Generalizable Approach to Quantifying Guideline-Directed Medical Therapy.

BACKGROUND: Quantifying guideline-directed medical therapy (GDMT) intensity is foundational for improving heart failure (HF) care. Existing measures discount dose intensity or use inconsistent weighting. METHODS: The Kansas City Medical Optimization (KCMO) score is the average of total daily to target dose percentages for eligible GDMT, reflecting the percentage of optimal GDMT prescribed (range, 0-100). In Change the Management of Patients With HF, we computed KCMO, HF collaboratory (0-7), and modified HF Collaboratory (0-100) scores for each patient at baseline and for 1-year change in established GDMT at the time (mineralocorticoid receptor antagonist, ß-blocker, ACE [angiotensin-converting enzyme] inhibitor/angiotensin receptor blocker/angiotensin receptor neprilysin inhibitor). We compared baseline and 1-year change distributions and the coefficient of variation (SD/mean) across scores. RESULTS: Among 4532 patients at baseline, mean KCMO, HF collaboratory, and modified HF Collaboratory scores were 38.8 (SD, 25.7), 3.4 (1.7), and 42.2 (22.2), respectively. The mean 1-year change (n=4061) for KCMO was -1.94 (17.8); HF collaborator, -0.11 (1.32); and modified HF Collaboratory, -1.35 (19.8). KCMO had the highest coefficient of variation (0.66), indicating greater variability around the mean than the HF collaboratory (0.49) and modified HF Collaboratory (0.53) scores, reflecting higher resolution of the variability in GDMT intensity across patients. CONCLUSIONS: KCMO measures GDMT intensity by incorporating dosing and treatment eligibility, provides more granularity than existing methods, is easily interpretable (percentage of ideal GDMT), and can be adapted as performance measures evolve. Further study of its association with outcomes and its usefulness for quality assessment and improvement is needed.

Automated External Defibrillator Use After Out-of-Hospital Cardiac Arrest at Recreational Facilities.

This cohort study examines bystander automated external defibrillator (AED) application and survival outcomes for out-of-hospital cardiac arrest at recreational facilities in US states with and without AED legislation.

Medications and conditions associated with weight loss in patients prescribed semaglutide based on real-world data.

OBJECTIVE: Approved by the Food and Drug Administration (FDA) in 2017 for diabetes and in 2021 for weight loss, semaglutide has seen widespread use among individuals who aim to lose weight. The aim of this study was to evaluate weight loss and the influence of clinical factors on semaglutide patients in real-world clinical practice. METHODS: Using data from 10 health systems within the Greater Plains Collaborative (a PCORnet Clinical Research Network), nearly 4000 clinical factors encompassing demographic, diagnosis, and prescription information were extracted for semaglutide patients. A gradient-boosting, machine-learning classifier was developed for weight-loss prediction and identification of the most impactful factors via SHapley Additive exPlanations (SHAP) value extrapolation. RESULTS: A total of 3555 eligible patients (539 of whom were observed 52 weeks following exposure) from March 2017 to April 2022 were studied. On average, individuals lost 4.44% (male individuals, 3.66%; female individuals, 5.08%) of their initial weight. History of diabetes mellitus diagnosis was associated with less weight loss, whereas prediabetes and linaclotide use were associated with more pronounced weight loss. CONCLUSIONS: Weight loss in patients prescribed semaglutide from real-world evidence was strong but attenuated compared with previous clinical trials. Machine-learning analysis of electronic health record data identified factors that warrant further research and consideration when tailoring weight-loss therapy.

Stratification of Lung Cancer Risk with Thoracic Imaging Phenotypes.

