Publications by authors named "George Anesi"

33 Publications

The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage.

Curr Opin Crit Care 2021 Jul 8. Epub 2021 Jul 8.

Division of Pulmonary, Allergy, and Critical Care Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Purpose Of Review: Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic.

Recent Findings: Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic.

Summary: The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.
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http://dx.doi.org/10.1097/MCC.0000000000000859DOI Listing
July 2021

Impact of COVID-19 on inpatient clinical emergencies: A single-center experience.

Resusc Plus 2021 Jun 4;6:100135. Epub 2021 May 4.

Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine, University of Pennsylvania, United States.

Aim: Determine changes in rapid response team (RRT) activations and describe institutional adaptations made during a surge in hospitalizations for coronavirus disease 2019 (COVID-19).

Methods: Using prospectively collected data, we compared characteristics of RRT calls at our academic hospital from March 7 through May 31, 2020 (COVID-19 era) versus those from January 1 through March 6, 2020 (pre-COVID-19 era). We used negative binomial regression to test differences in RRT activation rates normalized to floor (non-ICU) inpatient census between pre-COVID-19 and COVID-19 eras, including the sub-era of rapid COVID-19 census surge and plateau (March 28 through May 2, 2020).

Results: RRT activations for respiratory distress rose substantially during the rapid COVID-19 surge and plateau (2.38 (95% CI 1.39-3.36) activations per 1000 floor patient-days v. 1.27 (0.82-1.71) during the pre-COVID-19 era; p = 0.02); all-cause RRT rates were not significantly different (5.40 (95% CI 3.94-6.85) v. 4.83 (3.86-5.80) activations per 1000 floor patient-days, respectively; p = 0.52). Throughout the COVID-19 era, respiratory distress accounted for a higher percentage of RRT activations in COVID-19 versus non-COVID-19 patients (57% vs. 28%, respectively; p = 0.001). During the surge, we adapted RRT guidelines to reduce in-room personnel and standardize personal protective equipment based on COVID-19 status and risk to providers, created decision-support pathways for respiratory emergencies that accounted for COVID-19 status uncertainty, and expanded critical care consultative support to floor teams.

Conclusion: Increased frequency and complexity of RRT activations for respiratory distress during the COVID-19 surge prompted the creation of clinical tools and strategies that could be applied to other hospitals.
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http://dx.doi.org/10.1016/j.resplu.2021.100135DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096179PMC
June 2021

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Am J Respir Crit Care Med 2021 Jul;204(2):178-186

Palliative and Advanced Illness Research Center.

Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.
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http://dx.doi.org/10.1164/rccm.202012-4383OCDOI Listing
July 2021

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

Ann Intern Med 2021 05 19;174(5):613-621. Epub 2021 Jan 19.

University of Pennsylvania Health System, Philadelphia, Pennsylvania (G.L.A., J.J., M.O.H., J.H.A., J.B., C.B., P.J.B., C.L.C., L.M.C., M.F.C., J.M.C., J.D.C., T.C., K.R.C., B.D.F., E.G., J.C.G., S.G., A.H., C.W.H., M.H., P.K., Z.N.K., G.B.K., M.L., N.D.M., M.E.M., D.N., J.L.P., M.B.P., S.C.P., Z.A.Q., J.P.R., J.S., W.D.S., M.J.S., M.G.S., C.P.S., J.K.W., W.W., A.A.W., B.J.A., J.T.G.).

Background: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally.

Objective: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery.

Design: Single-health system, multihospital retrospective cohort study.

Setting: 5 hospitals within the University of Pennsylvania Health System.

Patients: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic.

Measurements: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions.

Results: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change.

Limitations: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications.

Conclusion: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms.

Primary Funding Source: Agency for Healthcare Research and Quality.
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http://dx.doi.org/10.7326/M20-5327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901669PMC
May 2021

National Psoriasis Foundation COVID-19 Task Force guidance for management of psoriatic disease during the pandemic: Version 2-Advances in psoriatic disease management, COVID-19 vaccines, and COVID-19 treatments.

