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  1. AU="Deppen, Stephen"
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  1. Artikel ; Online: Beyond the

    Sandler, Kim L / Deppen, Stephen A

    AJR. American journal of roentgenology

    2021  Band 218, Heft 5, Seite(n) 926

    Mesh-Begriff(e) Humans ; Veterans ; Veterans Health ; Early Detection of Cancer ; Follow-Up Studies ; Lung Neoplasms/diagnostic imaging
    Sprache Englisch
    Erscheinungsdatum 2021-09-29
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Comment
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.21.26852
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  2. Artikel ; Online: Intrinsic Evaluation of Contextual and Non-contextual Word Embeddings using Radiology Reports.

    Khan, Mirza S / Landman, Bennett A / Deppen, Stephen A / Matheny, Michael E

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2022  Band 2021, Seite(n) 631–640

    Abstract: 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 ... ...

    Abstract 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 (ρ
    Mesh-Begriff(e) Data Collection ; Humans ; Natural Language Processing ; PubMed ; Radiology ; Semantics
    Sprache Englisch
    Erscheinungsdatum 2022-02-21
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Deep Breaths: A Systematic Review of the Potential Effects of Employment in the Nuclear Industry on Mortality from Non-Malignant Respiratory Disease.

    Milder, Cato M / Howard, Sara C / Ellis, Elizabeth D / Deppen, Stephen A

    Radiation research

    2022  Band 198, Heft 4, Seite(n) 396–429

    Abstract: Ionizing radiation is an established carcinogen, but its effects on non-malignant respiratory disease (NMRD) are less clear. Cohorts exposed to multiple risk factors including radiation and toxic dusts conflate these relationships, and there is a need ... ...

    Abstract Ionizing radiation is an established carcinogen, but its effects on non-malignant respiratory disease (NMRD) are less clear. Cohorts exposed to multiple risk factors including radiation and toxic dusts conflate these relationships, and there is a need for clarity in previous findings. This systematic review was conducted to survey the body of existing evidence for radiation effects on NMRD in global nuclear worker cohorts. A PubMed search was conducted for studies with terms relating to radiation or uranium and noncancer respiratory outcomes. Papers were limited to the most recent report within a single cohort published between January 2000 and December 2020. Publication quality was assessed based upon UNSCEAR 2017 criteria. In total, 31 papers were reviewed. Studies included 29 retrospective cohorts, one prospective cohort, and one longitudinal cohort primarily comprising White men from the U.S., Canada and Western Europe. Ten studies contained subpopulations of uranium miners or millers. Papers reported standardized mortality ratio (SMR) analyses, regression analyses, or both. Neither SMR nor regression analyses consistently showed a relationship between radiation exposure and NMRD. A meta-analysis of excess relative risks (ERRs) for NMRD did not present evidence for a dose-response (overall ERR/Sv: 0.07; 95% CI: -0.07, 0.21), and results for more specific outcomes were inconsistent. Significantly elevated SMRs for NMRD overall were observed in two studies among the subpopulation of uranium miners and millers (combined n = 4229; SMR 1.42-1.43), indicating this association may be limited to mining and milling populations and may not extend to other nuclear workers. A quality review showed limited capacity of 17 out of 31 studies conducted to provide evidence for a causal relationship between radiation and NMRD; the higher-quality studies showed no consistent relationship. All elevated NMRD SMRs were among mining and milling cohorts, indicating different exposure profiles between mining and non-mining cohorts; future pooled cohorts should adjust for mining exposures or address mining cohorts separately.
    Mesh-Begriff(e) Carcinogens ; Employment ; Humans ; Lung Neoplasms/etiology ; Male ; Occupational Diseases/etiology ; Occupational Exposure/adverse effects ; Prospective Studies ; Respiration Disorders ; Retrospective Studies ; Risk Factors ; Uranium/adverse effects
    Chemische Substanzen Carcinogens ; Uranium (4OC371KSTK)
    Sprache Englisch
    Erscheinungsdatum 2022-07-29
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Systematic Review ; Research Support, N.I.H., Extramural
    ZDB-ID 80322-4
    ISSN 1938-5404 ; 0033-7587
    ISSN (online) 1938-5404
    ISSN 0033-7587
    DOI 10.1667/RADE-21-00014.1
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  4. Artikel ; Online: Risk factors for SARS-CoV-2 related mortality and hospitalization before vaccination: A meta-analysis.

    Marmor, Hannah N / Pike, Mindy / Zhao, Zhiguo Alex / Ye, Fei / Deppen, Stephen A

    PLOS global public health

    2022  Band 2, Heft 11, Seite(n) e0001187

    Abstract: The literature remains scarce regarding the varying point estimates of risk factors for COVID-19 associated mortality and hospitalization. This meta-analysis investigates risk factors for mortality and hospitalization, estimates individual risk factor ... ...

