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Dicpinigaitis AJ, Khamzina Y, Hall DE, Nassereldine H, Kennedy J, Seymour CW, Schmidt M, Reitz KM, Bowers CA. Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes. JAMA Netw Open 2024; 7:e2413166. [PMID: 38787554 PMCID: PMC11127118 DOI: 10.1001/jamanetworkopen.2024.13166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/23/2024] [Indexed: 05/25/2024] Open
Abstract
Importance Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI). Objective To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS). Design, Setting, and Participants RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023. Exposures RAI parameters and in-hospital mortality. Main Outcomes and Measures The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination. Results RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty. Conclusion and Relevance In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.
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Affiliation(s)
- Alis J. Dicpinigaitis
- Department of Neurology, New York Presbyterian–Weill Cornell Medical Center, New York, New York
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico
| | | | - Daniel E. Hall
- Department of Neurology, New York Presbyterian–Weill Cornell Medical Center, New York, New York
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Wolff Center, UPMC, Pittsburgh, Pennsylvania
| | - Hasan Nassereldine
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jason Kennedy
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher W. Seymour
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Meic Schmidt
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico
| | - Katherine M. Reitz
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Vascular Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christian A. Bowers
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico
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Kip KE, Diamond D, Mulukutla S, Marroquin OC. Is LDL cholesterol associated with long-term mortality among primary prevention adults? A retrospective cohort study from a large healthcare system. BMJ Open 2024; 14:e077949. [PMID: 38548371 PMCID: PMC10982736 DOI: 10.1136/bmjopen-2023-077949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/18/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVES Among primary prevention-type adults not on lipid-lowering therapy, conflicting results exist on the relationship between low-density lipoprotein cholesterol (LDL-C) and long-term mortality. We evaluated this relationship in a real-world evidence population of adults. DESIGN Retrospective cohort study. SETTING Electronic medical record data for adults, from 4 January 2000 through 31 December 2022, were extracted from the University of Pittsburgh Medical Center healthcare system. PARTICIPANTS Adults without diabetes aged 50-89 years not on statin therapy at baseline or within 1 year and classified as primary prevention-type patients. To mitigate potential reverse causation, patients who died within 1 year or had baseline total cholesterol (T-C) ≤120 mg/dL or LDL-C <30 mg/dL were excluded. MAIN EXPOSURE MEASURE Baseline LDL-C categories of 30-79, 80-99, 100-129, 130-159, 160-189 or ≥190 mg/dL. MAIN OUTCOME MEASURE All-cause mortality with follow-up starting 365 days after baseline cholesterol measurement. RESULTS 177 860 patients with a mean (SD) age of 61.1 (8.8) years and mean (SD) LDL-C of 119 (31) mg/dL were evaluated over a mean of 6.1 years of follow-up. A U-shaped relationship was observed between the six LDL-C categories and mortality with crude 10-year mortality rates of 19.8%, 14.7%, 11.7%, 10.7%, 10.1% and 14.0%, respectively. Adjusted mortality HRs as compared with the referent group of LDL-C 80-99 mg/dL were: 30-79 mg/dL (HR 1.23, 95% CI 1.17 to 1.30), 100-129 mg/dL (0.87, 0.83-0.91), 130-159 mg/dL (0.88, 0.84-0.93), 160-189 mg/dL (0.91, 0.84-0.98) and ≥190 mg/dL (1.19, 1.06-1.34), respectively. Unlike LDL-C, both T-C/HDL cholesterol (high-density lipoprotein cholesterol) and triglycerides/HDL cholesterol ratios were independently associated with long-term mortality. CONCLUSIONS Among primary prevention-type patients aged 50-89 years without diabetes and not on statin therapy, the lowest risk for long-term mortality appears to exist in the wide LDL-C range of 100-189 mg/dL, which is much higher than current recommendations. For counselling these patients, minimal consideration should be given to LDL-C concentration.
