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Kline JA, Camargo CA, Courtney DM, Kabrhel C, Nordenholz KE, Aufderheide T, Baugh JJ, Beiser DG, Bennett CL, Bledsoe J, Castillo E, Chisolm-Straker M, Goldberg EM, House H, House S, Jang T, Lim SC, Madsen TE, McCarthy DM, Meltzer A, Moore S, Newgard C, Pagenhardt J, Pettit KL, Pulia MS, Puskarich MA, Southerland LT, Sparks S, Turner-Lawrence D, Vrablik M, Wang A, Weekes AJ, Westafer L, Wilburn J. Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021. PLoS One 2021; 16:e0248438. [PMID: 33690722 PMCID: PMC7946184 DOI: 10.1371/journal.pone.0248438] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.
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Affiliation(s)
- Jeffrey A. Kline
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- * E-mail:
| | - Carlos A. Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - D. Mark Courtney
- Department of Emergency Medicine, University of Texas Southwestern, Dallas, Texas, United States of America
| | - Christopher Kabrhel
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kristen E. Nordenholz
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Thomas Aufderheide
- Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Joshua J. Baugh
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - David G. Beiser
- Section of Emergency Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Christopher L. Bennett
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California, United States of America
| | - Joseph Bledsoe
- Department of Emergency Medicine, Healthcare Delivery Institute, Intermountain Healthcare, Salt Lake City, Utah, United States of America
| | - Edward Castillo
- Department of Emergency Medicine, University of California, San Diego, California, United States of America
| | - Makini Chisolm-Straker
- Department of Emergency Medicine, Mt. Sinai School of Medicine, New York, New York, United States of America
| | - Elizabeth M. Goldberg
- Department of Emergency Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America
| | - Hans House
- Department of Emergency Medicine, University of Iowa School of Medicine, Iowa City, Iowa, United States of America
| | - Stacey House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louise, Missouri, United States of America
| | - Timothy Jang
- Department of Emergency Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | - Stephen C. Lim
- University Medical Center New Orleans, Louisiana State University School of Medicine, New Orleans, Louisiana, United States of America
| | - Troy E. Madsen
- Division of Emergency Medicine, Department Surgery, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Danielle M. McCarthy
- Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Andrew Meltzer
- Department of Emergency Medicine, George Washington University School of Medicine, Washington D.C., DC, United States of America
| | - Stephen Moore
- Department of Emergency Medicine, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States of America
| | - Craig Newgard
- Department of Emergency Medicine, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Justine Pagenhardt
- Department of Emergency Medicine, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Katherine L. Pettit
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Michael S. Pulia
- Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Michael A. Puskarich
- Department of Emergency Medicine, Hennepin County Medical Center and the University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Lauren T. Southerland
- Department of Emergency Medicine, Ohio State University Medical Center, Columbus, Ohio, United States of America
| | - Scott Sparks
- Department of Emergency Medicine, Riverside Regional Medical Center, Newport News, Virginia, United States of America
| | - Danielle Turner-Lawrence
- Department of Emergency Medicine, Beaumont Health, Royal Oak, Michigan, United States of America
| | - Marie Vrablik
- Department of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Alfred Wang
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Anthony J. Weekes
- Department of Emergency Medicine, Carolinas Medical Center at Atrium Health, Charlotte, North Carolina, United States of America
| | - Lauren Westafer
- Department of Emergency Medicine, Baystate Health, Springfield, Massachusetts, United States of America
| | - John Wilburn
- Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, Michigan, United States of America
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Xue J, Taha B, Reddy S, Wright RS, Aufderheide T. A new method to incorporate age and gender into the criteria for the detection of acute inferior myocardial infarction. J Electrocardiol 2002; 34 Suppl:229-34. [PMID: 11781961 DOI: 10.1054/jelc.2001.28904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent studies have shown that younger women are more likely to die during and after hospitalization for acute myocardial infarction (MI) than older women and men of all ages. This may be partly due to incorrect diagnosis or late detection of acute MI in younger women. At high specificity levels (>98%), the sensitivity of the initial ECG to detect acute MI may be as low as 30% when using traditional criteria by both physicians and computerized interpretation programs. This study examines if women of different age groups have a similar ECG presentation to men during acute inferior MI and if the diagnostic accuracies of the initial ECG are comparable. We analyzed chest pain ECGs from Mayo Clinic and Medical College of Wisconsin, which included 1,339 patients with acute inferior MI and 1,169 age-matched controls with noncardiac chest pain. We subdivided all groups by age (below and above 60 years) and compared ECG parameters (ST elevation, ST depression, QRS duration, R-wave amplitude, Q-wave duration and amplitude, QT interval) between genders. For inferior MI patients under age 60, women had lower ST elevations at the J point in lead II than men (57 +/- 91 microV vs. 86 +/- 117 microV, P < .02). This trend was reversed for patients over age 60 (lead a VF: 102 +/- 126 microV vs. 84+/-117 microV, P < .04; Lead III: 130+/-146 microV vs. 103+/-131 microV, P < .007). A neural network method was used to identify the most significant group of ECG parameters for detecting acute MI. An adaptive fuzzy logic method was developed for adapting to the threshold differences among the different gender and age groups. The new algorithm improved the sensitivity of acute inferior MI detection by more than 25% relative to old algorithm, while maintaining the high specificity around 98% for noncardiac chest pain patients.