    Abstract The literature remains scarce regarding the varying point estimates of risk factors for COVID-19 associated mortality and hospitalization. This meta-analysis investigates risk factors for mortality and hospitalization, estimates individual risk factor contribution, and determines drivers of published estimate variances. We conducted a systematic review and meta-analysis of COVID-19 related mortality and hospitalization risk factors using PRISMA guidelines. Random effects models estimated pooled risks and meta-regression analyses estimated the impact of geographic region and study type. Studies conducted in North America and Europe were more likely to have lower effect sizes of mortality attributed to chronic kidney disease (OR: 0.21, 95% CI: 0.09-0.52 and OR: 0.25, 95% CI: 0.10-0.63, respectively). Retrospective studies were more likely to have decreased effect sizes of mortality attributed to chronic heart failure compared to prospective studies (OR: 0.65, 95% CI: 0.44-0.95). Studies from Europe and Asia (OR: 0.42, 95% CI: 0.30-0.57 and OR: 0.49, 95% CI: 0.28-0.84, respectively) and retrospective studies (OR: 0.58, 95% CI: 0.47-0.73) reported lower hospitalization risk attributed to male sex. Significant geographic population-based variation was observed in published comorbidity related mortality risks while male sex had less of an impact on hospitalization among European and Asian populations or in retrospective studies.
    Sprache Englisch
    Erscheinungsdatum 2022-11-02
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2767-3375
    ISSN (online) 2767-3375
    DOI 10.1371/journal.pgph.0001187
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  5. Artikel ; Online: Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.

    Rankin, Danielle A / Peetluk, Lauren S / Deppen, Stephen / Slaughter, James Christopher / Katz, Sophie / Halasa, Natasha B / Khankari, Nikhil K

    BMJ open

    2023  Band 13, Heft 4, Seite(n) e067878

    Abstract: Objectives: To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.: Design: Systematic review.: Data sources: PubMed and Embase were searched from 1 January 1975 to 3 February ...

    Abstract Objectives: To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.
    Design: Systematic review.
    Data sources: PubMed and Embase were searched from 1 January 1975 to 3 February 2022.
    Eligibility criteria: We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded.
    Data extraction and synthesis: Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool).
    Results: Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment.
    Conclusions: Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application.
    Prospero registration number: CRD42022308917.
    Mesh-Begriff(e) Child ; Humans ; Bias ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Testing ; Prognosis ; Respiratory Tract Infections/diagnosis ; SARS-CoV-2 ; Virus Diseases/diagnosis
    Sprache Englisch
    Erscheinungsdatum 2023-04-21
    Erscheinungsland England
    Dokumenttyp Journal Article ; Systematic Review ; Research Support, N.I.H., Extramural
    ZDB-ID 2599832-8
    ISSN 2044-6055 ; 2044-6055
    ISSN (online) 2044-6055
    ISSN 2044-6055
    DOI 10.1136/bmjopen-2022-067878
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Provider-ordered viral testing and antibiotic administration practices among children with acute respiratory infections across healthcare settings in Nashville, Tennessee.

    Rankin, Danielle A / Katz, Sophie E / Amarin, Justin Z / Hayek, Haya / Stewart, Laura S / Slaughter, James C / Deppen, Stephen / Yanis, Ahmad / Romero, Yesenia Herazo / Chappell, James D / Khankari, Nikhil K / Halasa, Natasha B

    Antimicrobial stewardship & healthcare epidemiology : ASHE

    2024  Band 4, Heft 1, Seite(n) e29

    Abstract: Objective: Evaluate the association between provider-ordered viral testing and antibiotic treatment practices among children discharged from an ED or hospitalized with an acute respiratory infection (ARI).: Design: Active, prospective ARI ... ...

    Abstract Objective: Evaluate the association between provider-ordered viral testing and antibiotic treatment practices among children discharged from an ED or hospitalized with an acute respiratory infection (ARI).
    Design: Active, prospective ARI surveillance study from November 2017 to February 2020.
    Setting: Pediatric hospital and emergency department in Nashville, Tennessee.
    Participants: Children 30 days to 17 years old seeking medical care for fever and/or respiratory symptoms.
    Methods: Antibiotics prescribed during the child's ED visit or administered during hospitalization were categorized into (1) None administered; (2) Narrow-spectrum; and (3) Broad-spectrum. Setting-specific models were built using unconditional polytomous logistic regression with robust sandwich estimators to estimate the adjusted odds ratios and 95% confidence intervals between provider-ordered viral testing (ie, tested versus not tested) and viral test result (ie, positive test versus not tested and negative test versus not tested) and three-level antibiotic administration.
    Results: 4,107 children were enrolled and tested, of which 2,616 (64%) were seen in the ED and 1,491 (36%) were hospitalized. In the ED, children who received a provider-ordered viral test had 25% decreased odds (aOR: 0.75; 95% CI: 0.54, 0.98) of receiving a narrow-spectrum antibiotic during their visit than those without testing. In the inpatient setting, children with a negative provider-ordered viral test had 57% increased odds (aOR: 1.57; 95% CI: 1.01, 2.44) of being administered a broad-spectrum antibiotic compared to children without testing.
    Conclusions: In our study, the impact of provider-ordered viral testing on antibiotic practices differed by setting. Additional studies evaluating the influence of viral testing on antibiotic stewardship and antibiotic prescribing practices are needed.
    Sprache Englisch
    Erscheinungsdatum 2024-03-06
    Erscheinungsland England
    Dokumenttyp Journal Article
    ISSN 2732-494X
    ISSN (online) 2732-494X
    DOI 10.1017/ash.2024.24
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  7. Artikel ; Online: Biomarkers in Lung Cancer Screening: a Narrative Review.

