Roger J. Lewis

Bio: Roger J. Lewis is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Clinical trial & Randomized controlled trial. The author has an hindex of 56, co-authored 293 publications receiving 14437 citations. Previous affiliations of Roger J. Lewis include American Medical Association & UCLA Medical Center.

Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct.

Abstract: BackgroundThe effect of endovascular thrombectomy that is performed more than 6 hours after the onset of ischemic stroke is uncertain. Patients with a clinical deficit that is disproportionately severe relative to the infarct volume may benefit from late thrombectomy. MethodsWe enrolled patients with occlusion of the intracranial internal carotid artery or proximal middle cerebral artery who had last been known to be well 6 to 24 hours earlier and who had a mismatch between the severity of the clinical deficit and the infarct volume, with mismatch criteria defined according to age (<80 years or ≥80 years). Patients were randomly assigned to thrombectomy plus standard care (the thrombectomy group) or to standard care alone (the control group). The coprimary end points were the mean score for disability on the utility-weighted modified Rankin scale (which ranges from 0 [death] to 10 [no symptoms or disability]) and the rate of functional independence (a score of 0, 1, or 2 on the modified Rankin scale, whic...

Effect of Out-of-Hospital Pediatric Endotracheal Intubation on Survival and Neurological Outcome: A Controlled Clinical Trial

Abstract: ContextEndotracheal intubation (ETI) is widely used for airway management of children in the out-of-hospital setting, despite a lack of controlled trials demonstrating a positive effect on survival or neurological outcome.ObjectiveTo compare the survival and neurological outcomes of pediatric patients treated with bag-valve-mask ventilation (BVM) with those of patients treated with BVM followed by ETI.DesignControlled clinical trial, in which patients were assigned to interventions by calendar day from March 15, 1994, through January 1, 1997.SettingTwo large, urban, rapid-transport emergency medical services (EMS) systems.ParticipantsA total of 830 consecutive patients aged 12 years or younger or estimated to weigh less than 40 kg who required airway management; 820 were available for follow-up.InterventionsPatients were assigned to receive either BVM (odd days; n = 410) or BVM followed by ETI (even days; n = 420).Main Outcome MeasuresSurvival to hospital discharge and neurological status at discharge from an acute care hospital compared by treatment group.ResultsThere was no significant difference in survival between the BVM group (123/404 [30%]) and the ETI group (110/416 [26%]) (odds ratio [OR], 0.82; 95% confidence interval [CI], 0.61-1.11) or in the rate of achieving a good neurological outcome (BVM, 92/404 [23%] vs ETI, 85/416 [20%]) (OR, 0.87; 95% CI, 0.62-1.22).ConclusionThese results indicate that the addition of out-of-hospital ETI to a paramedic scope of practice that already includes BVM did not improve survival or neurological outcome of pediatric patients treated in an urban EMS system.

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Abstract: Importance Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures The primary end point was organ support–free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned –1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support–free days were 0 (IQR, –1 to 15), 0 (IQR, –1 to 13), and 0 (–1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support–free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support–free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration ClinicalTrials.gov Identifier:NCT02735707

The Propensity Score

Abstract: Two recent studies published in JAMA involved the analysis of observational data to estimate the effect of a treatment on patient outcomes. In the study by Roze et al,1 a large observational data set was analyzed to estimate the relationship between early echocardiography screening for patent ductus arteriosus and mortality among preterm infants. The authors compared mortality rates of 847 infants who were screened for patent ductus arteriosus and 666 who were not. The 2 infant groups were dissimilar; infants who were screened were younger, more likely female, and less likely to have received corticosteroids. The authors used propensity score matching to create 605 matched infant pairs from the original cohort to adjust for these differences. In the study by Huybrechts et al,2 the Medicaid Analytic eXtract data set was analyzed to estimate the association between antidepressant use during pregnancy and persistent pulmonary hypertension of the newborn. The authors included 3 789 330 women, of which 128 950 had used antidepressants. Women who used antidepressants were different from those who had not, with differences in age, race/ethnicity, chronic illnesses, obesity, tobacco use, and health care use. The authors adjusted for these differences using, in part, the technique of propensity score stratification.

