Validation of Acute Myocardial Infarction (AMI) in the FDA’s Mini-Sentinel Distributed Database

The Food and Drug Administration’s (FDA) Mini-Sentinel is a pilot program that aims to conduct active surveillance to detect and refine safety signals that emerge for marketed medical products. The purpose of this Mini-Sentinel AMI Validation project was to: (a) develop and design an abstraction and adjudication process to use when full text medical record review is required to confirm a coded diagnosis; and (b) to test this approach by validating a code algorithm for acute myocardial infarction (AMI)

Dental applications of ozone therapy: A review of literature

Ozone has been used successfully for the treatment of various diseases for more than a decade. Its unique properties include immunostimulant, analgesic, antihypnotic, detoxicating, antimicrobial, bioenergetic and biosynthetic actions. Its atraumatic, painless, non invasive nature, and relative absence of discomfort and side effects increase the patient’s acceptability and compliance thus making it an ideal treatment choice specially for pediatric patients. This review is an attempt to highlight various treatment modalities of ozone therapy and its possible clinical applications in future

Association of local capacity for endoscopy with individual use of colorectal cancer screening and stage at diagnosis

BACKGROUND: Limited capacity for endoscopy in areas in which African Americans and Hispanics live may be a reason for persistent disparities in colorectal cancer (CRC) screening and stage at diagnosis. METHODS: The authors linked data from the National Health Interview Survey on the use of CRC screening and data from Surveillance, Epidemiology, and End Results-Medicare on CRC stage with measures of county capacity for colonoscopy and sigmoidoscopy (endoscopy) derived from Medicare claims. RESULTS: Hispanics lived in counties with less capacity for endoscopy than African Americans or whites (for National Health Interview Survey, an average of 1224, 1569, and 1628 procedures per 100,000 individuals aged > or = 50 years, respectively). Individual use of CRC screening increased modestly as county capacity increased. For example, as the number of endoscopies per 100,000 residents increased by 750, the odds of being screened increased by 4%. Disparities in screening were mitigated or diminished by adjustment for area endoscopy capacity, racial/ethnic composition, and socioeconomic status. Similarly, among individuals with CRC, those who lived in counties with less endoscopy capacity were marginally less likely to be diagnosed at an early stage. Adjustment for area characteristics diminished disparities in stage for Hispanics compared with whites but not African Americans. CONCLUSIONS: Increasing the use of CRC screening may require interventions to improve capacity for endoscopy in some areas. The characteristics of the area where an individual resides may in part mediate disparities in CRC screening use for both African Americans and Hispanics, and disparities in cancer stage for Hispanics

Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusions: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways

Design for validation of acute myocardial infarction cases in Mini-Sentinel

PURPOSE: To describe the acute myocardial infarction (AMI) validation project, a test case for health outcome validation within the US Food and Drug Administration-funded Mini-Sentinel pilot program. METHODS: The project consisted of four parts: (i) case identification-developing an algorithm based on the International Classification of Diseases, Ninth Revision, to identify hospitalized AMI patients within the Mini-Sentinel Distributed Database; (ii) chart retrieval-establishing procedures that ensured patient privacy (collection and transfer of minimum necessary amount of information, and redaction of direct identifiers to validate potential cases of AMI); (iii) abstraction and adjudication-trained nurse abstractors gathered key data using a standardized form with cardiologist adjudication; and (iv) calculation of the positive predictive value of the constructed algorithm. RESULTS: Key decision points included (i) breadth of the AMI algorithm, (ii) centralized versus distributed abstraction, and (iii) approaches to maintaining patient privacy and to obtaining charts for public health purposes. We used an algorithm limited to International Classification of Diseases, Ninth Revision, codes 410.x0-410.x1. Centralized data abstraction was performed because of the modest number of charts requested ( CONCLUSIONS: We have established a process to validate AMI within Mini-Sentinel, which may be used for other health outcomes. Challenges include the following: (i) ensuring that only minimum necessary data are transmitted by Data Partners for centralized chart review, (ii) establishing procedures to maintain data privacy while still allowing for timely access to medical charts, and (iii) securing access to charts for public health uses that do not require approval from an institutional review board while maintaining patient privacy

Validation of acute myocardial infarction in the Food and Drug Administration\u27s Mini-Sentinel program

PURPOSE: To validate an algorithm based upon International Classification of Diseases, 9(th) revision, Clinical Modification (ICD-9-CM) codes for acute myocardial infarction (AMI) documented within the Mini-Sentinel Distributed Database (MSDD). METHODS: Using an ICD-9-CM-based algorithm (hospitalized patients with 410.x0 or 410.x1 in primary position), we identified a random sample of potential cases of AMI in 2009 from four Data Partners participating in the Mini-Sentinel Program. Cardiologist reviewers used information abstracted from hospital records to assess the likelihood of an AMI diagnosis based on criteria from the Joint European Society of Cardiology and American College of Cardiology Global Task Force. Positive predictive values (PPVs) of the ICD-9-based algorithm were calculated. RESULTS: Of the 153 potential cases of AMI identified, hospital records for 143 (93%) were retrieved and abstracted. Overall, the PPV was 86.0% (95% confidence interval; 79.2%, 91.2%). PPVs ranged from 76.3% to 94.3% across the four Data Partners. CONCLUSIONS: The overall PPV of potential AMI cases, as identified using an ICD-9-CM-based algorithm, may be acceptable for safety surveillance; however, PPVs do vary across Data Partners. This validation effort provides a contemporary estimate of the reliability of this algorithm for use in future surveillance efforts conducted using the Food and Drug Administration\u27s MSDD. Copyright © 2012 John Wiley & Sons, Ltd