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Affiliation(s)
- Kevin E Kip
- Clinical Analytics, University of Pittsburgh Medical Center Health System, Pittsburgh, Pennsylvania, USA
| | - David Diamond
- Department of Psychology, University of South Florida, Tampa, Florida, USA
| | - Suresh Mulukutla
- Clinical Analytics, University of Pittsburgh Medical Center Health System, Pittsburgh, Pennsylvania, USA
| | - Oscar C Marroquin
- Physician Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
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Holder-Murray J, Esper SA, Althans AR, Knight J, Subramaniam K, Derenzo J, Ball R, Beaman S, Luke C, La Colla L, Schott N, Williams B, Lorenzi E, Berry LR, Viele K, Berry S, Masters M, Meister KA, Wilkinson T, Garrard W, Marroquin OC, Mahajan A. REMAP Periop: a randomised, embedded, multifactorial adaptive platform trial protocol for perioperative medicine to determine the optimal enhanced recovery pathway components in complex abdominal surgery patients within a US healthcare system. BMJ Open 2023; 13:e078711. [PMID: 38154902 PMCID: PMC10759097 DOI: 10.1136/bmjopen-2023-078711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023] Open
Abstract
INTRODUCTION Implementation of enhanced recovery pathways (ERPs) has resulted in improved patient-centred outcomes and decreased costs. However, there is a lack of high-level evidence for many ERP elements. We have designed a randomised, embedded, multifactorial, adaptive platform perioperative medicine (REMAP Periop) trial to evaluate the effectiveness of several perioperative therapies for patients undergoing complex abdominal surgery as part of an ERP. This trial will begin with two domains: postoperative nausea/vomiting (PONV) prophylaxis and regional/neuraxial analgesia. Patients enrolled in the trial will be randomised to arms within both domains, with the possibility of adding additional domains in the future. METHODS AND ANALYSIS In the PONV domain, patients are randomised to optimal versus supraoptimal prophylactic regimens. In the regional/neuraxial domain, patients are randomised to one of five different single-injection techniques/combination of techniques. The primary study endpoint is hospital-free days at 30 days, with additional domain-specific secondary endpoints of PONV incidence and postoperative opioid consumption. The efficacy of an intervention arm within a given domain will be evaluated at regular interim analyses using Bayesian statistical analysis. At the beginning of the trial, participants will have an equal probability of being allocated to any given intervention within a domain (ie, simple 1:1 randomisation), with response adaptive randomisation guiding changes to allocation ratios after interim analyses when applicable based on prespecified statistical triggers. Triggers met at interim analysis may also result in intervention dropping. ETHICS AND DISSEMINATION The core protocol and domain-specific appendices were approved by the University of Pittsburgh Institutional Review Board. A waiver of informed consent was obtained for this trial. Trial results will be announced to the public and healthcare providers once prespecified statistical triggers of interest are reached as described in the core protocol, and the most favourable interventions will then be implemented as a standardised institutional protocol. TRIAL REGISTRATION NUMBER NCT04606264.
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Affiliation(s)
| | - Stephen A Esper
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alison R Althans
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joshua Knight
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kathirvel Subramaniam
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joseph Derenzo
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ryan Ball
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Shawn Beaman
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Charles Luke
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Luca La Colla
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Nicholas Schott
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian Williams
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | - Kert Viele
- Berry Consultants Statistical Innovation, Austin, Texas, USA
| | - Scott Berry
- Berry Consultants Statistical Innovation, Austin, Texas, USA
| | - Miranda Masters
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Katie A Meister
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Todd Wilkinson
