Browsing by Author "Steenburg, Scott D."

Now showing 1 - 9 of 9
  • Thumbnail Image
    Item
    American Society of Emergency Radiology Multicenter Blunt Splenic Trauma Study: CT and Clinical Findings
    (Radiological Society of North America, 2021) Lee, James T.; Slade, Emily; Uyeda, Jennifer; Steenburg, Scott D.; Chong, Suzanne T.; Tsai, Richard; Raptis, Demetrios; Linnau, Ken F.; Chinapuvvula, Naga R.; Dattwyler, Matthew P.; Dugan, Adam; Baghdanian, Arthur; Flink, Carl; Baghdanian, Armonde; LeBedis, Christina A.; Radiology and Imaging Sciences, School of Medicine
    Background: Treatment of blunt splenic trauma (BST) continues to evolve with improved imaging for detection of splenic vascular injuries. Purpose: To report on treatments for BST from 11 trauma centers, the frequency and clinical impact of splenic vascular injuries, and factors influencing treatment. Materials and Methods: Patients were retrospectively identified as having BST between January 2011 and December 2018, and clinical, imaging, and outcome data were recorded. Patient data were summarized descriptively, both overall and stratified by initial treatment received (nonoperative management [NOM], angiography, or surgery). Regression analyses were used to examine the primary outcomes of interest, which were initial treatment received and length of stay (LOS). Results: This study evaluated 1373 patients (mean age, 42 years ± 18; 845 men). Initial treatments included NOM in 849 patients, interventional radiology (IR) in 240 patients, and surgery in 284 patients. Rates from CT reporting were 22% (304 of 1373) for active splenic hemorrhage (ASH) and 20% (276 of 1373) for contained vascular injury (CVI). IR management of high-grade injuries increased 15.6%, from 28.6% (eight of 28) to 44.2% (57 of 129) (2011–2012 vs 2017–2018). Patients who were treated invasively had a higher injury severity score (odds ratio [OR], 1.04; 95% CI: 1.02, 1.05; P < .001), lower temperature (OR, 0.97; 95% CI: 0.97, 1.00; P = .03), and a lower hematocrit (OR, 0.96; 95% CI: 0.93, 0.99; P = .003) and were more likely to show ASH (OR, 8.05; 95% CI: 5.35, 12.26; P < .001) or CVI (OR, 2.70; 95% CI: 1.64, 4.44; P < .001) on CT images, have spleen-only injures (OR, 2.35; 95% CI: 1.45, 3.8; P < .001), and have been administered blood product for fewer than 24 hours (OR, 2.35; 95% CI: 1.58, 3.51; P < .001) compared with those chosen for NOM, after adjusting for key demographic and clinical variables. After adjustment, factors associated with a shorter LOS were female sex (OR, 0.84; 95% CI: 0.73, 0.96; P = .009), spleen-only injury (OR, 0.72; 95% CI: 0.6, 0.86; P < .001), higher admission hematocrit (OR, 0.98; 95% CI: 0.6, 0.86; P < .001), and presence of ASH at CT (OR, 0.74; 95% CI: 0.62, 0.88; P < .001). Conclusion: Contained vascular injury and active splenic hemorrhage (ASH) were frequently reported, and rates of interventional radiologic management increased during the study period. ASH was associated with a shorter length of stay, and patients with ASH had eight times the odds of undergoing invasive treatment compared with undergoing nonoperative management.
  • Thumbnail Image
    Item
    Computerized Tomography of the Acute Left Upper Quadrant Pain
    (Springer, 2016-08) Tirkes, Temel; Ballenger, Zachary; Steenburg, Scott D.; Altman, Daniel J.; Sandrasegaran, Kumaresan; Department of Radiology and Imaging Sciences, IU School of Medicine
    The purpose of this study was to evaluate the clinical utility of computerized tomography (CT) of the abdomen in the emergent setting of left upper quadrant pain. One hundred patients (average age: 45, range: 19–93 years, female: 57 %, male: 43 %) who presented to the emergency department (ED) and underwent CT scanning of abdomen with the given indication of left upper quadrant pain were included in this study. The results from CT examinations were compared to final diagnoses determined by either ED physician or clinician on a follow-up visit. Sensitivity of CT was 69 % (95 %CI: 52–83 %) for 39 patients who eventually were diagnosed with an acute abdominal abnormality. Twenty-seven patients had an acute abnormal finding on abdominal CT that represented the cause of the patient’s pain (positive predictive value of 100 %, 95 %CI: 87–100 %). Of the remaining 73 patients with negative CT report, 12 were diagnosed clinically (either in the ED or on follow-up visit to specialist) with a pathology that was undetectable on the CT imaging (negative predictive value of 83 %, 95 %CI: 73–91 %). None of the remaining 61 patients with negative CT were found to have pathology by clinical evaluation (specificity of 100 %, 95 %CI: 94–100 %). CT is a useful examination for patients with acute left upper quadrant pain in the emergency department setting with moderate sensitivity and excellent specificity.
