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1.
Acad Radiol ; 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1458676

ABSTRACT

INTRODUCTION: Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. METHODS: Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). RESULTS: The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. CONCLUSION: The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

2.
Cancer Med ; 10(18): 6327-6335, 2021 09.
Article in English | MEDLINE | ID: covidwho-1344970

ABSTRACT

BACKGROUND: We aimed to investigate the effects of COVID-19 on computed tomography (CT) imaging of cancer. METHODS: Cancer-related CTs performed at one academic hospital and three affiliated community hospitals in Massachusetts were retrospectively analyzed. Three periods of 2020 were considered as follows: pre-COVID-19 (1/5/20-3/14/20), COVID-19 peak (3/15/20-5/2/20), and post-COVID-19 peak (5/3/20-11/14/20). 15 March 2020 was the day a state of emergency was declared in MA; 3 May 2020 was the day our hospitals resumed to non-urgent imaging. The volumes were assessed by (1) Imaging indication: cancer screening, initial workup, active cancer, and surveillance; (2) Care setting: outpatient and inpatient, ED; (3) Hospital type: quaternary academic center (QAC), university-affiliated community hospital (UACH), and sole community hospitals (SCHs). RESULTS: During the COVID-19 peak, a significant drop in CT volumes was observed (-42.2%, p < 0.0001), with cancer screening, initial workup, active cancer, and cancer surveillance declining by 81.7%, 54.8%, 30.7%, and 44.7%, respectively (p < 0.0001). In the post-COVID-19 peak period, cancer screening and initial workup CTs did not recover (-11.7%, p = 0.037; -20.0%, p = 0.031), especially in the outpatient setting. CT volumes for active cancer recovered, but inconsistently across hospital types: the QAC experienced a 9.4% decline (p = 0.022) and the UACH a 41.5% increase (p < 0.001). Outpatient CTs recovered after the COVID-19 peak, but with a shift in utilization away from the QAC (-8.7%, p = 0.020) toward the UACH (+13.3%, p = 0.013). Inpatient and ED-based oncologic CTs increased post-peak (+20.0%, p = 0.004 and +33.2%, p = 0.009, respectively). CONCLUSIONS: Cancer imaging was severely impacted during the COVID-19 pandemic. CTs for cancer screening and initial workup did not recover to pre-COVID-19 levels well into 2020, a finding that suggests more patients with advanced cancers may present in the future. A redistribution of imaging utilization away from the QAC and outpatient settings, toward the community hospitals and inpatient setting/ED was observed.


Subject(s)
COVID-19/epidemiology , Neoplasms/diagnostic imaging , Pandemics/statistics & numerical data , Emergency Service, Hospital/statistics & numerical data , Hospitals , Humans , Inpatients/statistics & numerical data , Massachusetts/epidemiology , Outpatients/statistics & numerical data , Retrospective Studies , SARS-CoV-2/pathogenicity , Tomography, X-Ray Computed/methods
3.
Clin Imaging ; 80: 77-82, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1260688

ABSTRACT

INTRODUCTION: COVID-19 has resulted in decreases in absolute imaging volumes, however imaging utilization on a per-patient basis has not been reported. Here we compare per-patient imaging utilization, characterized by imaging studies and work relative value units (wRVUs), in an emergency department (ED) during a COVID-19 surge to the same period in 2019. METHODS: This retrospective study included patients presenting to the ED from April 1-May 1, 2020 and 2019. Patients were stratified into three primary subgroups: all patients (n = 9580, n = 5686), patients presenting with respiratory complaints (n = 1373, n = 2193), and patients presenting without respiratory complaints (n = 8207, n = 3493). The primary outcome was imaging studies/patient and wRVU/patient. Secondary analysis was by disposition and COVID status. Comparisons were via the Wilcoxon rank-sum or Chi-squared tests. RESULTS: The total patients, imaging exams, and wRVUs during the 2020 and 2019 periods were 5686 and 9580 (-41%), 6624 and 8765 (-24%), and 4988 and 7818 (-36%), respectively, and the percentage patients receiving any imaging was 67% and 51%, respectively (p < .0001). In 2020 there was a 170% relative increase in patients presenting with respiratory complaints. In 2020, patients without respiratory complaints generated 24% more wRVU/patient (p < .0001) and 33% more studies/patient (p < .0001), highlighted by 38% more CTs/patient. CONCLUSION: We report increased per-patient imaging utilization in an emergency department during COVID-19, particularly in patients without respiratory complaints.


Subject(s)
COVID-19 , Emergency Service, Hospital , COVID-19/diagnostic imaging , Humans , Retrospective Studies
4.
Am J Emerg Med ; 49: 52-57, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1244700

ABSTRACT

PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports. METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports. RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period. CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.


