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Zajic P, Engelbrecht T, Graf A, Metnitz B, Moreno R, Posch M, Rhodes A, Metnitz P. Intensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis. Crit Care 2024; 28:304. [PMID: 39277756 PMCID: PMC11401295 DOI: 10.1186/s13054-024-05090-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Accepted: 09/08/2024] [Indexed: 09/17/2024] Open
Abstract
BACKGROUND Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU). METHODS Retrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients' respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period. RESULTS 415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99-1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02-1.16), p = 0.008 for ≤ 50% and 1.10 (1.05-1.15), p < 0.0001 for 51-75%]. Exceptions were significant associations for caseloads > 100% between 2020 and 2022 [1.18 (1.06-1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21-30, lower [0.88 (0.78-0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01-1.19), p = 0.035 for 31-40, 1.28 (1.02-1.60), p = 0.033 for > 40]. CONCLUSIONS In a system with comparably high intensive care resources and mandatory staffing levels, patients' survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death.
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Affiliation(s)
- Paul Zajic
- Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria.
| | - Teresa Engelbrecht
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Alexandra Graf
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Barbara Metnitz
- Austrian Center for Documentation and Quality Assurance in Intensive Care, Vienna, Austria
| | - Rui Moreno
- Hospital de São José, Unidade Local de Saúde São José, Lisbon, Portugal
- Centro Clínico Académico de Lisboa, Lisbon, Portugal
- Faculdade de Ciências da Saúde, Universidade da Beira Interior, Lisbon, Portugal
| | - Martin Posch
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Andrew Rhodes
- Adult Critical Care, St. George's University Hospitals NHS Foundation Trust, St. George's University of London, London, UK
| | - Philipp Metnitz
- Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria
- Austrian Center for Documentation and Quality Assurance in Intensive Care, Vienna, Austria
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Fanelli C, Pistidda L, Terragni P, Pasero D. Infection Prevention and Control Strategies According to the Type of Multidrug-Resistant Bacteria and Candida auris in Intensive Care Units: A Pragmatic Resume including Pathogens R 0 and a Cost-Effectiveness Analysis. Antibiotics (Basel) 2024; 13:789. [PMID: 39200090 PMCID: PMC11351734 DOI: 10.3390/antibiotics13080789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/29/2024] [Accepted: 08/02/2024] [Indexed: 09/01/2024] Open
Abstract
Multidrug-resistant organism (MDRO) outbreaks have been steadily increasing in intensive care units (ICUs). Still, healthcare institutions and workers (HCWs) have not reached unanimity on how and when to implement infection prevention and control (IPC) strategies. We aimed to provide a pragmatic physician practice-oriented resume of strategies towards different MDRO outbreaks in ICUs. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios; education, isolation, decolonization, screening, and hygiene practices; outbreak reporting; cost-effectiveness; reproduction numbers (R0); and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician lose sight of the big picture. IPC strategies have proven their cost-effectiveness regardless of typology, country, and pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, the elaboration of a rapid outbreak recognition tool (i.e., an easy-to-use mathematical model) would improve early diagnosis and prevent spreading. Further studies are needed to express views in favor or against MDRO decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategy application has made and still makes ICUs major MDRO reservoirs in the community. In a not-too-distant future, genetic engineering and phage therapies could represent a plot twist in MDRO IPC strategies.
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Affiliation(s)
- Chiara Fanelli
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy (L.P.); (P.T.)
| | - Laura Pistidda
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy (L.P.); (P.T.)
| | - Pierpaolo Terragni
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy (L.P.); (P.T.)
- Head of Intensive Care Unit, University Hospital of Sassari, 07100 Sassari, Italy
| | - Daniela Pasero
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy (L.P.); (P.T.)
- Head of Intensive Care Unit, Civil Hospital of Alghero, 07041 Alghero, Italy
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Neupane M, De Jonge N, Angelo S, Sarzynski S, Sun J, Rochwerg B, Hick J, Mitchell SH, Warner S, Mancera A, Cooper D, Kadri SS. Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review. Crit Care Med 2024; 52:1097-1112. [PMID: 38517234 PMCID: PMC11176032 DOI: 10.1097/ccm.0000000000006263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
OBJECTIVES COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients. DATA SOURCES PubMed, Embase, and Web of Science. STUDY SELECTION English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients. DATA EXTRACTION Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations. DATA SYNTHESIS Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status. CONCLUSIONS Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.