J Am Acad Dermatol 2021 05 7;84(5):1254-1268. Epub 2021 Jan 7.

Division of Allergy, Immunology, and Rheumatology Division, University of Rochester Medical Center, Rochester, New York.

Objective: To update guidance regarding the management of psoriatic disease during the COVID-19 pandemic.

Study Design: The task force (TF) includes 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation staff. Clinical questions relevant to the psoriatic disease community were informed by inquiries received by the National Psoriasis Foundation. A Delphi process was conducted.

Results: The TF updated evidence for the original 22 statements and added 5 new recommendations. The average of the votes was within the category of agreement for all statements, 13 with high consensus and 14 with moderate consensus.

Limitations: The evidence behind many guidance statements is variable in quality and/or quantity.

Conclusions: These statements provide guidance for the treatment of patients with psoriatic disease on topics including how the disease and its treatments affect COVID-19 risk, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 (including novel vaccination), and what they should do if they develop COVID-19. The guidance is a living document that is continuously updated by the TF as data emerge.
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http://dx.doi.org/10.1016/j.jaad.2020.12.058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788316PMC
May 2021

Hospital transfers across U.S. regions to address the "space" shortage in a pandemic: a public good.

Authors:
George L Anesi

Clin Infect Dis 2020 Oct 18. Epub 2020 Oct 18.

Division of Pulmonary, Allergy, and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

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http://dx.doi.org/10.1093/cid/ciaa1596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665414PMC
October 2020

National Psoriasis Foundation COVID-19 Task Force Guidance for Management of Psoriatic Disease During the Pandemic: Version 1.

J Am Acad Dermatol 2020 Dec 4;83(6):1704-1716. Epub 2020 Sep 4.

Division of Allergy, Immunology, and Rheumatology, University of Rochester Medical Center, Rochester, New York.

Objective: To provide guidance about management of psoriatic disease during the coronavirus disease 2019 (COVID-19) pandemic.

Study Design: A task force (TF) of 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care was convened. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation (NPF) staff. Clinical questions relevant to the psoriatic disease community were informed by questions received by the NPF. A Delphi process was conducted.

Results: The TF approved 22 guidance statements. The average of the votes was within the category of agreement for all statements. All guidance statements proposed were recommended, 9 with high consensus and 13 with moderate consensus.

Limitations: The evidence behind many guidance statements is limited in quality.

Conclusion: These statements provide guidance for the management of patients with psoriatic disease on topics ranging from how the disease and its treatments impact COVID-19 risk and outcome, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 and what they should do if they develop COVID-19. The guidance is intended to be a living document that will be updated by the TF as data emerge.
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http://dx.doi.org/10.1016/j.jaad.2020.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471802PMC
December 2020

Watchful Waiting in the ICU? Considerations for the Allocation of ICU Resources.

Am J Respir Crit Care Med 2020 11;202(10):1332-1333

Division of Pulmonary, Allergy, and Critical Care.

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http://dx.doi.org/10.1164/rccm.202007-2873EDDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667905PMC
November 2020

Association of an Emergency Department-embedded Critical Care Unit with Hospital Outcomes and Intensive Care Unit Use.

Ann Am Thorac Soc 2020 12;17(12):1599-1609

Department of Emergency Medicine.

