PERSONALIZED MEDICINE AT FDA

The transformation of health care from one-size-fits-all, trial-and-error medicine to a targeted approach utilizing each patient’s molecular information continues to accelerate as the U.S. Food and Drug Administration more regularly and rapidly approves diagnostic tools and treatments that expand the frontiers of personalized medicine. Personalized medicine, sometimes called individualized or precision medicine, is a rapidly evolving field in which physicians use diagnostic tests to determine which medical treatments will work best for each patient or use medical interventions to alter molecular mechanisms, often genetic, that cause disease or influence a patient’s response to certain treatments. By combining molecular data with an individual’s medical history, circumstances and values, health care providers can develop targeted treatment and prevention plans. Following the approval of 11 new personalized medicines last year, personalized medicines now account for more than one of every four drugs the agency has approved in the past six years. This figure represents a sharp increase since 2005, when personalized medicines accounted for 5 percent of the new therapies approved each year. In 2019, the agency also expanded the indications for several existing personalized therapies; approved a new gene therapy for the treatment of a rare disease; and qualified the first digital technology platform via its pre-certification program. These new drugs and technologies will help physicians develop safer and more efficacious targeted treatment regimens.

The full report can be downloaded below.  

Can AI flag disease outbreaks faster than humans? Not quite

Did an artificial-intelligence system beat human doctors in warning the world of a severe outbreak of COVID-19 in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It’s difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The full AP News article can be viewed at this link.  

Digital Therapeutics Leaders Focus On Reimbursement

This past week the DTx (Digital Therapeutics) West conference was held in Silicon Valley. CEOs, venture capitalists, and executives met to debate the state of digital healthcare. Surrounded by technology giants such as Oracle and Google, DTx leaders discussed how the industry needs to solve its reimbursement conundrum in order to keep growing. 

The full Forbes article can be viewed at this link.  

Interim Estimates of 2019–20 Seasonal Influenza Vaccine Effectiveness — United States, February 2020

During the 2019–20 influenza season, influenza-like illness (ILI)* activity first exceeded the national baseline during the week ending November 9, 2019, signaling the earliest start to the influenza season since the 2009 influenza A(H1N1) pandemic. Activity remains elevated as of mid-February 2020. In the United States, annual vaccination against seasonal influenza is recommended for all persons aged ≥6 months (1). During each influenza season, CDC estimates seasonal influenza vaccine effectiveness in preventing laboratory-confirmed influenza associated with medically attended acute respiratory illness (ARI). This interim report used data from 4,112 children and adults enrolled in the U.S. Influenza Vaccine Effectiveness Network (U.S. Flu VE Network) during October 23, 2019– January 25, 2020. Overall, vaccine effectiveness (VE) against any influenza virus associated with medically attended ARI was 45% (95% confidence interval [CI] = 36%–53%). 

The full CDC interim report can be downloaded below.  

A Deep Learning Approach to Antibiotic Discovery

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

The full article can be viewed at this link.  

Trump's next health care move: Giving Silicon Valley your medical data

The Trump administration's push to give patients more control over their health records could turn over a massive trove of very personal data to giant tech companies, app designers and data brokers.

If proposed policy changes go through, patients would be able to download their health records on to their smartphones and direct it to apps of their choice. But there’s a major privacy pitfall: As soon as those records leave the software system of the doctor or hospital, they are no longer protected by HIPAA, the landmark medical privacy law.

That’s where Google and Apple step in to hoover up the data. The tech giants have lobbied the administration to require access. That might allow for unprecedented convenience, letting patients more easily share data for a second opinion or enabling a researcher to find participants for a clinical trial. But it also opens up a Wild West of data sharing on the most intimate health care details for millions of Americans.

The full Politico article can be viewed at this link.  

The Rural Health Safety Net Under Pressure: Rural Hospital Vulnerability 

This analysis was developed by The Chartis Center for Rural Health and designed to model the probability of closure for all rural hospitals as a function of various indicators of closure and provide new insight into the underlying characteristics of hospitals that are more vulnerable to closure.

The full analysis can be downloaded below.  

The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping

Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF).

A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (>90 day) revascularization and death.

1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, P<0.0001) respectively, even after adjusting for age and co-morbidity. For each 1 unit decrease in MPR the adjusted HR for death and MACE were 2.45 (95% CI 1.42-4.24, P=0.001) and 1.74 (95% CI 1.36-2.22, P<0.0001) respectively. In patients without regional perfusion defects on clinical read and no known macrovascular coronary artery disease (n=783), MPR remained independently associated with death and MACE, with stress MBF remaining associated with MACE only.

In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

The full article can be viewed at this link.  

Why health care AI can’t replace medicine’s human component

The AMA deliberately uses the term augmented intelligence (AI)—rather than the more common term “artificial intelligence”—when referring to machine-learning computer algorithms that hold the potential to produce dramatic breakthroughs for health care research, population health risk-stratification and diagnostic support.

And there’s a good reason for that.

“In health care, machines are not acting alone but rather in concert and in careful guidance with humans, i.e., us—physicians,” said AMA Board of Trustees Chair Jesse M. Ehrenfeld, MD, MPH. “There is and will continue to be a human component to medicine, which cannot be replaced. AI is best optimized when it is designed to leverage human intelligence.”

The full AMA article can be viewed at this link.  

Application of Blockchain to Maintaining Patient Records in Electronic Health Record for Enhanced Privacy, Scalability, and Availability

Electronic Health Record (EHR) systems are increasingly used as an effective method to share patients’ records among different hospitals. However, it is still a challenge to access scattered patient data through multiple EHRs. Our goal is to build a system to access patient records easily among EHRs without relying on a centralized supervisory system.

We apply consortium blockchain to compose a distributed system using Hyperledger Fabric incorporating existent EHRs. Peer nodes hold the same ledger on which the address of a patient record in an EHR is written. Individual patients are identified by unique certificates issued by a local certificate authorities that collaborate with each other in a channel of the network. To protect a patient’s privacy, we use a proxy re-encryption scheme when the data are transferred. We designed and implemented various chaincodes to handle business logic agreed by member organizations of the network.

We developed a prototype system to implement our concept and tested its performance including chaincode logic. The results demonstrated that our system can be used by doctors to find patient’s records and verify patient’s consent on access to the data. Patients also can seamlessly receive their past records from other hospitals. The access log is stored transparently and immutably in the ledger that is used for auditing purpose.

Our system is feasible and flexible with scalability and availability in adapting to existing EHRs for strengthening security and privacy in managing patient records. Our research is expected to provide an effective method to integrate dispersed patient records among medical institutions.

The full article can be downloaded below.