Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Personalising Neoadjuvant Chemotherapy for Locally Advanced Colon Cancer: Protocols for the International Phase III FOxTROT2 and FOxTROT3 Randomised-Controlled Trials

Aim FOxTROT1 established a new standard of care for managing locally advanced colon cancer (CC) with neoadjuvant chemotherapy (NAC). Six weeks of neoadjuvant oxaliplatin and fluoropyrimidine (OxFp) chemotherapy was associated with greater 2-year disease-free survival (DFS) when compared to proceeding straight to surgery (STS). There is now a need to refine the use of NAC and identify those most likely to benefit. FOxTROT2 will investigate NAC in older adults and those with frailty. FOxTROT3 will assess whether intensified triplet NAC provides additional benefits over OxFp. Methods FOxTROT2 and FOxTROT3 are international, open-label, phase III randomised-controlled trials. Eligible patients will be identified by the multidisciplinary team. Patient age, frailty and comorbidities will be considered to guide trial entry. Participants will be randomised 2:1 to the intervention or control arm: six weeks of dose-adapted neoadjuvant OxFp vs. STS in FOxTROT2 and six weeks of neoadjuvant modified oxaliplatin, 5FU and irinotecan (mFOLFOXIRI) vs. OxFp in FOxTROT3. The primary endpoint in FOxTROT2 is 3-year DFS. In FOxTROT3, tumour regression grade and 3-year DFS are co-primary endpoints. Discussion FOxTROT2 and FOxTROT3 will establish the FOxTROT platform, a key part of our long-term strategy to develop neoadjuvant treatments for CC. FOxTROT2 will investigate NAC in a population under-represented in FOxTROT1 and wider research. FOxTROT3 will assess whether it is possible to induce greater early tumour responses and whether this translates to superior long-term outcomes. Looking ahead, the FOxTROT platform will facilitate further trial comparisons and extensive translational research to optimise the use of NAC in CC

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment : a review

Quantitative image analysis techniques have gained an undeniable role in several fields of research during the last decade. In the field of biological wastewater treatment (WWT) processes, several computer applications have been developed for monitoring microbial entities, either as individual cells or in different types of aggregates. New descriptors have been defined that are more reliable, objective, and useful than the subjective and time-consuming parameters classically used to monitor biological WWT processes. Examples of this application include the objective prediction of filamentous bulking, known to be one of the most problematic phenomena occurring in activated sludge technology. It also demonstrated its usefulness in classifying protozoa and metazoa populations. In high-rate anaerobic processes, based on granular sludge, aggregation times and fragmentation phenomena could be detected during critical events, e.g., toxic and organic overloads. Currently, the major efforts and needs are in the development of quantitative image analysis techniques focusing on its application coupled with stained samples, either by classical or fluorescent-based techniques. The use of quantitative morphological parameters in process control and online applications is also being investigated. This work reviews the major advances of quantitative image analysis applied to biological WWT processes.The authors acknowledge the financial support to the project PTDC/EBB-EBI/103147/2008 and the grant SFRH/BPD/48962/2008 provided by Fundacao para a Ciencia e Tecnologia (Portugal)

Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries

Background: The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs. Methods: First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals' SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score. Findings: In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from 119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1–84·9), which varied between HIC (88·5 [89·0–88·0]), MIC (81·8 [82·5–81·1]), and LIC (66·8 [64·9–68·7]) settings. In the third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which 625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-effects model, a 10-point increase in SPI corresponded to a 3·6% (95% CI 3·0–4·1; p<0·0001) increase in SVR. This was consistent in HIC (4·8% [4·1–5·5]; p<0·0001), MIC (2·8 [2·0–3·7]; p<0·0001), and LIC (3·8 [1·3–6·7%]; p<0·0001) settings. Interpretation: The SPI contains 23 indicators that are globally applicable, relevant across different system stressors, vary at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs. Funding: National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery, NIHR Academy, Association of Coloproctology of Great Britain and Ireland, Bowel Research UK, British Association of Surgical Oncology, British Gynaecological Cancer Society, and Medtronic

COVID-19-related absence among surgeons: development of an international surgical workforce prediction model

Background: During the initial COVID-19 outbreak up to 28.4 million elective operations were cancelled worldwide, in part owing to concerns that it would be unsustainable to maintain elective surgery capacity because of COVID-19-related surgeon absence. Although many hospitals are now recovering, surgical teams need strategies to prepare for future outbreaks. This study aimed to develop a framework to predict elective surgery capacity during future COVID-19 outbreaks. Methods: An international cross-sectional study determined real-world COVID-19-related absence rates among surgeons. COVID-19-related absences included sickness, self-isolation, shielding, and caring for family. To estimate elective surgical capacity during future outbreaks, an expert elicitation study was undertaken with senior surgeons to determine the minimum surgical staff required to provide surgical services while maintaining a range of elective surgery volumes (0, 25, 50 or 75 per cent). Results Based on data from 364 hospitals across 65 countries, the COVID-19-related absence rate during the initial 6 weeks of the outbreak ranged from 20.5 to 24.7 per cent (mean average fortnightly). In weeks 7–12, this decreased to 9.2–13.8 per cent. At all times during the COVID-19 outbreak there was predicted to be sufficient surgical staff available to maintain at least 75 per cent of regular elective surgical volume. Overall, there was predicted capacity for surgeon redeployment to support the wider hospital response to COVID-19. Conclusion: This framework will inform elective surgical service planning during future COVID-19 outbreaks. In most settings, surgeon absence is unlikely to be the factor limiting elective surgery capacity

Right Iliac Fossa Pain Treatment (RIFT) Study: protocol for an international, multicentre, prospective observational study

Introduction Patients presenting with right iliac fossa (RIF) pain are a common challenge for acute general surgical services. Given the range of potential pathologies, RIF pain creates diagnostic uncertainty and there is subsequent variation in investigation and management. Appendicitis is a diagnosis which must be considered in all patients with RIF pain; however, over a fifth of patients undergoing appendicectomy, in the UK, have been proven to have a histologically normal appendix (negative appendicectomy). The primary aim of this study is to determine the contemporary negative appendicectomy rate. The study’s secondary aims are to determine the rate of laparoscopy for appendicitis and to validate the Appendicitis Inflammatory Response (AIR) and Alvarado prediction scores. Methods and analysis This multicentre, international prospective observational study will include all patients referred to surgical specialists with either RIF pain or suspected appendicitis. Consecutive patients presenting within 2-week long data collection periods will be included. Centres will be invited to participate in up to four data collection periods between February and August 2017. Data will be captured using a secure online data management system. A centre survey will profile local policy and service delivery for management of RIF pain. Ethics and dissemination Research ethics are not required for this study in the UK, as determined using the National Research Ethics Service decision tool. This study will be registered as a clinical audit in participating UK centres. National leads in countries outside the UK will oversee appropriate registration and study approval, which may include completing full ethical review. The study will be disseminated by trainee-led research collaboratives and through social media. Peer-reviewed publications will be published under corporate authorship including ‘RIFT Study Group’ and ‘West Midlands Research Collaborative’