Delayed Administration of Recombinant Plasma Gelsolin Improves Survival in a Murine Model of Penicillin-Susceptible and Penicillin-Resistant Pneumococcal Pneumonia

By Zhiping Yang, Alice Bedugnis, Susan Levinson, Mark Dinubile, Thomas Stossel, Quan Lu, Lester Kobzik. Published in The Journal of Infectious Diseases. To be published November 1, 2019.  

 

Therapy to enhance host immune defenses may improve outcomes in serious infections, especially for antibiotic-resistant pathogens. Recombinant human plasma gelsolin (rhu-pGSN), a normally circulating protein, has beneficial effects in diverse preclinical models of inflammation and injury. We evaluated delayed therapy (24–48 hours after challenge) with rhu-pGSN in a mouse model of pneumococcal pneumonia. rhu-pGSN without antibiotics increased survival and reduced morbidity and weight loss after infection with either penicillin-susceptible or penicillin-resistant pneumococci (serotypes 3 and 14, respectively). rhu-pGSN improves outcomes in a highly lethal pneumococcal pneumonia model when given after a clinically relevant delay, even in the setting of antimicrobial resistance.

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Preventing Bloodstream Infections and Death in Zambian Neonates: Impact of a Low-cost Infection Control Bundle

By Lawrence Mwananyanda, Cassandra Pierre, James Mwansa, Carter Cowden, A Russell Localio, Monica L Kapasa, Sylvia Machona, Chileshe Lukwesa Musyani, Moses M Chilufya, Gertrude Munanjala, Angela Lyondo, Matthew A Bates, Susan E Coffin, Davidson H Hamer. Published in Clinical Infectious Diseases. To be published October 15, 2019.   

 

Sepsis is a leading cause of neonatal mortality in low-resource settings. As facility-based births become more common, the proportion of neonatal deaths due to hospital-onset sepsis has increased. We conducted a prospective cohort study in a neonatal intensive care unit in Zambia where we implemented a multifaceted infection prevention and control (IPC) bundle consisting of IPC training, text message reminders, alcohol hand rub, enhanced environmental cleaning, and weekly bathing of babies ≥1.5 kg with 2% chlorhexidine gluconate. Hospital-associated sepsis, bloodstream infection (BSI), and mortality (>3 days after admission) outcome data were collected for 6 months prior to and 11 months after bundle implementation. Most enrolled neonates had a birth weight ≥1.5 kg (2131/2669 [79.8%]). Hospital-associated mortality was lower during the intervention than baseline period (18.0% vs 23.6%, respectively). Total mortality was lower in the intervention than prior periods. Half of enrolled neonates (50.4%) had suspected sepsis; 40.8% of cultures were positive. Most positive blood cultures yielded a pathogen (409/549 [74.5%]), predominantly Klebsiella pneumoniae (289/409 [70.1%]). The monthly rate and incidence density rate of suspected sepsis were lower in the intervention period for all birth weight categories, except babies weighing <1.0 kg. The rate of BSI with pathogen was also lower in the intervention than baseline period. A simple IPC bundle can reduce sepsis and death in neonates hospitalized in high-risk, low-resource settings. Further research is needed to validate these findings in similar settings and to identify optimal implementation strategies for improvement and sustainability.

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N95 Respirators vs Medical Masks for Preventing Influenza Among Health Care Personnel

By Lewis J. Radonovich, Michael S. Simberkoff, Mary T. Bessesen, Alexandria C. Brown, Derek A. T. Cummings, Charlotte A. Gaydos, Jenna G. Los, Amanda E. Krosche, Cynthia L. Gibert, Geoffrey J. Gorse, Ann-Christine Nyquist, Nicholas G. Reich, Maria C. Rodriguez-Barradas, Connie Savor Price, Trish M. Perl, for the ResPECT investigators

JAMA. September 3, 2019

 

A cluster randomized pragmatic effectiveness study conducted at 137 outpatient study sites at 7 US medical centers between September 2011 and May 2015, with final follow-up in June 2016. Each year for 4 years, during the 12-week period of peak viral respiratory illness, pairs of outpatient sites (clusters) within each center were matched and randomly assigned to the N95 respirator or medical mask groups.

Overall, 1993 participants in 189 clusters were randomly assigned to wear N95 respirators (2512 HCP-seasons of observation) and 2058 in 191 clusters were randomly assigned to wear medical masks (2668 HCP-seasons) when near patients with respiratory illness.

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Deep Learning in Medicine—Promise, Progress, and Challenges

By Fei Wang, Lawrence Peter Casalino, and Dhruv Khullar

JAMA Network Open, December 17, 2018

 

Recent years have seen a surge of interest in machine learning and artificial intelligence techniques in health care.1 Deep learning2 represents the latest iteration in a progression of artificial intelligence technologies that have allowed machines to mimic human intelligence in increasingly sophisticated and independent ways.3 Early medical artificial intelligence systems relied heavily on experts to train computers by encoding clinical knowledge as logic rules for specific clinical scenarios. More advanced machine learning systems train themselves to learn these rules by identifying and weighing relevant features from the data, such as pixels from medical images, or raw information from electronic health records (EHRs).

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Making Machine Learning Models Clinically Useful

By Nigam H. Shah, Arnold Milstein, Steven C. Bagley.

JAMA. August 8, 2019.

 

Recent advances in supervised machine learning have improved diagnostic accuracy and prediction of treatment outcomes, in some cases surpassing the performance of clinicians.1 In supervised machine learning, a mathematical function is constructed via automated analysis of training data, which consists of input features (such as retinal images) and output labels (such as the grade of macular edema). With large training data sets and minimal human guidance, a computer learns to generalize from the information contained in the training data. The result is a mathematical function, a model, that can be used to map a new record to the corresponding diagnosis, such as an image to grade macular edema. Although machine learning–based models for classification or for predicting a future health state are being developed for diverse clinical applications, evidence is lacking that deployment of these models has improved care and patient outcomes.

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