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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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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