Live Mammographic Screening Interpretation Versus Offline Same-Day Screening Interpretation at a Tertiary Cancer Center.

OBJECTIVES: The aim of this study was to compare screening mammography performance metrics for immediate (live) interpretation versus offline interpretation at a cancer center. METHODS: An institutional review board-approved, retrospective comparison of screening mammography metrics at a cancer center for January 1, 2018, to December 31, 2019 (live period), and September 1, 2020, to March 31, 2022 (offline period), was performed. Before July 2020, screening examinations were interpreted while patients waited (live period), and diagnostic workup was performed concurrently. After the coronavirus disease 2019 shutdown from March to mid-June 2020, offline same-day interpretation was instituted. Patients with abnormal screening results returned for separate diagnostic evaluation. Screening metrics of positive predictive value 1 (PPV1), cancer detection rate (CDR), and abnormal interpretation rate (AIR) were compared for 17 radiologists who interpreted during both periods. Statistical significance was assessed using χ2 analysis. RESULTS: In the live period, there were 7,105 screenings, 635 recalls, and 51 screen-detected cancers. In the offline period, there were 7,512 screenings, 586 recalls, and 47 screen-detected cancers. Comparison of live screening metrics versus offline metrics produced the following results: AIR, 8.9% (635 of 7,105) versus 7.8% (586 of 7,512) (P = .01); PPV1, 8.0% (51 of 635) versus 8.0% (47 of 586); and CDR, 7.2/1,000 versus 6.3/1,000 (P = .50). When grouped by >10% AIR or <10% AIR for the live period, the >10% AIR group showed a significant decrease in AIR for offline interpretation (from 12.7% to 9.7%, P < .001), whereas the <10% AIR group showed no significant change (from 7.4% to 6.7%, P = .17). CONCLUSIONS: Conversion to offline screening interpretation from immediate interpretation at a cancer center was associated with lower AIR and similar CDR and PPV1. This effect was seen largely in radiologists with AIR > 10% in the live setting.

Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction.

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. RESULTS: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24-3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71-1.82). CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

Exacerbation of Inequities in Use of Diagnostic Radiology During the Early Stages of Reopening After COVID-19.

OBJECTIVE: Assess diagnostic radiology examination utilization and associated social determinants of health during the early stages of reopening after state-mandated shutdown of nonurgent services because of coronavirus disease 2019 (COVID-19). METHODS: This institutional review board-approved, retrospective study assessed all patients with diagnostic radiology examinations performed at an academic medical center with eight affiliated outpatient facilities before (January 1, 2020, to March 8, 2020) and after (June 7, 2020, to July 15, 2020) the COVID-19 shutdown. Examinations during the shut down (March 9, 2020, to June 6, 2020) were excluded. Patient-specific factors (eg, race, ethnicity), imaging modalities, and care settings were extracted from the Research Data Warehouse. Primary outcome was the number of diagnostic radiology examinations per day compared pre- and post-COVID-19 shutdown. Univariate analysis and multivariable logistic regression determined features associated with completing an examination. RESULTS: Despite resumption of nonurgent services, marked decrease in radiology examination utilization persisted in all care settings post-COVID-19 shutdown (869 examinations per day preshutdown [59,080 examinations in 68 days] versus 502 examinations per day postshutdown [19,594 examinations in 39 days]), with more significantly decreased odds ratios for having examinations in inpatient and outpatient settings versus in the emergency department. Inequities worsened, with patients from communities with high rates of poverty, unemployment, and chronic disease having significantly lower odds of undergoing radiology examinations post-COVID-19 shutdown. Patients of Asian race and Hispanic ethnicity had significantly lower odds ratios for having examinations post-COVID-19 shutdown compared with White and non-Hispanic patients, respectively. DISCUSSION: The COVID-19 pandemic has exacerbated known pre-existing inequities in diagnostic radiology utilization. Resources should be allocated to address subgroups of patients who may be less likely to receive necessary diagnostic radiology examinations, potentially leading to compromised patient safety and quality of care.