A small but growing number of hospitals are experimenting with emergency department-embedded critical care units (CCUs) in an effort to improve the quality of care for critically ill patients with sepsis and acute respiratory failure (ARF). To evaluate the potential impact of an emergency department-embedded CCU at the Hospital of the University of Pennsylvania among patients with sepsis and ARF admitted from the emergency department to a medical ward or intensive care unit (ICU) from January 2016 to December 2017. The exposure was eligibility for admission to the emergency department-embedded CCU, which was defined as meeting a clinical definition for sepsis or ARF and admission to the emergency department during the intervention period on a weekday. The primary outcome was hospital length of stay (LOS); secondary outcomes included total emergency department plus ICU LOS, hospital survival, direct admission to the ICU, and unplanned ICU admission. Primary interrupted time series analyses were performed using ordinary least squares regression comparing monthly means. Secondary retrospective cohort and before-after analyses used multivariable Cox proportional hazard and logistic regression. In the baseline and intervention periods, 3,897 patients met the inclusion criteria for sepsis and 1,865 patients met the criteria for ARF. Among patients admitted with sepsis, opening of the emergency department-embedded CCU was not associated with hospital LOS (β = -1.82 d; 95% confidence interval [CI], -4.50 to 0.87;  = 0.17 for the first month after emergency department-embedded CCU opening compared with baseline; β = -0.26 d; 95% CI, -0.58 to 0.06;  = 0.10 for subsequent months). Among patients admitted with ARF, the emergency department-embedded CCU was not associated with a significant change in hospital LOS for the first month after emergency department-embedded CCU opening (β = -3.25 d; 95% CI, -7.86 to 1.36;  = 0.15) but was associated with a 0.64 d/mo shorter hospital LOS for subsequent months (β = -0.64 d; 95% CI, -1.12 to -0.17;  = 0.01). This result persisted among higher acuity patients requiring ventilatory support but was not supported by alternative analytic approaches. Among patients admitted with sepsis who did not require mechanical ventilation or vasopressors in the emergency department, the emergency department-embedded CCU was associated with an initial 9.9% reduction in direct ICU admissions in the first month (β = -0.099; 95% CI, -0.153 to -0.044;  = 0.002), followed by a 1.1% per month increase back toward baseline in subsequent months (β = 0.011; 95% CI, 0.003-0.019;  = 0.009). This relationship was supported by alternative analytic approaches and was not seen in ARF. No associations with emergency department plus ICU LOS, hospital survival, or unplanned ICU admission were observed among patients with sepsis or ARF. The emergency department-embedded CCU was not associated with clinical outcomes among patients admitted with sepsis or ARF. Among less sick patients with sepsis, the emergency department-embedded CCU was initially associated with reduced rates of direct ICU admission from the emergency department. Additional research is necessary to further evaluate the impact and utility of the emergency department-embedded CCU model.
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http://dx.doi.org/10.1513/AnnalsATS.201912-912OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706601PMC
December 2020

COVID-19 and cardiac arrhythmias.

Heart Rhythm 2020 Sep 22;17(9):1439-1444. Epub 2020 Jun 22.

Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address:

Background: Early studies suggest that coronavirus disease 2019 (COVID-19) is associated with a high incidence of cardiac arrhythmias. Severe acute respiratory syndrome coronavirus 2 infection may cause injury to cardiac myocytes and increase arrhythmia risk.

Objectives: The purpose of this study was to evaluate the risk of cardiac arrest and arrhythmias including incident atrial fibrillation (AF), bradyarrhythmias, and nonsustained ventricular tachycardia (NSVT) in a large urban population hospitalized for COVID-19. We also evaluated correlations between the presence of these arrhythmias and mortality.

Methods: We reviewed the characteristics of all patients with COVID-19 admitted to our center over a 9-week period. Throughout hospitalization, we evaluated the incidence of cardiac arrests, arrhythmias, and inpatient mortality. We also used logistic regression to evaluate age, sex, race, body mass index, prevalent cardiovascular disease, diabetes, hypertension, chronic kidney disease, and intensive care unit (ICU) status as potential risk factors for each arrhythmia.

Results: Among 700 patients (mean age 50 ± 18 years; 45% men; 71% African American; 11% received ICU care), there were 9 cardiac arrests, 25 incident AF events, 9 clinically significant bradyarrhythmias, and 10 NSVTs. All cardiac arrests occurred in patients admitted to the ICU. In addition, admission to the ICU was associated with incident AF (odds ratio [OR] 4.68; 95% confidence interval [CI] 1.66-13.18) and NSVT (OR 8.92; 95% CI 1.73-46.06) after multivariable adjustment. Also, age and incident AF (OR 1.05; 95% CI 1.02-1.09) and prevalent heart failure and bradyarrhythmias (OR 9.75; 95% CI 1.95-48.65) were independently associated. Only cardiac arrests were associated with acute in-hospital mortality.

