Tyler O'Neal, Staff Editor GOVERNMENT

Rush unveils quality composite rank

New composite rank aggregates hospital rating system scores into single, consumer-friendly score

Rush University Medical Center researchers have proposed a rating system that standardizes and combines data from five leading hospital rating systems into an easy-to-understand composite score of one to 10 that will help guide consumer's hospitals choice.

In a paper published July 2 in the American Journal of Medical Quality, the authors first cited research showing that despite almost two decades of public reporting of quality metrics, consumers have found hospital rating systems "to be limited and lacking in personalization or relevance for individual consumers." This lack of consumer engagement, the authors suggest, is likely driven by the substantial variability that exists between the ranking of top performing hospitals in different ranking systems: The U.S. News & World Report Best Hospitals List, the Vizient Quality and Accountability Study, the Centers for Medicare & Medicaid Services (CMS) Star Rating, the Leapfrog Hospital Safety Grade, and the Truven Top 100 Hospitals list. Rush University Medical Center's chief analytics officer Dr. Bala Hota is the lead author of 'Disagreement Between Hospital Rating Systems: Measuring the Correlation of Multiple Benchmarks and Developing a Quality Composite Rank'.{module In-article}

Lead author Dr. Bala Hota, the Medical Center's chief analytics officer, noted that while each of the rating organizations provides valuable data and insight that help drive hospital quality improvement efforts, their complexity and variability have made them difficult for consumers to use.

"The science behind each rating systems is very complex and measures different outcomes, domains and even time periods," Hota said. "And while this wealth of data supporting the ratings is vital to hospitals, consumers are confused when the ratings disagree."

Thus nearly two years ago, Hota and his Rush colleagues began gathering the data and assembling an objective framework to needed assess the overall similarity of rating systems to one another. The paper, "Disagreement Between Hospital Rating Systems: Measuring the Correlation of Multiple Benchmarks and Developing a Quality Composite Rank" details how they aggregated scoring data from multiple hospital ranking systems to generate a single measure, the Quality Composite Rank (QCR).

For the study, the scores for 70 high-performing hospitals ranked by the various raking systems were combined into a core data set of ten performance measures. Using a series of statistical correlation approaches that accounted for differences and similarities in what each rating organization measured, researchers were able to better identify variations and ultimately generate a single digit composite score that rewards hospitals for consistency across ratings systems.

"Standardizing what is measured more objectively identifies hospitals that do well in multiple measurement systems. Hospitals with the best QCR scores had higher quality scores across more areas and measured by more scoring systems. We believe that suggests a more sustained and institutional commitment to quality care," Hota said.

More importantly, the authors believe a single-digit QCR composite score built from the various ratings systems will benefit patients.

"The most important metrics are those that help patients navigate the health system. But publicly-reported quality measures that the public does not understand defeats their purpose," said Omar Lateef, DO, Rush University Medical Center chief executive officer and paper co-author.

"When patients see conflicting ratings, they must then reconcile that information in their mind. What we've done is to develop a measure that quantitatively does that reconciliation."

NYU’s Jennifer Crodelle develops model exploring daily rhythms in pain sensitivity

Tyler O'Neal, Staff Editor GOVERNMENT

Findings could open new paths toward better pain management strategies

A new computational model successfully predicts how daily pain sensitivity rhythms affect pain processing, both in healthy adults and in people with neuropathic pain. Jennifer Crodelle of New York University and colleagues present these findings in PLOS Computational Biology. {module In-article}g

Just as processes like metabolism and alertness exhibit a daily rhythm, pain sensitivity changes over the course of the day. Sensitivity is usually highest in the middle of the night and lowest in late afternoon. However, this rhythm is flipped for people with neuropathic pain, who feel severe pain in response to a typically non-painful stimulus. For these patients, the lowest pain sensitivity occurs at night.

The mechanisms underlying both normal and neuropathic pain rhythms have been unclear. To gain new insights, Crodelle and colleagues built a mathematical model that simulates how pain is transmitted from a nerve to the spinal cord's dorsal horn, where pain is initially processed.

