ASN Kidney Week 2018 looks to future of patient care

Issue: December 2018

ByMark E. Neumann

If you attended ASN Kidney Week 2018, you may have walked away with optimism about the future of patient care. Certainly, there were the traditional sessions on diagnosis and treatment of kidney disease, along with poster presentations by the thousands on improving care for patients. Topics focused on everything from science to economics. There were well attended sessions on the treatment of acute kidney injury in the ICU and updates on diabetes care. Late-breaking news covered the use of new therapeutic agents and results from ongoing clinical trials.

But there was also a deeper dive into areas of treatment that focused less on the therapy and more on preventive care: monitoring patient health by improving staff-to-patient communication. As kidney care starts heading in the direction that CMS and the Medicare program have been embracing – namely, value-based care driven by outcomes vs. the time-worn fee-for-service approach – the use of artificial intelligence and sharing medical information between patients and staff has helped expand the thinking on how dialysis and transplantation can be delivered in the future.

Of particular interest was the use of machine learning to help detect complications before these arise. In a shared-savings environment like the Comprehensive ESRD Care Demonstration, that involves 30,000 patients in ESRD seamless care organizations, the focus is on limiting hospitalizations. Such an approach not only saves money for the payer – in this case, Medicare – and provides financial rewards for the care provider, but also benefits the patient whose routine, outpatient care is not disrupted.

At ASN Kidney Week 2018, several presentations focused on ways the information technology community can improve communication for home dialysis users. Peritonitis has always been the Achilles heel of PD. Difficult to predict and painful for patients who experience it, peritonitis often brings with it a patient’s fear about the risks of home therapy and thoughts about going to in-center dialysis.

Fresenius Medical Care has placed a focus on reducing the risk of peritonitis through machine learning and predictive models.

“... We are finding new and better ways to personalize care that will enhance the patient experience and improve outcomes,” Bill Valle, CEO of Fresenius Medical Care North America, said in a statement. Added company chief medical officer Franklin W. Maddux, MD, “We are especially excited about our work to improve the connectivity of our home product portfolio that is increasing engagement and outcomes for this growing patient population. We are committed to making home therapies an option for as many patients as possible.”

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Projects include utility of a predictive model with more than 1,500 variables to identify the patient’s probability of a hospitalization within 7 days of a hemodialysis treatment. Another uses machine learning to help predict elevated serum phosphate levels in patients with ESRD.

Finally, with a focus on reducing the risk of peritonitis in patients on PD, the product and patient services company is using technology to predict patient risk of peritonitis episodes, ie, finding ways to identify those patients who are at higher risk for catheter infections.

Other companies like Baxter Renal Care are also developing connectivity products for patients. The company’s shared source technology helped to reduce hospitalizations and deliver an increase in cost efficiencies for PD therapy, according to a presentation at ASN Kidney Week 2018. Data showed “statistically significant reduced hospitalization for home patients using Baxter’s Sharesource telehealth platform with an (automated PD) system,” according to a Baxter press release.

“While many factors contribute to these findings, it is believed remote patient management technology helps support greater communication between patients and their health care providers, which can improve adherence to treatment and identify potential complications before they become serious,” the release noted.

Therapy that works is an important part of any treatment plan, but what if we could avoid – or delay – the therapy with new technologies that predict when the body needs attention? Placing a priority on kidney treatment vs. kidney failure is a worthwhile goal.