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Oscar C Marroquin
- Clinical Analytics, UPMC, Pittsburgh, Pennsylvania, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Aman Mahajan
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Kip KE, McCreary EK, Collins K, Minnier TE, Snyder GM, Garrard W, McKibben JC, Yealy DM, Seymour CW, Huang DT, Bariola JR, Schmidhofer M, Wadas RJ, Angus DC, Kip PL, Marroquin OC. Evolving Real-World Effectiveness of Monoclonal Antibodies for Treatment of COVID-19 : A Cohort Study. Ann Intern Med 2023; 176:496-504. [PMID: 37011399 PMCID: PMC10074437 DOI: 10.7326/m22-1286] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Treatment guidelines and U.S. Food and Drug Administration emergency use authorizations (EUAs) of monoclonal antibodies (mAbs) for treatment of high-risk outpatients with mild to moderate COVID-19 changed frequently as different SARS-CoV-2 variants emerged. OBJECTIVE To evaluate whether early outpatient treatment with mAbs, overall and by mAb product, presumed SARS-CoV-2 variant, and immunocompromised status, is associated with reduced risk for hospitalization or death at 28 days. DESIGN Hypothetical pragmatic randomized trial from observational data comparing mAb-treated patients with a propensity score-matched, nontreated control group. SETTING Large U.S. health care system. PARTICIPANTS High-risk outpatients eligible for mAb treatment under any EUA with a positive SARS-CoV-2 test result from 8 December 2020 to 31 August 2022. INTERVENTION Single-dose intravenous mAb treatment with bamlanivimab, bamlanivimab-etesevimab, sotrovimab, bebtelovimab, or intravenous or subcutaneous casirivimab-imdevimab administered within 2 days of a positive SARS-CoV-2 test result. MEASUREMENTS The primary outcome was hospitalization or death at 28 days among treated patients versus a nontreated control group (no treatment or treatment ≥3 days after SARS-CoV-2 test date). RESULTS The risk for hospitalization or death at 28 days was 4.6% in 2571 treated patients and 7.6% in 5135 nontreated control patients (risk ratio [RR], 0.61 [95% CI, 0.50 to 0.74]). In sensitivity analyses, the corresponding RRs for 1- and 3-day treatment grace periods were 0.59 and 0.49, respectively. In subgroup analyses, those receiving mAbs when the Alpha and Delta variants were presumed to be predominant had estimated RRs of 0.55 and 0.53, respectively, compared with 0.71 for the Omicron variant period. Relative risk estimates for individual mAb products all suggested lower risk for hospitalization or death. Among immunocompromised patients, the RR was 0.45 (CI, 0.28 to 0.71). LIMITATIONS Observational study design, SARS-CoV-2 variant presumed by date rather than genotyping, no data on symptom severity, and partial data on vaccination status. CONCLUSION Early mAb treatment among outpatients with COVID-19 is associated with lower risk for hospitalization or death for various mAb products and SARS-CoV-2 variants. PRIMARY FUNDING SOURCE None.
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Affiliation(s)
- Kevin E Kip
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (K.E.K., K.C., W.G., J.C.M., O.C.M.)
| | - Erin K McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (E.K.M., G.M.S., J.R.B.)
| | - Kevin Collins
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (K.E.K., K.C., W.G., J.C.M., O.C.M.)
| | - Tami E Minnier
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (T.E.M., P.L.K.)
| | - Graham M Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (E.K.M., G.M.S., J.R.B.)
| | - William Garrard
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (K.E.K., K.C., W.G., J.C.M., O.C.M.)
| | - Jeffrey C McKibben
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (K.E.K., K.C., W.G., J.C.M., O.C.M.)
| | - Donald M Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (D.M.Y., R.J.W.)
| | - Christopher W Seymour
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (C.W.S., D.C.A.)
| | - David T Huang
- Department of Emergency Medicine and Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (D.T.H.)
| | - J Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (E.K.M., G.M.S., J.R.B.)
| | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (M.S.)
| | - Richard J Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (D.M.Y., R.J.W.)
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (C.W.S., D.C.A.)
| | - Paula L Kip
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (T.E.M., P.L.K.)
| | - Oscar C Marroquin
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (K.E.K., K.C., W.G., J.C.M., O.C.M.)
