The Perioperative Patient Experience During COVID-19.

BACKGROUND: Access to elective surgical procedures has been impacted by the COVID-19 pandemic. METHODS: We sought to understand the patient experience by developing and distributing an anonymous online survey to those who underwent non-emergency surgery at a large academic tertiary medical center between March and October 2020. RESULTS: The survey was completed by 184 patients; the majority were white (84%), female (74.6%), and ranged from 18 to 88 years old. Patients were likely unaware of case delay as only 23.6% reported a delay, 82% of which agreed with that decision. Conversely, 44% felt that the delay negatively impacted their quality of life. Overall, 82.7% of patients indicated high satisfaction with their care. African American patients more often indicated a "neutral" vs "satisfactory" hospital experience (P < .05) and considered postponing their surgery (P < .01). Interestingly, younger patients (<60) were more likely than older (≥60) patients to note anxiety associated with having surgery during the pandemic (P < .01), feeling unprepared for discharge (P < .02), not being allowed visitors (P < .02), and learning about the spread of COVID-19 from health care providers (P < .02). DISCUSSION: These results suggest that patients are resilient and accepting of changes to health care delivery during the current pandemic; however, certain patient populations may have higher levels of anxiety which could be addressed by their care provider. These findings can help inform and guide ongoing and future health care delivery adaptations in response to care disruptions.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 â€‹mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p â€‹< â€‹0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p â€‹< â€‹0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p â€‹= â€‹0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

A Multifaceted Approach to Opioid Education, Prescribing, and Disposal for Patients with Breast Cancer Undergoing Surgery.

BACKGROUND: Standardized prescribing practices are recommended to decrease opioid abuse, however, data regarding the handling and disposal of leftover narcotics are lacking. This quality improvement project and analysis evaluated implementation of standardized prescribing, opioid education, and a narcotic disposal system. METHODS: This initiative was implemented over a 1-y period among patients who underwent breast surgery. The project included the following: 1) implementation of standardized prescribing, 2) voluntary and anonymous survey analysis, and 3) preoperative education regarding risks of opioids, charcoal disposal bag distribution, and follow-up survey to assess use and use of intervention. RESULTS: Preintervention surveys were completed by 53 patients, and 60% (n = 32) underwent lumpectomy. Narcotic prescriptions were filled by 90%; median number of pills taken was 3 (range 0-24), however 93% felt that a non-narcotic was more effective. Eighty three percentage of patients had unused pills, and 58% kept these pills in an unlocked cabinet. Postintervention surveys were completed by 66 patients, and 48% (n = 32) underwent lumpectomy. Narcotic prescriptions were filled by 88%, median number of pills taken was 4 (range 0-40), and 89% of patients had pills leftover. Sixty seven percentage of patients found the education handout useful and charcoal bag use was reported by 37% (n = 17). The median postoperative pain control satisfaction score was 4.5 (5-point Likert scale, 1 = very dissatisfied, 5 = very satisfied) on both preintervention and postintervention surveys. CONCLUSIONS: This study, which included standardized prescribing parameters, opioid education, and implementation of a disposal method, was found to be feasible, beneficial, and did not compromise postoperative pain control.