Natural Language Processing Framework in Early Detection of Amyloidosis: The ALARM Study

Early diagnosis of systemic amyloidosis (SA) is associated with improved outcomes. However, early diagnosis remains limited by variable, nonspecific, and multisystemic presentation.

In this study, we used natural language processing (NLP) to develop models for predicting SA diagnosis.

Patients with a diagnosis of heart failure (HF) and/or neuropathy and sufficient chart data were identified at Oregon Health & Science University (development cohort) and unstructured text data from electronic health records were used to construct NLP-predictive models for the diagnosis of SA. These models were validated in an independent population with HF at Baylor Scott & White Health System (validation cohort).

We identified 109,275 patients with a diagnosis of HF and/or neuropathy (age 57.6 ± 18.6 years, 52.9% females, and 89.6% White) in the development cohort. Of the 420 models evaluated, gradient boosted regression applied to combined notes had the best diagnostic accuracy (area under the curve [AUC] = 0.88), with 73.5% sensitivity, 86.0% specificity, 3.4% positive predictive value, and 99.8% negative predictive value in predicting SA. In the validation cohort (116,658 patients with HF, age 71.6 ± 14.3 years, 47.3% females, 75.8% White), the model had 54.1% sensitivity, 94.0% specificity, 8.8% positive predictive value, and 99.1% negative predictive value, with an AUC of 0.85.

NLP-based prediction models have an excellent diagnostic performance for SA in patients with HF and/or neuropathy. NLP-enabled workflow can potentially lead to earlier diagnosis of amyloidosis, starting therapy and possibly improving outcomes.