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Reitz KM, Althouse AD, Forman DE, Zuckerbraun BS, Vodovotz Y, Zamora R, Raffai RL, Hall DE, Tzeng E. MetfOrmin BenefIts Lower Extremities with Intermittent Claudication (MOBILE IC): randomized clinical trial protocol. BMC Cardiovasc Disord 2023; 23:38. [PMID: 36681798 PMCID: PMC9862509 DOI: 10.1186/s12872-023-03047-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Peripheral artery disease (PAD) affects over 230 million people worldwide and is due to systemic atherosclerosis with etiology linked to chronic inflammation, hypertension, and smoking status. PAD is associated with walking impairment and mobility loss as well as a high prevalence of coronary and cerebrovascular disease. Intermittent claudication (IC) is the classic presenting symptom for PAD, although many patients are asymptomatic or have atypical presentations. Few effective medical therapies are available, while surgical and exercise therapies lack durability. Metformin, the most frequently prescribed oral medication for Type 2 diabetes, has salient anti-inflammatory and promitochondrial properties. We hypothesize that metformin will improve function, retard the progression of PAD, and improve systemic inflammation and mitochondrial function in non-diabetic patients with IC. METHODS 200 non-diabetic Veterans with IC will be randomized 1:1 to 180-day treatment with metformin extended release (1000 mg/day) or placebo to evaluate the effect of metformin on functional status, PAD progression, cardiovascular disease events, and systemic inflammation. The primary outcome is 180-day maximum walking distance on the 6-min walk test (6MWT). Secondary outcomes include additional assessments of functional status (cardiopulmonary exercise testing, grip strength, Walking Impairment Questionnaires), health related quality of life (SF-36, VascuQoL), macro- and micro-vascular assessment of lower extremity blood flow (ankle brachial indices, pulse volume recording, EndoPAT), cardiovascular events (amputations, interventions, major adverse cardiac events, all-cause mortality), and measures of systemic inflammation. All outcomes will be assessed at baseline, 90 and 180 days of study drug exposure, and 180 days following cessation of study drug. We will evaluate the primary outcome with linear mixed-effects model analysis with covariate adjustment for baseline 6MWT, age, baseline ankle brachial indices, and smoking status following an intention to treat protocol. DISCUSSION MOBILE IC is uniquely suited to evaluate the use of metformin to improve both systematic inflammatory responses, cellular energetics, and functional outcomes in patients with PAD and IC. TRIAL REGISTRATION The prospective MOBILE IC trial was publicly registered (NCT05132439) November 24, 2021.
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Affiliation(s)
- Katherine M Reitz
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Division of Vascular Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | | | - Daniel E Forman
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatrics Research, Education, and Clinical Care, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Brian S Zuckerbraun
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
| | | | - Daniel E Hall
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatrics Research, Education, and Clinical Care, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- Wolff Center, UPMC, Pittsburgh, PA, USA
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Edith Tzeng
- Department of Surgery, University of Pittsburgh, South Tower, Rm 351.6, 200 Lothrop Street, Pittsburgh, PA, 15213, USA.
- Division of Vascular Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Surgery, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA.
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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Morris AH, Horvat C, Stagg B, Grainger DW, Lanspa M, Orme J, Clemmer TP, Weaver LK, Thomas FO, Grissom CK, Hirshberg E, East TD, Wallace CJ, Young MP, Sittig DF, Suchyta M, Pearl JE, Pesenti A, Bombino M, Beck E, Sward KA, Weir C, Phansalkar S, Bernard GR, Thompson BT, Brower R, Truwit J, Steingrub J, Hiten RD, Willson DF, Zimmerman JJ, Nadkarni V, Randolph AG, Curley MAQ, Newth CJL, Lacroix J, Agus MSD, Lee KH, deBoisblanc BP, Moore FA, Evans RS, Sorenson DK, Wong A, Boland MV, Dere WH, Crandall A, Facelli J, Huff SM, Haug PJ, Pielmeier U, Rees SE, Karbing DS, Andreassen S, Fan E, Goldring RM, Berger KI, Oppenheimer BW, Ely EW, Pickering BW, Schoenfeld DA, Tocino I, Gonnering RS, Pronovost PJ, Savitz LA, Dreyfuss D, Slutsky AS, Crapo JD, Pinsky MR, James B, Berwick DM. Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy. J Am Med Inform Assoc 2022; 30:178-194. [PMID: 36125018 PMCID: PMC9748596 DOI: 10.1093/jamia/ocac143] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Abstract
How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.
