Kumar, Praveen M

Abstract Study Objectives Narcolepsy has a complex phenotype owing to differences in symptomatology, disease severity, and comorbidities. This is the first study to use aggregate electronic health record (EHR) data and natural language processing (NLP) algorithms to characterize the demographics and comorbidities of a large cohort of patients with narcolepsy. Methods First-time Mayo Clinic patients (2000–2020) who had ≥1 narcolepsy-specific ICD-9/10 code and ≥1 disease-supportive statement in the clinical notes (identified using an NLP algorithm) were identified. A control cohort was propensity matched for birth year, age at first institutional encounter, sex, race, ethnicity, number of diagnosis codes, and mortality. Common comorbidities were compared and ranked between cohorts. Results In the EHR database (N = 6 389 186 patients), 2057 patients with narcolepsy were identified (median age, 32 years; 59.6% female; 92.6% white; and 89.2% non-Hispanic) and propensity matched with a control cohort. Among the top 20 comorbidities occurring more frequently in the narcolepsy cohort compared with the control cohort (odds ratio [OR], 1.67–3.94; p < .001]) were sleep disorders (restless legs syndrome, obstructive sleep apnea, and insomnia), mood disorders (depression, dysthymia, and anxiety), and pain disorders (chronic pain syndrome, migraine, fibromyalgia, carpal tunnel syndrome, and myalgia). Other comorbidities significantly associated with narcolepsy (OR, 1.33–1.95) were irritable bowel syndrome (p < .001), asthma (p < .001), cervical spondylosis (p < .01), syncope (p < .01), and hypothyroidism (p < .05). Conclusions This propensity-matched cohort study demonstrates increased psychiatric, sleep, and pain disorders in patients living with narcolepsy and challenges beyond narcolepsy-specific symptoms in this population. Understanding common narcolepsy-associated comorbidities may assist in tailoring treatment modalities.

11176 Background: Progression-free survival (PFS) is frequently measured in oncology clinical trials. In analyses outside of the trial setting, various strategies have been utilized to assess real-world PFS (rwPFS), including manual abstraction of clinical records, natural language processing (NLP) of oncology notes and radiology reports, and longitudinal analyses of radiologic images. Here we develop and validate a new semi-automated workflow that combines NLP of clinical notes with structured electronic health record data to facilitate the evaluation of rwPFS in patients with metastatic breast cancer (mBC). Methods: This study analyzes de-identified EHR data using nference nSights. The data is extracted following privacy-preserving protocols and is HIPAA compliant. The overall cohort included 316 patients with HR-positive, HER2-negative mBC who initiated Palbociclib and Letrozole combination therapy between January 1, 2015 and December 31, 2021. We developed and implemented an ensemble of deep-learning NLP frameworks to capture progression events from unstructured clinical notes and radiology reports. A change in the line of therapy, as determined by structured drug order/administration records, was also considered a progression event. We used manually curated “ground-truth” datasets to evaluate the performance of the progression-event capture workflow at the levels of both sentences (N = 1000) and patients (N = 100) by calculating sensitivity, specificity, precision, accuracy, and F1 scores. Progression events and censoring events (death, loss to follow-up, end of study period) were considered to compute rwPFS. Results: At the sentence level, progression events were captured from clinical notes and radiology reports with a sensitivity of 99.8%, specificity of 96.7%, and accuracy of 98.2% (Table). At the patient level, initial progression was correctly captured within a window of +/-30 days with a sensitivity of 92.5%, specificity of 83.0%, and accuracy of 88.0% (Table). In a sample of 100 patients, the median rwPFS was determined to be 25 months (95% CI; 15-35 months) by manual curation and 22 months (95% CI; 15-35 months) by the semi-automated workflow. In the overall cohort, median rwPFS was 20 months (95% CI; 18-25 months). Conclusions: An ensemble of NLP algorithms extracted progression events from clinical notes and radiology reports with high accuracy. A semi-automated workflow enabled rapid and accurate determination of rwPFS in mBC patients receiving a combination chemotherapy regimen. Further evaluation of this workflow to estimate rwPFS in other cancers and therapeutic settings is warranted. [Table: see text]

