ABSTRACT
Objectives: Bariatric surgery has evolved over the past two decades yet assessing trends of bariatric surgery utilization in the growing eligible population is lacking.This study aimed to update the trends in bariatric surgery utilization, changes in types of procedures performed, and the characteristics of patients who underwent bariatric surgery in the US, using real-world data. Method(s): This cross-sectional study was conducted using the TriNetX, a federated electronic medical records network from 2012 to 2021, for adult patients 18 years old or older who had bariatric surgery. Descriptive statistical analysis was conducted to assess patients' demographics and characteristics. Annual secular trend analyses were conducted for the annual rate of bariatric surgery, and the specific procedural types and proportions of laparoscopic surgeries. Result(s): A steady increase in the number of procedures performed in the US over the first six years of the study, a plateau for the following two years, and then a decline in 2020 and 2021 (during the coronavirus-19 pandemic). The annual rate of bariatric surgery was lowest in 2012 at 59.2 and highest in 2018 at 79.6 surgeries per 100,000 adults. During the study period, 96.2% to 98.8% of procedures performed annually were conducted laparoscopically as opposed to the open technique. Beginning in 2012, the Roux-en-Y (RYGB) procedure fell to represent only 17.1% of cases in 2018, along with a sharp decline in the adjustable gastric band (AGB) procedure, replaced by a sharp increase in the sleeve gastrectomy (SG) procedure to represent over 74% of cases in 2018. Conclusion(s): Bariatric surgery utilization in the US showed a moderate decline in the number of RYGB procedures, which was offset by a substantial increase in the number of SG procedures and a precipitous drop in the annual number of AGB procedures.Copyright © 2023
ABSTRACT
Objectives: The effectiveness of commercial clinical named entity recognition (NER) systems in recognizing COVID-19-related concepts is not well understood. The objective of this study was to measure the performance of the Amazon Comprehend Medical ICD-10-CM Ontology Linking API (ACM-ICD10) for detecting the presence of COVID-19-related concepts in a clinical document sample obtained from COVID-19 positive patients. Methods: We obtained all clinical notes of patients who had tested positive for COVID-19 at a large medical institution in the OMNY Health System Database during one month in late 2020. Patients were excluded if the U07.1 ICD-10-CM code was not present in their structured diagnosis data. We processed the notes using ACM-ICD10 and obtained the ICD-10 concept mappings for each note. Results: After exclusions, 63 patients remained for analysis. These 63 patients had 791 diagnoses in their structured diagnosis data. Processing the 1,488 notes of the 63 patients through ACM-ICD10 yielded 81,599 ICD-10 concept mappings, none of which contained the U07.1 code. However, mappings were observed that related to symptoms and alternative diagnoses of COVID-19 including “dyspnea, unspecified” (R06.00;518 times;0.6%), “shortness of breath” (R06.02;505 times;0.6%), “unspecified infectious disease” (B99.9;466 times;0.6%), and “viral infection, unspecified” (B34.9;292 times;0.4%). Specifically, text containing the “covid” substring was mapped to ICD-10 concepts 75 times, most commonly to “viral infection, unspecified” (B34.9;5 times) and “encephalitis and encephalomyelitis, unspecified” (G04.90;5 times). Conclusions: While ACM-ICD10 was not effective at mapping COVID-19-related text to its standard code (U07.1) as recommended by the CDC, it often mapped such text to symptoms and signs of COVID-19 infection. These results suggest that commercial NER tools may need to be recalibrated periodically to account for new and emerging illnesses.