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Affiliation(s)
- Alan H Morris
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Christopher Horvat
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian Stagg
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
| | - David W Grainger
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Michael Lanspa
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James Orme
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Terry P Clemmer
- Department of Internal Medicine (Critical Care), Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Lindell K Weaver
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Frank O Thomas
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Colin K Grissom
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Ellie Hirshberg
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Thomas D East
- SYNCRONYS - Chief Executive Officer, Albuquerque, New Mexico, USA
| | - Carrie Jane Wallace
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Michael P Young
- Department of Critical Care, Renown Regional Medical Center, Reno, Nevada, USA
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA
| | - Mary Suchyta
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James E Pearl
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Antinio Pesenti
- Faculty of Medicine and Surgery—Anesthesiology, University of Milan, Milano, Lombardia, Italy
| | - Michela Bombino
- Department of Emergency and Intensive Care, San Gerardo Hospital, Monza (MB), Italy
| | - Eduardo Beck
- Faculty of Medicine and Surgery - Anesthesiology, University of Milan, Ospedale di Desio, Desio, Lombardia, Italy
| | - Katherine A Sward
- Department of Biomedical Informatics, College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Shobha Phansalkar
- Wolters Kluwer Health—Clinical Solutions—Medical Informatics, Wolters Kluwer Health, Newton, Massachusetts, USA
| | - Gordon R Bernard
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - B Taylor Thompson
- Pulmonary and Critical Care Division, Department of Internal Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Roy Brower
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Jonathon Truwit
- Department of Internal Medicine, Pulmonary and Critical Care, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jay Steingrub
- Department of Internal Medicine, Pulmonary and Critical Care, University of Massachusetts Medical School, Baystate Campus, Springfield, Massachusetts, USA
| | - R Duncan Hiten
- Department of Internal Medicine, Pulmonary and Critical Care, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Douglas F Willson
- Pediatric Critical Care, Department of Pediatrics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jerry J Zimmerman
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| | - Vinay Nadkarni
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adrienne G Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martha A Q Curley
- University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Christopher J L Newth
- Childrens Hospital Los Angeles, Department of Anesthesiology and Critical Care, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Jacques Lacroix
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Université de Montréal Faculté de Médecine, Montreal, Quebec, Canada
| | - Michael S D Agus
- Division of Medical Pediatric Critical Care, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kang Hoe Lee
- Department of Intensive Care Medicine, Ng Teng Fong Hospital and National University Centre of Transplantation, National University Singapore Yong Loo Lin School of Medicine, Singapore
| | - Bennett P deBoisblanc
- Department of Internal Medicine, Pulmonary and Critical Care, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Frederick Alan Moore
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| | - R Scott Evans
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Dean K Sorenson
- Department of Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Anthony Wong
- Department of Data Science Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Michael V Boland
- Department of Ophthalmology, Massachusetts Ear and Eye Infirmary, Harvard Medical School, Boston, Massachusetts, USA
| | - Willard H Dere
- Endocrinology and Metabolism Division, Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Alan Crandall
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
- Posthumous
| | - Julio Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Stanley M Huff
- Department of Medical Informatics, Intermountain Healthcare, Department of Biomedical Informatics, University of Utah, and Graphite Health, Salt Lake City, Utah, USA
| | - Peter J Haug
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Ulrike Pielmeier
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Stephen E Rees
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Dan S Karbing
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Steen Andreassen