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Affiliation(s)
- Maniraj Neupane
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Nathaniel De Jonge
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
| | - Sahil Angelo
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh
| | - Sadia Sarzynski
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Junfeng Sun
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Bram Rochwerg
- Department of Health Research Methods, Evidence and Impact and Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - John Hick
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN
| | | | - Sarah Warner
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Alex Mancera
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Diane Cooper
- Office of Research Services, Division of Library Services, National Institutes of Health, Bethesda, MD
| | - Sameer S. Kadri
- Clinical Epidemiology Section, Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
- Critical Care Medicine Branch, National Heart, Lung and Blood Institute, Bethesda, MD
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Gershengorn HB, Garland A, Costa DK, Dzierba AL, Fowler R, Kramer AA, Liu VX, Lizano D, Scales DC, Wunsch H. ICU Staffing in the United States. Chest 2024:S0012-3692(24)00631-7. [PMID: 38788896 DOI: 10.1016/j.chest.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/11/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND The last national estimates of US ICU physician staffing are 25 years old and lack information about interprofessional teams. RESEARCH QUESTION How are US adult ICUs currently staffed? STUDY DESIGN AND METHODS We conducted a cross-sectional survey (May 4, 2022-February 2, 2023) of adult ICU clinicians (targeting nurse/physician leadership) contacted using 2020 American Hospital Association (AHA) database information and, secondarily, through professional organizations. The survey included questions about interprofessional ICU staffing availability and roles at steady state (pre-COVID-19). We linked survey data to hospital data in the AHA database to create weighted national estimates by extrapolating ICU staffing data to nonrespondent hospitals based on hospital characteristics. RESULTS The cohort consisted of 596 adult ICUs (response rates: AHA contacts: 2.1%; professional organizations: unknown) with geographic diversity and size variability (median, 20 beds; interquartile range, 12-25); most cared for mixed populations (414 [69.5%]), yet medical (55 [9.2%]), surgical (70 [11.7%]), and specialty (57 [9.6%]) ICUs were well represented. A total of 554 (93.0%) had intensivists available, with intensivists covering all patients in 75.6% of these and onsite 24 h/d in one-half (53.3% weekdays; 51.8% weekends). Of all ICUs, 69.8% had physicians-in-training and 77.7% had nurse practitioners/physician assistants. For patients on mechanical ventilation, nurse to patient ratios were 1:2 in 89.6% of ICUs. Clinical pharmacists were available in 92.6%, and respiratory therapists were available in 98.8%. We estimated 85.1% (95% CI, 85.7%-84.5%) of hospitals nationally had ICUs with intensivists, 51.6% (95% CI, 50.6%-52.5%) had physicians-in-training, 72.1% (95% CI, 71.3%-72.9%) had nurse practitioners/physician assistants, 98.5% (95% CI, 98.4%-98.7%) had respiratory therapists, and 86.9% (95% CI, 86.4%-87.4%) had clinical pharmacists. For patients on mechanical ventilation, 86.4% (95% CI, 85.8%-87.0%) used 1:2 nurses/patients. INTERPRETATION We found that intensivist presence in adult US ICUs has greatly increased over 25 years. Intensivists, respiratory therapists, and clinical pharmacists are commonly available, and each nurse usually provides care for two patients on mechanical ventilation. However, team composition and workload vary.
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Affiliation(s)
- Hayley B Gershengorn
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, FL; Division of Critical Care Medicine, Albert Einstein College of Medicine, Bronx, NY.
| | - Allan Garland
- Department of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Deena K Costa
- Yale School of Nursing, West Haven, CT; Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT
| | - Amy L Dzierba
- Department of Pharmacy, New York-Presbyterian Hospital, New York, NY; Center for Acute Respiratory Failure, Columbia University College of Physicians and Surgeons and New York-Presbyterian Hospital, New York, NY
| | - Robert Fowler
- University Health Network & Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Tory Trauma Program, Sunnybrook Hospital, Toronto, ON, Canada
| | | | - Vincent X Liu
- Division of Research, Kaiser Permanente, Oakland, CA
| | - Danny Lizano
- Physician Assistant Program, Fort Lauderdale Dr. Pallavi Patel College of Health Care Sciences Health Professions Division, Nova Southeastern University, Fort Lauderdale, FL; Florida Kendall Hospital, Miami, FL
| | - Damon C Scales
- Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Hannah Wunsch
- Department of Anesthesiology, Weill Cornell Medical College, New York, NY; Sunnybrook Research Institute, Toronto, ON, Canada; Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
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Endo H, Okamoto H, Hashimoto S, Miyata H. Association Between In-hospital Mortality and the Institutional Factors of Intensive Care Units with a Focus on the Intensivist- to-bed Ratio: A Retrospective Cohort Study. J Intensive Care Med 2024:8850666241245645. [PMID: 38567432 DOI: 10.1177/08850666241245645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Purpose: To elucidate the relationship between in-hospital mortality and the institutional factors of intensive care units (ICUs), with a focus on the intensivist-to-bed ratio. Methods: A retrospective cohort study was conducted using a Japanese ICU database, including adult patients admitted between April 1, 2020 and March 31, 2021. We used a multilevel logistic regression model to investigate the associations between in-hospital mortality and the following institutional factors: the intensivist-to-bed ratios on weekdays or over weekends/holidays, different work shifts, hospital-to-ICU-bed ratio, annual-ICU-admission-to-bed ratio, type of hospital, and the presence of other medical staff. Results: The study population comprised 46 503 patients admitted to 65 ICUs. The in-hospital mortality rate was 8.1%. The median numbers of ICU beds and intensivists were 12 (interquartile range [IQR] 8-14) and 4 (IQR 2-9), respectively. In-hospital mortality decreased significantly as the intensivist-to-bed ratio at 10 am on weekdays increased: the average contrast indicated a 20% (95% confidence interval [CI]: 1%-38%) reduction when the ratio increased from 0 to 0.5, and a 38% (95% CI: 9%-67%) reduction when the ratio increased from 0 to 1. The other institutional factors did not present a significant effect. Conclusions: The intensivist-to-bed ratio at 10 am on weekdays had a significant effect on in-hospital mortality. Further investigation is needed to understand the processes leading to improved outcomes.
