Diagnosis and Treatment of Cardiac Amyloidosis Related to Plasma Cell Dyscrasias

Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis

OBJECTIVE

To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG).

METHODS

We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets.

RESULTS

The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85.

CONCLUSION

An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.

Predictors of cardiac involvement and survival in patients with primary systemic light-chain amyloidosis: roles of the clinical, chemical, and 3-D speckle tracking echocardiography parameters

Background

Light-chain (AL) amyloidosis is the most common type of systemic amyloidosis with poor prognosis. Currently, the predictors of cardiac involvement and prognostic staging systems are primarily based on conventional echocardiography and serological biomarkers. We used three-dimensional speckle tracking echocardiography (STE-3D) measurements of strain, hypothesizing that it could detect cardiac involvement and aid in prediction of mortality.

Methods

We retrospectively analysed 74 consecutive patients with biopsy-proven AL amyloidosis. Among them, 42 showed possible cardiac involvement and 32 without cardiac involvement. LV global longitudinal strain (GLS), global radial strain, global circumferential strain and global area strain (GAS) measurements were obtained.

Results

The GLS and GAS were considered significant predictors of cardiac involvement. The cut-off values discriminating cardiac involvement were 16.10% for GLS, 32.95% for GAS. During the median follow-up of 12.5 months (interquartile range 4–25 months), 20 (27%) patients died. For the Cox proportional model survival analysis, heart rate, cardiac troponin T, NT-proBNP levels, E/e’, GLS, and GAS were univariate predictors of death. Multivariate Cox model showed that GLS ≤ 14.78% and cardiac troponin T ≥ 0.049 mg/l levels were independent predictors of survival.

Conclusions

STE-3D measurements of LV myocardial mechanics could detect cardiac involvement in patients with AL amyloidosis; GLS and cardiac biomarkers can provided prognostic information for mortality prediction.

Myocardial amyloidosis following multiple myeloma in a 38-year-old female patient: A case report

Multiple myeloma (MM) is an immunoglobulin-producing tumor of plasma cells, which occurs commonly in the elderly. The incidence of myocardial amyloidosis with MM is extremely low and early clinical manifestations are nonspecific. The diversity of clinical manifestations and first episode symptoms often cause misdiagnosis in young patients with myocardial amyloidosis following MM. In this study, we analyzed the clinical data of a young woman with MM and impaired cardiac function combined with echocardiography, electrocardiography (ECG), laboratory data, cell Congo Red staining, and other manifestations to diagnose amyloidosis. Considering the rapid progression, short survival, and poor prognosis in most patients, a clear, definitive, and timely diagnosis is essential for the treatment of patients with MM complicated with myocardial amyloidosis.