Differences in the Impact of Obesity and Bariatric Surgery on Patients Hospitalized for Atrial Flutter and Atrial Fibrillation: A Nationwide Analysis, 2016-2020.

BACKGROUND: The "obesity paradox" claims that although obesity is a risk factor for atrial fibrillation, obese patients have lower inpatient mortality when admitted due to atrial fibrillation. This study aims to analyze if the obesity paradox still holds true after weight loss from bariatric surgery.  Methods: This study analyzed discharge data from the National Inpatient Sample, 2016-2020. Patients admitted due to atrial fibrillation or atrial flutter, with or without obesity, and with or without a past medical history of bariatric surgery were identified using ICD-10-CM and ICD-10-PCS codes. The primary outcome was mortality. Secondary outcomes included length of stay, resource utilization, necessity for endotracheal intubation, and necessity for cardioversion. STATA v.13 was used for univariate and multivariate analysis (StataCorp LLC, Texas, USA). RESULTS: Among 2,292,194 patients who had a primary diagnosis of atrial fibrillation or atrial flutter, 494,830 were obese and 25,940 had bariatric surgery. Mortality was not significantly different in post-bariatric surgery patients when compared to the general population (OR 0.76; 95% [CI 0.482-1.2; p=0.24]). Mortality was significantly lower in obese patients when compared to the general population (OR 0.646; 95% [CI 0.583-0.717; p<0.001]). Therefore, post-bariatric surgery patients had a higher mortality than obese patients when compared to the general population. Obese patients spent more days in the hospital (regression 0.219; 95% [CI 0.19-0.248, p<0.001]), had higher resource utilization (regression 3491.995; 95% [CI 2870.085-4113.905, p<0.001]), more cardioversions (OR 1.434; 95% [CI 1.404-1.465; p<0.001]), and no difference in endotracheal intubation rate (OR 1.02; 95% [CI 0.92-1.127; p=0.724]) when compared to the general population. Post-bariatric patients had no difference in length of stay (regression -0.053; 95% [CI -0.137-0.031; p=0.218]) and resource utilization (regression 577.297; 95% [CI -1069.801-2224.396; p=0.492]), fewer endotracheal intubations (OR 0.583; 95% [CI 0.343-0.99; p=0.046]), and more cardioversions (OR 1.223; 95% [CI 1.134-1.32; p<0.001]) when compared to the general population. CONCLUSION: Compared to the general population, post-bariatric patients had higher inpatient mortality than obese patients when admitted due to atrial fibrillation or atrial flutter. This research reinforces the presence of the obesity paradox following bariatric surgery with respect to mortality.

A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.

This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.

Charlson and Elixhauser Comorbidity Indices for Prediction of Mortality and Hospital Readmission in Patients With Acute Pulmonary Embolism.

Several risk stratification systems aid clinicians in classifying pulmonary embolism (PE) severity and prognosis. We compared 2 clinical PE scoring systems, the PESI and sPESI scores, with 2 comorbidity indices, the Charlson Comorbidity Index (CCI) and the val Walraven Elixhauser Comorbidity Index (ECI), to determine the utility of each in predicting mortality and hospital readmission. Information was collected from 436 patients presenting with PE via retrospective chart review. The PESI, sPESI, CCI, and ECI scores were calculated for each patient. Multivariate analysis was used to determine each system's ability to predict in-hospital mortality, 90-day mortality, overall mortality, and all-cause hospital readmission. The impact of various demographic and clinical characteristics of each patient on these outcomes was also assessed. The PESI score was found to be an independent predictor of in-hospital mortality and 90-day mortality. The PESI score and the CCI were able to independently predict overall mortality. None of the 4 risk scores independently predicted hospital readmission. Other factors including hypoalbuminemia, serum BNP, coagulopathy, anemia, and diabetes were associated with increased mortality and readmission at various endpoints. The PESI score was the best tool for predicting mortality at any endpoint. The CCI may have utility in predicting long-term outcomes. Further work is needed to better determine the roles of the CCI and ECI in predicting patient outcomes in PE. The potential prognostic implications of low serum albumin and anemia at the time of PE also warrant further investigation.

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients' clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation. Results: There were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline. Conclusion: The NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.

Drivers of Thyroid Ultrasound Use: A Retrospective Observational Study.

OBJECTIVE: Excessive use of thyroid ultrasound (TUS) contributes to the overdiagnosis of thyroid nodules and thyroid cancer. In this study, we evaluated drivers of and clinical trajectories following TUS orders. METHODS: We conducted a retrospective review of 500 adult patients who underwent an initial TUS between 2015 and 2017 at Mayo Clinic in Rochester, MN. A framework was employed to classify the indication for TUS, and it was characterized as inappropriate when ordered without a guideline-based indication. Medical records were reviewed for up to 12 months following the TUS, and clinical outcomes were evaluated. RESULTS: The mean age mean age (SD) was 53.6 years (16.6), 63.8% female, and 86.6% white. TUS orders were triggered by incidental findings on unrelated imaging (31.6%), thyroid symptoms (20.4%), thyroid abnormalities on routine physical examination (17.2%), and thyroid dysfunction workup (11.8%). In females and males, the most common reason were incidental findings on imaging (female, 91/319, 28.5% and male, 67/181, 37.0%). In primary care practice, TUS orders were mostly triggered by symptoms (71/218, 32.5%), while thyroid dysfunction workup was the primary reason in endocrinology (28/100, 28.0%). We classified 11.2% (56/500) TUS orders as likely to have been ordered inappropriately based on current guidelines. Finally, 119 patients (119/500, 23.8%) had a thyroid biopsy with 11.8% had thyroid cancer (14/119. 11.8%). CONCLUSIONS: Incidental findings on imaging, symptoms, and routine physical exam findings in asymptomatic patients were the most prevalent drivers of TUS. Furthermore, 1 in 10 TUS were likely inappropriately ordered based on current practice guidelines.