Machine Learning Can Identify Patients With HF at Risk for Wild-Type ATTR-CM Earlier

Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underrecognized cause of heart failure (HF) and delayed diagnosis of wild-type ATTR-CM (ATTRwt-CM) leads to inappropriate assessments and treatment before diagnosis, as well as worse clinical outcomes after diagnosis, explained Casey Reed, PharmD, MBA, MSc, MS, CSP, advanced medical engagement lead, Pfizer, during a presentation of the findings at the annual meeting of the Academy of Managed Care Pharmacy.

Reed was the lead author on a platinum-winning poster that presented the findings of applying a machine learning model to characterize the patients with HF who are at high risk of ATTRwt-CM.

She noted that the delayed diagnosis and worse outcomes after diagnosis highlight “the importance of screening patients for this disease state sooner and efficiently in order to improve patient outcomes overall.”

Reed and her coauthors conducted a retrospective, case-control study using Humana administrative claims for a random sample of patients diagnosed with ATTRwt-CM (n = 119) and patients with HF but no diagnosis of ATTRwt-CM (n = 119). Patients were between the ages of 69 and 85 years when they had their first HF diagnosis claim (index date) and were enrolled in a Medicare Advantage plan ≥ 12 months pre-index and ≥ 6 months postindex.

These groups were used to evaluate the model, and the researchers found the model had sensitivity of 88%, specificity of 65%, and accuracy of 77%. The model predictions were correct 89% of the time based on the receiver operating characteristics area under the concentration-tine curve, Reed explained.

After evaluating the machine learning model, they ran it on 266,906 patients with HF without a diagnosis of ATTRwt-CM. They found 4.1% were suspected to be at high risk of ATTwt-CM and 25.5% were suspected to be at risk. According to Reed, the confirmed diagnosis population leans heavily male (78.2%), but the mix in the suspected risks is more even: 53.1% male in the suspected high-risk group and 50.9% in the suspected at-risk group.

“So that may show some practice disparities…with how we're diagnosing females, potentially, with this disease state,” she said.

The researchers also looked at health care resource utilization (HCRU) for the 119 patients with confirmed ATTRwt-CM and found that patients with HF a higher proportion of patients had claims for several health care resources before the ATTRwt-CM diagnosis vs after the diagnosis.

They found:

• All-cause hospitalizations decreased from 65% in the 1 to 2 years before ATTRwt-CM diagnosis to 48% after ATTRwt-CM diagnosis
• All-cause hospital readmission claims decreased 46% to 26%
• Emergency department visits decreased 60% to 45%
• Prescription rates decreased from 93% to 45%

HCRU was highest in the year leading up to the diagnosis of ATTRwt-CM compared with the 1 to 2 years before or after diagnosis.

“So, again, that fits in with the story of effectively and efficiently managing these resources and managing care for these patients and getting them appropriately diagnosed,” Reed said.

When looking at patients with suspected high risk of ATTRwt-CM, the data showed acute HCRU rates were higher for these patients compared with those who had a confirmed diagnosis.

“Patients at suspected high risk for this disease state had high resource utilization and may benefit from earlier suspicion of the disease,” Reed said. “And then this tool, the machine learning algorithm, may be used to help find those patients.”

Reference

Reed C, Nair R, Schepart A, Sheer R, Casey E, Simmons R. Applying a machine learning model in a claims database to characterize patients with heart failure at high risk for wild-type transthyretin amyloid cardiomyopathy. Presented at: Academy of Managed Care Pharmacy Annual Meeting 2022; Chicago, Illinois; March 29-April 1, 2022. Poster E27.