    Marmor, Hannah N / Zorn, J Tyler / Deppen, Stephen A / Massion, Pierre P / Grogan, Eric L

    Current challenges in thoracic surgery

    2021  Band 5

    Abstract: Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to ... ...

    Abstract Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.
    Sprache Englisch
    Erscheinungsdatum 2021-03-01
    Erscheinungsland China
    Dokumenttyp Journal Article
    ISSN 2664-3278
    ISSN (online) 2664-3278
    DOI 10.21037/ccts-20-171
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  8. Artikel ; Online: The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers.

    Kammer, Michael N / Rowe, Dianna J / Deppen, Stephen A / Grogan, Eric L / Kaizer, Alexander M / Barón, Anna E / Maldonado, Fabien

    Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology

    2022  Band 31, Heft 9, Seite(n) 1752–1759

    Abstract: Background: Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on ... ...

    Abstract Background: Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.
    Methods: Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.
    Results: Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 > 0.97 for each) with fitted parameters closely aligned with professional society guidelines.
    Conclusions: The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.
    Impact: We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.
    Mesh-Begriff(e) Female ; Humans ; Lung ; Male ; Ovarian Neoplasms/diagnosis ; Ovarian Neoplasms/pathology ; Probability ; Prostate/pathology ; Research Design
    Sprache Englisch
    Erscheinungsdatum 2022-06-22
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1153420-5
    ISSN 1538-7755 ; 1055-9965
    ISSN (online) 1538-7755
    ISSN 1055-9965
    DOI 10.1158/1055-9965.EPI-22-0190
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3693: Hefte anzeigen Standort:
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  9. Artikel ; Online: Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

    Marmor, Hannah N / Jackson, Laurel / Gawel, Susan / Kammer, Michael / Massion, Pierre P / Grogan, Eric L / Davis, Gerard J / Deppen, Stephen A

    Clinica chimica acta; international journal of clinical chemistry

    2022  Band 534, Seite(n) 106–114

    Abstract: Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.: Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A ... ...

    Abstract Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.
    Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.
    Results: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.
    Conclusions: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.
    Mesh-Begriff(e) Antigens, Neoplasm ; Biomarkers ; Humans ; Keratin-19 ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/pathology ; Multiple Pulmonary Nodules/diagnosis ; Multiple Pulmonary Nodules/pathology ; Tomography, X-Ray Computed
    Chemische Substanzen Antigens, Neoplasm ; Biomarkers ; Keratin-19 ; antigen CYFRA21.1
    Sprache Englisch
    Erscheinungsdatum 2022-07-20
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ZDB-ID 80228-1
    ISSN 1873-3492 ; 0009-8981
    ISSN (online) 1873-3492
    ISSN 0009-8981
    DOI 10.1016/j.cca.2022.07.010
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Using Clinical Risk Models for Lung Nodule Classification.

    Deppen, Stephen A / Grogan, Eric L

    Seminars in thoracic and cardiovascular surgery

    2015  Band 27, Heft 1, Seite(n) 30–35

    Abstract: Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be ... ...

    Abstract Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be evaluating smaller and screening discovered nodules. Multiple calculators exist that can help the clinician diagnose lung cancer at the bedside. The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) model helps to determine who needs lung cancer screening, and the McWilliams and Mayo models help to guide the primary care clinician or pulmonologist with diagnosis by estimating the probability of cancer in patients with indeterminate pulmonary nodules. The Thoracic Research Evaluation And Treatment (TREAT) model assists surgeons to determine who needs a surgical biopsy among patients referred for suspicious lesions. Additional work is needed to develop decision support tools that will facilitate the use of these models in clinical practice, to complement the clinician's judgment and enhance shared decision making with the patient at the bedside.
    Mesh-Begriff(e) Humans ; Lung Neoplasms/classification ; Multiple Pulmonary Nodules/classification ; Risk Assessment/methods ; Risk Factors
    Sprache Englisch
    Erscheinungsdatum 2015-04-07
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ZDB-ID 1038278-1
    ISSN 1532-9488 ; 1043-0679
    ISSN (online) 1532-9488
    ISSN 1043-0679
    DOI 10.1053/j.semtcvs.2015.04.001
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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