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Affiliation(s)
- Hideki Endo
- Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan
| | - Hiroshi Okamoto
- Department of Critical Care Medicine, St. Luke's International Hospital, Tokyo, Japan
| | - Satoru Hashimoto
- Non Profit Organization, ICU Collaboration Network, Tokyo, Japan
| | - Hiroaki Miyata
- Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan
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Herasevich S, Pinevich Y, Lindroth HL, Herasevich V, Pickering BW, Barwise AK. Who needs clinician attention first? A qualitative study of critical care clinicians' needs that enable the prioritization of care for populations of acutely ill patients. Int J Med Inform 2023; 177:105118. [PMID: 37295137 PMCID: PMC10527757 DOI: 10.1016/j.ijmedinf.2023.105118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/15/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard. METHODS We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software. RESULTS We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU. CONCLUSIONS This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.
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Affiliation(s)
- Svetlana Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN.
| | - Yuliya Pinevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN; Department of Anesthesiology, Republican Clinical Medical Center, Minsk, Belarus
| | - Heidi L Lindroth
- Department of Nursing, Mayo Clinic, Rochester, MN; Center for Health Innovation and Implementation Science, Center for Aging Research, School of Medicine, Indiana University, Indianapolis, IN
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Amelia K Barwise
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN; Bioethics Research Program, Mayo Clinic, Rochester, MN
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Keats K, Sikora A, Heavner MS, Chen X, Smith SE. Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio. Crit Care Explor 2023; 5:e0956. [PMID: 37644971 PMCID: PMC10461940 DOI: 10.1097/cce.0000000000000956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients. METHODS Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis. RESULTS Our protocol describes the processes and methods for an observational study in the ICU. CONCLUSIONS This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.
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Affiliation(s)
- Kelli Keats
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA
| | - Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA
| | - Mojdeh S Heavner
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD
| | - Xianyan Chen
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA
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Kahn JM, Yabes JG, Bukowski LA, Davis BS. Intensivist physician-to-patient ratios and mortality in the intensive care unit. Intensive Care Med 2023; 49:545-553. [PMID: 37133740 PMCID: PMC10155655 DOI: 10.1007/s00134-023-07066-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/01/2023] [Indexed: 05/04/2023]
Abstract
PURPOSE A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients. METHODS We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days. RESULTS The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14). CONCLUSIONS Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.
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Affiliation(s)
- Jeremy M Kahn
- CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 602B Allan Magee Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15213, USA.
- Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, PA, 15213, USA.
| | - Jonathan G Yabes
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Leigh A Bukowski
- CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 602B Allan Magee Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15213, USA
| | - Billie S Davis
- CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 602B Allan Magee Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15213, USA
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Herasevich S, Pinevich Y, Lipatov K, Barwise AK, Lindroth HL, LeMahieu AM, Dong Y, Herasevich V, Pickering BW. Evaluation of Digital Health Strategy to Support Clinician-Led Critically Ill Patient Population Management: A Randomized Crossover Study. Crit Care Explor 2023; 5:e0909. [PMID: 37151891 PMCID: PMC10158897 DOI: 10.1097/cce.0000000000000909] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023] Open
Abstract
To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR). DESIGN Single center randomized crossover study. SETTING Quaternary care academic hospital. SUBJECTS Attending and in-training critical care physicians, and advanced practice providers. INTERVENTIONS AMP. MEASUREMENTS AND MAIN RESULTS We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078). CONCLUSIONS When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting.
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Affiliation(s)
- Svetlana Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Yuliya Pinevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
- Department of Anesthesiology, Republican Clinical Medical Center, Minsk, Belarus
| | - Kirill Lipatov
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic Health Systems, Eau Claire, WI
| | - Amelia K Barwise
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Bioethics Research Program, Mayo Clinic, Rochester, MN
| | - Heidi L Lindroth
- Department of Nursing, Mayo Clinic, Rochester, MN
- Center for Health Innovation and Implementation Science, Center for Aging Research, School of Medicine, Indiana University, Indianapolis, IN
| | | | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
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Klick JC, Syed M, Leong R, Miranda H, Cotter EK. Health and Well-Being of Intensive Care Unit Physicians: How to Ensure the Longevity of a Critical Specialty. Anesthesiol Clin 2023; 41:303-316. [PMID: 36872006 PMCID: PMC9985495 DOI: 10.1016/j.anclin.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
A second epidemic of burnout, fatigue, anxiety, and moral distress has emerged concurrently with the coronavirus disease 2019 (COVID-19) pandemic, and critical care physicians are especially affected. This article reviews the history of burnout in health care workers, presents the signs and symptoms, discusses the specific impact of the COVID-19 pandemic on intensive care unit caregivers, and attempts to identify potential strategies to combat the Great Resignation disproportionately affecting health care workers. The article also focuses on how the specialty can amplify the voices and highlight the leadership potential of underrepresented minorities, physicians with disabilities, and the aging physician population.