Conclusion: Cardiac arrests and arrhythmias are likely the consequence of systemic illness and not solely the direct effects of COVID-19 infection.
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http://dx.doi.org/10.1016/j.hrthm.2020.06.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307518PMC
September 2020

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units.

Ann Am Thorac Soc 2020 11;17(11):1440-1447

Division of Research, Kaiser Permanente, Oakland, California.

Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations. We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes. Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression. Among high-acuity patients with sepsis ( = 90,150) and acute respiratory failure (ARF;  = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile;  < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all  ≥ 0.13). Strain was also not meaningfully associated with patient characteristics. Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.
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http://dx.doi.org/10.1513/AnnalsATS.202003-228OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640725PMC
November 2020

Covid-19 related hospital admissions in the United States: needs and outcomes.

BMJ 2020 05 27;369:m2082. Epub 2020 May 27.

Center for Emergency Care Policy and Research, Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

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http://dx.doi.org/10.1136/bmj.m2082DOI Listing
May 2020

A Conceptual and Adaptable Approach to Hospital Preparedness for Acute Surge Events Due to Emerging Infectious Diseases.

Crit Care Explor 2020 Apr 29;2(4):e0110. Epub 2020 Apr 29.

Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington Medical Center, Seattle, WA.

At the time this article was written, the World Health Organization had declared a global pandemic due to the novel coronavirus disease 2019, the first pandemic since 2009 H1N1 influenza A. Emerging respiratory pathogens are a common trigger of acute surge events-the extreme end of the healthcare capacity strain spectrum in which there is a dramatic increase in care demands and/or decreases in care resources that trigger deviations from normal care delivery processes, reliance on contingencies and external resources, and, in the most extreme cases, nonroutine decisions about resource allocation. This article provides as follows: 1) a conceptual introduction and approach to healthcare capacity strain including the etiologies of patient volume, patient acuity, special patient care demands, and resource reduction; 2) a framework for considering key resources during an acute surge event-the "four Ss" of preparedness: space (beds), staff (clinicians and operations), stuff (physical equipment), and system (coordination); and 3) an adaptable approach to and discussion of the most common domains that should be addressed during preparation for and response to acute surge events, with an eye toward combating novel respiratory viral pathogens.
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http://dx.doi.org/10.1097/CCE.0000000000000110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188427PMC
April 2020

Preparedness Tested: Severe Cerebral Malaria Presenting as a High-Risk Person Under Investigation for Ebola Virus Disease at a US Hospital.

Disaster Med Public Health Prep 2020 May 8:1-6. Epub 2020 May 8.

Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

In 2019, a 42-year-old African man who works as an Ebola virus disease (EVD) researcher traveled from the Democratic Republic of Congo (DRC), near an ongoing EVD epidemic, to Philadelphia and presented to the Hospital of the University of Pennsylvania Emergency Department with altered mental status, vomiting, diarrhea, and fever. He was classified as a "wet" person under investigation for EVD, and his arrival activated our hospital emergency management command center and bioresponse teams. He was found to be in septic shock with multisystem organ dysfunction, including circulatory dysfunction, encephalopathy, metabolic lactic acidosis, acute kidney injury, acute liver injury, and diffuse intravascular coagulation. Critical care was delivered within high-risk pathogen isolation in the ED and in our Special Treatment Unit until a diagnosis of severe cerebral malaria was confirmed and EVD was definitively excluded.This report discusses our experience activating a longitudinal preparedness program designed for rare, resource-intensive events at hospitals physically remote from any active epidemic but serving a high-volume international air travel port-of-entry.
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http://dx.doi.org/10.1017/dmp.2020.53DOI Listing
May 2020

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

Ann Intern Med 2020 07 7;173(1):21-28. Epub 2020 Apr 7.

