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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Making Machine Learning Models Clinically Useful

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

JAMA Network Open, 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.2

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Antibody Treatment against Angiopoietin-Like 4 Reduces Pulmonary Edema and Injury in Secondary Pneumococcal Pneumonia

By Liang Li, Benjamin Jie Wei Foo, Ka Wai Kwok, Noriho Sakamoto, Hiroshi Mukae, Koichi Izumikawa, Stéphane Mandard, Jean-Pierre Quenot, Laurent Lagrost, Wooi Keong Teh, Gurjeet Singh Kohli, Pengcheng Zhu, Hyungwon Choi, Martin Lindsay Buist, Ju Ee Seet, Liang Yang, Fang He, Vincent Tak Kwong Chow, Nguan Soon Tan

mBio. June 4, 2019

 

Secondary bacterial lung infection by Streptococcus pneumoniae (S. pneumoniae) poses a serious health concern, especially in developing countries. We posit that the emergence of multiantibiotic-resistant strains will jeopardize current treatments in these regions. Deaths arising from secondary infections are more often associated with acute lung injury, a common consequence of hypercytokinemia, than with the infection per se. Given that secondary bacterial pneumonia often has a poor prognosis, newer approaches to improve treatment outcomes are urgently needed to reduce the high levels of morbidity and mortality. Using a sequential dual-infection mouse model of secondary bacterial lung infection, we show that host-directed therapy via immunoneutralization of the angiopoietin-like 4 c-isoform (cANGPTL4) reduced pulmonary edema and damage in infected mice. RNA sequencing analysis revealed that anti-cANGPTL4 treatment improved immune and coagulation functions and reduced internal bleeding and edema. Importantly, anti-cANGPTL4 antibody, when used concurrently with either conventional antibiotics or antipneumolysin antibody, prolonged the median survival of mice compared to monotherapy. Anti-cANGPTL4 treatment enhanced immune cell phagocytosis of bacteria while restricting excessive inflammation. This modification of immune responses improved the disease outcomes of secondary pneumococcal pneumonia. Taken together, our study emphasizes that host-directed therapeutic strategies are viable adjuncts to standard antimicrobial treatments.

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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

 

By Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich & Shravya Shetty

Nature Medicine, May 20, 2019

 

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.

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Artificial Intelligence in Health Care Will the Value Match the Hype?

By Ezekiel J. Emanuel and Robert M. Wachter

JAMA Network Open, May 20, 2019

 

Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine’s “magic bullet” phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an “AI revolution,” one that will finally result in high-value care.

While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action.

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Machine Learning at the Clinical Bedside—The Ghost in the Machine

By Joseph J. Zorc, James M. Chamberlain, Lalit Bajaj

 

JAMA Pediatrics, May 13, 2019

In this issue of JAMA Pediatrics, Bertsimas et al1 describe a novel machine-learning approach to derive a revised version of the head injury prediction rule developed by the Pediatric Emergency Care Applied Research Network (PECARN). The PECARN rule was derived and validated using a prospectively collected data set of more than 42 000 patients to classify which children with head injury are at very low risk of clinically significant intracranial abnormalities.2 The ultimate goal of such a decision rule is to reduce unnecessary computed tomographic imaging and associated radiation. Bertsimas et al1 analyzed a public use data set from the PECARN study using a technique called optimal classification trees. The revised rule has improved specificity and predictive value, identifying 33% more children younger than 2 years, and 14% more children 2 years or older as having a very low risk for intracranial injury compared with the PECARN rule, without missing any additional cases of intracranial injury. Although this is good use of the public use data sets now required for federally funded research, interpreting machine-learning techniques may be challenging for clinicians to understand and apply as the techniques become increasingly complex. Although we live in an era of precision medicine, with the ability to tailor personalized recommendations, it is also an era emphasizing shared decision making between clinicians and patients. It may be difficult for clinicians to counsel patients about the implications of a rule that is perceived as a black box or ghost in the machine, which may provide recommendations for unclear reasons.

 

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Can health promotion videos ‘go viral’? A non-randomised, controlled, before-and-after pilot study to measure the spread and impact of local language mobile videos in Burkina Faso

By Tessa Swigart, Jennifer Hollowell ORCID Icon, Pieter Remes, Matthew Lavoie, Joanna Murray, Mireille Belem, Rita Lamoukri

Global Health Action, May 8, 2019

 

Mobile phones present a new health communications opportunity but use of mobile videos warrants more exploration. Our study tested a new idea: to produce health promotion videos in languages for which films have never previously been produced to see if they were widely shared.

To investigate whether the novelty of films in local languages focusing on health messages would be shared ‘virally’ among the target population.

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Infectious Diseases Society of America Position Statement on Telehealth and Telemedicine as Applied to the Practice of Infectious Diseases

By Jeremy D Young, Rima Abdel-Massih, Thomas Herchline, Lewis McCurdy, Kay J Moyer, John D Scott, Brian R Wood, Javeed Siddiqui

Clinical Infectious Diseases, May 2019

 

Over the last 2 decades, telemedicine has effectively demonstrated its ability to increase access to care. This access has the ability to deliver quality clinical care and offer potential savings to the healthcare system. With increasing frequency, physicians, clinics, and medical centers are harnessing modern telecommunications technologies to manage a multitude of acute and chronic conditions, as well as incorporating telehealth into teaching and research. The technologies spanning telehealth, telemedicine, and mobile health (mHealth) are rapidly evolving, and the Infectious Diseases Society of America (IDSA) has prepared this updated position statement to educate its membership on the use of telemedicine and telehealth technologies. IDSA supports the appropriate and evidence-based use of telehealth technologies to provide up-to-date, timely, cost-effective subspecialty care to resource-limited populations.

