Predictors of developing renal dysfunction following diagnosis of transthyretin cardiac amyloidosis.

BACKGROUND: In patients with transthyretin cardiac amyloidosis (ATTR-CA), renal dysfunction is a poor prognostic indicator. Limited data are available on variables that portend worsening renal function (wRF) among ATTR-CA patients. OBJECTIVES: This study assesses which characteristics place patients at higher risk for the development of wRF (defined as a drop of ≥10% in glomerular filtration rate [GFR]) within the first year following diagnosis of ATTR-CA. METHODS: We included patients with ATTR-CA (n = 134) evaluated between 2/2016 and 12/2022 and followed for up to 1 year at our amyloid clinic. Patients were stratified into two groups: a group with maintained renal function (mRF) and a group with wRF and compared using appropriate testing. Significant variables in the univariate analysis were included in the multivariable logistic regression model to determine characteristics associated with wRF. RESULTS: Within a follow-up period of 326 ± 118 days, the median GFR% change measured -6% [-18%, +8]. About 41.8% (n = 56) had wRF, while the remainder had mRF. In addition, in patients with no prior history of chronic kidney disease (CKD), 25.5% developed de novo CKD. On multivariable logistic regression, only New York Heart Association (NYHA) class ≥III (odds ratio [OR]: 3.9, 95% confidence interval [CI]: [1.6-9.3]), history of ischemic heart disease (IHD) (OR: 0.3, 95% CI: [0.1-0.7]), and not receiving SGLT-2i (OR: 0.1, 95% CI: [0.02-0.5]) were significant predictors of wRF. CONCLUSION: Our study demonstrated that the development of de novo renal dysfunction or wRF is common following the diagnosis of ATTR-CA. Additionally, we identified worse NYHA class and no prior history of IHD as significant predictors associated with developing wRF, while receiving SGLT-2i therapy appeared to be protective in this population.

A Comparison of Smoking History in the Electronic Health Record With Self-Report.

INTRODUCTION: Knowing patients' smoking history helps guide who may benefit from preventive services such as lung cancer screening. The accuracy of smoking history electronic health records remains unclear. METHODS: This was a secondary analysis of data collected from a portal-based lung cancer screening decision aid. Participants of an academically affiliated health system, aged 55-76 years, completed an online survey that collected a detailed smoking history including years of smoking, years since quitting, and smoking intensity. Eligibility for lung cancer screening was defined using the Centers for Medicare and Medicaid Services criteria. Data analysis was performed May-December 2018, and data collection occurred between November 2016 and February 2017. RESULTS: A total of 336 participants completed the survey and were included in the analysis. Of 175 participants with self-reported smoking intensity, 72% had packs per day and 62% had pack-years recorded in the electronic health record. When present, smoking history in the electronic health records correlated well with self-reported years of smoking (r =0.78, p≤0.0001) and years since quitting (r =0.94, p≤0.0001). Self-reported smoking intensity, including pack-years (r =0.62, p<0.0001) and packs per day (r =0.65, p≤0.0001), was less correlated. Of those participants eligible for lung cancer screening by self-report, only 35% met criteria for screening by electronic health records data alone. Others were either incorrectly classified as ineligible (23%) or had incomplete data (41%). CONCLUSIONS: The electronic health records frequently misses critical elements of a smoking history, and when present, it often underestimates smoking intensity, which may impact who receives lung cancer screening.