Association postdoctoral fellowship (to Dr. Amorim); and Andrew David Heitman Neuroendovascular Research Fund and the Rappaport Foundation (to Dr. Westover). Preliminary findings of this study were presented at the 14th Annual Neurocritical Care Society Meeting, National Harbor, MD, September 15–18, 2016. Dr. Amorim's institution received funding from the National Institutes of Health (NIH), Neurocritical Care Society, and American Heart Association. Drs. Amorim, Mark, and Westover received support for article research from the NIH. Dr. Lee received funding from SleepMed/DigiTrace, Advance Medical, and United Diagnostics. Drs. Lee's and Mark's institutions received funding from the NIH. Dr. Herman's institution received funding from UCB Pharma, Sage Therapeutics, Neurospace, Epilepsy Therapy Development Project, Acorda Therapeutics, Pfizer, and Philips. Dr. Hirsch's institution received funding from Upsher-Smith and Monteris. He received funding from Adamas; consultation fees for advising from Aquestive, Ceribell, Eisai, and Medtronic; honoraria for speaking from Neuropace; and royalties for authoring chapters for UpToDate-Neurology and from Wiley for coauthoring a book on electroencephalograms in critical care. Dr. Scirica's institution received funding from Merck, Eisai, and Novartis, and he received consulting fees from AbbVie, Allergan, AstraZeneca, Boehringer Ingelheim, Covance, Eisai, Elsevier Practice Update Cardiology, GlaxoSmithKline, Lexicon, Merck, NovoNordisk, Sanofi, and equity in Health [at] Scale. Dr. Brown's institution received funding from Massachusetts General Hospital and Massachusetts Institute of Technology. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: mwestover@mgh.harvard.edu; edilbertoamorim@gmail.com. Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Enablers and Barriers to Implementing ICU Follow-Up Clinics and Peer Support Groups Following Critical Illness: The Thrive Collaboratives Objectives: Data are lacking regarding implementation of novel strategies such as follow-up clinics and peer support groups, to reduce the burden of postintensive care syndrome. We sought to discover enablers that helped hospital-based clinicians establish post-ICU clinics and peer support programs, and identify barriers that challenged them. Design: Qualitative inquiry. The Consolidated Framework for Implementation Research was used to organize and analyze data. Setting: Two learning collaboratives (ICU follow-up clinics and peer support groups), representing 21 sites, across three continents. Subjects: Clinicians from 21 sites. Measurement and Main Results: Ten enablers and nine barriers to implementation of "ICU follow-up clinics" were described. A key enabler to generate support for clinics was providing insight into the human experience of survivorship, to obtain interest from hospital administrators. Significant barriers included patient and family lack of access to clinics and clinic funding. Nine enablers and five barriers to the implementation of "peer support groups" were identified. Key enablers included developing infrastructure to support successful operationalization of this complex intervention, flexibility about when peer support should be offered, belonging to the international learning collaborative. Significant barriers related to limited attendance by patients and families due to challenges in creating awareness, and uncertainty about who might be appropriate to attend and target in advertising. Conclusions: Several enablers and barriers to implementing ICU follow-up clinics and peer support groups should be taken into account and leveraged to improve ICU recovery. Among the most important enablers are motivated clinician leaders who persist to find a path forward despite obstacles. This does not necessarily represent the views of the U.S. government or Department of Veterans Affairs. Drs. Haines, McPeake, Boehm, and Sevin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All other authors contributed substantially to the study design, data analysis and interpretation, and the writing of the article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Drs. Haines's, McPeake's, Hibbert's, Boehm's, Aparanji's, Bastin's, Drumright's, Holdsworth's, Johnson's, Kloos's, Meyer's, Quasim's, Saft's, Stollings's, and Sevin's institutions received funding from the Society of Critical Care Medicine (SCCM). Dr. Haines, McPeake, Boehm, and Sevin are currently receiving funding from SCCM to undertake this work, although the supporting source had no input into the design, data collection and analysis, although approved the final article for submission for publication. Dr. Boehm's institution received funding from the National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) (1K12HL137943-01) and Vanderbilt Clinical and Translational Science Award. The funding source reviewed and approved the article for submission. Drs. Boehm and Iwashyna received support for article research from the NIH. Dr. Hope's institution received funding from NHLBI K01-HL140279, and he received funding from American Association of Critical Care Nurses. Dr. Khan's institution received funding from the NIH. Dr. Kross's institution received funding from the NIH and the American Lung Association. Dr. Quasim's institution received funding from the Health Foundation. Dr. Saft received funding from Medtronic. Dr. Stollings received funding from Intermountain Health. Dr. Weinhouse received funding from UptoDate. Dr. Hopkins's institution received funding from Intermountain Research and Medical Foundation. Dr. Iwashyna's institution received funding from NIH K12, and he disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: Kimberley.haines@wh.org.au Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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