scholarly journals SP4.2.7 Outcomes following reorganisation of colorectal cancer pathway using FIT testing and a locally developed risk stratification score in a busy District General Hospital in United Kingdom during the COVID-19 pandemic

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Akil Gani ◽  
Vardhini Vijay ◽  
Carol Allgrove ◽  
Shanmuga Vivekanandan
Keyword(s):  
Colorectal Cancer ◽  
Risk Stratification ◽  
Diagnostic Yield ◽  
District General Hospital ◽  
Clinical Settings ◽  
Test Results ◽  
National Guidance ◽  
Fit Testing ◽  
The Impact ◽  
Risk Stratification Score

Abstract Aims/Introduction Limited availability of endoscopy and CT colonography led to significant pressures on the colorectal cancer pathway during the COVID-19 pandemic. Risk stratification was necessary to determine the order in which patients on the tracking list could access diagnostics. Based on national guidance, patient symptoms and FIT test results, we used a locally developed risk stratification score (RSS). The aim of this study was to evaluate the impact the RSS had on patient diagnoses over a period of 8 months Methodology A prospectively maintained database was used to assess the outcomes. Based on their symptoms, patients on the colorectal 2ww pathway were invited to provide FIT samples if eligible. Diagnostics were prioritised using RSS. Results were analysed to assess how the cancer diagnostic yield during the pandemic compared to a similar period the year before. Results There were 1133 patients referred on the colorectal 2ww pathway between May - December 2020. 884 of these patients had FIT testing and 249 were not eligible. Of the 69 colorectal cancers diagnosed, 25 were in the FIT group and 44 in those not eligible. The RSS stratified to high or indeterminate risk in the non-FIT group was 31 and 23 in the FIT group. Compared to the previous year’s number of 59 patients diagnosed between May - December 2019, no statistical difference was found. Conclusion Our study shows that FIT testing and RSS led to an equivalent pick up rate of colorectal cancer and can be utilised in clinical settings with limited resources.

scholarly journals Recovery, restoration, and risk: a cross-sectional survey of the impact of COVID-19 on GPs in the first UK city to lock down

BJGP Open ◽  
2020 ◽  
pp. BJGPO.2020.0151
Author(s):  
Namrata Trivedi ◽  
Vivek Trivedi ◽  
Arumugam Moorthy ◽  
Hina Trivedi
Keyword(s):  
Mental Health ◽  
Risk Stratification ◽  
Response Scale ◽  
Cross Sectional Survey ◽  
Work Patterns ◽  
Cross Sectional ◽  
Government Interventions ◽  
National Guidance ◽  
The Impact ◽  
Second Wave

BackgroundThe COVID-19 pandemic has impacted GPs immensely. Work patterns have changed, risk stratification has been proposed, and the mental health of clinicians has been adversely affected. The COVID-19 prevalence among GPs is unknown. This study focuses on assessing the impact of COVID-19 on GPs in Leicestershire, the first UK city to lock down locally.AimThis survey assessed the prevalence of COVID-19 in GPs and explored GP work patterns in comparison with national guidance. It used a validated perceived stress tool to evaluate the impact of COVID-19 on GP stress perception.Design & settingThe cross-sectional retrospective survey was sent to all the GPs in Leicestershire.MethodA total of 111 GPs in Leicestershire took part voluntarily in an anonymised questionnaire-based study. A 29-item survey using SmartSurvey software was designed with multiple choice and Likert response scale questions.ResultsCOVID-19 prevalence in GPs in Leicestershire was 8.1%; 70.3% of GPs were of Black, Asian, and minority ethnic (BAME) origin; 91.9% of GPs had undergone risk stratification; and 79.3% of GPs felt supported by their practice, but only 59.5% felt supported with mental health. GPs described feeling more stressed during the COVID-19 pandemic than they had been previously.ConclusionThis is the first study evaluating COVID-19 prevalence among GPs in Leicestershire. Despite government interventions, GPs felt less supported with their mental health compared with pre-COVID-19 times. Thus, the NHS in England should focus on GP stress and wellbeing as they work towards the restoration and recovery of primary care while battling the second wave.

scholarly journals TP10.1.6Short term outcomes of emergency colorectal cancer resections during COVID-19 pandemic;Experience of a district general hospital

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Aloka Suwanna Danwaththa Liyanage ◽  
Krishnan Gokul ◽  
Bheemanakone Harish Babu
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Primary Outcome ◽  
Admission Rate ◽  
District General Hospital ◽  
Infection Rates ◽  
Readmission Rates ◽  
Additional Risk ◽  
The Impact ◽  
Multicentre Cohort

