Using natural language processing to identify opioid use disorder in electronic health record data

Background: As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention strategies, presents challenges. The objective of this study was to ascertain prevalence of OUD using two methods to identify OUD in electronic health records (EHR): applying natural language processing (NLP) for text mining of unstructured clinical notes and using ICD-10-CM diagnostic codes. Methods: Data were drawn from EHR records for hospital and emergency department patient visits to a large regional academic medical center from 2017 to 2019. International Classification of Disease, 10th Edition, Clinic Modification (ICD-10-CM) discharge codes were extracted for each visit. To develop the rule-based NLP algorithm, a stepwise process was used. First, a small sample of visits from 2017 was used to develop initial dictionaries. Next, EHR corresponding to 30,124 visits from 2018 were used to develop and evaluate the rule-based algorithm. A random sample of the results were manually reviewed to identify and address shortcomings in the algorithm, and to estimate sensitivity and specificity of the two methods of ascertainment. Last, the final algorithm was then applied to 29,212 visits from 2019 to estimate OUD prevalence. Results: While there was substantial overlap in the identified records (n = 1,381 [59.2 %]), overall n = 2,332 unique visits were identified. Of the total unique visits, 430 (18.4 %) were identified only by ICD-10-CM codes, and 521 (22.3 %) were identified only by NLP. The prevalence of visits with evidence of an OUD diagnosis in this sample, ascertained using only ICD-10-CM codes, was 1,811/29,212 (6.1 %). Including the additional 521 visits identified only by NLP, the estimated prevalence of OUD is 2,332/29,212 (7.9 %), an increase of 29.5 % compared to the use of ICD-10-CM codes alone. The estimated sensitivity and specificity of the NLP-based OUD classification were 81.8 % and 97.5 %, respectively, relative to gold-standard manual review by an expert addiction medicine physician. Conclusion: NLP-based algorithms can automate data extraction and identify evidence of opioid use disorder from unstructured electronic healthcare records. The most complete ascertainment of OUD in EHR was combined NLP with ICD-10-CM codes. NLP should be considered for epidemiological studies involving EHR data

An evaluation of injurious falls and Fall-Risk-Increasing-Drug (FRID) prescribing in ambulatory care in older adults

Background: Falls are a major public health problem affecting millions of older adults each year. Little is known about FRID prescribing behaviors after injurious falls occur. The primary objective of this study was to investigate whether an injurious fall is associated with being prescribed a new FRID. Methods: We conducted a cross-sectional analysis using data from the National Ambulatory Medical Care Survey (2016). We included visits from patients age ≥ 65 years and classified visits based on presence of an injurious fall. The outcome of interest was prescription of new FRID between those with and without an injurious fall. Multivariable logistic regression weighted for sampling and adjusted for demographics, health history and other medications was used. Age and Alzheimer’s disease were examined as potential effect measure modifiers. Odds ratios and 95% confidence intervals were reported. Bayes factor upper bounds were also reported to quantify whether the data were better predicted by the null hypothesis or the alternative hypothesis. Results: The sample included 239,016,482 ambulatory care visits. 5,095,734 (2.1%) of the visits were related to an injurious fall. An injurious fall was associated with a non-statistically significant increase in odds of at least one new FRID prescription: adjusted OR = 1.6 (95% CI 0.6, 4.0). However, there was non-statistically significant evidence that the association depended on patient age, with OR = 2.6 (95% CI 0.9, 7.4) for ages 65–74 versus OR = 0.4 (95% CI 0.1, 1.6) for ages ≥ 75. In addition to age, Alzheimer’s disease was also identified as a statistically significant effect measure modifier, but stratum specific estimates were not determined due to small sample sizes. Conclusions: Ambulatory care visits involving an injurious fall showed a non-statistically significant increase in odds of generating a new FRID prescription, but this association may depend on age

Frequency of LATE neuropathologic change across the spectrum of Alzheimer’s disease neuropathology: combined data from 13 community-based or population-based autopsy cohorts

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) and Alzheimer’s disease neuropathologic change (ADNC) are each associated with substantial cognitive impairment in aging populations. However, the prevalence of LATE-NC across the full range of ADNC remains uncertain. To address this knowledge gap, neuropathologic, genetic, and clinical data were compiled from 13 high-quality community- and population-based longitudinal studies. Participants were recruited from United States (8 cohorts, including one focusing on Japanese–American men), United Kingdom (2 cohorts), Brazil, Austria, and Finland. The total number of participants included was 6196, and the average age of death was 88.1 years. Not all data were available on each individual and there were differences between the cohorts in study designs and the amount of missing data. Among those with known cognitive status before death (n = 5665), 43.0% were cognitively normal, 14.9% had MCI, and 42.4% had dementia—broadly consistent with epidemiologic data in this age group. Approximately 99% of participants (n = 6125) had available CERAD neuritic amyloid plaque score data. In this subsample, 39.4% had autopsy-confirmed LATE-NC of any stage. Among brains with “frequent” neuritic amyloid plaques, 54.9% had comorbid LATE-NC, whereas in brains with no detected neuritic amyloid plaques, 27.0% had LATE-NC. Data on LATE-NC stages were available for 3803 participants, of which 25% had LATE-NC stage > 1 (associated with cognitive impairment). In the subset of individuals with Thal Aβ phase = 0 (lacking detectable Aβ plaques), the brains with LATE-NC had relatively more severe primary age-related tauopathy (PART). A total of 3267 participants had available clinical data relevant to frontotemporal dementia (FTD), and none were given the clinical diagnosis of definite FTD nor the pathological diagnosis of frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). In the 10 cohorts with detailed neurocognitive assessments proximal to death, cognition tended to be worse with LATE-NC across the full spectrum of ADNC severity. This study provided a credible estimate of the current prevalence of LATE-NC in advanced age. LATE-NC was seen in almost 40% of participants and often, but not always, coexisted with Alzheimer’s disease neuropathology