OBJECTIVES: To identify and describe the profile of potential transthyretin cardiac amyloidosis (ATTR-CM) cases in the Brazilian public health system (SUS), using a predictive machine learning (ML) model.

METHODS: This was a retrospective descriptive database study that aimed to estimate the frequency of potential ATTR-CM cases in the Brazilian public health system using a supervised machine learning model, with data extracted from DATASUS outpatient and inpatient datasets from January 2015 to December 2021. To build the model, a list of ICD-10 codes and procedures potentially related with ATTR-CM was created based on literature review and validated by experts.

RESULTS: From 2015 to 2021, the ML model classified 262 hereditary ATTR-CM (hATTR-CM) and 1,581 wild-type ATTR-CM (wtATTR-CM) potential cases (hATTR-CM-like and wtATTR-CM-like). Overall, the median age of hATTR-CM and wtATTR-CM patients was 66.8 and 59.9 years, respectively. The ICD-10 codes most presented as hATTR-CM and wtATTR-CM were related to heart failure and arrythmias. Regarding healthcare utilization, hATTR-CM and hATTR-CM-like had similar profiles on proportion of patients with outpatient visits (hATTR-CM 98.0% vs. 92.0% hATTR-CM-like) and different profile related to proportion of hospitalized patients (hATTR-CM 94.4% vs. 32.7% hATTR-CM-like). In wtATTR-CM groups, although both proportions on outpatient visits and hospitalizations were similar, the length of stay (LOS) on hospitalizations was different in wtATTR-CM-like (wtATTR-CM median LOS 5.0 (IQR:2.0 - 10.0] vs. median LOS 7.0 [IQR:3.0 - 14.0])

CONCLUSIONS: Our findings highlight the underdiagnosis of ATTR-CM in Brazil using a machine learning approach. These data may be useful in the development of healthcare guidelines for early diagnosis and treatment, addressing patients’ unmet and improving the estimated population size affected with this disease.