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Affiliation(s)
- John C Klick
- Department of Anesthesiology, University of Vermont Medical Center, University of Vermont Larner College of Medicine, 111 Colchester Avenue, Burlington, VT 05401, USA
| | - Madiha Syed
- Department of Intensive Care & Resuscitation, Anesthesiology Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code G58, Cleveland, OH 44195, USA
| | - Ron Leong
- Thomas Jefferson University Hospital, Sidney Kimmel Medial College, 111 South 11th Street, Gibbon Building, Suite 8130, Philadelphia, PA 19107, USA
| | - Haley Miranda
- Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas Medical Center, 3901 Rainbow Boulevard, MS 1034, Kansas City, KS 66160, USA
| | - Elizabeth K Cotter
- Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas Medical Center, 3901 Rainbow Boulevard, MS 1034, Kansas City, KS 66160, USA.
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Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study. Crit Care Explor 2023; 5:e0864. [PMID: 36778910 PMCID: PMC9904765 DOI: 10.1097/cce.0000000000000864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN SETTING AND PARTICIPANTS This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61-1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57-1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001). CONCLUSIONS AND RELEVANCE Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.
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Strain on the ICU resources and patient outcomes in the COVID-19 pandemic: A Swedish national registry cohort study. Eur J Anaesthesiol 2023; 40:13-20. [PMID: 36156044 DOI: 10.1097/eja.0000000000001760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The Coronavirus 2019 (COVID-19) pandemic has led to an unprecedented strain on the ICU resources. It is not known how the ICU resources employed in treating COVID-19 patients are related to inpatient characteristics, use of organ support or mortality. OBJECTIVES To investigate how the use of ICU resources relate to use of organ support and mortality in COVID-19 patients. DESIGN A national register-based cohort study. SETTING All Swedish ICUs from March 2020 to November 2021. PATIENTS All patients admitted to Swedish ICUs with a primary diagnosis of COVID-19 reported to the national Swedish Intensive Care Register (SIR). MAIN OUTCOME MEASURES Organ support (mechanical ventilation, noninvasive ventilation, high-flow oxygen therapy, prone positioning, surgical and percutaneous tracheostomy, central venous catheterisation, continuous renal replacement therapy and intermittent haemodialysis), discharge at night, re-admission, transfer and ICU and 30-day mortality. RESULTS Seven thousand nine hundred and sixty-nine patients had a median age of 63 years, and 70% were men. Median daily census was 167% of habitual census, daily new admissions were 20% of habitual census and the median occupancy was 82%. Census and new admissions were associated with mechanical ventilation, OR 1.37 (95% CI 1.28 to 1.48) and OR 1.44 (95% CI 1.13 to 1.84), respectively, but negatively associated with noninvasive ventilation, OR 0.83 (95% CI 0.77 to 0.89) and OR 0.40 (95% CI 0.30 to 52) and high-flow oxygen therapy, OR 0.72 (95% CI 0.67 to 0.77) and OR 0.77 (95% CI 0.61 to 0.97). Occupancy above 90% of available beds was not associated with mechanical ventilation or noninvasive ventilation, but with high-flow oxygen therapy, OR 1.36 (95% CI 1.21 to 1.53). All measures of pressure on resources were associated with transfer to other hospitals, but none were associated with discharge at night, ICU mortality or 30-day mortality. CONCLUSIONS Pressure on ICU resources was associated with more invasive respiratory support, indicating that during these times, ICU resources were reserved for sicker patients.
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Variation in Bed-to-Physician Ratios During Weekday Daytime Hours in ICUs in Australia and New Zealand. Crit Care Med 2022; 50:1737-1747. [PMID: 35862614 DOI: 10.1097/ccm.0000000000005623] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVES To determine common "bed-to-physician" ratios during weekday hours across ICUs and assess factors associated with variability in this ratio. DESIGN Retrospective cohort study. SETTING All ICUs in Australia/New Zealand that participated in a staffing survey administered in 2017-2018. PATIENTS ICU admissions from 2016 to 2018. METHODS We linked survey data with patient-level data. We defined: 1) bed-to-intensivist ratio as the number of usually available ICU beds divided by the number of onsite weekday daytime intensivists; and 2) bed-to-physician ratio as the number of available ICU beds divided by the total number of physicians (intensivists + nonintensivists, including trainees). We calculated the median and interquartile range (IQR) of bed-to-intensivist ratio and bed-to-physician ratios during weekday hours. We assessed variability in each by type of hospital and ICU and by severity of illness of patients, defined by the predicted hospital mortality. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of the 123 (87.2%) of Australia/New Zealand ICUs that returned staffing surveys, 114 (92.7%) had an intensivist present during weekday daytime hours, and 116 (94.3%) reported at least one nonintensivist physician. The median bed-to-intensivist ratio was 8.0 (IQR, 6.0-11.4), which decreased to a bed-to-physician ratio of 3.0 (IQR, 2.2-4.9). These ratios varied with mean severity of illness of the patients in the unit. The median bed-to-intensivist ratio was highest (13.5) for ICUs with a mean predicted mortality > 2-4%, and the median bed-to-physician ratio was highest (5.7) for ICUs with a mean predicted mortality of > 4-6%. Both ratios decreased and plateaued in ICUs with a mean predicted mortality for patients greater than 8% (median bed-to-intensivist ratio range, 6.8-8.0, and bed-to-physician ratio range of 2.4-2.7). CONCLUSIONS Weekday bed-to-physician ratios in Australia/New Zealand ICUs are lower than the bed-to-intensivist ratios and have a relatively fixed ratio of less than 3 for units taking care of patients with a higher average severity of illness. These relationships may be different in other countries or healthcare systems.