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.a. body habitus, is increasingly understood to be important, but has not been integrated into risk models. Chest low dose computed tomography (LDCT) is the standard imaging study in lung cancer screening, with the capability to discriminate differences in body composition and organ arrangement in the thorax. We hypothesize that the primary phenotypes identified using lung screening chest LDCT can form a representation of body habitus and add predictive power for lung cancer risk stratification. In this pilot study, we evaluated the feasibility of body habitus image-based phenotyping on a large lung screening LDCT dataset. A thoracic imaging manifold was estimated based on an intensity-based pairwise (dis)similarity metric for pairs of spatial normalized chest LDCT images. We applied the hierarchical clustering method on this manifold to identify the primary phenotypes. Body habitus features of each identified phenotype were evaluated and associated with future lung cancer risk using time-to-event analysis. We evaluated the method on the baseline LDCT scans of 1,200 male subjects sampled from National Lung Screening Trial. Five primary phenotypes were identified, which were associated with highly distinguishable clinical and body habitus features. Time-to-event analysis against future lung cancer incidences showed two of the five identified phenotypes were associated with elevated future lung cancer risks (HR=1.61, 95% CI = [1.08, 2.38], p=0.019; HR=1.67, 95% CI = [0.98, 2.86], p=0.057). These results indicated that it is feasible to capture the body habitus by image-base phenotyping using lung screening LDCT and the learned body habitus representation can potentially add value for future lung cancer risk stratification.

AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection.

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. The added value of these measurements was assessed with sex- and cause-specific Cox proportional hazards models with and without the AI-derived body composition measurements for predicting lung cancer incidence, lung cancer death, CVD death, and all-cause mortality. Models were adjusted for confounding variables including age; body mass index; quantitative emphysema; coronary artery calcification; history of diabetes, heart disease, hypertension, and stroke; and other PLCOM2012 lung cancer risk factors. Goodness-of-fit improvements were assessed with the likelihood ratio test. Results Among 20 768 included participants (median age, 61 years [IQR, 57-65 years]; 12 317 men), 865 were diagnosed with lung cancer and 4180 died during follow-up. Including the AI-derived body composition measurements improved risk prediction for lung cancer death (male participants: χ2 = 23.09, P < .001; female participants: χ2 = 15.04, P = .002), CVD death (males: χ2 = 69.94, P < .001; females: χ2 = 16.60, P < .001), and all-cause mortality (males: χ2 = 248.13, P < .001; females: χ2 = 94.54, P < .001), but not for lung cancer incidence (male participants: χ2 = 2.53, P = .11; female participants: χ2 = 1.73, P = .19). Conclusion The body composition measurements automatically derived from baseline low-dose CT examinations added predictive value for lung cancer death, CVD death, and all-cause death, but not for lung cancer incidence in the NLST. Clinical trial registration no. NCT00047385 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fintelmann in this issue.

Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective.

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. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.

Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification.

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. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures.

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.

OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs. METHOD: We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model). RESULTS: Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g. , AUC: 0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model. CONCLUSIONS: Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance.

Extending the value of routine lung screening CT with quantitative body composition assessment.

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). A two-stage fully automatic pipeline is developed to assess the cross-sectional area of body composition components including subcutaneous adipose tissue (SAT), muscle, visceral adipose tissue (VAT), and bone on T5, T8 and T10 vertebral levels. The pipeline is developed using 61 cases of the VerSe'20 dataset, 40 annotated cases of NLST, and 851 inhouse screening cases. On a test cohort consisting of 30 cases from the inhouse screening cohort (age 55 - 73, 50% female) and 42 cases of NLST (age 55 - 75, 59.5% female), the pipeline achieves a root mean square error (RMSE) of 7.25 mm (95% CI: [6.61, 7.85]) for the vertebral level identification and mean Dice similarity score (DSC) 0.99 ± 0.02, 0.96 ± 0.03, and 0.95 ± 0.04 for SAT, muscle, and VAT, respectively for body composition segmentation. The pipeline is generalized to the CT arm of the NLST dataset (25,205 subjects, 40.8% female, 1,056 lung cancer incidences). Time-to-event analysis for lung cancer incidence indicates inverse association between measured muscle cross-sectional area and incidental lung cancer risks (p < 0.001 female, p < 0.001 male). In conclusion, automatic body composition analysis using routine lung screening LDCT is feasible.

Inference-based correction of multi-site height and weight measurement data in the All of Us research program.

