Publications by authors named "Mirza Khan"

Background: Although they are fast-growing populations in the United States, little is known about survival outcomes of Hispanic and Asian patients after in-hospital cardiac arrest.

Methods And Results: In Get With The Guidelines-Resuscitation, we identified Asian, Hispanic, and White adults with in-hospital cardiac arrest during 2005 to 2023. Using multivariable models, we compared rates of survival to discharge separately for Asian and Hispanic patients versus White patients, as well as rates of sustained return of spontaneous circulation for ≥20 minutes and favorable neurologic survival as secondary outcomes.

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  • A study examined patients with acute myocardial infarction (AMI) who lack standard modifiable risk factors (SMuRFs) to see how it affects their health status over time.
  • Out of 4,076 patients studied, those without SMuRFs showed initially better health status scores but their long-term improvements were similar to those with SMuRFs.
  • Findings suggest that AMI patients without SMuRFs can achieve comparable health status after 12 months, indicating less need for aggressive secondary prevention measures.
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Importance: The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a commonly used outcome in heart failure trials. While comparing means between treatment groups improves statistical power, mean treatment effects do not necessarily reflect the clinical benefit experienced by individual patients.

Objective: To evaluate the association between mean KCCQ treatment effects and the proportions of patients experiencing clinically important improvements across a range of clinical trials and heart failure etiologies.

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  • Survival rates for in-hospital cardiac arrest (IHCA) have decreased since the COVID-19 pandemic, raising concerns about the consistency of top-performing hospitals.
  • A study analyzed 243 hospitals with at least two years of data from before and after the pandemic, finding a mean risk-standardized survival rate (RSSR) drop from 26.8% to 21.7%.
  • Despite variations in patient demographics, the correlation between pre- and post-pandemic survival rates was relatively strong (0.55), indicating that better-performing hospitals before the pandemic generally maintained their status afterwards.
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Background: How frequently out-of-hospital cardiac arrest (OHCA) occurs within a reasonable walking distance to the nearest public automated external defibrillator (AED) has not been well studied.

Methods: As Kansas City, Missouri has a comprehensive city-wide public AED registry, we identified adults with an OHCA in Kansas City during 2019-2022 in the Cardiac Arrest Registry to Enhance Survival. Using AED location data from the registry, we computed walking times between OHCAs and the nearest registered AED using the Haversine formula, a mapping algorithm to calculate walking distance in miles from one location to another.

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The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification.

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  • The Kansas City Medical Optimization (KCMO) score was developed to more accurately quantify the intensity of guideline-directed medical therapy (GDMT) for heart failure patients by averaging daily doses compared to target doses.
  • In a study with over 4,500 patients, baseline scores showed low average KCMO (38.8), indicating underutilization of optimal therapy, while a 1-year follow-up revealed slight declines in scores, suggesting challenges in improving GDMT intensity.
  • KCMO demonstrated the highest variability among scoring methods, implying it provides a clearer picture of differences in GDMT intensity among patients, but further research is needed to determine its impact on patient outcomes and quality of care.
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  • Semaglutide, approved by the FDA for diabetes in 2017 and for weight loss in 2021, has become popular for weight loss, prompting a study to evaluate its effectiveness in real-world settings.* -
  • The study analyzed data from nearly 4,000 patients across 10 health systems, utilizing machine learning to predict weight loss outcomes and identify key influencing factors.* -
  • Results showed an average weight loss of 4.44%, with factors like a diabetes diagnosis linked to less weight loss, while prediabetes and linaclotide use correlated with greater weight loss, suggesting the need for personalized treatment approaches.*
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Background An artificial intelligence (AI) algorithm has been developed for fully automated body composition assessment of lung cancer screening noncontrast low-dose CT of the chest (LDCT) scans, but the utility of these measurements in disease risk prediction models has not been assessed. Purpose To evaluate the added value of CT-based AI-derived body composition measurements in risk prediction of lung cancer incidence, lung cancer death, cardiovascular disease (CVD) death, and all-cause mortality in the National Lung Screening Trial (NLST). Materials and Methods In this secondary analysis of the NLST, body composition measurements, including area and attenuation attributes of skeletal muscle and subcutaneous adipose tissue, were derived from baseline LDCT examinations by using a previously developed AI algorithm.

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In lung cancer screening, estimation of future lung cancer risk is usually guided by demographics and smoking status. The role of constitutional profiles of human body, a.k.

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Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data.

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Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with incidental lung cancer risk in the screening population is still unclear. We study the feasibility of body composition analysis using chest low dose computed tomography (LDCT).

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  • The study aims to improve lung cancer diagnosis in patients with indeterminate pulmonary nodules by using a deep learning network that integrates clinical data and CT images, potentially reducing unnecessary procedures and risk for patients.
  • Researchers employed a retrospective design and a two-path deep learning approach, validating their model using data from multiple sites and comparing it against established clinical and image-only prediction models.
  • Results showed that the new co-learning model outperformed traditional prediction methods in accuracy (AUC), achieving notably higher scores on multiple validation datasets, thus enhancing early cancer detection capabilities.
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Many clinical natural language processing methods rely on non-contextual word embedding (NCWE) or contextual word embedding (CWE) models. Yet, few, if any, intrinsic evaluation benchmarks exist comparing embedding representations against clinician judgment. We developed intrinsic evaluation tasks for embedding models using a corpus of radiology reports: term pair similarity for NCWEs and cloze task accuracy for CWEs.

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Purpose: To develop a model to estimate lung cancer risk using lung cancer screening CT and clinical data elements (CDEs) without manual reading efforts.

Materials And Methods: Two screening cohorts were retrospectively studied: the National Lung Screening Trial (NLST; participants enrolled between August 2002 and April 2004) and the Vanderbilt Lung Screening Program (VLSP; participants enrolled between 2015 and 2018). Fivefold cross-validation using the NLST dataset was used for initial development and assessment of the co-learning model using whole CT scans and CDEs.

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Objective: Measurement and data entry of height and weight values are error prone. Aggregation of medical record data from multiple sites creates new challenges prompting the need to identify and correct errant values. We sought to characterize and correct issues with height and weight measurement values within the All of Us (AoU) Research Program.

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A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the brain, standardized coordinate systems (e.

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This study was designed to investigate the prevalence and associated risk factors of Shigella flexneri isolated from drinking water and retail raw food samples in Peshawar, Pakistan. A total of 1,020 different samples were collected from various areas of Peshawar between January 2016 and May 2017, followed by identification of S. flexneri through biochemical, serological, and 16S rRNA gene sequencing.

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We describe the initial presentation, diagnostic work-up and treatment of three adult immunocompetent men who presented within a short time frame of each other to an academic medical centre with acute respiratory distress syndrome. Their presentation was found to be secondary to a large inoculum of histoplasmosis from remodelling a building with bat droppings infestation. We discuss the pathophysiology of histoplasmosis and highlight the importance of exposure history in patients with acute respiratory failure and why patients with the occupational risk of exposure to fungal inoculum should wear protective respirator gear.

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Introduction: Epigenetic modifications play an important role in progression and development of resistance in BRAF positive metastatic melanoma. Therefore, we hypothesized that the action of vemurafenib (BRAF inhibitor) can be made more effective by combining with low dose decitabine (a DNA methyltransferase inhibitor). The primary objective of this phase lb study was to determine the dose limiting toxicity and maximum tolerated dose of combination of subcutaneous decitabine with oral vemurafenib in patients with BRAF positive metastatic melanoma with or without any prior treatment.

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