Collaboration, strong payment model key to nephrology, primary care efforts to slow CKD
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There are many ways to quantify the burden of chronic kidney disease.
In Advancing American Kidney Health (AAKH), an executive order signed by President Donald J. Trump a year ago, a primary goal is to reduce the number of new patients diagnosed with end-stage kidney disease by 25% during the next 10 years. This means aggressive identification and treatment of kidney disease in its early stages.
“Progression is the key,” Michael Germain, MD, professor of medicine at Tufts University School of Medicine, told Nephrology News & Issues. “In [polycystic kidney disease] PKD, we can predict progression early and treat to slow progression. As we get better at genetic markers, then we can do the same in other causes of CKD.”
The CDC estimates 37 million adults in the United States have CKD, but most do not recognize the signs or know they have it. In the agency’s most recent report, the CDC notes that Black people and Hispanic people carry the greatest risk for CKD (see Table). Diabetes and high blood pressure are the major causes of CKD in adults. Other risk factors include heart disease, obesity, family history, previous damage to the kidneys and older age.
Patients at risk
To get to patients early and offer treatment, planning is important, Paul Komenda, MD, FRCPC, MHA, an epidemiologist and professor of medicine at the University of Manitoba and research director of the Chronic Disease Innovation Centre in Winnipeg, Canada, told Nephrology News & Issues. “The priorities are screening and surveillance,” he said. “How do we find the cases, and then how do we treat?
“I think the strategy first involves primary care physicians, who risk stratify the CKD patient population and determine who should be treated more aggressively. Then we provide public health education on CKD, followed by the nephrologist and the multidisciplinary care team helping the patient manage their late-stage disease.”
Komenda said the AAKH will have an impact on slowing progression of kidney disease.
Source: Paul Komenda, MD, FRCPC, MHA
“Pre-dialysis care has been woefully insufficient in the U.S.; AAKH should change that,” he said.
Lessons can be learned from other specialties like oncology on how to make CKD surveillance more effective.
“We [risk stratify] patients who are at high risk for breast cancer. We can do this for kidney disease as well, putting together a patient profile, preparing a history and monitoring the patient for the clear signs of CKD, such as hypertension and diabetes,” Komenda said.
In a recent paper he co-authored in Current Opinions in Nephrology and Hypertension, Komenda said laboratory values can serve as an important indicator of CKD progression, along with early evaluation programs like the NKF’s Kidney Early Evaluation Program, which reached more than 185,000 individuals at increased risk for developing kidney disease between August 2000 and June 2013. In Canada, the First Nations Community Based Screening to Improve Kidney Health and Dialysis project set a goal to improve kidney health in the country’s rural indigenous communities. Of 1,700 individuals tested, 25.5% showed signs of CKD.
“Importantly, it was noted that if screening had only been offered to patients with diabetes and hypertension, 97 (28.3% of people) who were found to have CKD would have been missed,” Komenda wrote.
Awareness among those with CKD continues to be an issue for clinicians trying to take a more aggressive approach toward treatment of the disease.
“Data suggest that 7% of people with CKD are aware that they have it, so education is a big issue,” Elizabeth Montgomery, vice president of learning strategies and primary care programs for the NKF, said in a presentation at the Southwest Nephrology Conference earlier this year. “The latest data suggest the national benchmark for CKD, meaning the percentage of people at risk that are actually receiving testing, is somewhere around 13%.”
Engage primary care
To improve that percentage, the NKF created the CKD Change Package, an online compendium of information to help clinicians understand how other practices have successfully implemented CKD guidelines.
“For many organizations, the first step to implementing a CKD program is to increase the visibility of CKD as an important entity to follow,” the NKF wrote on the CKD Change Package website. “To achieve this, it is suggested that a strong analysis of CKD be created from both a financial and morbidity/mortality perspective.”
The NKF added, “It is important to engage the highest level of medical leadership in these discussions. Primary care leaders should spearhead this intervention and be fully engaged in the initial conversations regarding CKD in a population health model. Include atypical stakeholders, like a physician/hospital organization or employee benefit representative, to provide support for the CKD program.”
Source: CDC. Chronic Kidney Disease in the United States, 2019. Atlanta, GA: HHS, CDC.
In the NKF project, a pilot program was built to evaluate if CKD intervention in primary care made a difference in patient outcomes and cost. An online risk tool was developed to ascertain information about kidney health. The risk tool asked about diabetes, hypertension, heart disease, weight, height and whether a family member is on dialysis or had a transplant. The goal was to prompt conversations between primary care clinicians and potential CKD patients to educate about and assess for CKD. A total of 30,000 people clicked into the system; 95% said the content was helpful and they would discuss it with their physician.