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Affiliation(s)
- J Xue
- GE Medical Systems-Information Technologies, Milwaukee, WI 53223, USA
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Abstract
Electromechanical dissociation (EMD) is the presenting rhythm in approximately 17% of all prehospital cardiorespiratory arrests. Yet, we know comparatively little about the demographic profile of these patients. The purpose of this study was to review historical and resuscitative parameters to help create a demographic profile. For a 6-year period of time from January 1st, 1980 to December 31st, 1985, 503 adult patients presented to a prehospital system in non-traumatic, nonpoisoned, cardiorespiratory arrest with an initial rhythm of electromechanical dissociation. The overall average response time was 6.1 +/- 3.2 min. Sixty percent of the patients were witnessed arrests and 65% had bystander initiated CPR. Forty-six percent of the patients had a cardiac history: myocardial infarction 13%, CHF 11% and other 21%. Other pertinent past medical history included diabetes 15%, COPD 10% and seizures 3%. The average age was 69.8 +/- 13.7 years. Fifty-seven percent were male. Forty-three percent were on cardiac medication including: digoxin, 24%; nitroglycerin, 12%; potassium supplements, 9%; propranolol, 8%; isordil, 6%; quinidine, 3%; nitropaste, 3%; and other cardiac medications, 15%. One hundred forty-eight (29%) patients developed a pulse at some time during resuscitative efforts, of these 17 (3.4%) patients responded with a pulse immediately after intubation. The mean time of resuscitation to sustaining pulse was 20 +/- 11 min and the mean resuscitation time to sustaining pressure was 22 +/- 11 min. Nineteen percent were successfully resuscitated, defined as a conveyance of a patient with a pulse and a rhythm to an emergency department. Four point four percent were saved, defined as a patient discharged alive from the hospital. Approximately 53% of the successfully resuscitated patients and 45% of the save patients were determined to have a probable respiratory event as the primary etiology of their arrest. This study attempts to provide some insight into the demographic profile of the patients in EMD.
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Affiliation(s)
- H A Stueven
- Section of Trauma and Emergency Medicine, Medical College of Wisconsin, Milwaukee 53226
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Abstract
Electromechanical dissociation (EMD) is inconsistently defined in the literature. Our definition is the presence of discernible electrical complexes (excluding ventricular tachycardia and ventricular fibrillation) and the absence of palpable pulses. It has been noted that EMD may present with a variety of morphological complexes. It was the purpose of this study to categorize the electrical morphologic characteristics of patients presenting in EMD and to correlate morphology with patient outcome and response to therapy. From the 6-year period, January 1st, 1980 to December 31st, 1985, 503 evaluable adult patients presented to an urban paramedic system in non-traumatic, non-poisoned, cardiorespiratory arrest and were determined to be in EMD. The rhythm strips obtained from paramedics on all patients were retrospectively reviewed and were arbitrarily categorized in the following manner: Group 1, normal QRS width, isoelectric ST and normal appearing T-waves; Group 2A, atrial activity, widened QRS width (greater than or equal to 0.12 ms) or abnormal ST and/or T-waves (ST depression, elevation, slurring or T-wave inversion); Group 2B, same as Group 2A but without atrial activity; Group 3, essentially monophasic, slurred RST complexes. The respective initial distribution was Group 1, 147 (29%); Group 2A, 248 (49%); Group 2B, 60 (12%); Group 3, 48 (10%). The relative frequency of morphologies preceding the attainment of a pulse was as follows: Group 1, 30 (24%); Group 2A, 82 (65%); Group 2B, 8 (6%); Group 3, 6 (5%) (P less than or equal to 0.01 with no significant difference between Group 2B and 3).(ABSTRACT TRUNCATED AT 250 WORDS)
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Affiliation(s)
- H A Stueven
- Section of Trauma and Emergency Medicine, Medical College of Wisconsin, Milwaukee 53226
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Abstract
Electromechanical dissociation (EMD) is a major arrest rhythm for which there is often inadequate treatment. The purpose of this study was to evaluate the different pharmacological and non-pharmacological interventions considered in the treatment of EMD. During the 6-year period, January 1st, 1980 to December 31st, 1985, 503 evaluable adult patients presented in a non-traumatic, non-poisoning cardiopulmonary arrest with the initial rhythm of EMD. One hundred nineteen patients obtained a pulse during resuscitation efforts following drug administration. The average time to obtaining pulses after the last drug administration was 1.97 +/- 2.21 min. The following drugs were last administered prior to transient pulses: bicarbonate, 31/119 (26%); epinephrine, 26/119 (22%); atropine, 26/119 (22%); dopamine, 13/119 (11%); calcium, 11/119 (9%); isoproterenol, 7/119 (6%); other drugs, 5/119 (4%). Ninety-five percent of the successful resuscitations received eight or less drug interventions and all saves received three or less drug interventions. Two hundred twenty-four patients (44.5%) had 288 non-pharmacological interventions. Twenty-three patients developed a pulse after intervention in the following distribution: MAST suit (N = 9), pericardiocentesis (N = 6), fluid challenge (N = 5), needle thoracostomy (N = 1), and intervention combinations (N = 2). The time interval between intervention and the onset of pulse was as follows: MAST suit, 4 +/- 2.8 min; pericardiocentesis, 3.7 +/- 3.6 min; fluid challenge, 4.8 +/- 4.1 min; needle thoracostomy, 6 min. The overall save rate for intervention patients was 0.9% whereas for those not having intervention it was 7.2% (P less than or equal to 0.0003).(ABSTRACT TRUNCATED AT 250 WORDS)
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Affiliation(s)
- B Vanags
- Section of Trauma and Emergency Medicine, Medical College of Wisconsin, Milwaukee 53226
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