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Eddy Fan
- Internal Medicine, Pulmonary and Critical Care Division, Institute of Health Policy, Management and Evaluation, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Roberta M Goldring
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Kenneth I Berger
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Beno W Oppenheimer
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - E Wesley Ely
- Internal Medicine, Pulmonary and Critical Care, Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Tennessee Valley Veteran’s Affairs Geriatric Research Education Clinical Center (GRECC), Nashville, Tennessee, USA
| | - Brian W Pickering
- Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, USA
| | - David A Schoenfeld
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Irena Tocino
- Department of Radiology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Russell S Gonnering
- Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Peter J Pronovost
- Department of Anesthesiology and Critical Care Medicine, University Hospitals, Highland Hills, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Lucy A Savitz
- Northwest Center for Health Research, Kaiser Permanente, Oakland, California, USA
| | - Didier Dreyfuss
- Assistance Publique—Hôpitaux de Paris, Université de Paris, Sorbonne Université - INSERM unit UMR S_1155 (Common and Rare Kidney Diseases), Paris, France
| | - Arthur S Slutsky
- Interdepartmental Division of Critical Care Medicine, Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
| | - James D Crapo
- Department of Internal Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael R Pinsky
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Brent James
- Department of Internal Medicine, Clinical Excellence Research Center (CERC), Stanford University School of Medicine, Stanford, California, USA
| | - Donald M Berwick
- Institute for Healthcare Improvement, Cambridge, Massachusetts, USA
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7
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McCreary EK, Bariola JR, Minnier TE, Wadas RJ, Shovel JA, Albin D, Marroquin OC, Kip KE, Collins K, Schmidhofer M, Wisniewski MK, Nace DA, Sullivan C, Axe M, Meyers R, Weissman A, Garrard W, Peck-Palmer OM, Wells A, Bart RD, Yang A, Berry LR, Berry S, Crawford AM, McGlothlin A, Khadem T, Linstrum K, Montgomery SK, Ricketts D, Kennedy JN, Pidro CJ, Haidar G, Snyder GM, McVerry BJ, Yealy DM, Angus DC, Nakayama A, Zapf RL, Kip PL, Seymour CW, Huang DT. The comparative effectiveness of COVID-19 monoclonal antibodies: A learning health system randomized clinical trial. Contemp Clin Trials 2022; 119:106822. [PMID: 35697146 PMCID: PMC9187853 DOI: 10.1016/j.cct.2022.106822] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/09/2022] [Accepted: 06/06/2022] [Indexed: 11/28/2022]
Abstract
Background Monoclonal antibodies (mAb) that neutralize SARS-CoV-2 decrease hospitalization and death compared to placebo in patients with mild to moderate COVID-19; however, comparative effectiveness is unknown. We report the comparative effectiveness of bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab. Methods A learning health system platform trial in a U.S. health system enrolled patients meeting mAb Emergency Use Authorization criteria. An electronic health record-embedded application linked local mAb inventory to patient encounters and provided random mAb allocation. Primary outcome was hospital-free days to day 28. Primary analysis was a Bayesian model adjusting for treatment location, age, sex, and time. Inferiority was defined as 99% posterior probability of an odds ratio < 1. Equivalence was defined as 95% posterior probability the odds ratio is within a given bound. Findings Between March 10 and June 25, 2021, 1935 patients received treatment. Median hospital-free days were 28 (IQR 28, 28) for each mAb. Mortality was 0.8% (1/128), 0.8% (7/885), and 0.7% (6/922) for bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab, respectively. Relative to casirivimab-imdevimab (n = 922), median adjusted odds ratios were 0.58 (95% credible interval [CI] 0.30–1.16) and 0.94 (95% CI 0.72–1.24) for bamlanivimab (n = 128) and bamlanivimab-etesevimab (n = 885), respectively. These odds ratios yielded 91% and 94% probabilities of inferiority of bamlanivimab versus bamlanivimab-etesevimab and casirivimab-imdevimab, and an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab. Interpretation Among patients with mild to moderate COVID-19, bamlanivimab-etesevimab or casirivimab-imdevimab treatment resulted in 86% probability of equivalence. No treatment met prespecified criteria for statistical equivalence. Median hospital-free days to day 28 were 28 (IQR 28, 28) for each mAb. Funding and registration This work received no external funding. The U.S. government provided the reported mAb. This trial is registered at ClinicalTrials.gov, NCT04790786.