Radiology Workload Changes During the COVID-19 Pandemic: Implications for Staff Redeployment.

RATIONALE AND OBJECTIVES: Quantify changes in total and by-subspecialty radiology workload due to deferring nonurgent services during the initial COVID-19 pandemic, and describe operational strategies implemented due to shifts in priority. MATERIALS AND METHODS: This retrospective, Institutional Review Board-exempt, study was performed between February 3, 2020 and April 19, 2020 at a large academic medical center. During March 9-15 (intervention period), nonurgent outpatient service deferments began. Five-week periods pre- (baseline) and postintervention (COVID) were defined. Primary outcomes were radiology volume (reports per day) overall and in 11 subspecialty divisions. Linear regression assessed relationship between baseline vs. COVID volumes stratified by division. Secondary outcomes included changes in relative value units (RVUs), inpatient and outpatient volumes. RESULTS: There were 62,791 baseline reports vs. 23,369 during COVID; a 60% overall precipitous volume decrease (p < 0.001). Mean volume decrease pre- and during-COVID was significant (p < 0.001) amongst all individual divisions. Mean volume decrease differed amongst divisions: Interventional Radiology experienced least disruption (29% volume decrease), 7 divisions experienced 40%-60% decreases, and Musculoskeletal, Breast, and Cardiovascular imaging experienced >75% volume decrease. Total RVUs decreased 60% (71,186 baseline; 28,476 COVID). Both outpatient and inpatient report volumes decreased; 72% (41,115 baseline; 11,326 COVID) and 43% (12,626 baseline vs. 6,845 COVID), respectively. In labor pool tracking data, 21.8% (162/744) total radiology employees were reassigned to other hospital duties during the intervention period. CONCLUSION: Precipitous radiology workload reductions impacted subspecialty divisions with marked variation. Data-driven operational decisions during COVID-19 assisted workflow and staffing assignment changes. Ongoing adjustments will be needed as healthcare systems transition operations to a "new normal."

Radiologist Reporting and Operational Management for Patients With Suspected COVID-19.

PURPOSE: The aim of this study was to evaluate the adoption and outcomes of locally designed reporting guidelines for patients with possible coronavirus disease 2019 (COVID-19). METHODS: A departmental guideline was developed for radiologists that specified reporting terminology and required communication for patients with imaging findings suggestive of COVID-19, on the basis of patient test status and imaging indication. In this retrospective study, radiology reports completed from March 1, 2020, to May 3, 2020, that mentioned COVID-19 were reviewed. Reports were divided into patients with known COVID-19, patients with "suspected" COVID-19 (having an order indication of respiratory or infectious signs or symptoms), and "unsuspected patients" (other order indications, eg, trauma or non-chest pain). The primary outcome was the percentage of COVID-19 reports using recommended terminology; the secondary outcome was percentages of suspected and unsuspected patients diagnosed with COVID-19. Relationships between categorical variables were assessed using the Fisher exact test. RESULTS: Among 77,400 total reports, 1,083 suggested COVID-19 on the basis of imaging findings; 774 of COVID-19 reports (71%) used recommended terminology. Of 574 patients without known COVID-19 at the time of interpretation, 345 (60%) were eventually diagnosed with COVID-19, including 61% (315 of 516) of suspected and 52% (30 of 58) of unsuspected patients. Nearly all unsuspected patients (46 of 58) were identified on CT. CONCLUSIONS: Radiologists rapidly adopted recommended reporting terminology for patients with suspected COVID-19. The majority of patients for whom radiologists raised concern for COVID-19 were subsequently diagnosed with the disease, including the majority of clinically unsuspected patients. Using unambiguous terminology and timely notification about previously unsuspected patients will become increasingly critical to facilitate COVID-19 testing and contact tracing as states begin to lift restrictions.