Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS.

RATIONALE AND OBJECTIVES: Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement. MATERIALS AND METHODS: All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or "No Information Available." A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm. RESULTS: Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as "No Information Available," and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report. CONCLUSION: Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS.

Associations of clinical and circulating metabolic biomarkers with low physical fitness and function in adults with chronic lymphocytic leukemia.

Many patients with chronic lymphocytic leukemia (CLL) experience physical dysfunction and low overall fitness. It remains unknown what factors drive CLL physical dysfunction. We assessed physical function and metabolic lipoprotein panels in 106 patients with CLL. In univariate analyses of clinical factors, a longer time since diagnosis was associated with a higher likelihood of dysfunctional aerobic fitness (OR = 3.56, 95% CI: 1.37-9.22; p = 0.002) and physical performance (SPPB: OR = 2.03, 95% CI: 1.20-3.44; p = 0.004). Having received treatment was associated with a higher likelihood of dysfunctional aerobic fitness (OR = 1.57, 95% CI: 1.02-2.40; p = 0.036), SPPB (OR = 1.85, 95% CI: 1.13-3.03; p = 0.011) and grip strength (OR = 1.67, 95% CI: 1.10-2.55; p = 0.015). We found that several small HDL particle parameters, higher levels of citrate (OR = 2.01, 95% CI: 1.22-3.31; p = 0.030), and lower levels of hemoglobin (OR = 0.50, 95% CI: 0.31-0.82; p = 0.030) were associated with a higher likelihood of dysfunctional aerobic fitness. Multivariable least absolute shrinkage and selection operator (LASSO)-penalized regression analyses using variable importance measures (VIM) showed that 7.8-nm HDL particles (VIM = 1.000) and total HDL particle levels (VIM = 1.000) were more informative than clinical measures for the odds of dysfunctional aerobic fitness and 6-min walk functional fitness, respectively, while 10.3-nm HDL particles (VIM = 0.383) were more informative for grip strength. Time since diagnosis (VIM = 0.680) and having received treatment (VIM = 0.490) were more informative than lipoprotein measures for the odds of having dysfunctional SPPB. Taken together, we establish significant relationships between clinical and metabolic factors and physical characteristics that might prompt early use of ancillary support services.

The Effect of Largemouth Bass Virus on Bass Populations in Kansas Impoundments.

Largemouth Bass virus (LMBV) first became a concern in Kansas when it was identified as a potential cause of decreased catch rates at Crawford State Fishing Lake in 2007. The discovery of LMBV in eight additional impoundments from 2008 to 2017 increased concern about the prevalence and effects of LMBV in Kansas. In response, the Kansas Department of Wildlife, Parks, and Tourism tested 25 Largemouth Bass Micropterus salmoides impoundments for the presence of LMBV. The objectives of this study were to quantify the incidence of LMBV and examine differences in population metrics (i.e., body condition, relative abundance, and growth). A total of 1,260 Largemouth Bass were collected by using standard spring electrofishing surveys, and sagittal otoliths were collected from all of the sampled fish to estimate growth rates. Of the 25 study impoundments, 14 tested positive for LMBV. There was no evidence of LMBV effects on body condition, relative abundance of quality-length fish, or growth rates. The initial dates of LMBV infection of Largemouth Bass in these impoundments are unknown. The LMBV-positive populations in Kansas may have been exposed to the virus many years ago, and the fish may be in the process of rebounding from any potential negative effects.

Comparison of Forceps, Cryoprobe, and Thoracoscopic Lung Biopsy for the Diagnosis of Interstitial Lung Disease - The CHILL Study.

RATIONALE: Transbronchial lung cryobiopsy (TBLC) has emerged as a less invasive method to obtain a tissue diagnosis in patients with interstitial lung disease (ILD). The diagnostic yield of TBLC compared to surgical lung biopsy (SLB) remains uncertain. OBJECTIVES: The aim of this study was to determine the diagnostic accuracy of forceps transbronchial lung biopsy (TBLB) and TBLC compared to SLB when making the final diagnosis based on multidisciplinary discussion (MDD). METHODS: Patients enrolled in the study underwent sequential TBLB and TBLC followed immediately by SLB. De-identified cases, with blinding of the biopsy method, were reviewed by a blinded pathologist and then discussed at a multidisciplinary conference. MAIN RESULTS: Between August 2013 and October 2017, we enrolled 16 patients. The raw agreement between TBLC and SLB for the MDD final diagnosis was 68.75% with a Cohen's kappa of 0.6 (95% CI 0.39, 0.81). Raw agreement and Cohen's kappa of TBLB versus TBLC and TBLB versus SLB for the MDD final diagnosis were much lower (50%, 0.21 [95% CI 0, 0.42] and 18.75%, 0.08 [95% CI -0.03, 0.19], respectively). TBLC was associated with mild bleeding (grade 1 bleeding requiring suction to clear) in 56.2% of patients. CONCLUSIONS: In patients with ILD who have an uncertain type based on clinical and radiographic data and require tissue sampling to obtain a specific diagnosis, TBLC showed moderate correlation with SLB when making the diagnosis with MDD guidance. TBLB showed poor concordance with both TBLC and SLB MDD diagnoses.

