Publications by authors named "Dennis Murphree"

Article Synopsis
  • The study focuses on differentiating perianal issues caused by hidradenitis suppurativa (HS) and perianal fistulizing inflammatory bowel disease (IBD), as they often present similar symptoms.
  • A retrospective review of patient data at the Mayo Clinic identified 15 key clinical features using machine learning to help distinguish between HS and IBD cases.
  • Findings revealed significant differences in symptoms, with HS patients showing more skin-related issues and IBD patients experiencing additional gastrointestinal symptoms, which may lead to better diagnosis and treatment strategies.
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  • The study evaluates an AI model (A2E) that analyzes ECGs to predict survival in patients with cardiac amyloidosis (CA), focusing on three patient groups: light chain amyloidosis (AL), wild-type transthyretin amyloidosis (ATTRwt), and hereditary transthyretin amyloidosis (ATTRv).
  • Data from 2533 CA patients were analyzed, and results showed that those with higher A2E scores had a significantly increased risk of death.
  • The findings suggest that the A2E model provides useful prognostic information, helping to better assess the risk of death in patients with both AL and ATTR amyloidosis.
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Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient tissue comparison for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient tissue comparison.

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Predictive modeling is becoming an essential tool for clinical decision support, but health systems with smaller sample sizes may construct suboptimal or overly specific models. Models become over-specific when beside true physiological effects, they also incorporate potentially volatile site-specific artifacts. These artifacts can change suddenly and can render the model unsafe.

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  • Researchers tested an AI-enhanced ECG for detecting cardiac amyloidosis (CA) in a group of 440 diagnosed patients, compared to a control group of 6,600 who were matched by age and sex.
  • The AI's performance showed an area under the curve (AUC) of 0.84, which is a decrease from an earlier study that reported an AUC of 0.91, indicating slight deterioration in accuracy.
  • The AI performed well across different racial groups but struggled with Hispanic patients and conditions like left ventricular hypertrophy and left bundle branch block, highlighting the need for targeted improvements in these areas.
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Article Synopsis
  • * The study utilized deep neural networks to analyze resting Doppler arterial waveforms from DM patients to predict all-cause mortality, major adverse cardiac events (MACE), and limb events (MALE) over five years.
  • * Results indicated that patients in the highest prediction quartile (based on their arterial waveforms) had significantly increased risk for death, MACE, and MALE, highlighting the usefulness of this AI-based approach in clinical settings.
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Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies.

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  • Patients with peripheral artery disease face higher risks for serious heart and limb issues, prompting the need for effective risk assessment tools to improve outcomes.
  • This study utilized deep neural networks to analyze resting Doppler waveforms from the posterior tibial artery to predict risks of major adverse events over five years in a large patient population.
  • The findings showed that this AI method reliably identified patients at higher risk for death and significant cardiac and limb problems, suggesting its potential as a valuable tool for early intervention and management.
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Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of sophisticated clinical decision support systems to provide real-time feedback to clinicians that could have a role in optimizing the diagnostic workup of BCC. There were 287 annotated whole-slide images of frozen sections from tangential biopsies, of which 121 contained BCC, that were used to train and test an AI pipeline to recognize BCC.

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Lichen planus (LP) can affect multiple body sites including skin, mucosae, scalp and nails, causing considerable impact on patients' quality of life. Currently, there are no LP patient-reported outcome measures (PROMs) that address all body sites potentially affected by LP. We developed a LP Quality of Life Questionnaire (LPQoL), informed by an expert consortium and patient survey study, to address this gap.

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Article Synopsis
  • * The study evaluated the effectiveness of deep neural networks analyzing resting Doppler waveforms to identify PAD through ankle-brachial index (ABI) testing in a sample of 11,748 patients, with a focus on accuracy metrics.
  • * Results showed that the predictive model performed excellently with an AUC of 0.94, high sensitivity (0.83), and good specificity (0.88), indicating that AI can accurately identify PAD in clinical settings.
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Background: Preoperative risk scores facilitate patient selection, but postoperative risk scores may offer valuable information for predicting outcomes. We hypothesized that the postoperative Sequential Organ Failure Assessment (SOFA) score would predict mortality after left ventricular assist device (LVAD) implantation.