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. MATERIALS AND METHODS: Using the AoU Researcher Workbench, we assessed site-level measurement value distributions to infer unit types. We also used plausibility checks with exceptions for conditions with possible outlier values, eg obesity, and assessed for excess deviation within individual participant's records. RESULTS: 15.8% of height and 22.4% of weight values had missing unit type information. DISCUSSION: We identified several measurement unit related issues: the use of different units of measure within and between sites, missing units, and incorrect labeling of units. Failure to account for these in patient data repositories may lead to erroneous study results and conclusions. CONCLUSION: Discrepancies in height and weight measurement data may arise from missing or mislabeled units. Using site- and participant-level analyses while accounting for outlier value-associated clinical conditions, we can infer measurement units and apply corrections. These methods are adaptable and expandable within AoU and other data repositories.

Cancer Risk Estimation Combining Lung Screening CT with Clinical Data Elements.

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. The VLSP dataset was used for external testing of the developed model. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve were used to measure the performance of the model. The developed model was compared with published risk-prediction models that used only CDEs or imaging data alone. The Brock model was also included for comparison by imputing missing values for patients without a dominant pulmonary nodule. RESULTS: A total of 23 505 patients from the NLST (mean age, 62 years ± 5 [standard deviation]; 13 838 men, 9667 women) and 147 patients from the VLSP (mean age, 65 years ± 5; 82 men, 65 women) were included. Using cross-validation on the NLST dataset, the AUC of the proposed co-learning model (AUC, 0.88) was higher than the published models predicted with CDEs only (AUC, 0.69; P < .05) and with images only (AUC, 0.86; P < .05). Additionally, using the external VLSP test dataset, the co-learning model had a higher performance than each of the published individual models (AUC, 0.91 [co-learning] vs 0.59 [CDE-only] and 0.88 [image-only]; P < .05 for both comparisons). CONCLUSION: The proposed co-learning predictive model combining chest CT images and CDEs had a higher performance for lung cancer risk prediction than models that contained only CDE or only image data; the proposed model also had a higher performance than the Brock model.Keywords: Computer-aided Diagnosis (CAD), CT, Lung, Thorax Supplemental material is available for this article. © RSNA, 2021.

Development and Characterization of a Chest CT Atlas.

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.g., atlases) have enabled separate consideration of local features from gross/global structure. To date, no analogous standard atlas has been presented to enable spatial mapping and harmonization in chest computational tomography (CT). In this paper, we propose a thoracic atlas built upon a large low dose CT (LDCT) database of lung cancer screening program. The study cohort includes 466 male and 387 female subjects with no screening detected malignancy (age 46-79 years, mean 64.9 years). To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. Briefly, with 50 scans of 50 randomly selected female subjects as fine tuning dataset, we search for the optimal configuration of the non-rigid registration module in a range of adjustable parameters including: registration searching radius, degree of keypoint dispersion, regularization coefficient and similarity patch size, to minimize the registration failure rate approximated by the number of samples with low Dice similarity score (DSC) for lung and body segmentation. We evaluate the optimized pipeline on a separate cohort (100 scans of 50 female and 50 male subjects) relative to two baselines with alternative non-rigid registration module: the same software with default parameters and an alternative software. We achieve a significant improvement in terms of registration success rate based on manual QA. For the entire study cohort, the optimized pipeline achieves a registration success rate of 91.7%. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes, including body mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary artery calcification (CAC).

Prevalence and associated risk factors of Shigella flexneri isolated from drinking water and retail raw foods in Peshawar, Pakistan.

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. Potential risk factors associated with the development and spreading of S. flexneri infection were also investigated. Overall, 45 (4.41%) samples were positive for Shigella species. Among these samples, the predominant species was S. flexneri (n = 44) followed by S. boydii (n = 1). Interestingly, S. sonnei and S. dysenteriae isolates were not found in any sample. The isolation rate of S. flexneri in drinking water samples, market raw milk, and fruits/vegetables from Peshawar were 6.47%, 3.5%, and 2.9%, respectively. The phylogenetic reconstruction showed genetic diversity among three clades, as clades I and II have isolates of S. flexneri that were circulating within the drinking water, milk, fruits/vegetables, while clade III isolates were recovered from milk samples. Most of S. flexneri were detected in June to September. Potential risk factors of S. flexneri were water sources contaminated by toilet wastes (p = 0.04), surface water drainage (p = 0.0002), hospital wastes (p = 0.01), unhygienic handling (p < 0.05), and transportation of raw food (p = 0.04). In conclusion, S. flexneri isolates of closely related lineage originating from non-clinical samples might be associated with an increased human risk to shigellosis in Pakistan, as significant numbers of S. flexneri were observed in the drinking water and retail raw food samples. PRACTICAL APPLICATION: This study demonstrated the presence of S. flexneri in drinking water and retail raw food samples which seem to possess a serious threat to public health. Potential sources of food and water contamination should properly be monitored by public health authorities to reduce cases of shigellosis.