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Agarwal A, Chen JT, Coopersmith CM, Denson JL, Dickert NW, Ferrante LE, Gershengorn HB, Gosine AD, Hayward BJ, Kaur N, Khan A, Lamberton C, Landsittel D, Lyons PG, Mikkelsen ME, Nadig NR, Pietropaoli AP, Poole BR, Viglianti EM, Sevransky JE. SWEAT ICU-An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs. Crit Care Explor 2022; 4:e0774. [PMID: 36259061 PMCID: PMC9575792 DOI: 10.1097/cce.0000000000000774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality. DESIGN Cross-sectional observational study. SETTING Fourteen academic centers in the United States from August 2020 to July 2021. SUBJECTS We enrolled ICU physicians and collected data on adult ICU patients under the physician's care on the single physician-selected study day for each physician. MEASUREMENTS and MAIN RESULTS The primary exposure was workload (self-reported number of patients' physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10-14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24-2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92-1.91). CONCLUSIONS In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.
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Affiliation(s)
- Ankita Agarwal
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA
- Emory Critical Care Center, Emory Healthcare, Atlanta, GA
| | - Jen-Ting Chen
- Division of Critical Care Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY
| | - Craig M Coopersmith
- Emory Critical Care Center, Emory Healthcare, Atlanta, GA
- Department of Surgery, Emory University School of Medicine, Atlanta, GA
| | - Joshua L Denson
- Section of Pulmonary Diseases, Critical Care, and Environmental Medicine, Tulane University School of Medicine, New Orleans, LA
| | - Neal W Dickert
- Department of Medicine, Emory University School of Medicine, Atlanta, GA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Lauren E Ferrante
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Hayley B Gershengorn
- Division of Critical Care Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Adhiraj D Gosine
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Bradley J Hayward
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Navneet Kaur
- Division of Pulmonary and Critical Care Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Akram Khan
- Division of Pulmonary Critical Care, Oregon Health and Science University, Portland, OR
| | - Courtney Lamberton
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Douglas Landsittel
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN
| | - Patrick G Lyons
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Mark E Mikkelsen
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Nandita R Nadig
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL
| | - Anthony P Pietropaoli
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Rochester Medical Center, Rochester, NY
| | - Brian R Poole
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Utah, Salt Lake City, UT
| | - Elizabeth M Viglianti
- Division Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, MI
| | - Jonathan E Sevransky
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA
- Emory Critical Care Center, Emory Healthcare, Atlanta, GA
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Jimenez JV, Sifuentes-Osornio J, Hyzy RC. Understanding the Impact of Intensive Care Unit Personnel on Intensive Care Unit Mortality during Times of High Demand. Ann Am Thorac Soc 2022; 19:1623-1624. [PMID: 35522445 PMCID: PMC9447386 DOI: 10.1513/annalsats.202203-231le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Jose Victor Jimenez
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador ZubiránMexico City, Mexico
- University of MichiganAnn Arbor, Michigan
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Benes J, Jankowski M, Szułdrzynski K, Zahorec R, Lainscak M, Ruszkai Z, Podbregar M, Zatloukal J, Kletecka J, Kusza K, Szrama J, Ramic E, Galkova K, Krbila S, Valky J, Ivanic J, Kurnik M, Mikó A, Kiss T, Hetényi B, Hegyi P, Sustic A, Molnar Z. SepsEast Registry indicates high mortality associated with COVID-19 caused acute respiratory failure in Central-Eastern European intensive care units. Sci Rep 2022; 12:14906. [PMID: 36050403 PMCID: PMC9436166 DOI: 10.1038/s41598-022-18991-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The coronavirus disease (COVID-19) pandemic caused unprecedented research activity all around the world but publications from Central-Eastern European countries remain scarce. Therefore, our aim was to characterise the features of the pandemic in the intensive care units (ICUs) among members of the SepsEast (Central-Eastern European Sepsis Forum) initiative. We conducted a retrospective, international, multicentre study between March 2020 and February 2021. All adult patients admitted to the ICU with pneumonia caused by COVID-19 were enrolled. Data on baseline and treatment characteristics, organ support and mortality were collected. Eleven centres from six countries provided data from 2139 patients. Patient characteristics were: median 68, [IQR 60-75] years of age; males: 67%; body mass index: 30.1 [27.0-34.7]; and 88% comorbidities. Overall mortality was 55%, which increased from 2020 to 2021 (p = 0.004). The major causes of death were respiratory (37%), cardiovascular (26%) and sepsis with multiorgan failure (21%). 1061 patients received invasive mechanical ventilation (mortality: 66%) without extracorporeal membrane oxygenation (n = 54). The rest of the patients received non-invasive ventilation (n = 129), high flow nasal oxygen (n = 317), conventional oxygen therapy (n = 122), as the highest level of ventilatory support, with mortality of 50%, 39% and 22%, respectively. This is the largest COVID-19 dataset from Central-Eastern European ICUs to date. The high mortality observed especially in those receiving invasive mechanical ventilation renders the need of establishing national-international ICU registries and audits in the region that could provide high quality, transparent data, not only during the pandemic, but also on a regular basis.