Methods: We retrospectively reviewed data from 294 continuous-flow LVAD implantations performed at Mayo Clinic Rochester during 2007 to 2015.

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Objective: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs.

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Background: Utilization of inpatient palliative care services (PCS) has been infrequently studied in patients with cardiac arrest complicating acute myocardial infarction (AMI-CA).

Methods: Adult AMI-CA admissions were identified from the National Inpatient Sample (2000-2017). Outcomes of interest included temporal trends and predictors of PCS use and in-hospital mortality, length of stay, hospitalization costs and discharge disposition in AMI-CA admissions with and without PCS use.

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Aims: The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD).

Methods And Results: This study included 1893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior.

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Background: Recent data suggests improved outcomes among cardiac intensive care unit (CICU) patients treated with norepinephrine, especially patients with severe shock. We aimed to describe the association between norepinephrine and mortality in CICU patients with severe shock, defined as those requiring high-dose vasopressors (HDV).

Materials And Results: We retrospectively evaluated Mayo Clinic CICU patients treated with vasopressors from 2007 to 2015.

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Objective: To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG).

Methods: We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls.

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There are limited contemporary data on the management and outcomes of acute myocardial infarction (AMI) in patients with concomitant acute respiratory infections. Hence, using the National Inpatient Sample from 2000-2017, adult AMI admissions with and without concomitant respiratory infections were identified. We evaluated in-hospital mortality, utilization of cardiac procedures, hospital length of stay, hospitalization costs, and discharge disposition.

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Background Impaired right ventricular (RV) pulmonary artery coupling has been associated with higher mortality in patients with chronic heart disease, but few studies have examined this metric in critically ill patients. We sought to evaluate the association between RV pulmonary artery coupling, defined by the ratio of tricuspid annular peak systolic tissue Doppler velocity (TASV)/estimated RV systolic pressure (RVSP), and mortality in cardiac intensive care unit patients. Methods and Results Using a database of unique cardiac intensive care unit admissions from 2007 to 2018, we included patients with TASV/RVSP ratio measured within 1 day of hospitalization.

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Aims: Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS.

Methods And Results: Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.

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Aims: There are limited contemporary data on the use of initial fibrinolysis in ST-segment elevation myocardial infarction cardiogenic shock (STEMI-CS). This study sought to compare the outcomes of STEMI-CS receiving initial fibrinolysis vs. primary percutaneous coronary intervention (PPCI).

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Objective: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team.

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Background: The benefit of red blood cell (RBC) transfusion in anemic critically-ill patients with cardiovascular disease is uncertain, as is the optimal threshold at which RBC transfusion should be considered. We sought to examine the association between RBC transfusion and mortality stratified by nadir Hgb level and admission diagnosis among cardiac intensive care unit (CICU) patients.

Methods: Retrospective single-center cohort of 11,754 CICU patients admitted between 2007 and 2018.

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Purpose: Patients with stage I/IIA cutaneous melanoma (CM) are currently not eligible for adjuvant therapies despite uncertainty in relapse risk. Here, we studied the ability of a recently developed model which combines clinicopathologic and gene expression variables (CP-GEP) to identify stage I/IIA melanoma patients who have a high risk for disease relapse.

Patients And Methods: Archival specimens from a cohort of 837 consecutive primary CMs were used for assessing the prognostic performance of CP-GEP.

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Background: There are limited data on the complications with a percutaneous left ventricular assist device (pLVAD) vs. intra-aortic balloon pump (IABP) in acute myocardial infarction-cardiogenic shock (AMI-CS).

Objective: To assess the trends, rates and predictors of complications.

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