An Introduction to Classification and Regression Tree (CART) Analysis

Abstract: Introduction A common goal of many clinical research studies is the development of a reliable clinical decision rule, which can be used to classify new patients into clinically-important categories. Examples of such clinical decision rules include triage rules, whether used in the out-of-hospital setting or in the emergency department, and rules used to classify patients into various risk categories so that appropriate decisions can be made regarding treatment or hospitalization. Traditional statistical methods are cumbersome to use, or of limited utility, in addressing these types of classification problems. There are a number of reasons for these difficulties. First, there are generally many possible " predictor " variables which makes the task of variable selection difficult. Traditional statistical methods are poorly suited for this sort of multiple comparison. Second, the predictor variables are rarely nicely distributed. Many clinical variables are not normally distributed and different groups of patients may have markedly different degrees of variation or variance. Third, complex interactions or patterns may exist in the data. For example, the value of one variable (e.g., age) may substantially affect the importance of another variable (e.g., weight). These types of interactions are generally difficult to model, and virtually impossible to model when the number of interactions and variables becomes substantial. Fourth, the results of traditional methods may be difficult to use. For example, a multivariate logistic regression model yields a probability of disease, which can be calculated using the regression coefficients and the characteristics of the patient, yet such models are rarely utilized in clinical practice. Clinicians generally do not think in terms of probability but, rather in terms of categories, such as " low risk " versus " high risk. " Regardless of the statistical methodology being used, the creation of a clinical decision rule requires a relatively large dataset. For each patient in the dataset, one variable (the dependent variable), records whether or not that patient had the condition which we hope to predic t accurately in future patients. Examples might include significant injury after trauma, myocardial infarction, or subarachnoid hemorrhage in the setting of headache. In addition, other variables record the values of patient characteristics which we believe might help us to predict the value of the dependent variable. For example, if one hopes to predict the presence of subarachnoid hemorrhage, a possible predictor variable might be whether or not the patient's headache was sudden in onset; another possible …

Bayesian data analysis.

Abstract: Bayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST). Ironically, specific Bayesian models of cognition and perception may not long endure the ravages of empirical verification, but generic Bayesian methods for data analysis will eventually dominate. It is time that Bayesian data analysis became the norm for empirical methods in cognitive science. This article reviews a fatal flaw of NHST and introduces the reader to some benefits of Bayesian data analysis. The article presents illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Infectious Diseases Society of America/American Thoracic Society Consensus Guidelines on the Management of Community-Acquired Pneumonia in Adults

Abstract: Lionel A. Mandell, Richard G. Wunderink, Antonio Anzueto, John G. Bartlett, G. Douglas Campbell, Nathan C. Dean, Scott F. Dowell, Thomas M. File, Jr. Daniel M. Musher, Michael S. Niederman, Antonio Torres, and Cynthia G. Whitney McMaster University Medical School, Hamilton, Ontario, Canada; Northwestern University Feinberg School of Medicine, Chicago, Illinois; University of Texas Health Science Center and South Texas Veterans Health Care System, San Antonio, and Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas; Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi School of Medicine, Jackson; Division of Pulmonary and Critical Care Medicine, LDS Hospital, and University of Utah, Salt Lake City, Utah; Centers for Disease Control and Prevention, Atlanta, Georgia; Northeastern Ohio Universities College of Medicine, Rootstown, and Summa Health System, Akron, Ohio; State University of New York at Stony Brook, Stony Brook, and Department of Medicine, Winthrop University Hospital, Mineola, New York; and Cap de Servei de Pneumologia i Allergia Respiratoria, Institut Clinic del Torax, Hospital Clinic de Barcelona, Facultat de Medicina, Universitat de Barcelona, Institut d’Investigacions Biomediques August Pi i Sunyer, CIBER CB06/06/0028, Barcelona, Spain.

Heart Disease and Stroke Statistics—2007 Update A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Abstract: 1. About These Statistics…e70 2. Cardiovascular Diseases…e72 3. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris…e89 4. Stroke…e99 5. High Blood Pressure…e111 6. Congenital Cardiovascular Defects…e116 7. Heart Failure…e119 8. Other Cardiovascular Diseases…e122 9. Risk Factor: Smoking/Tobacco Use…e128 10. Risk Factor: High Blood Cholesterol and Other Lipids…e132 11. Risk Factor: Physical Inactivity…e136 12. Risk Factor: Overweight and Obesity…e139 13. Risk Factor: Diabetes Mellitus…e143 14. End-Stage Renal Disease and Chronic Kidney Disease…e149 15. Metabolic Syndrome…e151 16. Nutrition…e153 17. Quality of Care…e155 18. Medical Procedures…e159 19. Economic Cost of Cardiovascular Diseases…e162 20. At-a-Glance Summary Tables…e164 21. Glossary and Abbreviation Guide…e168 Writing Group Disclosures…e171 Appendix I: List of Statistical Fact Sheets: http://www.americanheart.org/presenter.jhtml?identifier=2007 We thank Drs Robert Adams, Philip Gorelick, Matt Wilson, and Philip Wolf (members of the Statistics Committee or Stroke Statistics Subcommittee); Brian Eigel; Gregg Fonarow; Kathy Jenkins; Gail Pearson; and Michael Wolz for their valuable comments and contributions. We would like to acknowledge Tim Anderson and Tom Schneider for their editorial contributions and Karen Modesitt for her administrative assistance. # 1. About These Statistics {#article-title-2} The American Heart Association (AHA) works with the Centers for Disease Control and Prevention’s National Center for Health Statistics (CDC/NCHS); the National Heart, Lung, and Blood Institute (NHLBI); the National Institute of Neurological Disorders and Stroke (NINDS); and other government agencies to derive the annual statistics in this Update. This chapter describes the most important sources and the types of data we use from them. For more details and an alphabetical list of abbreviations, see Chapter 21 of this document, the Glossary and Abbreviation Guide. The surveys used are:

Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association.

Abstract: Background and Purpose- The purpose of these guidelines is to provide an up-to-date comprehensive set of recommendations in a single document for clinicians caring for adult patients with acute arterial ischemic stroke. The intended audiences are prehospital care providers, physicians, allied health professionals, and hospital administrators. These guidelines supersede the 2013 Acute Ischemic Stroke (AIS) Guidelines and are an update of the 2018 AIS Guidelines. Methods- Members of the writing group were appointed by the American Heart Association (AHA) Stroke Council's Scientific Statements Oversight Committee, representing various areas of medical expertise. Members were not allowed to participate in discussions or to vote on topics relevant to their relations with industry. An update of the 2013 AIS Guidelines was originally published in January 2018. This guideline was approved by the AHA Science Advisory and Coordinating Committee and the AHA Executive Committee. In April 2018, a revision to these guidelines, deleting some recommendations, was published online by the AHA. The writing group was asked review the original document and revise if appropriate. In June 2018, the writing group submitted a document with minor changes and with inclusion of important newly published randomized controlled trials with >100 participants and clinical outcomes at least 90 days after AIS. The document was sent to 14 peer reviewers. The writing group evaluated the peer reviewers' comments and revised when appropriate. The current final document was approved by all members of the writing group except when relationships with industry precluded members from voting and by the governing bodies of the AHA. These guidelines use the American College of Cardiology/AHA 2015 Class of Recommendations and Level of Evidence and the new AHA guidelines format. Results- These guidelines detail prehospital care, urgent and emergency evaluation and treatment with intravenous and intra-arterial therapies, and in-hospital management, including secondary prevention measures that are appropriately instituted within the first 2 weeks. The guidelines support the overarching concept of stroke systems of care in both the prehospital and hospital settings. Conclusions- These guidelines provide general recommendations based on the currently available evidence to guide clinicians caring for adult patients with acute arterial ischemic stroke. In many instances, however, only limited data exist demonstrating the urgent need for continued research on treatment of acute ischemic stroke.

Heart Disease and Stroke Statistics—2006 Update A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Abstract: 1. About These Statistics 2. Cardiovascular Diseases 3. Coronary Heart Disease, Acute Coronary Syndrome and Angina Pectoris 4. Stroke and Stroke in Children 5. High Blood Pressure (and End-Stage Renal Disease) 6. Congenital Cardiovascular Defects 7. Heart Failure 8. Other Cardiovascular Diseases 9. Risk Factors 10. Metabolic Syndrome 11. Nutrition 12. Quality of Care 13. Medical Procedures 14. Economic Cost of Cardiovascular Diseases 15. At-a-Glance Summary Tables 16. Glossary and Abbreviation Guide 17. Acknowledgment 18. References Appendix I: List of Statistical Fact Sheets. URL: http://www.americanheart.org/presenter.jhtml?identifier=2007 The American Heart Association works with the Centers for Disease Control and Prevention’s National Center for Health Statistics (CDC/NCHS), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute of Neurological Disorders and Stroke (NINDS), and other government agencies to derive the annual statistics in this update. This section describes the most important sources we use. For more details and an alphabetical list of abbreviations, see the Glossary and Abbreviation Guide. All statistics are for the most recent year available. Prevalence, mortality and hospitalizations are computed for 2003 unless otherwise noted. Mortality as an underlying or contributing cause of death is for 2002. Economic cost estimates are for 2006. Due to late release of data, some disease mortality are not updated to 2003. Mortality for 2003 are underlying preliminary data, obtained from the NCHS publication National Vital Statistics Report: Deaths: Preliminary Data for 2003 (NVSR, 2005;53:15) and from unpublished tabulations furnished by Robert Anderson of NCHS. US and state death rates and prevalence rates are age-adjusted per 100 000 population (unless otherwise specified) using the 2000 …