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Affiliation(s)
- Jan Benes
- Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Pilsen, Pilsen, Czech Republic
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
| | - Miłosz Jankowski
- Department of Anesthesiology and Intensive Therapy, Central Clinical Hospital of the Ministry of Interior and Administration, Warsaw, Poland
- Jagiellonian University Medical College, Krakow, Poland
| | - Konstanty Szułdrzynski
- Department of Anesthesiology and Intensive Therapy, Central Clinical Hospital of the Ministry of Interior and Administration, Warsaw, Poland
- Jagiellonian University Medical College, Krakow, Poland
| | - Roman Zahorec
- Anesthesiology and Intensive Medicine, Medical School, Comenius University, Bratislava, Slovakia
| | - Mitja Lainscak
- Division of Cardiology, General Hospital Murska Sobota, Murska Sobota, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Zoltán Ruszkai
- Department of Anesthesiology and Intensive Therapy, Flór Ferenc Hospital County Pest, Kistarcsa, Hungary
| | - Matej Podbregar
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department for Internal Care Medicine, General Hospital Celje, Celje, Slovenia
| | - Jan Zatloukal
- Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Pilsen, Pilsen, Czech Republic
| | - Jakub Kletecka
- Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Pilsen, Pilsen, Czech Republic
| | - Krzysztof Kusza
- Department of Anesthesiology and Intensive Therapy and Pain Management, Poznan University of Medical Sciences, Poznan, Poland
| | - Jakub Szrama
- Department of Anesthesiology and Intensive Therapy and Pain Management, Poznan University of Medical Sciences, Poznan, Poland
| | - Estera Ramic
- Department of Anesthesiology, Reanimatology, Intensive Care and Emergency Medicine, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Katarina Galkova
- Department of Anaesthesiology and Intensive Care, Faculty Hospital, Nitra, Slovakia
| | - Stefan Krbila
- Department of Anaesthesia and Intensive Therapy, University Hospital Nové Zámky, Nové Zamky, Slovakia
| | - Josef Valky
- Department Anesthesiology and Intensive Therapy, University Hospital Banska Bystrica, Banska Bystrica, Slovakia
| | - Jaka Ivanic
- Department of Ananesthesiology and Perioperative Medicine, General Hospital Murska Sobota, Murska Sobota, Slovenia
| | - Marko Kurnik
- Department for Internal Care Medicine, General Hospital Celje, Celje, Slovenia
| | - Angéla Mikó
- Department of Anesthesiology and Intensive Therapy, Flór Ferenc Hospital County Pest, Kistarcsa, Hungary
| | - Tamás Kiss
- Department of Anesthesiology and Intensive Therapy, School of Medicine, University of Pécs, Pécs, Hungary
| | - Barbara Hetényi
- Department of Anesthesiology and Intensive Therapy, School of Medicine, University of Pécs, Pécs, Hungary
| | - Peter Hegyi
- Institute for Translational Medicine, Medical School, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division for Pancreatic Disorders, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Alan Sustic
- Department of Anesthesiology, Reanimatology, Intensive Care and Emergency Medicine, Faculty of Medicine, University of Rijeka, Rijeka, Croatia.
- Department of Clinical Medical Science II, Faculty of Health Studies, University of Rijeka, Rijeka, Croatia.
| | - Zsolt Molnar
- Department of Anesthesiology and Intensive Therapy and Pain Management, Poznan University of Medical Sciences, Poznan, Poland.
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
- Department of Anaesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary.