  • Thumbnail Image
    Item
    Diagnostic Yield and Clinical Utility of Abdominopelvic CT Following Emergent Laparotomy for Trauma
    (RSNA, 2016-09) Haste, Adam K.; Brewer, Brian L.; Steenburg, Scott D.; Department of Surgery, IU School of Medicine
    Purpose To determine the incidence of unexpected injuries that are diagnosed with computed tomography (CT) after emergent exploratory laparotomy for trauma and whether identification of such injuries results in additional surgery or angiography. Materials and Methods This HIPAA-compliant retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. The trauma databases of two urban level 1 trauma centers were queried over a period of more than 5 years for patients who underwent abdominopelvic CT within 48 hours of emergent exploratory laparotomy for trauma. Comparisons were made between CT findings and those described in the surgical notes. Descriptive statistics were generated, and 95% confidence intervals (CIs) were determined by using an exact method based on a binomial distribution. Results The study cohort consisted of 90 patients, including both blunt and penetrating trauma victims with a median injury severity score of 17.5 (interquartile range, 9.25–34). Seventy-three percent (66 of 90) of patients sustained penetrating trauma, 82% (74 of 90) of whom were male. A total of 19 patients (21.1%; 95% CI: 13.2, 31.0) had additional injuries within the surgical field that were not identified during laparotomy. There were 17 unidentified solid organ injuries, and eight patients had active bleeding within the surgical field. Eight patients (8.9%; 95% CI: 3.9, 16.8) had unexpected injuries at CT that were substantial enough to warrant additional surgery or angiography. In addition, previously undiagnosed fractures were found in 45 patients (50%; 95% CI: 39.3, 60.7). Conclusion Performing CT after emergent exploratory laparotomy for trauma is useful in identifying unexpected injuries and confirming suspected injuries that were not fully explored at initial surgery.
  • Thumbnail Image
    Item
    Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals
    (Oxford University Press, 2022) Peng, Le; Luo, Gaoxiang; Walker, Andrew; Zaiman, Zachary; Jones, Emma K.; Gupta, Hemant; Kersten, Kristopher; Burns, John L.; Harle, Christopher A.; Magoc, Tanja; Shickel, Benjamin; Steenburg, Scott D.; Loftus, Tyler; Melton, Genevieve B.; Wawira Gichoya, Judy; Sun, Ju; Tignanelli, Christopher J.; Radiology and Imaging Sciences, School of Medicine
    Objective: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. Materials and methods: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). Results: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. Conclusion: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.
  • Thumbnail Image
    Item
    Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs
    (Elsevier, 2022-02) Gichoya, Judy W.; Sinha, Priyanshu; Davis, Melissa; Dunkle, Jeffrey W.; Hamlin, Scott A.; Herr, Keith D.; Hoff, Carrie N.; Letter, Haley P.; McAdams, Christopher R.; Puthoff, Gregory D.; Smith, Kevin L.; Steenburg, Scott D.; Banerjee, Imon; Trivedi, Hari; Radiology and Imaging Sciences, School of Medicine
    BACKGROUND: Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED). MATERIALS AND METHODS: We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other - Infectious, Other - Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement. RESULTS: 1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result. CONCLUSION: At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.
  • Thumbnail Image
    Item
    Optimizing Electronic Release of Imaging Results through an Online Patient Portal
    (RSNA, 2019-01) Woolen, Sean A.; Kazerooni, Ella A.; Steenburg, Scott D.; Nan, Bin; Ma, Tianwen; Wall, Amber; Linna, Nathaniel B.; Gayed, Matthew J.; Kushdilian, Michael V.; Parent, Kelly; Cahalan, Shannon; Alameddine, Mitchell; Ladd, Lauren M.; Davenport, Matthew S.; Radiology and Imaging Sciences, School of Medicine
    Purpose To determine an optimal embargo period preceding release of radiologic test results to an online patient portal. Materials and Methods This prospective discrete choice conjoint survey with modified orthogonal design was administered to patients by trained interviewers at four outpatient sites and two institutions from December 2016 to February 2018. Three preferences for receiving imaging results associated with a possible or known cancer diagnosis were evaluated: delay in receipt of results (1, 3, or 14 days), method of receipt (online portal, physician’s office, or phone), and condition of receipt (before, at the same time as, or after health care provider). Preferences (hereafter, referred to as utilities) were derived from parameter estimates (β) of multinomial regression stratified according to study participant and choice set. Results Among 464 screened participants, the response and completion rates were 90.5% (420 of 464) and 99.5% (418 of 420), respectively. Participants preferred faster receipt of results (P < .001) from their physician (P < .001) over the telephone (P < .001). Each day of delay decreased preference by 13 percentage points. Participants preferred immediate receipt of results through an online portal (utility, –.57) if made to wait more than 6 days to get results in the office and more than 11 days to get results by telephone. Compared with receiving results in their physician’s office on day 7 (utility, –.60), participants preferred immediate release through the online portal without physician involvement if followed by a telephone call within 6 days (utility, –0.49) or an office visit within 2 days (utility, –.53). Older participants preferred physician-directed communication (P < .001). Conclusion The optimal embargo period preceding release of results through an online portal depends on the timing of traditional telephone- and office-based styles of communication.
  • Thumbnail Image
    Item
    Penetrating neck trauma: A review of image-based evaluation and management
    (2016-01) Steenburg, Scott D.; Leatherwood, Danny; Department of Radiology and Imaging Sciences, IU School of Medicine
  • Thumbnail Image
    Item
    Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study
    (Radiological Society of North America, 2022-06-01) Sun, Ju; Peng, Le; Li, Taihui; Adila, Dyah; Zaiman, Zach; Melton-Meaux, Genevieve B.; Ingraham, Nicholas E.; Murray, Eric; Boley, Daniel; Switzer, Sean; Burns, John L.; Huang, Kun; Allen, Tadashi; Steenburg, Scott D.; Wawira Gichoya, Judy; Kummerfeld, Erich; Tignanelli, Christopher J.; Radiology and Imaging Sciences, School of Medicine
    Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.
  • Thumbnail Image
    Item
    Transitioning to Independent Practice: A Successful Fourth-Year Radiology Resident Curriculum
    (Elsevier, 2017-12) Heitkamp, Darel E.; Ford, Jason M.; Madden, Colleen M.; Smith, Kevin L.; Nartker, Matthew J.; Ponting, John M.; Steenburg, Scott D.; Aaron, Vasantha D.; Kamer, Aaron P.; Radiology and Imaging Sciences, School of Medicine