Intrinsic Evaluation of Contextual and Non-contextual Word Embeddings using Radiology Reports.

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. Using surveys, we quantified the agreement between clinician judgment and embedding model representations. We compare embedding models trained on a custom radiology report corpus (RRC), a general corpus, and PubMed and MIMIC-III corpora (P&MC). Cloze task accuracy was equivalent for RRC and P&MC models. For term pair similarity, P&MC-trained NCWEs outperformed all other NCWE models (ρspearman 0.61 vs. 0.27-0.44). Among models trained on RRC, fastText models often outperformed other NCWE models and spherical embeddings provided overly optimistic representations of term pair similarity.

Enzymes mediating resistance to chlorpyriphos in Aphis fabae (Homoptera: Aphididae).

The black bean aphid, Aphis fabae (Homoptera: Aphididae), is a widespread pest that has more than 200 hosts in the world. Insecticide resistance (IR) due to frequent applications is the major limitation in integrated pest management programs. Biochemical resistance is a common type of IR in which the insecticide is detoxified by one or more enzymes of the pest before reaching its target site. In this study, the IR of A. fabae populations to chlorpyrifos was evaluated in two single sprayed fields (fields A and C) and one replicated spraying field (field B) in comparison with a susceptible population (field H) during 2015. After treatments, total protein content and the activity of two detoxifying enzymes, esterases (ESTs) and glutathione S-transferases (GSTs), and acetylcholinesterase (AChE) in the populations were determined. Results clearly showed higher total protein content for the field populations compared to the susceptible population. The total protein content in field B population was significantly more than other populations. The total protein contents in Field A, B and C were 2.81, 2.89 and 1.06-fold more than susceptible strain, respectively. Higher actives of enzymes were observed in fields A, B, and C populations compared to the susceptible population (field H). The highest activity of GSTs and ESTs was observed in the field B population. Taken together, the present study demonstrated a significant IR to chlorpyrifos in the sprayed populations of A. fabae that can be attributed to the higher activity of their detoxification enzymes.

Disseminated pulmonary histoplasmosis in immunocompetent patients: a common epidemiological exposure.

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.

Targeting epigenetics for treatment of BRAF mutated metastatic melanoma with decitabine in combination with vemurafenib: A phase lb study.

INTRODUCTION: Epigenetic modifications play an important role in progression and development of resistance in V600EBRAF 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 V600EBRAF positive metastatic melanoma with or without any prior treatment. EXPERIMENTAL DESIGN: The study employed 3+3 dose escalation combining subcutaneous decitabine at different doses and schedules (4 cohorts) with the standard oral dose of vemurafenib 960 mg twice daily. Preclinical assessment and further analysis were also performed in A375 melanoma cell line. RESULTS: Fourteen patients received study treatment. No dose limiting toxicity was encountered and maximum tolerated dose was not reached. Important toxicities included fatigue, increased creatinine, neutropenia, leucopenia, hypophosphatemia, rash and hyperuricemia. Three patients achieved complete response, three had partial response and five had stable disease. Preclinical assessment demonstrated action of the combination which delayed the development of acquired resistance and improved duration of treatment sensitivity. CONCLUSIONS: The combination of oral vemurafenib with subcutaneous decitabine is safe and showed activity in V600EBRAF positive metastatic melanoma. Since most responses were seen in cohort 1, which utilized low-dose, long-term decitabine, future studies of this combination treatment should utilize longer duration of decitabine, at the lowest dose of 0.1 mg/kg.