Psychological impact of the COVID-19 outbreak in community pharmacists: A longitudinal study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has negatively affected the mental health of frontline health care workers, including pharmacists. OBJECTIVES: The aim of this longitudinal study was to assess the psychological impact of the COVID-19 outbreak in French owner community pharmacists. METHODS: We conducted a postal-based survey to assess the psychological difficulties of the COVID-19 outbreak in French owner community pharmacists based on 3 psychologically validated self-report questionnaires: Perceived Stress Scale (PSS), Impact of Event Scale-revised (IES-R), and Maslach Burnout Inventory. The baseline assessment was during the first sanitary lockdown period and the second one 5 months later. RESULTS: The sample consists of 135 owner community pharmacists. At follow-up, 67 answered the questionnaires (response rate: 49.6%). The mean scores of the PSS and IES-R significantly decreased (P = 0.002). Fifteen pharmacists reported significant posttraumatic stress symptoms (23.1%) at baseline and 11 at follow-up (16.4%, P = 0.02). Age and sex were not significantly associated with persistent posttraumatic stress or burnout symptoms. CONCLUSION: This is the first longitudinal study that showed the psychological impact of owner community pharmacists as health care workers dealing with their community's COVID-19 outbreak. Based on validated self-report questionnaires, stress, posttraumatic stress, and burnout symptoms decreased during follow-up. It is necessary to continue monitoring psychological difficulties for health care workers, especially during consecutive waves of the COVID-19 outbreak.

The SmartOR: a distributed sensor network to improve operating room efficiency.

BACKGROUND: Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system and analyzed data from multiple operating rooms. STUDY DESIGN: OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon. RESULTS: From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification. CONCLUSION: The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.

A robust and non-obtrusive automatic event tracking system for operating room management to improve patient care.

BACKGROUND: Optimization of OR management is a complex problem as each OR has different procedures throughout the day inevitably resulting in scheduling delays, variations in time durations and overall suboptimal performance. There exists a need for a system that automatically tracks procedural progress in real time in the OR. This would allow for efficient monitoring of operating room states and target sources of inefficiency and points of improvement. STUDY DESIGN: We placed three wireless sensors (floor-mounted pressure sensor, ventilator-mounted bellows motion sensor and ambient light detector, and a general room motion detector) in two ORs at our institution and tracked cases 24 h a day for over 4 months. RESULTS: We collected data on 238 total cases (107 laparoscopic cases). A total of 176 turnover times were also captured, and we found that the average turnover time between cases was 35 min while the institutional goal was 30 min. Deeper examination showed that 38 % of laparoscopic cases had some aspect of suboptimal activity with the time between extubation and patient exiting the OR being the biggest contributor (16 %). CONCLUSION: Our automated system allows for robust, wireless real-time OR monitoring as well as data collection and retrospective data analyses. We plan to continue expanding our system and to project the data in real time for all OR personnel to see. At the same time, we plan on adding key pieces of technology such as RFID and other radio-frequency systems to track patients and physicians to further increase efficiency and patient safety.

Esophageal stent migration: Testing few hypothesis with a simplified mathematical model.

Esophageal stent placement has significantly improved the quality of life in patients with malignant as well as benign esophageal obstructing lesions. Despite its early success and rapid adoption, stent migration still occurs in as many as 30% of cases especially with fully covered stents. To date, few models of interaction between the stent and the esophageal wall have been published and these have only focused on the deployment of the stent or the static mechanical stress distribution of the stent material. To elucidate the mechanism behind esophageal stent migration we developed a simplified radially symmetric computational model of esophageal peristalsis and the stent. A thorough review of the literature on esophageal peristalsis was performed and pertinent data were implemented into the model. Similarly, mechanical properties of an existing esophageal stent were used for the stent model. A sensitivity analysis of the parameters of the model enabled identification of the key elements of stent design that influence the degree of stent migration including flares design, stent length as well as longitudinal and radial stiffness. A comparison of the model to the migration rate reported in clinical studies for various types of fully covered stents further verified our model, which can significantly contribute to the development of a more stable esophageal stent with lower rates of migration.

Interrogating a multifactorial model of breast conserving therapy with clinical data.

Most women with early stage breast cancer do not require removal of the entire breast to treat their cancer; instead, up to 70% of women can be effectively and safely treated by breast conserving therapy (BCT) with surgical removal of the tumor only (lumpectomy) followed by radiation treatment of the remaining breast tissue. Unfortunately, the final contour and cosmesis of the treated breast is suboptimal in approximately 30% of patients. The ability to accurately predict breast contour after BCT for breast cancer could significantly improve patient decision-making regarding the choice of surgery for breast cancer. Our overall hypothesis is that the complex interplay among mechanical forces due to gravity, breast tissue constitutive law distribution, inflammation induced by radiotherapy and internal stress generated by the healing process play a dominant role in determining the success or failure of lumpectomy in preserving the breast contour and cosmesis. We have shown here from a first patient study that even in the idealistic situation of excellent cosmetic outcome this problem requires multiscale modeling. We propose a method to decide which component of the model works best for each phase of healing and what parameters should be considered dominant and patient specific. This patient study is part of a clinical trial registered on ClinicalTrial.gov, identifier NCT02310711.

Is obesity an independent prognosis factor in woman breast cancer?

BACKGROUND: Breast cancer and obesity represent important public health issues in most western countries. A number of studies found a negative prognosis effect of obesity or excess of weight in woman breast cancer. However, to date, this issue remains controversial. The objectives of this study were to confirm the prognosis role of obesity on a large cohort of patients and to investigate a potential independent effect. MATERIALS AND METHODS: We constituted a cohort of 14,709 patients who were recruited and treated at the Curie Institute (Paris) from 1981 to 1999. These patients were followed prospectively for a first unilateral invasive breast cancer without distant metastasis. Obesity was defined by a Body Mass Index (BMI) above 30 kg/m(2) according to the World Health Organization recommendations. RESULTS: Obese patients (8%) presented more extended tumors at diagnosis time suggesting a delayed breast cancer diagnosis. However, obesity appeared as a negative prognosis factor for several events in respectively univariate and multivariate survival analysis: metastasis recurrence (HR = 1.32[1.19-1.48]; HR = 1.12[1.00-1.26]), disease free interval (1.20[1.08-1.32]; 1.10[0.99-1.22]), overall survival (1.43[1.28-1.60]; 1.12[0.99-1.25]) and second primary cancer outcome (1.57[1.19-2.07]; 1.43[1.09-1.89]). Even if obese patients presented more advanced tumors at diagnosis time, multivariate analysis showed that there was a relevant independent effect. Other BMI codings, distinguishing overweight patients or using BMI as a continuous variable, showed a consistent correlation between BMI's value and prognosis effect. Interaction analysis revealed a more important obesity effect in the presence of tumor estrogen receptors and among limited extent tumors. CONCLUSIONS: This survey confirms the prognosis role of obesity on one of the largest cohort by investigating several prognosis events. While independent obesity effect linked to hormonal disorders appeared consistent as obesity's mechanism, we stress that obesity prognosis effect was also related to breast cancer presentation at diagnosis time.