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Background The strongest risk factor of neurodegenerative diseases (NDDs) is aging. Spontaneous asparaginyl deamidation leading to formation of isoaspartate (isoAsp) has been correlated with protein aggregation in NDDs. Methods Two cohorts consisting of 140 subjects were studied. Cohort 1 contained patients with AD and healthy controls, while Cohort 2 recruited subjects with mild cognitive impairment (MCI), vascular dementia (VaD), frontotemporal dementia (FTD), Parkinson’s disease (PD) and healthy controls. The levels of isoAsp in plasma human albumin (HSA), the most abundant protein in plasma, as well as the levels of immunoglobulin G (IgG) specific against deamidated HSA were measured. Apart from the memory tests, plasma biomarkers for NDDs reported in literature were also quantified, including amyloid beta (Aβ) peptides Aβ40 and Aβ42, neurofilament light protein (NfL), glial fibrillary acidic protein (GFAP) and phosphorylated tau 181 (p-tau181) protein. Results Deamidation products of blood albumin were significantly elevated in vascular dementia and frontotemporal dementia (P < 0.05), but less so in PD. Intriguingly, the deamidation levels were significantly (P < 0.01) associated with the memory test scores for all tested subjects. Deamidation biomarkers performed superiorly (accuracy up to 92%) compared with blood biomarkers Aß42/Aß40, NfL, GFAP and p-tau181 in separating mild cognitive impairment from healthy controls. Conclusion We demonstrated the diagnostic capacity of deamidation-related biomarkers in predicting NDDs at the early stage of disease, and the biomarker levels significantly correlated with cognitive decline, strongly supporting the role of deamidation in triggering neurodegeneration and early stages of disease development. Prospective longitudinal studies with a longer observation period and larger cohorts should provide a more detailed picture of the deamidation role in NDD progression.
Background The strongest risk factor of neurodegenerative diseases (NDDs) is aging. Spontaneous asparaginyl deamidation leading to formation of isoaspartate (isoAsp) has been correlated with protein aggregation in NDDs. Methods Two cohorts consisting of 140 subjects were studied. Cohort 1 contained patients with AD and healthy controls, while Cohort 2 recruited subjects with mild cognitive impairment (MCI), vascular dementia (VaD), frontotemporal dementia (FTD), Parkinson’s disease (PD) and healthy controls. The levels of isoAsp in plasma human albumin (HSA), the most abundant protein in plasma, as well as the levels of immunoglobulin G (IgG) specific against deamidated HSA were measured. Apart from the memory tests, plasma biomarkers for NDDs reported in literature were also quantified, including amyloid beta (Aβ) peptides Aβ40 and Aβ42, neurofilament light protein (NfL), glial fibrillary acidic protein (GFAP) and phosphorylated tau 181 (p-tau181) protein. Results Deamidation products of blood albumin were significantly elevated in vascular dementia and frontotemporal dementia (P < 0.05), but less so in PD. Intriguingly, the deamidation levels were significantly (P < 0.01) associated with the memory test scores for all tested subjects. Deamidation biomarkers performed superiorly (accuracy up to 92%) compared with blood biomarkers Aß42/Aß40, NfL, GFAP and p-tau181 in separating mild cognitive impairment from healthy controls. Conclusion We demonstrated the diagnostic capacity of deamidation-related biomarkers in predicting NDDs at the early stage of disease, and the biomarker levels significantly correlated with cognitive decline, strongly supporting the role of deamidation in triggering neurodegeneration and early stages of disease development. Prospective longitudinal studies with a longer observation period and larger cohorts should provide a more detailed picture of the deamidation role in NDD progression.
Background The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or “hidden” in unstructured text fields and not readily available for clinicians to act upon. Objective We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. Methods We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians’ notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, “mild dementia” and “advanced Alzheimer disease”). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. Results We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. Conclusions Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.
BACKGROUND The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or “hidden” in unstructured text fields and not readily available for clinicians to act upon. OBJECTIVE We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. METHODS We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on <i>ICD-9</i> (<i>International Classification of Diseases, Ninth Revision</i>) and <i>ICD-10</i> (<i>International Statistical Classification of Diseases, Tenth Revision</i>) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians’ notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, “mild dementia” and “advanced Alzheimer disease”). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. RESULTS We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an <i>F</i><sub>1</sub>-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and <i>F</i><sub>1</sub>-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. CONCLUSIONS Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.
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