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Affiliation(s)
- Erin K McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - J Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Tami E Minnier
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Richard J Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Judith A Shovel
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Debbie Albin
- Supply Chain Management/HC Pharmacy, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Oscar C Marroquin
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kevin E Kip
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kevin Collins
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - David A Nace
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Colleen Sullivan
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Meredith Axe
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Russell Meyers
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alexandra Weissman
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William Garrard
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Octavia M Peck-Palmer
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alan Wells
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Robert D Bart
- Health Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Anne Yang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | | | | | | | - Tina Khadem
- Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kelsey Linstrum
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Stephanie K Montgomery
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Daniel Ricketts
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jason N Kennedy
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Caroline J Pidro
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ghady Haidar
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Graham M Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bryan J McVerry
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Donald M Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Derek C Angus
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Anna Nakayama
- Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Rachel L Zapf
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Paula L Kip
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Christopher W Seymour
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David T Huang
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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8
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McCreary EK, Bariola JR, Wadas RJ, Shovel JA, Wisniewski MK, Adam M, Albin D, Minnier T, Schmidhofer M, Meyers R, Marroquin OC, Collins K, Garrard W, Berry LR, Berry S, Crawford AM, McGlothlin A, Linstrum K, Nakayama A, Montgomery SK, Snyder GM, Yealy DM, Angus DC, Kip PL, Seymour CW, Huang DT, Kip KE. Association of Subcutaneous or Intravenous Administration of Casirivimab and Imdevimab Monoclonal Antibodies With Clinical Outcomes in Adults With COVID-19. JAMA Netw Open 2022; 5:e226920. [PMID: 35412625 PMCID: PMC9006104 DOI: 10.1001/jamanetworkopen.2022.6920] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
IMPORTANCE Monoclonal antibody (mAb) treatment decreases hospitalization and death in high-risk outpatients with mild to moderate COVID-19; however, only intravenous administration has been evaluated in randomized clinical trials of treatment. Subcutaneous administration may expand outpatient treatment capacity and qualified staff available to administer treatment, but the association with patient outcomes is understudied. OBJECTIVES To evaluate whether subcutaneous casirivimab and imdevimab treatment is associated with reduced 28-day hospitalization and death compared with nontreatment among mAb-eligible patients and whether subcutaneous casirivimab and imdevimab treatment is clinically and statistically similar to intravenous casirivimab and imdevimab treatment. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study evaluated high-risk outpatients in a learning health system in the US with mild to moderate COVID-19 symptoms from July 14 to October 26, 2021, who were eligible for mAb treatment under emergency use authorization. A nontreated control group of eligible patients was also studied. EXPOSURES Subcutaneous injection or intravenous administration of the combined single dose of 600 mg of casirivimab and 600 mg of imdevimab. MAIN OUTCOMES AND MEASURES The primary outcome was the 28-day adjusted risk ratio or adjusted risk difference for hospitalization or death. Secondary outcomes included 28-day adjusted risk ratios and differences in hospitalization, death, a composite end point of emergency department admission and hospitalization, and rates of adverse events. Among 1959 matched adults with mild to moderate COVID-19, 969 patients (mean [SD] age, 53.8 [16.7] years; 547 women [56.4%]) who received casirivimab and imdevimab subcutaneously had a 28-day rate of hospitalization or death of 3.4% (22 of 653 patients) compared with 7.0% (92 of 1306 patients) in nontreated controls (risk ratio, 0.48; 95% CI, 0.30-0.80; P = .002). Among 2185 patients treated with subcutaneous (n = 969) or intravenous (n = 1216; mean [SD] age, 54.3 [16.6] years; 672 women [54.4%]) casirivimab and imdevimab, the 28-day rate of hospitalization or death was 2.8% vs 1.7%, which resulted in an adjusted risk difference of 1.5% (95% CI, -0.6% to 3.5%; P = .16). Among all infusion patients, there was no difference in intensive care unit admission (adjusted risk difference, 0.7%; 95% CI, -3.5% to 5.0%) or need for mechanical ventilation (adjusted risk difference, 0.2%; 95% CI, -5.8% to 5.5%). CONCLUSIONS AND RELEVANCE In this cohort study of high-risk outpatients with mild to moderate COVID-19 symptoms, subcutaneously administered casirivimab and imdevimab was associated with reduced hospitalization and death when compared with no treatment. These results provide preliminary evidence of potential expanded use of subcutaneous mAb treatment, particularly in areas that are facing treatment capacity and/or staffing shortages.