INTRODUCTION: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

Abstract Introduction Narcolepsy is a chronic sleep disorder with multiple comorbid conditions. Sodium oxybate (SXB) is strongly recommended for treatment of narcolepsy. This study used aggregate electronic health record (EHR) data to characterize demographic characteristics and comorbidities of patients with narcolepsy treated with or without SXB. Methods An EHR-based search identified first-time Mayo Clinic patients between 1975–2020. Patients had ≥1 narcolepsy-specific ICD-9/-10 code and ≥1 diagnostic mention of narcolepsy in clinical notes (natural-language-processing algorithm). Patients with narcolepsy treated with SXB were age/sex matched with a cohort without SXB treatment. Common comorbidities were identified using ICD-9/-10 codes and compared between cohorts (odds ratio [OR]). Results Of 4387 patients identified, 8% received SXB treatment (n=351; mean [IQR] age at first diagnosis code observed at Mayo Clinic, 32 [23.2-46.1] y; 65.5% female; 92.3% white); 4036 patients had no SXB treatment (mean [IQR] age at first diagnosis code observed at Mayo Clinic, 44.8 [29.8-59.0] y; 58.0% female; 88.9% white). The 10 most commonly reported comorbidities (overall population) were insomnia (46.6%), fatigue (46.3%), depression (42.2%), hypertension (36.2%), hyperlipidemia (34.2%), obstructive sleep apnea (32.3%), diabetes mellitus (31.3%), arrhythmia (27.3%), idiopathic hypersomnia (IH; 26.8%), and coronary artery disease (17.6%). A cohort of 351 patients without SXB were age/sex matched to patients with SXB for comparison of comorbidities. In the unadjusted analysis, P values were significant for differences between cohorts (OR [95% CI] SXB vs no SXB) for fatigue (0.72 [0.54-0.97]; P< 0.05; adjusted P>0.9) and IH (0.60 [0.43-0.84]; P< 0.01; adjusted P=0.29). After P value adjustment (Bonferroni correction), there were no significant differences between these cohorts in ORs for any comorbidity. The matched cohorts, which were younger than the overall population, had numerically lower rates of these diagnoses vs the overall population, except fatigue and IH in patients without SXB and depression in patients with SXB. Conclusion Among age-/gender-matched cohorts of patients with/ without SXB, there were no significant differences in comorbidities. Prevalence of comorbid IH/narcolepsy diagnoses highlight the diagnostic challenge in differentiating IH from narcolepsy type 2. SXB is highly effective but used by < 1:10 patients with narcolepsy in the Mayo Clinic health system. Support (if any) Avadel Pharmaceuticals

Abstract Introduction Sodium oxybate (SXB) is strongly recommended for treatment of narcolepsy. Patients treated with available immediate-release oxybates are required to awaken for a second dose 2.5–4 hours after the bedtime dose to cover a full night of sleep. This study characterized SXB treatment patterns and discontinuation in narcolepsy patients. Methods Mayo Clinic patients from 1975–2020 were identified using an electronic health record–based search. Patients with ≥1 narcolepsy-specific ICD-9/-10 code and ≥1 diagnostic mention of narcolepsy in clinical notes (identified using a natural-language-processing [NLP] algorithm) were included. NLP was used to identify SXB non-use events with manual chart reviews performed to characterize reasons for discontinuation, switching, or missing the second dose and effects of missing the second dose. Results Of the patients with narcolepsy prescribed SXB (n=351; mean age at first diagnosis code observed at Mayo Clinic, 32 y [IQR: 23.2–46.1]; 65.5% female; 92.3% white), 113 (32.2%) had clinical notes indicating discontinuation of SXB, with 71 (20.2%) including reasons for discontinuation. The most common reasons for discontinuation (n≥5) included lack of efficacy (n=11), side effects (n=10), gastrointestinal side effects (n=10), neurological side effects (n=9), psychiatric side effects (n=8), lack of access (insurance/cost, n=10), and resolution/absence of cataplexy (n=5). Mentions of switching from SXB to other drugs (n=12 patients) included switching to stimulants (mixed amphetamine salts, n=3; dextroamphetamine, n=2; methylphenidate, n=1), sedative hypnotics (zolpidem, n=2), antidepressants (mirtazapine, n=1), and wake-promoting agents (solriamfetol, n=1). Of the 38 patients (10.8%) with mentions of missing the second nightly SXB dose, reasons were recorded for 24 (63.2%), including inability to wake up (n=14), forgetting the second dose (n=3), and having to wake up early the following day (n=3). Consequences of missing the second dose were recorded for a small subset (increased cataplexy, n=4; lower daytime alertness, n=3; awakens earlier, n=1; tiredness, n=1). Conclusion Limited data are available regarding real-world use of SXB. This novel study used a combination of NLP algorithm and manual chart review to identify difficulties patients have with the second, middle-of-the night immediate-release oxybate dose and patient-experienced consequences of not adhering to the prescribed dosing regimen. Support (if any) Avadel Pharmaceuticals