Subject(s)
Appendicitis/diagnostic imaging , Diverticulitis/diagnostic imaging , Emergency Service, Hospital , Intestinal Obstruction/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Abdomen/diagnostic imaging , COVID-19/epidemiology , Humans , Massachusetts/epidemiology , Natural Language Processing , Retrospective Studies , SARS-CoV-2 , Utilization Review
5.
J Med Internet Res ; 23(5): e26666, 2021 05 25.
Article in English | MEDLINE | ID: covidwho-1190248

ABSTRACT

BACKGROUND: There are many alternatives to direct journal access, such as podcasts, blogs, and news sites, that allow physicians and the general public to stay up to date with medical literature. However, there is a scarcity of literature that investigates the readership characteristics of open-access medical news sites and how these characteristics may have shifted during the COVID-19 pandemic. OBJECTIVE: This study aimed to assess readership and survey data to characterize open-access medical news readership trends related to the COVID-19 pandemic and overall readership trends regarding pandemic-related information delivery. METHODS: Anonymous, aggregate readership data were obtained from 2 Minute Medicine, an open-access, physician-run medical news organization that has published over 8000 original, physician-written texts and visual summaries of new medical research since 2013. In this retrospective observational study, the average number of article views, number of actions (defined as the sum of the number of views, shares, and outbound link clicks), read times, and bounce rates (probability of leaving a page in <30 s) were compared between COVID-19 articles published from January 1 to May 31, 2020 (n=40) and non-COVID-19 articles (n=145) published in the same time period. A voluntary survey was also sent to subscribed 2 Minute Medicine readers to further characterize readership demographics and preferences, which were scored on a Likert scale. RESULTS: COVID-19 articles had a significantly higher median number of views than non-COVID-19 articles (296 vs 110; U=748.5; P<.001). There were no significant differences in average read times (P=.12) or bounce rates (P=.12). Non-COVID-19 articles had a higher median number of actions than COVID-19 articles (2.9 vs 2.5; U=2070.5; P=.02). On a Likert scale of 1 (strongly disagree) to 5 (strongly agree), our survey data revealed that 65.5% (78/119) of readers agreed or strongly agreed that they preferred staying up to date with emerging literature about COVID-19 by using sources such as 2 Minute Medicine instead of journals. A greater proportion of survey respondents also indicated that open-access news sources were one of their primary sources for staying informed (86/120, 71.7%) compared to the proportion who preferred direct journal article access (61/120, 50.8%). The proportion of readers indicating they were reading one or less full-length medical studies a month were lower following introduction to 2 Minute Medicine compared to prior (21/120, 17.5% vs 38/120, 31.6%; P=.005). CONCLUSIONS: The readership significantly increased for one open-access medical literature platform during the pandemic. This reinforces the idea that open-access, physician-written sources of medical news represent an important alternative to direct journal access for readers who want to stay up to date with medical literature.


Subject(s)
Biomedical Research/statistics & numerical data , COVID-19 , Open Access Publishing/statistics & numerical data , Reading , Surveys and Questionnaires , Adult , Aged , Female , Humans , Male , Middle Aged , Pandemics , Retrospective Studies , Young Adult
6.
J Am Coll Radiol ; 18(6): 843-852, 2021 06.
Article in English | MEDLINE | ID: covidwho-1131426

ABSTRACT

Reports are rising of patients with unilateral axillary lymphadenopathy, visible on diverse imaging examinations, after recent coronavirus disease 2019 vaccination. With less than 10% of the US population fully vaccinated, we can prepare now for informed care of patients imaged after recent vaccination. The authors recommend documenting vaccination information (date[s] of vaccination[s], injection site [left or right, arm or thigh], type of vaccine) on intake forms and having this information available to the radiologist at the time of examination interpretation. These recommendations are based on three key factors: the timing and location of the vaccine injection, clinical context, and imaging findings. The authors report isolated unilateral axillary lymphadenopathy (i.e., no imaging findings outside of visible lymphadenopathy), which is ipsilateral to recent (prior 6 weeks) vaccination, as benign with no further imaging indicated. Clinical management is recommended, with ultrasound if clinical concern persists 6 weeks after the final vaccination dose. In the clinical setting to stage a recent cancer diagnosis or assess response to therapy, the authors encourage prompt recommended imaging and vaccination (possibly in the thigh or contralateral arm according to the location of the known cancer). Management in this clinical context of a current cancer diagnosis is tailored to the specific case, ideally with consultation between the oncology treatment team and the radiologist. The aim of these recommendations is to (1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination and (2) avoid further delays in vaccinations and recommended imaging for best patient care during the pandemic.


Subject(s)
COVID-19 , Lymphadenopathy , COVID-19 Vaccines , Humans , Lymphadenopathy/diagnostic imaging , Radiologists , SARS-CoV-2 , Vaccination
7.
J Med Screen ; 28(2): 210-212, 2021 06.
Article in English | MEDLINE | ID: covidwho-1117126

ABSTRACT

The COVID-19 pandemic has led to delays in cancer diagnosis, in part due to postponement of cancer screening. We used Google Trends data to assess public attention to cancer screening during the first peak of the COVID-19 pandemic. Search volume for terms related to established cancer screening tests ("colonoscopy," "mammogram," "lung cancer screening," and "pap smear") showed a marked decrease of up to 76% compared to the pre-pandemic period, a significantly greater drop than for search volume for terms denoting common chronic diseases. Maintaining awareness of cancer screening during future public health crises may decrease delays in cancer diagnosis.


