The diagnosis of AL amyloidosis is often delayed despite patients reporting multiple symptoms and seeing different specialists for care over several months to years. This context underscores the critical unmet need for reducing the time from the initial onset of symptoms to the diagnosis of the disease. To address this need, we leveraged a large EHR dataset to investigate the timing and co-occurrence of specific precursor diagnoses occurring before the diagnosis of AL amyloidosis. We were particularly interested to understand when and how certain precursor diagnoses such as dyspnea, fatigue, edema, pain, proteinuria, among others were established as diagnoses within the medical history as a diagnostic code in relation to the AL amyloidosis diagnosis. These precursor diagnoses were derived from symptoms and signs endorsed by many AL amyloidosis patients [4, 10]. We were interested in understanding whether these get catalogued as diagnoses within medical history by ICD codes and if so, how early before the diagnosis of AL amyloidosis. By identifying the proportion of patients with these precursor diagnoses and examining their timing and co-occurrence, our study sheds light on the diagnostic process using EHR data in this rare multisystemic condition.

Our prior work suggests that AL amyloidosis patients have a high prevalence of precursor diagnoses [6]. In the current analyses, we studied the pattern and timing of these diagnoses prior to the diagnosis of AL amyloidosis using the same data source. Concordant with findings by others [7] and as reported in patient surveys [3], our analysis confirms the high prevalence of several symptoms of the disease present and diagnosed in EHR seen in this disease well before the diagnosis of AL amyloidosis has been made with a median time of 3.2 to 21.4 months before AL amyloidosis diagnosis, providing support to efforts to develop predictive algorithms toward early diagnosis.

The most common precursor diagnoses include dyspnea, fatigue, heart failure, edema, altered bowels, neuropathy, and chronic kidney disease. Fatigue is the most common symptom of the disease as reported by 80% of AL amyloidosis patients [4]. Our data show that fatigue is also the most catalogued of the precursor diagnoses as an ICD code, seen in 45% of patients with median time 15.6 months before the diagnosis of AL amyloidosis. Other common AL amyloidosis symptoms including dyspnea and edema are also commonly identified as ICD codes by healthcare providers at one year or longer prior to the diagnosis of AL amyloidosis. This concordance with known symptoms of the disease document the feasibility to using EHR data of diagnosis codes toward creating algorithms that could improve time from symptom onset to AL amyloidosis diagnosis.

When assessing co-occurrence of precursor diagnoses, the strongest correlation was often seen with precursor diagnoses within the same organ system/category, for e.g., cardiomyopathy and heart failure, or nephrotic syndrome and renal disease. This was concordant with expected AL amyloidosis pathology, in that, with organ involvement, the disease would be expected to cause multiple symptoms and signs related to that organ system. Other precursor diagnoses belonged to different organ systems/categories but made intuitive sense as a downstream effect of one of the precursors e.g., autonomic neuropathy and syncope or macroglossia and dysphagia.

The majority of AL amyloidosis patients have more than one organ involvement, thus correlations between organ systems were of greater interest in our analysis. Here we saw numerous strong correlations across various organ system categories, autonomic neuropathy and dyspnea, neuropathy and purpura, fatigue and purpura, fatigue and autonomic neuropathy. The two organ systems which showed the greatest correlation included cardiac and gastrointestinal, cardiac and multisystemic, gastrointestinal and other, neurologic and multisystemic, and neurologic with other. Some other ICD code-based precursors which we had not considered in our current analysis could be added in future iterations include weight loss, constipation, elevated alkaline phosphatase, alopecia, nail dystrophy, spontaneous ecchymosis, and nail dystrophy.

It is crucial to acknowledge and consider the limitations of our study, inherent to the use of EHR data in research, when interpreting the results. For example, misdiagnoses, coding errors, and variations in which symptoms are recorded can lead to incorrect associations or missed precursor diagnoses. Different healthcare organizations within the TriNetX network may have variations in EHR systems and diagnostic coding practices, thus limiting the generalizability of the findings. The early symptoms of AL amyloidosis are nonspecific and can mimic other, more common, conditions. Our study assumes that precursor diagnoses represent early symptoms of AL amyloidosis, but we have not adjusted for comorbidities that may drive the onset of many of the precursor diagnoses. For example, presence of diabetes may lead to neuropathy, proteinuria, and cardiomyopathy. Our approach in selecting a cohort with at least 3 years of backward medical history allows us to assess a baseline prevalence period in the first year of EHR history and then incidence in the subsequent two years preceding the AL amyloidosis diagnosis. On the other hand, our requirement for the 3-year medical history would exclude patients who established within a medical system with the constellation of amyloidosis symptoms within a short time frame. Presumably a proportion of these are patients suspected of having AL from a smaller practice (where there may have been diagnostic delays) and are referred to a larger center for expeditious workup. Because the goal of this study was to establish the timing of precursor diagnoses, we had to start with a cohort with ‘established patients’ within a medical system with ongoing interactions with it, but then happen to develop AL. Lastly, we lack detailed clinical context making it challenging to understand the severity and clinical significance of precursor diagnoses. Nevertheless, our approach is relevant because it is reflective of symptoms that are clinically recognized by healthcare providers. An important strength of this analysis is the racial diversity within our dataset, often lacking in published clinical research in AL amyloidosis from the U.S [11].

In conclusion, leveraging real time EHR data enabled us to identify a large and diverse cohort of AL amyloidosis patients from which to examine diagnostic patterns and demonstrate the potential for earlier diagnosis of this complex disease. Our findings lay the foundation to develop clinical algorithms using ICD codes aimed at earlier recognition of AL amyloidosis.