Summary Convalescent plasma can provide passive immunity during viral outbreaks, but the benefit is uncertain for the treatment of novel coronavirus disease 2019 (COVID‐19). Our goal is to assess the efficacy of COVID‐19 convalescent plasma (CCP). In all, 526 hospitalized patients with laboratory‐confirmed SARS‐CoV‐2 at an academic health system were analyzed. Among them, 263 patients received CCP and were compared to 263 matched controls with standard treatment. The primary outcome was 28‐day mortality with a subanalysis at 7 and 14 days. No statistical difference in 28‐day mortality was seen in CCP cases (25·5%) compared to controls (27%, P = 0·06). Seven‐day mortality was statistically better for CCP cases (9·1%) than controls (19·8%, P < 0·001) and continued at 14 days (14·8% vs. 23·6%, P = 0·01). After 72 h, CCP transfusion resulted in transitioning from nasal cannula to room air (median 4 days vs. 1 day, P = 0·02). The length of stay was longer in CCP cases than controls (14·3 days vs. 11·4 days, P < 0·001). Patients with COVID‐19 who received CCP had a decreased risk of death at 7 and 14 days, but not 28 days after transfusion. To date, this is the largest study demonstrating a mortality benefit for the use of CCP in patients with COVID‐19 compared to matched controls.

No abstract

Objectives:The increase in patient safety reporting systems has led to the challenge of effectively analyzing these data to identify and mitigate safety hazards. Patient safety analysts, who manage reports, may be ill-equipped to make sense of report data. We sought to understand the cognitive needs of patient safety analysts as they work to leverage patient safety reports to mitigate risk and improve patient care.Methods: Semistructured interviews were conducted with 21 analysts, from 11 hospitals across 3 healthcare systems. Data were parsed into utterances and coded to extract major themes.Results: From 21 interviews, 516 unique utterances were identified and categorized into the following 4 stages of data analysis: input (15.1% of utterances), transformation (14.1%), extrapolation (30%), and output (14%). Input utterances centered on the source (35.9% of inputs) and preprocessing of data. Transformation utterances centered on recategorizing patient safety events (57.5% of transformations) or integrating external data sources (42.5% of transformations). The focus of interviews was on extrapolation and trending data (56.1% of extrapolations); alarmingly, 16.1% of trend utterances explicitly mentioned a reliance on memory. The output was either a report (56.9% of outputs) or an action (43.1% of outputs). Conclusions:Major gaps in the analysis of patient safety report data were identified. Despite software to support reporting, many reports come from other sources. Transforming data are burdensome because of recategorization of events and integration with other data sources, processes that can be automated. Surprisingly, trend identification was mostly based on patient analyst memory, highlighting a need for new tools that better support analysts.

A radiologist's search pattern can directly influence patient management. A missed finding is a missed opportunity for intervention. Multiple studies have attempted to describe and quantify search patterns but have mainly focused on chest radiographs and chest CTs. Here, we describe and quantify the visual search patterns of 17 radiologists as they scroll through 6 CTs of the abdomen and pelvis. Search pattern tracings varied among individuals and remained relatively consistent per individual between cases. Attendings and trainees had similar eye metric statistics with respect to time to first fixation (TTFF), number of fixations in the region of interest (ROI), fixation duration in ROI, mean saccadic amplitude, or total number of fixations. Attendings had fewer numbers of fixations per second versus trainees (p < 0.001), suggesting efficiency due to expertise. In those cases that were accurately interpreted, TTFF was shorter (p = 0.04), the number of fixations per second and number of fixations in ROI were higher (p = 0.04, p = 0.02, respectively), and fixation duration in ROI was increased (p = 0.02). We subsequently categorized radiologists as Bscanners^or Bdrillers^by both qualitative and quantitative methods and found no differences in accuracy with most radiologists being categorized as Bdrillers.^This study describes visual search patterns of radiologists in interpretation of CTs of the abdomen and pelvis to better approach future endeavors in determining the effects of manipulations such as fatigue, interruptions, and computer-aided detection.