University of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.Z.L., G.L.A., P.J.B., J.D.C., C.W.H., M.E.M., S.D.H.).

Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.

Objective: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.

Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.

Setting: 3 hospitals in an academic health system.

Patients: All people living in the greater Philadelphia region.

Measurements: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.

Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.

Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction.

Conclusion: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

Primary Funding Source: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.
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http://dx.doi.org/10.7326/M20-1260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153364PMC
July 2020

Potentially Preventable Intensive Care Unit Admissions in the United States, 2006-2015.

Ann Am Thorac Soc 2020 01;17(1):81-88

Palliative and Advanced Illness Research Center.

Increasing intensive care unit (ICU) beds and the critical care workforce are often advocated to address an aging and increasingly medically complex population. However, reducing potentially preventable ICU stays may be an alternative to ensure adequate capacity. To determine the proportions of ICU admissions meeting two definitions of being potentially preventable using nationally representative U.S. claims databases. We analyzed claims from 2006 to 2015 from all Medicare Fee-for-Service (FFS) beneficiaries and from a large national payer offering a private insurance (PI) plan and a Medicare Advantage (MA) plan. Potentially preventable hospitalizations were identified using existing definitions for ambulatory care sensitive conditions (ACSCs) and life-limiting malignancies (LLMs). We analyzed 420,369,434 person-years of insurance coverage, during which there were 99,793,416 acute inpatient hospitalizations, of which 16,646,977 (16.7%) were associated with an ICU admission. Of these, the proportions with an ACSC were 12.9%, 12.7%, and 15.8%, and with an LLM were 5.2%, 5.4%, and 6.4%, among those with PI, MA, and FFS, respectively. Over 10 years, the absolute percentages of ACSC-associated ICU stays declined (PI = -1.1%, MA -6.4%, FFS -6.4%; all  < 0.001 for all trends). Smaller changes were noted among LLM-associated ICU stays, declining in the MA cohort (-0.8%) and increasing in the FFS (+0.3%) and PI (+0.2%) populations ( < 0.001 for all trends). An appreciable proportion of U.S. ICU admissions may be preventable with community-based interventions. Investment in the outpatient infrastructure required to prevent these ICU admissions should be considered as a complementary, if not alternative, strategy to expanding ICU capacity to meet future demand.
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http://dx.doi.org/10.1513/AnnalsATS.201905-366OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944341PMC
January 2020

Understanding irresponsible use of intensive care unit resources in the USA.

Lancet Respir Med 2019 07 20;7(7):605-612. Epub 2019 May 20.

Division of Pulmonary, Allergy, and Critical Care, University of Pennsylvania, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA; Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA; Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.

Use of intensive care unit (ICU) resources in the USA far outpaces that of other countries. This increased use is not accompanied by superior clinical outcomes and is at times discordant with patient desires. This Series paper seeks to identify major drivers of ICU resource use in the USA, and to offer steps towards better aligning ICU resource use with clinical needs and patient preferences. After considering several factors, such as organisational, ethical, and economic factors, we suggest that there are four intersecting drivers of irresponsible use of ICU resources in the USA: first, excess ICU bed capacity and a scarcity of data to understand which patients that truly benefit from ICU compared with ward care; second, clinicians misinterpreting the goals and means of patient autonomy; third, an extreme fear of rationing by the general public; and fourth, fee-for-service driven use of advanced medical technologies and procedures that beget ICU expansion.
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http://dx.doi.org/10.1016/S2213-2600(19)30088-8DOI Listing
July 2019

Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa.

J Intensive Care Med 2020 Oct 4;35(10):1104-1111. Epub 2018 Dec 4.

Pietermaritzburg Department of Anaesthesia, Critical Care and Pain Management, Pietermaritzburg, South Africa.

Objective: To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting.

Methods: We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression.

Results: No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality.