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Burns ML, Saager L, Cassidy RB, Mentz G, Mashour GA, Kheterpal S. Association of Anesthesiologist Staffing Ratio With Surgical Patient Morbidity and Mortality. JAMA Surg 2022; 157:807-815. [PMID: 35857304 DOI: 10.1001/jamasurg.2022.2804] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Importance Recent studies have investigated the effect of overlapping surgeon responsibilities or nurse to patient staffing ratios on patient outcomes, but the association of overlapping anesthesiologist responsibilities with patient outcomes remains unexplored to our knowledge. Objective To examine the association between different levels of anesthesiologist staffing ratios and surgical patient morbidity and mortality. Design, Setting, and Participants A retrospective, matched cohort study consisting of major noncardiac inpatient surgical procedures performed from January 1, 2010, to October 31, 2017, was conducted in 23 US academic and private hospitals. A total of 866 453 adult patients (aged ≥18 years) undergoing major inpatient surgery within the Multicenter Perioperative Outcomes Group electronic health record registry were included. Anesthesiologist sign-in and sign-out times were used to calculate a continuous time-weighted average staffing ratio variable for each operation. Propensity score-matching methods were applied to create balanced sample groups with respect to patient-, operative-, and hospital-level confounders and resulted in 4 groups based on anesthesiologist staffing ratio. Groups consisted of patients receiving care from an anesthesiologist covering 1 operation (group 1), more than 1 to no more than 2 overlapping operations (group 1-2), more than 2 to no more than 3 overlapping operations (group 2-3), and more than 3 to no more than 4 overlapping operations (group 3-4). Data analysis was performed from October 2019 to October 2021. Exposure Undergoing a major inpatient surgical operation that involved an anesthesiologist providing care for up to 4 overlapping operations. Main Outcomes and Measures The primary composite outcome was 30-day mortality and 6 major surgical morbidities (cardiac, respiratory, gastrointestinal, urinary, bleeding, and infectious complications) derived from International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision discharge diagnosis codes. Results In all, 578 815 adult patients (mean [SD] age, 55.7 [16.2] years; 55.1% female) were analyzed. After matching operations according to anesthesiologist staffing ratio, 48 555 patients were in group 1; 247 057, group 1-2; 216 193, group 2-3; and 67 010, group 3-4. Increasing anesthesiologist coverage responsibilities was associated with an increase in risk-adjusted surgical patient morbidity and mortality. Compared with patients in group 1-2, those in group 2-3 had a 4% relative increase in risk-adjusted mortality and morbidity (5.06% vs 5.25%; adjusted odds ratio [AOR], 1.04; 95% CI, 1.01-1.08; P = .02) and those in group 3-4 had a 14% increase in risk-adjusted mortality and morbidity (5.06% vs 5.75%; AOR, 1.15; 95% CI, 1.09-1.21; P < .001). Conclusions and Relevance This study's findings suggest that increasing overlapping coverage by anesthesiologists is associated with increased surgical patient morbidity and mortality. Therefore, the potential effects of staffing ratios in perioperative team models should be considered in clinical coverage efforts.
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Affiliation(s)
- Michael L Burns
- Department of Anesthesiology, University of Michigan, Ann Arbor
| | - Leif Saager
- Klinik für Anästhesiologie, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Ruth B Cassidy
- Department of Anesthesiology, University of Michigan, Ann Arbor
| | - Graciela Mentz
- Department of Anesthesiology, University of Michigan, Ann Arbor
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Konishi T, Fujiogi M, Michihata N, Matsui H, Tanabe M, Seto Y, Yasunaga H. Association between body mass index and incidence of breast cancer in premenopausal women: a Japanese nationwide database study. Breast Cancer Res Treat 2022; 194:315-325. [DOI: 10.1007/s10549-022-06638-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/16/2022] [Indexed: 11/02/2022]
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Jimenez JV, Olivas-Martinez A, Rios-Olais FA, Ayala-Aguillón F, Gil-López F, Leal-Villarreal MADJ, Rodríguez-Crespo JJ, Jasso-Molina JC, Enamorado-Cerna L, Dardón-Fierro FE, Martínez-Guerra BA, Román-Montes CM, Alvarado-Avila PE, Juárez-Meneses NA, Morales-Paredes LA, Chávez-Suárez A, Gutierrez-Espinoza IR, Najera-Ortíz MP, Martínez-Becerril M, Gonzalez-Lara MF, Ponce de León-Garduño A, Baltazar-Torres JÁ, Rivero-Sigarroa E, Dominguez-Cherit G, Hyzy RC, Kershenobich D, Sifuentes-Osornio J. Outcomes in Temporary ICUs Versus Conventional ICUs: An Observational Cohort of Mechanically Ventilated Patients With COVID-19-Induced Acute Respiratory Distress Syndrome. Crit Care Explor 2022; 4:e0668. [PMID: 35372841 PMCID: PMC8963854 DOI: 10.1097/cce.0000000000000668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Throughout the COVID-19 pandemic, thousands of temporary ICUs have been established worldwide. The outcomes and management of mechanically ventilated patients in these areas remain unknown. OBJECTIVES To investigate mortality and management of mechanically ventilated patients in temporary ICUs. DESIGN SETTING AND PARTICIPANTS Observational cohort study in a single-institution academic center. We included all adult patients with severe COVID-19 hospitalized in temporary and conventional ICUs for invasive mechanical ventilation due to acute respiratory distress syndrome from March 23, 2020, to April 5, 2021. MAIN OUTCOMES AND MEASURES To determine if management in temporary ICUs increased 30-day in-hospital mortality compared with conventional ICUs. Ventilator-free days, ICU-free days (both at 28 d), hospital length of stay, and ICU readmission were also assessed. RESULTS We included 776 patients (326 conventional and 450 temporary ICUs). Thirty-day in-hospital unadjusted mortality (28.8% conventional vs 36.0% temporary, log-rank test p = 0.023) was higher in temporary ICUs. After controlling for potential confounders, hospitalization in temporary ICUs was an independent risk factor associated with mortality (hazard ratio, 1.4; CI, 1.06-1.83; p = 0.016).There were no differences in ICU-free days at 28 days (6; IQR, 0-16 vs 2; IQR, 0-15; p = 0.5) or ventilator-free days at 28 days (8; IQR, 0-16 vs 5; IQR, 0-15; p = 0.6). We observed higher reintubation (18% vs 12%; p = 0.029) and readmission (5% vs 1.6%; p = 0.004) rates in conventional ICUs despite higher use of postextubation noninvasive mechanical ventilation (13% vs 8%; p = 0.025). Use of lung-protective ventilation (87% vs 85%; p = 0.5), prone positioning (76% vs 79%; p = 0.4), neuromuscular blockade (96% vs 98%; p = 0.4), and COVID-19 pharmacologic treatment was similar. CONCLUSIONS AND RELEVANCE We observed a higher 30-day in-hospital mortality in temporary ICUs. Although both areas had high adherence to evidence-based management, hospitalization in temporary ICUs was an independent risk factor associated with mortality.
