Michael S. Abers
Harvard University
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Featured researches published by Michael S. Abers.
Clinical Infectious Diseases | 2017
Daniel M. Musher; Michael S. Abers; John G. Bartlett
Understanding of the microbiology of pneumonia has evolved. The role of pneumococcus has greatly declined. “Atypical” agents cause only a very small proportion of cases. Viruses are prominent. Intensive investigations fail to identify a causative organism in more than 50% of cases.
Clinical Infectious Diseases | 2016
Michael S. Abers; Barcleigh Sandvall; Rahul Sampath; Carlo Zuno; Natalie Uy; Victor L. Yu; Charles E. Stager; Daniel M. Musher
Postobstructive pneumonia is often regarded as a bacterial infection. Despite overlap, the clinical syndrome of postobstructive pneumonia differs in many regards from that of bacterial pneumonia, suggesting that the observed infiltrate does not reflect bacterial infection in the alveoli.
Open Forum Infectious Diseases | 2015
Michael S. Abers; Musie Ghebremichael; Allison K. Timmons; H. Shaw Warren; Mark C. Poznansky; Jatin M. Vyas
Prolonged neutropenia is generally thought to be the major factor for invasive pulmonary aspergillosis (IPA). In the present study, we characterize the frequency, severity, and duration of neutropenia that immediately precedes IPA. Prolonged neutropenia was identified in only one third of all IPA cases and occurred exclusively in hematologic patients.
Critical Care | 2016
Yousef Hannawi; Michael S. Abers; Romergryko G. Geocadin; Marek A. Mirski
Abnormal movements are frequently encountered in patients with brain injury hospitalized in intensive care units (ICUs), yet characterization of these movements and their underlying pathophysiology is difficult due to the comatose or uncooperative state of the patient. In addition, the available diagnostic approaches are largely derived from outpatients with neurodegenerative or developmental disorders frequently encountered in the outpatient setting, thereby limiting the applicability to inpatients with acute brain injuries. Thus, we reviewed the available literature regarding abnormal movements encountered in acutely ill patients with brain injuries. We classified the brain injury into the following categories: anoxic, vascular, infectious, inflammatory, traumatic, toxic-metabolic, tumor-related and seizures. Then, we identified the abnormal movements seen in each category as well as their epidemiologic, semiologic and clinicopathologic correlates. We propose a practical paradigm that can be applied at the bedside for diagnosing abnormal movements in the ICU. This model seeks to classify observed abnormal movements in light of various patient-specific factors. It begins with classifying the patient’s level of consciousness. Then, it integrates the frequency and type of each movement with the availability of ancillary diagnostic tests and the specific etiology of brain injury.
Open Forum Infectious Diseases | 2016
Suzanne Mccluskey; Philipp Schuetz; Michael S. Abers; Benjamin Bearnot; Maria Morales; Debora Hoffman; Shreya Patel; Lauren Rosario; Victor Chiappa; Blair A. Parry; Ryan Callahan; Sheila A. Bond; Kent Lewandrowski; William D. Binder; Michael R. Filbin; Jatin M. Vyas; Michael K. Mansour
Abstract Background Procalcitonin (PCT) is a prohormone that rises in bacterial pneumonia and has promise in reducing antibiotic use. Despite these attributes, there are inconclusive data on its use for clinical prognostication. We hypothesize that serial PCT measurements can predict mortality, intensive care unit (ICU) admission, and bacteremia. Methods A prospective cohort study of inpatients diagnosed with pneumonia was performed at a large tertiary care center in Boston, Massachusetts. Procalcitonin was measured on days 1 through 4. The primary endpoint was a composite adverse outcome defined as all-cause mortality, ICU admission, and bacteremia. Regression models were calculated with area under the receiver operating characteristic curve (AUC) as a measure of discrimination. Results Of 505 patients, 317 patients had a final diagnosis of community-acquired pneumonia (CAP) or healthcare-associated pneumonia (HCAP). Procalcitonin was significantly higher for CAP and HCAP patients meeting the composite primary endpoint, bacteremia, and ICU admission, but not mortality. Incorporation of serial PCT levels into a statistical model including the Pneumonia Severity Index (PSI) improved the prognostic performance of the PSI with respect to the primary composite endpoint (AUC from 0.61 to 0.66), bacteremia (AUC from 0.67 to 0.85), and need for ICU-level care (AUC from 0.58 to 0.64). For patients in the highest risk class PSI >130, PCT was capable of further risk stratification for prediction of adverse outcomes. Conclusion Serial PCT measurement in patients with pneumonia shows promise for predicting adverse clinical outcomes, including in those at highest mortality risk.
Lancet Infectious Diseases | 2018
Ishan S Kamat; Harish Eswaran; Michael S. Abers; Daniel M. Musher
496 www.thelancet.com/infection Vol 18 May 2018 Colli-Monaldi Hospital, Napoli, Italy (ED-M); Centre for Anti-Infective Agents, Vienna, Austria (UT); Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, Netherlands (JWM); Department of Medicine E, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel (LL); and Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Israel (YC, LL)
QJM: An International Journal of Medicine | 2015
Michael S. Abers; Natalie Uy; Daniel M. Musher
We appreciate the gracious and insightful comments by Chalmers1 on our recent opinion paper.2 To fully consider the points raised by Chalmers, we decided to calculate the positive and negative likelihood ratio (+LR and –LR, respectively) for several scoring systems, using data from our recently published prospective study of community-acquired pneumonia (CAP) at the Michael E. DeBakey Veteran Affairs Medical Center. The details of this study are published elsewhere.3 Of 191 immunocompetent patients with CAP, 31 (16.2%) were admitted to the intensive care unit (ICU) and/or died within 30 days of admission. The performance of several CAP scores as predictors of a poor outcome (ICU admission and/or 30 day mortality) are shown in Table 1.
QJM: An International Journal of Medicine | 2014
Michael S. Abers; Natalie Uy; Daniel M. Musher
We appreciate the gracious and insightful comments by Chalmers1 on our recent opinion paper.2 To fully consider the points raised by Chalmers, we decided to calculate the positive and negative likelihood ratio (+LR and –LR, respectively) for several scoring systems, using data from our recently published prospective study of community-acquired pneumonia (CAP) at the Michael E. DeBakey Veteran Affairs Medical Center. The details of this study are published elsewhere.3 Of 191 immunocompetent patients with CAP, 31 (16.2%) were admitted to the intensive care unit (ICU) and/or died within 30 days of admission. The performance of several CAP scores as predictors of a poor outcome (ICU admission and/or 30 day mortality) are shown in Table 1.
Clinical Infectious Diseases | 2018
Michael S. Abers; Daniel M. Musher
Clinical Infectious Diseases | 2018
Daniel M. Musher; Michael S. Abers; John G. Bartlett