The researchers found that their model successfully reproduces experimental results on pain sensitivity and predicts how these results are affected by time of day. For instance, it predicts the time-of-day effects on pain inhibition, the phenomenon in which one feels a lessening of pain from applying light pressure, such as by grabbing a stubbed toe.

The model also suggests a potential mechanism for the flipped sensitivity rhythm in people with neuropathic pain: a change from inhibition to excitation in the synaptic connections between nerve cells. This finding points to targets for further experimental study and potential treatment.

"Our modeling results provide a first step in understanding how the daily rhythm in pain sensitivity affects normal pain processing across the day and potentially how the daily rhythm can benefit pain management strategies in clinical settings," Crodelle says. "For example, pain relief medication could be titrated appropriately across the day, thus reducing the total amount of medication needed."

Potential next steps are to incorporate factors that may influence the daily pain sensitivity rhythm, such as sleep deprivation and jet lag. The model could also aid investigations into how pain sensitivity is reduced by a chronic pain treatment known as spinal cord stimulation.

Italian scientists develop computational model revealing the mechanism of replication of prions in mad cow disease

Tyler O'Neal, Staff Editor GOVERNMENT

The study, from the Dulbecco Telethon Institute and the University of Trento in collaboration with the Italian National Institute of Nuclear Physics, will open up new research avenues to design drugs against incurable neurodegenerative disorders

The study was carried out in the Dulbecco Telethon Laboratory of Prions & Amyloids at CIBIO, lead by Emiliano Biasini, University of Trento and involved the team led by Prof. Pietro Faccioli, a physicist from the same university and affiliated to the Italian National Institute of Nuclear Physics.

Prions are unusual infectious agents made by aberrantly folded forms of a physiological protein called the cellular prion protein, or PrPC. These pathogens are known to replicate in absence of genetic material by recruiting normal PrPC molecules at the surface of cells and forcing them to change conformation and become infectious themselves. The resulting accumulation of prion particles in the nervous system lies at the root of neurodegenerative conditions known as transmissible spongiform encephalopathies, including Creutzfeldt-Jakob disease, fatal familial insomnia and Gerstmann-Sträussler-Scheinker in human, but also a variety of other pathologies in mammals such as the famous mad cow disease, which in the nineties caused a large epidemics in UK and Europe and several cases of cross-species transmission to human caused by the ingestion of infected meat. {module In-article}

Even though we know the existence of prions since 1982, thanks to the work of Nobel Laureate Stanley Prusiner, direct information regarding the structure of these non-canonical infectious agents is still lacking,” says Emiliano Biasini, Assistant Telethon Scientist and Associate Professor at the Department CIBIO, University of Trento. ”In fact, their insoluble and aggregated nature hampers the use of classical high resolution techniques for studying protein structures such as X-ray crystallography or nuclear magnetic resonance. However, such information is instrumental to rationally design drugs against these agents. In an attempt to fill this gap, we found unexpected help from a discipline usually considered far away from biology or chemistry, that is particles physics.”

Telethon researchers revised previous models of prion structure and proposed a novel architecture consistent with recent experimental data. This new model allowed Pietro Faccioli's group to apply their innovative algorithms for the reliable prediction of protein conformational transitions to the prion replication mechanism ”Cross-disciplinarity has been the key,” explains Giovanni Spagnolli, Ph.D. student at the Department CIBIO, University of Trento and first author of the paper. ”Without the contribution of the colleagues from physics we would have never been able to afford the kind of calculation required to simulate such complex systems. For the first time we reconstructed a physically-plausible mechanism of prion replication, which now allow us to formulate new hypotheses and design new drug discovery schemes to tackle the neurodegenerative processes unleashed by these infectious agents.

“The calculation algorithms that allowed the reconstruction of prion replication are derived from mathematical methods of theoretical physics, originally formulated to study phenomena of the subatomic world, such as the quantum tunneling effect. These mathematical methods have been adapted here to allow the simulation of complex biomolecular processes such as the folding and aggregation of proteins,” said Pietro Faccioli, Associate Professor at the Department of Physics, University of Trento and affiliated to the Italian National Institute for Nuclear Physics.