Conclusions: In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.
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http://dx.doi.org/10.1177/0885066618815804DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548699PMC
October 2020

Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department.

Ann Am Thorac Soc 2018 11;15(11):1328-1335

1 Division of Pulmonary, Allergy, and Critical Care.

Rationale: Intensive care unit (ICU) capacity strain refers to the potential limits placed on an ICU's ability to provide high-quality care for all patients who may need it at a given time. Few studies have investigated how fluctuations in ICU capacity strain might influence care outside the ICU.

Objectives: To determine whether ICU capacity strain is associated with initial level of inpatient care and outcomes for emergency department (ED) patients hospitalized for sepsis.

Methods: We performed a retrospective cohort study of patients with sepsis admitted from the ED to a medical ward or ICU at three hospitals within the University of Pennsylvania Health System between 2012 and 2015. Patients were excluded if they required life support therapies, defined as invasive or noninvasive ventilatory support or vasopressors, at the time of admission. The exposures were four measures of ICU capacity strain at the time of the ED disposition decision: ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy. The primary outcome was the decision to admit to a ward or to an ICU. Secondary analyses assessed the association of ICU capacity strain with in-hospital outcomes, including mortality.

Results: Among 77,142 hospital admissions from the ED, 3,067 patients met the study's eligibility criteria. The ICU capacity strain metrics varied between and within study hospitals over time. In unadjusted analyses, ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy were all negatively associated with ICU admission. In the fully adjusted model including patient-level covariates, only ICU occupancy remained associated with ICU admission (odds ratio, 0.87; 95% confidence interval, 0.79-0.96; P = 0.005), such that a 10% increase in ICU occupancy (e.g., one additional patient in a 10-bed ICU) was associated with a 13% decrease in the odds of ICU admission. Among the subset of patients admitted initially from the ED to a medical ward, ICU occupancy at the time of admission was associated with increased odds of hospital mortality (odds ratio, 1.61; 95% confidence interval, 1.21-2.14; P = 0.001).

Conclusions: The odds that patients in the ED with sepsis who do not require life support therapies will be admitted to the ICU are reduced when those ICUs experience high occupancy but not high levels of other previously explored measures of capacity strain. Patients with sepsis admitted to the wards during times of high ICU occupancy had increased odds of hospital mortality.
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http://dx.doi.org/10.1513/AnnalsATS.201804-241OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6850726PMC
November 2018

Assessing a population-level policy intervention seeking to avert in-hospital clinical deterioration.

Authors:
George L Anesi

Resuscitation 2017 12 28;121:A8-A9. Epub 2017 Sep 28.

Division of Pulmonary, Allergy, and Critical Care, Hospital of the University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA; Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. Electronic address:

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http://dx.doi.org/10.1016/j.resuscitation.2017.09.027DOI Listing
December 2017

Time to selected quit date and subsequent rates of sustained smoking abstinence.

J Behav Med 2017 Dec 21;40(6):989-997. Epub 2017 Jun 21.

Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.

In efforts to combat tobacco dependence, most smoking cessation programs offer individuals who smoke the choice of a target quit date. However, it is uncertain whether the time to the selected quit date is associated with participants' chances of achieving sustained abstinence. In a pre-specified secondary analysis of a randomized clinical trial of four financial-incentive programs or usual care to encourage smoking cessation (Halpern et al. in N Engl J Med 372(22):2108-2117, doi: 10.1056/NEJMoa1414293 , 2015), study participants were instructed to select a quit date between 0 and 90 days from enrollment. Among those who selected a quit date and provided complete baseline data (n = 1848), we used multivariable logistic regression to evaluate the association of the time to the selected quit date with 6- and 12-month biochemically-confirmed abstinence rates. In the fully adjusted model, the probability of being abstinent at 6 months if the participant selected a quit date in weeks 1, 5, 10, and 13 were 39.6, 22.6, 10.9, and 4.3%, respectively.
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http://dx.doi.org/10.1007/s10865-017-9868-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476186PMC
December 2017

Validation of an Administrative Definition of ICU Admission Using Revenue Center Codes.