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. METHODS: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. RESULTS: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. CONCLUSIONS: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images.

BACKGROUND: Immunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology helps surmount these challenges by combining image data with its metadata to provide a meaningful biological context, modeled after how a human expert would make use of contextual information to identify histological structures, that constrains and simplifies the process of segmentation and object identification. RESULTS: We propose an innovative approach for the semi-supervised analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that facilitate automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung database of the Molecular Atlas of Lung Development (LungMAP1) program CONCLUSION: We describe a novel ontology-guided approach to segmentation and classification of complex immunofluorescence images of the developing mouse lung. The ontology is used to automatically generate constraints for each image based on its biomedical context, which facilitates image segmentation and classification.

Triangulating Methodologies from Software, Medicine and Human Factors Industries to Measure Usability and Clinical Efficacy of Medication Data Visualization in an Electronic Health Record System.

Within the last decade, use of Electronic Health Record (EHR) systems has become intimately integrated into healthcare practice in the United States. However, large gaps remain in the study of clinical usability and require rigorous and innovative approaches for testing usability principles. In this study, validated tools from the core functions that EHRs serve-software, medicine and human factors-were combined to holistically understand and objectively measure usability of medication data displays. The first phase of this study included 132 medical trainee participants who were randomized to one of two simulated EHR environments with either a medication list or a medication timeline visualization. Within these environments human-computer interaction metrics, clinical reasoning and situation awareness tests, and usability surveys captured their multi-faceted interactions. Results showed no statistically significant differences in the two displays from software and situation awareness perspectives, though there were higher statistically significant usability scores of the medication timeline (intervention) as compared to the medication list (control). This first phase of a novel design in triangulating methodologies revealed several limitations from which future experiments will be adjusted with hopes of yielding further insight and a generalizable testing platform for evolving EHR interfaces.

The effects of exercise on cardiovascular biomarkers in patients with chronic heart failure.

BACKGROUND: Exercise training is recommended for chronic heart failure (HF) patients to improve functional status and reduce risk of adverse outcomes. Elevated plasma levels of amino-terminal pro-brain natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and cardiac troponin T (cTnT) are associated with increased risk of adverse outcomes in this patient population. Whether exercise training leads to improvements in biomarkers and how such improvements relate to clinical outcomes are unclear. METHODS AND RESULTS: Amino-terminal pro-brain natriuretic peptide, hs-CRP, and cTnT levels were assessed at baseline and 3 months in a cohort of 928 subjects from the HF-ACTION study, a randomized clinical trial of exercise training versus usual care in chronic HF patients with reduced left ventricular ejection fraction (<35%). Linear and logistic regressions were used to assess 3-month biomarker levels as a function of baseline value, treatment assignment (exercise training vs usual care), and volume of exercise. Linear regression and Cox proportional hazard modeling were used to evaluate the relations between changes in biomarker levels and clinical outcomes of interest that included change in peak oxygen consumption (peak VO2), hospitalizations, and mortality. Exercise training was not associated with significant changes in levels of NT-proBNP (P = .10), hs-CRP (P = .80), or detectable cTnT levels (P = .83) at 3 months. Controlling for baseline biomarker levels or volume of exercise did not alter these findings. Decreases in plasma concentrations of NT-proBNP, but not hs-CRP or cTnT, were associated with increases in peak VO2 (P < .001) at 3 months and decreased risk of hospitalizations or mortality (P ≤ .04), even after adjustment for a comprehensive set of known predictors. CONCLUSIONS: Exercise training did not lead to meaningful changes in biomarkers of myocardial stress, inflammation, or necrosis in patients with chronic HF. Only improvements in NT-proBNP translated to reductions in peak VO2 and reduced risk of clinical events.