Abstract Aim Surgical and oncological outcomes of emergency colorectal cancer (CRC) surgeries are poor compared to those of elective resections. SARS-Cov-2 ambience has added an additional risk on these patients during the course of their perioperative journey. The impact of peri-operative SARS-CoV-2 infection on the outcomes of these unique patients is still under scrutiny. We aimed to analyse a cohort of patients that underwent emergency CRC surgeries during pandemic in our setting. Methods We analysed a prospectively maintained database of all patients who underwent emergency CRC surgeries since 11th of March 2020 to 31st of December 2020. Primary outcome measures were Length of stay (LoS) and 90 day mortality. Secondary outcomes were post-operative complications, SARS-CoV-2 infection rates and 30 day readmission rates. Results We performed a total of 18 emergency CRC surgeries (Male: Female 1:1). Median age was 76.5 years (Range 39-89 years). Median LoS was 13 days (Range 5-110 days). 90 day mortality was 17% (3/18) and of the two patients who died (2/3), their cause of death was COVID-19 related. 4 (22%) patients had peri-operative SARS-CoV-2 infections. 30 day re-admission rate was 16% (3/18). 78% (14/18) of the patients had their cancer resected. 61% (11/18) of the procedures were palliative. Conclusions Peri-operative SARS-CoV-2 infection may have add on effect on the morbidity and mortality on patients undergoing emergency CRC surgeries. Data from large scale multicentre cohort studies would provide more insight in to this.

scholarly journals Impact on quantitative fit test results after application of prophylactic hydrocolloid dressing under N95 respirators

2021 ◽  
pp. 1-16
Author(s):  
Irene Ng ◽  
Benjamin Kave ◽  
Fiona Begg ◽  
Sarah Sage ◽  
Reny Segal ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Respiratory Protection ◽  
Test Results ◽  
Pass Rates ◽  
Hydrocolloid Dressing ◽  
Fit Testing ◽  
Pressure Injury ◽  
N95 Respirators ◽  
The Impact

Abstract Objective Discomfort and device-related pressure injury (DRPI) caused by N95 filtering facepiece respirators (FFRs) are common. The use of prophylactic hydrocolloid dressings is one of the strategies that may improve comfort and reduce DRPI. In this study, we investigated the impact of these dressings on N95 respirator fit. Methods We performed a repeat quantitative fit testing through the Respiratory Protection Program on 134 healthcare workers (HCWs), who applied hydrocolloid dressings on the bridge of their nose under the N95 FFRs that they passed the initial fit test but reported discomfort with. Results We found that the fit test pass rates, with the hydrocolloid dressings in place, for the semi-rigid cup style (3MTM 1860), the vertical flat-fold style (BYD), the duckbill style (BSN medical ProShield® and Halyard Fluidshield*), and the three-panel flat-fold style (3MTM Aura) N95 FFRs were 94% (108/115), 85% (44/52), 81% (87/108) and 100% (3/3) respectively. There was a statistically significant reduction in the overall fit factors for both the vertical flat-fold and duckbill type N95 respirators, after the application of hydrocolloid dressings. Conclusions Hydrocolloid dressings are likely to disturb the mask seal for non-rigid style N95 FFRs, in particular, the vertical flat-fold style and the duckbill style N95 FFRs. Given the risk of mask seal disturbance of N95 respirators as shown in this study, we advocate that any HCW requiring the use of prophylactic dressings should undergo repeat quantitative fit testing with the dressing in place prior to using the dressing and mask in combination.

scholarly journals Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA

2020 ◽  
Author(s):  
Igor Burstyn ◽  
Neal D. Goldstein ◽  
Paul Gustafson
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Clinical Settings ◽  
Test Results ◽  
Bias Analysis ◽  
Laboratory Test Results ◽  
Misclassification Errors ◽  
The Impact

The aim of our work was to better understand misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves. We examined publically available time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, about the sensitivity and specificity of the PCR-based diagnostic test. Data originated from Alberta, Canada (available on 3/28/2020) and city of Philadelphia, USA (available on 3/31/2020). Our analysis revealed that the data were compatible with near-perfect specificity but it was challenging to gain information about sensitivity (prior and posterior largely overlapped). We applied these insights to uncertainty/bias analysis of epidemic curves into jurisdictions under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the observed and adjusted epidemic curves likely fall within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves, a task for which the Bayesian method we presented is well-suited.

scholarly journals Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA

2020 ◽  
Author(s):  
Igor Burstyn ◽  
Neal D. Goldstein ◽  
Paul Gustafson
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Clinical Settings ◽  
Test Results ◽  
Bias Analysis ◽  
Laboratory Test Results ◽  
Misclassification Errors ◽  
The Impact

AbstractThe aim of our work was to better understand misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves. We examined publically available time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, about the sensitivity and specificity of the PCR-based diagnostic test. Data originated from Alberta, Canada (available on 3/28/2020) and city of Philadelphia, USA (available on 3/31/2020). Our analysis revealed that the data were compatible with near-perfect specificity but it was challenging to gain information about sensitivity (prior and posterior largely overlapped). We applied these insights to uncertainty/bias analysis of epidemic curves into jurisdictions under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the observed and adjusted epidemic curves likely fall within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves, a task for which the Bayesian method we presented is well-suited.

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