Crit Care Med 2017 Aug;45(8):e758-e762

1Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.2Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA.3Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Objectives: Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients.

Design: Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system.

Setting: The University of Pennsylvania Health System in Philadelphia, PA, United States.

Patients: All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015.

Interventions: None.

Measurements And Main Results: Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p < 0.001).

Conclusions: Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.
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http://dx.doi.org/10.1097/CCM.0000000000002374DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511059PMC
August 2017

Intensive Care Medicine in 2050: toward an intensive care unit without waste.

Intensive Care Med 2017 04 8;43(4):554-556. Epub 2016 Dec 8.

Division of Pulmonary, Allergy, and Critical Care Medicine, Hospital of the University of Pennsylvania, 3600 Spruce Street, Gates Building, Room GA 5044, Philadelphia, PA, 19104, USA.

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http://dx.doi.org/10.1007/s00134-016-4641-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360479PMC
April 2017

Choice architecture in code status discussions with terminally ill patients and their families.

Intensive Care Med 2016 Jun 7;42(6):1065-7. Epub 2016 Mar 7.

Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, 726 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104-6021, USA.

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http://dx.doi.org/10.1007/s00134-016-4294-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4846484PMC
June 2016

The "decrepit concept" of confidentiality, 30 years later.

Authors:
George L Anesi

Virtual Mentor 2012 Sep 1;14(9):708-11. Epub 2012 Sep 1.

Internal Medicine, Massachusetts General Hospital, USA.

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http://dx.doi.org/10.1001/virtualmentor.2012.14.9.jdsc1-1209DOI Listing
September 2012

Bilateral ear swelling and erythema after chemotherapy: a case of ara-C ears.

J Clin Oncol 2012 Jun 16;30(16):e146. Epub 2012 Apr 16.

Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, USA.

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http://dx.doi.org/10.1200/JCO.2011.39.5970DOI Listing
June 2012

Spontaneous coronary artery dissection in Ehlers-Danlos syndrome.

Ann Thorac Surg 2011 Nov 31;92(5):1883-4. Epub 2011 Oct 31.

Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio 44106, USA.

Ehlers-Danlos syndrome is a heterogeneous group of connective tissue disorders with type IV, the vascular subtype, behaving as the most severe largely due to spontaneous arterial aneurysm and dissection. In this case report we describe a spontaneous left anterior descending coronary artery dissection treated with coronary artery bypass graft in a patient with Ehlers-Danlos syndrome type IV.
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http://dx.doi.org/10.1016/j.athoracsur.2011.03.136DOI Listing
November 2011

Recombinant yeast screen for new inhibitors of human acetyl-CoA carboxylase 2 identifies potential drugs to treat obesity.

Proc Natl Acad Sci U S A 2010 May 3;107(20):9093-8. Epub 2010 May 3.

Department of Chemistry, University of Chicago, Chicago, IL 60637, USA.

Acetyl-CoA carboxylase (ACC) is a key enzyme of fatty acid metabolism with multiple isozymes often expressed in different eukaryotic cellular compartments. ACC-made malonyl-CoA serves as a precursor for fatty acids; it also regulates fatty acid oxidation and feeding behavior in animals. ACC provides an important target for new drugs to treat human diseases. We have developed an inexpensive nonradioactive high-throughput screening system to identify new ACC inhibitors. The screen uses yeast gene-replacement strains depending for growth on cloned human ACC1 and ACC2. In "proof of concept" experiments, growth of such strains was inhibited by compounds known to target human ACCs. The screen is sensitive and robust. Medium-size chemical libraries yielded new specific inhibitors of human ACC2. The target of the best of these inhibitors was confirmed with in vitro enzymatic assays. This compound is a new drug chemotype inhibiting human ACC2 with 2.8 muM IC(50) and having no effect on human ACC1 at 100 muM.
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http://dx.doi.org/10.1073/pnas.1003721107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889071PMC
May 2010
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