 

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People Welcomed This Innovation with Two Hands: A Qualitative Report of an mHealth Intervention for Community Case Management in Malawi

Nicole Ide , Victoria Hardy, Griphin Chirambo, Ciara Heavin, Yvonne O’Connor, John O’Donoghue, Nikolaos Mastellos, Kanika Dharmayat, Bo Andersson, Sven Carlsson, Adamson Muula, Matthew Thompson

 

Annals of Global Health, April 2019

 

Community Case Management (CCM) aims to improve health outcomes among children under five with malaria, diarrhea, and pneumonia, but its effectiveness in Malawi is limited by inconsistent standards of delivery characteristic of paper-based interventions. This may lead to negative impacts on child health outcomes and inefficient use of health system resources. This study evaluated the acceptability and impact of the Supporting LIFE Community Case Management App (SL eCCM App) by Health Surveillance Assistants (HSAs) and caregivers in two districts of Northern Malawi.

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Neutrophil extracellular traps in the central nervous system hinder bacterial clearance during pneumococcal meningitis

By Tirthankar Mohanty, Jane Fisher, Anahita Bakochi, Ariane Neumann, José Francisco Pereira Cardoso, Christofer A. Q. Karlsson, Chiara Pavan, Iben Lundgaard, Bo Nilson, Peter Reinstrup, Johan Bonnevier, Davi

Published in: Nature Communications April 10, 2019.  

 

Neutrophils are crucial mediators of host defense that are recruited to the central nervous system (CNS) in large numbers during acute bacterial meningitis caused by Streptococcus pneumoniae. Neutrophils release neutrophil extracellular traps (NETs) during infections to trap and kill bacteria. Intact NETs are fibrous structures composed of decondensed DNA and neutrophil-derived antimicrobial proteins. Here we show NETs in the cerebrospinal fluid (CSF) of patients with pneumococcal meningitis, and their absence in other forms of meningitis with neutrophil influx into the CSF caused by viruses, Borrelia and subarachnoid hemorrhage. In a rat model of meningitis, a clinical strain of pneumococci induced NET formation in the CSF. Disrupting NETs using DNase I significantly reduces bacterial load, demonstrating that NETs contribute to pneumococcal meningitis pathogenesis in vivo. We conclude that NETs in the CNS reduce bacterial clearance and degrading NETs using DNase I may have significant therapeutic implications.

 

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Smartphone-enabled video-observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial

by Alistair Story, PhD; Robert W Aldridge, PhD; Catherine M Smith, PhD; Elizabeth Garber, MSc; Joe Hall, MSc; Gloria Ferenando, MSc; et al.

Published in The Lancet, 21 February 2019. DOI: https://doi.org/10.1016/S0140-6736(18)32993-3

 

 

Directly observed treatment (DOT) has been the standard of care for tuberculosis since the early 1990s, but it is inconvenient for patients and service providers. Video-observed therapy (VOT) has been conditionally recommended by WHO as an alternative to DOT. Researchers tested whether levels of treatment observation were improved with VOT.

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High-performance medicine: the convergence of human and artificial intelligence

by Eric J. Topol

Published in Nature Medicine, 07 January 2019. 

 

 

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.

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Questions for Artificial Intelligence in Health Care

By Thomas M. Maddox, John S. Rumsfeld, and Philip R. O. Payne

JAMA Network Open, January 2019

 

Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems.1 This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, that AI will revolutionize health care for patients and populations. However, key questions must be answered to translate its promise into action.

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Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

by Jia Zhang, MSc;  Laura Tüshaus, PhD;  Néstor Nuño Martínez, MSc;  Monica Moreo, MSc;  Hector Verastegui, Lic;  Stella M Hartinger, PhD;  Daniel Mäusezahl, PhD;  Walter Karlen, Prof Dr 

Published in JMR Publications: The Leading eHealth Publisher, 14 December 2018

 

 

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.

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What is an appropriate level of evidence for a digital health intervention?

by Felix Greaves, Indra Joshi, Mark Campbell, Samantha Roberts, Neelam Patel, John Powell

Published in The Lancet, 10 December 2018. DOI: https://doi.org/10.1016/S0140-6736(18)33129-5

 

 

Harnessing new digital technologies to support the delivery of health services centred around the needs of patients has been embraced by the National Health Service (NHS) in England. Digital technologies—eg, apps, wearables, and software algorithms—have the potential to support a technology-enabled health system in which care interactions are moved away from formal settings and citizens are encouraged to manage their own health and illness. The scalability and often low marginal cost of digital interventions suggest they might deliver cost benefits to stretched services facing the demands of ageing populations living longer with higher levels of chronic disease. At the same time, a publicly funded health system has both financial and moral reasons to spend money conscientiously and judiciously to provide evidence-based effective care for its citizens.  Article access can be found here.  
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Clinical Decision Support in the Era of Artificial Intelligence

by Edward H. Shortliffe, MD, PhD and Martin J. Sepulveda, MD, ScD

Published in JAMA, 05 November 2018. doi:10.1001/jama.2018.17163

 

This Viewpoint article by Shortliffe and Sepulveda is "focused on the subset of decision support systems that are designed to be used interactively by clinicians as they seek to reach decisions, regardless of the underlying analytic methodology that they incorporate." Through this publication, explore the authors' take on clinical decision support systems (CDSSs) and how they have the potential to be used effectively in healthcare settings:

 

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