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Affiliation(s)
- Erin K. McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - J. Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Richard J. Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Judith A. Shovel
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Mary Kay Wisniewski
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Michelle Adam
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Debbie Albin
- Supply Chain Management/HC Pharmacy, UPMC, Pittsburgh, Pennsylvania
| | - Tami Minnier
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Russell Meyers
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | | | | | | | | | | | | | - Kelsey Linstrum
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Anna Nakayama
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | | | - Graham M. Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Donald M. Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Derek C. Angus
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Paula L. Kip
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Christopher W. Seymour
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - David T. Huang
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Kevin E. Kip
- Clinical Analytics, UPMC, Pittsburgh, Pennsylvania
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9
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Auriemma CL, Taylor SP, Harhay MO, Courtright KR, Halpern SD. Hospital-free Days: A Pragmatic and Patient-centered Outcome for Trials Among Critically and Seriously Ill Patients. Am J Respir Crit Care Med 2021; 204:902-909. [PMID: 34319848 DOI: 10.1164/rccm.202104-1063pp] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Hospital-free days (HFDs), alternatively known as "days alive and outside the hospital," is increasingly used as a primary or secondary outcome in randomized trials among critically and seriously ill patients. This novel outcome measure addresses an existing gap in the availability of patient-centered, reliably obtained outcome measures among patients with acute respiratory failure, advanced lung diseases, lung transplantation, and other serious and critical illnesses. Traditional outcomes such as mortality, organ-failure-free days, and longitudinal patient-reported measures have distinct drawbacks that limit their suitability as endpoints in trials of patients with serious illness, particularly those trials with pragmatic designs. By contrast, HFDs provides a summary measure of important health events and is easily calculated from administrative or electronic health record data, thereby balancing the goals of patient-centeredness and pragmatic measurement. However, before HFDs can be widely adopted as an endpoint in trials of patients with respiratory and critical illnesses, several questions must be addressed regarding the optimal definition, measurement, and analysis of HFDs. In this perspective, we outline important considerations relevant to the use of HFDs as a trial endpoint and suggest directions for further development of the measure.
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Affiliation(s)
- Catherine L Auriemma
- University of Pennsylvania, 6572, Medicine, Philadelphia, Pennsylvania, United States;
| | | | - Michael O Harhay
- University of Pennsylvania, Biostatistics, Epidemiology and Informatics, Philadelphia, Pennsylvania, United States
| | - Katherine R Courtright
- University of Pennsylvania Perelman School of Medicine, 14640, Medicine, Philadelphia, Pennsylvania, United States
| | - Scott D Halpern
- University of Pennsylvania Perelman School of Medicine, 14640, Philadelphia, Pennsylvania, United States
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10
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Implementation of the Randomized Embedded Multifactorial Adaptive Platform for COVID-19 (REMAP-COVID) trial in a US health system-lessons learned and recommendations. Trials 2021; 22:100. [PMID: 33509275 PMCID: PMC7841377 DOI: 10.1186/s13063-020-04997-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/22/2020] [Indexed: 12/18/2022] Open
Abstract
Background The Randomized Embedded Multifactorial Adaptive Platform for COVID-19 (REMAP-COVID) trial is a global adaptive platform trial of hospitalized patients with COVID-19. We describe implementation at the first US site, the UPMC health system, and offer recommendations for implementation at other sites. Methods To implement REMAP-COVID, we focused on six major areas: engaging leadership, trial embedment, remote consent and enrollment, regulatory compliance, modification of traditional trial management procedures, and alignment with other COVID-19 studies. Results We recommend aligning institutional and trial goals and sharing a vision of REMAP-COVID implementation as groundwork for learning health system development. Embedment of trial procedures into routine care processes, existing institutional structures, and the electronic health record promotes efficiency and integration of clinical care and clinical research. Remote consent and enrollment can be facilitated by engaging bedside providers and leveraging institutional videoconferencing tools. Coordination with the central institutional review board will expedite the approval process. Protocol adherence, adverse event monitoring, and data collection and export can be facilitated by building electronic health record processes, though implementation can start using traditional clinical trial tools. Lastly, establishment of a centralized institutional process optimizes coordination of COVID-19 studies. Conclusions Implementation of the REMAP-COVID trial within a large US healthcare system is feasible and facilitated by multidisciplinary collaboration. This investment establishes important groundwork for future learning health system endeavors. Trial registration NCT02735707. Registered on 13 April 2016.
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