Subject(s)
COVID-19 , Early Detection of Cancer , Information Seeking Behavior , Information Storage and Retrieval/trends , Search Engine/trends , Breast Neoplasms/diagnostic imaging , Colonoscopy/trends , Female , Humans , Lung Neoplasms/diagnosis , Male , Mammography/trends , Search Engine/statistics & numerical data , Vaginal Smears/trends
8.
J Am Coll Radiol ; 18(7): 1000-1008, 2021 07.
Article in English | MEDLINE | ID: covidwho-1091800

ABSTRACT

PURPOSE: Disproportionally high rates of coronavirus disease 2019 (COVID-19) have been noted among communities with limited English proficiency, resulting in an unmet need for improved multilingual care and interpreter services. To enhance multilingual care, the authors created a freely available web application, RadTranslate, that provides multilingual radiology examination instructions. The purpose of this study was to evaluate the implementation of this intervention in radiology. METHODS: The device-agnostic web application leverages artificial intelligence text-to-speech technology to provide standardized, human-like spoken examination instructions in the patient's preferred language. Standardized phrases were collected from a consensus group consisting of technologists, radiologists, and ancillary staff members. RadTranslate was piloted in Spanish for chest radiography performed at a COVID-19 triage outpatient center that served a predominantly Spanish-speaking Latino community. Implementation included a tablet displaying the application in the chest radiography room. Imaging appointment duration was measured and compared between pre- and postimplementation groups. RESULTS: In the 63-day test period after launch, there were 1,267 application uses, with technologists voluntarily switching exclusively to RadTranslate for Spanish-speaking patients. The most used phrases were a general explanation of the examination (30% of total), followed by instructions to disrobe and remove any jewelry (12%). There was no significant difference in imaging appointment duration (11 ± 7 and 12 ± 3 min for standard of care versus RadTranslate, respectively), but variability was significantly lower when RadTranslate was used (P = .003). CONCLUSIONS: Artificial intelligence-aided multilingual audio instructions were successfully integrated into imaging workflows, reducing strain on medical interpreters and variance in throughput and resulting in more reliable average examination length.


Subject(s)
COVID-19 , Limited English Proficiency , Artificial Intelligence , Humans , Pandemics , SARS-CoV-2
9.
J Am Coll Radiol ; 17(11): 1460-1468, 2020 11.
Article in English | MEDLINE | ID: covidwho-1065254

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline.


Subject(s)
COVID-19/epidemiology , Health Services Needs and Demand , Radiology Department, Hospital/organization & administration , Workload , Boston/epidemiology , Forecasting , Humans , Models, Organizational , Pandemics , Planning Techniques , SARS-CoV-2
10.
Acad Radiol ; 28(4): 572-576, 2021 04.
Article in English | MEDLINE | ID: covidwho-1032325

ABSTRACT

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS: We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS: Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION: An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Lung , Radiography, Thoracic , Radiologists , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
11.
medRxiv ; 2020 Sep 18.
Article in English | MEDLINE | ID: covidwho-808139

ABSTRACT

PURPOSE: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. MATERIALS AND METHODS: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. RESULTS: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. CONCLUSIONS: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

12.
Acad Radiol ; 27(10): 1353-1362, 2020 10.
Article in English | MEDLINE | ID: covidwho-713681

ABSTRACT

RATIONALE AND OBJECTIVES: While affiliated imaging centers play an important role in healthcare systems, little is known of how their operations are impacted by the COVID-19 pandemic. Our goal was to investigate imaging volume trends during the pandemic at our large academic hospital compared to the affiliated imaging centers. MATERIALS AND METHODS: This was a descriptive retrospective study of imaging volume from an academic hospital (main hospital campus) and its affiliated imaging centers from January 1 through May 21, 2020. Imaging volume assessment was separated into prestate of emergency (SOE) period (before SOE in Massachusetts on March 10, 2020), "post-SOE" period (time after "nonessential" services closure on March 24, 2020), and "transition" period (between pre-SOE and post-SOE). RESULTS: Imaging volume began to decrease on March 11, 2020, after hospital policy to delay nonessential studies. The average weekly imaging volume during the post-SOE period declined by 54% at the main hospital campus and 64% at the affiliated imaging centers. The rate of imaging volume recovery was slower for affiliated imaging centers (slope = 6.95 for weekdays) compared to main hospital campus (slope = 7.18 for weekdays). CT, radiography, and ultrasound exhibited the lowest volume loss, with weekly volume decrease of 41%, 49%, and 53%, respectively, at the main hospital campus, and 43%, 61%, and 60%, respectively, at affiliated imaging centers. Mammography had the greatest volume loss of 92% at both the main hospital campus and affiliated imaging centers. CONCLUSION: Affiliated imaging center volume decreased to a greater degree than the main hospital campus and showed a slower rate of recovery. Furthermore, the trend in imaging volume and recovery were temporally related to public health announcements and COVID-19 cases.


Subject(s)
Betacoronavirus , Coronavirus Infections , Pandemics , Pneumonia, Viral , COVID-19 , Hospitals , Humans , Massachusetts , Retrospective Studies , SARS-CoV-2 , Urban Health Services
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