“We are partnered with the [Veterans Administration] VA to test this system,” Montgomery said. “We will have feedback not only from veterans, but from primary care clinicians about the effectiveness, usefulness [and] the design of this program,” she said.
Komenda cited a paper by Tangri and colleagues that reviewed the concept of the Kidney Failure Risk Equation. The researchers looked at 31 cohorts involving 721,357 participants with CKD stages 3-5 in more than 30 countries. The data was collected from 1982 through 2014. During a median follow-up of 4 years, 23,829 cases of ESKD were identified.
“The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts; discrimination in subgroups by age, race, and diabetes status was similar ... ,” Tangri and colleagues wrote.
AAKH payment models
As part of the implementation of AAKH, CMS has developed several payment models, both mandatory and volunteer, with a focus on earlier intervention. The agency has some history in bringing together subspecialties to provide a value-based, patient-centered care approach; the Kidney Care Choices model developed as part of the AAKH builds on the existing Comprehensive End Stage Renal Disease Care Model structure. That model brings together dialysis facilities, nephrologists and other health care providers from ESKD-focused accountable care organizations. The Kidney Care Choices model adds financial incentives for health care providers to manage the care of Medicare beneficiaries with CKD stages 4 and 5 and ESKD.
“The model will help delay the onset of dialysis and to incentivize kidney transplantation,” according to the AAKH website.
In the Kidney Care First Option, participating nephrology practices will receive adjusted fixed payments each quarter on a per-patient basis for managing the care of patients with late-stage CKD and patients with ESKD. The payments will be adjusted based on health outcomes and utilization compared to the participating practice’s own experience and national standards, as well as performance on quality measures. In addition, participating practices will receive a bonus payment for every patient assigned to the group who receives a kidney transplant based on the transplant remaining healthy for up to 3 years after the surgery.
Germain said the model has merit. “The idea is good, but the reimbursement is inadequate, and the execution has been poor,” he said.
CMS received more than 300 comments on the proposed payment models.
Detection, then treatment
A key to reducing the number of cases of ESKD includes mechanisms to screen patients who are at risk. Some organizations, like Cricket Health, are using artificial intelligence (AI) to spot clinical indicators of CKD. In the company’s recent white paper, “Machine learning for chronic kidney disease detection and risk stratification,” Carmen A. Peralta, MD, Chief Medical Officer of Cricket Health, and colleagues highlight the economic burden of CKD and describe the methodology and application of AI.
“Though CKD is highly prevalent and represents a large economic burden in the United States, efforts to manage the condition at earlier stages, delay disease progression and reduce complications have been fairly limited,” the authors wrote. Cricket is applying AI technology to claims data to develop models that predict estimated eGFR. “Utilizing in-house [machine learning] ML models in kidney care management ensures that Cricket Health is well-positioned to identify and risk-stratify undiagnosed or misclassified CKD patients for risk-bearing entities with access to claims data,” the authors wrote.
If AI can be used to identify the risk factors for CKD, the debate centers around who should lead the team to direct treatment strategies: a primary care physician or a nephrologist. Komenda and Germain said there is a role for both if the goal is to help slow the progression of kidney disease and reach the AAKH goal.
“In most cases, the nephrologist should be in charge for patients with CKD 3 to 5 (unless not progressing), dialysis and transplant,” Germain said. “In some cases, the primary care physician likes to be in charge, but in my area, they want us to take the lead.” – by Mark E. Neumann
- References:
- Curtis S, et al. Curr Opin Nephrol Hypertens. 2020; doi:10.1097/MNH.000000000000059.
- www.cdc.gov/kidneydisease/publications-resources/2019-national-facts.html
- www.cms.gov/newsroom/fact-sheets/kidney-care-choices-kcc-model
- www.crickethealth.com/2019/11/cricket-health-announces-new-machine-learning-model-for-detecting-chronic-kidney-disease
- www.healio.com/news/nephrology/20200401/nephrology-primary-care-can-work-together-on-ckd-detection
- www.jamanetwork.com/journals/jama/fullarticle/2481005
- www.prnewswire.com/news-releases/cricket-health-announces-new-machine-learning-model-for-detecting-chronic-kidney-disease-300952390.html
- For more information:
- Michael Germain, MD, is professor of medicine at Tufts University School of Medicine and is with Baystate Health in Springfield, Massachusetts. He can be reached at michael.germain@baystatehealth.org.
- Paul Komenda, MD, FRCPC, MHA, is professor of medicine at the University of Manitoba and research director of the Chronic Disease Innovation Centre in Winnipeg, Manitoba, Canada. He can be reached at pkomenda@sogh.mb.ca.
- Elizabeth Montgomery is vice president of learning strategies and primary care programs for the NKF and can be reached at elizabeth.montgomery@kidney.org.
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