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Affiliation(s)
- Jose Victor Jimenez
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Antonio Olivas-Martinez
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Fausto Alfredo Rios-Olais
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Frida Ayala-Aguillón
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Fernando Gil-López
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | | | - Juan José Rodríguez-Crespo
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Juan C Jasso-Molina
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Linda Enamorado-Cerna
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | | | - Bernardo A Martínez-Guerra
- Department of Infectious Disease, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Carla Marina Román-Montes
- Department of Infectious Disease, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Pedro E Alvarado-Avila
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Noé Alonso Juárez-Meneses
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Luis Alberto Morales-Paredes
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Adriana Chávez-Suárez
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Irving Rene Gutierrez-Espinoza
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - María Paula Najera-Ortíz
- Department of Nursing, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Marina Martínez-Becerril
- Department of Nursing, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - María Fernanda Gonzalez-Lara
- Department of Infectious Disease, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Alfredo Ponce de León-Garduño
- Department of Infectious Disease, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - José Ángel Baltazar-Torres
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Eduardo Rivero-Sigarroa
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Guillermo Dominguez-Cherit
- Department of Critical Care Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Escuela de Medicina y Ciencias de la Salud TecSalud del Tecnológico de Monterrey, Monterrey, Mexico
| | - Robert C Hyzy
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - David Kershenobich
- Escuela de Medicina y Ciencias de la Salud TecSalud del Tecnológico de Monterrey, Monterrey, Mexico
| | - José Sifuentes-Osornio
- Department of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
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Kerlin MP, Caruso P. Towards evidence-based staffing: the promise and pitfalls of patient-to-intensivist ratios. Intensive Care Med 2022; 48:225-226. [PMID: 35024883 PMCID: PMC8755975 DOI: 10.1007/s00134-021-06614-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/24/2021] [Indexed: 11/24/2022]
Affiliation(s)
- Meeta Prasad Kerlin
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, 302 Blockley Hall, Philadelphia, PA, 19103, USA.
| | - Pedro Caruso
- Intensive Care Unit, AC Camargo Cancer Center, São Paulo, Brazil.,Pulmonary Division, Heart Institute (InCor), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
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21
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Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand. Intensive Care Med 2021; 48:179-189. [PMID: 34854939 PMCID: PMC8638228 DOI: 10.1007/s00134-021-06575-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/30/2021] [Indexed: 11/29/2022]
Abstract
Purpose The impact of intensivist workload on intensive care unit (ICU) outcomes is incompletely described and assessed across healthcare systems and countries. We sought to examine the association of patient-to-intensivist ratio (PIR) with hospital mortality in Australia/New Zealand (ANZ) ICUs. Methods We conducted a retrospective study of adult admissions to ANZ ICUs (August 2016–June 2018) using two cohorts: “narrow”, based on previously used criteria including restriction to ICUs with a single daytime intensivist; and “broad”, refined by individual ICU daytime staffing information. The exposure was average daily PIR and the outcome was hospital mortality. We used summary statistics to describe both cohorts and multilevel multivariable logistic regression models to assess the association of PIR with mortality. In each, PIR was modeled using restricted cubic splines to allow for non-linear associations. The broad cohort model included non-PIR physician and non-physician staffing covariables. Results The narrow cohort of 27,380 patients across 67 ICUs (predicted mortality: median 1.2% [IQR 0.4–1.4%]; mean 5.9% [sd 13.2%]) had a median PIR of 10.1 (IQR 7–14). The broad cohort of 91,206 patients across 73 ICUs (predicted mortality: 1.9% [0.6–6.5%]; 7.6% [14.9%]) had a median PIR of 7.8 (IQR 5.8–10.2). We found no association of PIR with mortality in either the narrow (PIR 1st spline term odds ratio [95% CI]: 1 [0.94, 1.06], Wald testing of spline terms p = 0.61) or the broad (1.02 [0.97, 1.07], p = 0.4) cohort. Conclusion We found no association of PIR with hospital mortality across ANZ ICUs. The low cohort predicted mortality may limit external validity. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06575-z.
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