Mortality and pulmonary complications in patients undergoing surgery with perioperative sars-cov-2 infection: An international cohort study

Background The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (740%) had emergency surgery and 280 (248%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (261%) patients. 30-day mortality was 238% (268 of 1128). Pulmonary complications occurred in 577 (512%) of 1128 patients; 30-day mortality in these patients was 380% (219 of 577), accounting for 817% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 175 [95% CI 128-240], p<00001), age 70 years or older versus younger than 70 years (230 [165-322], p<00001), American Society of Anesthesiologists grades 3-5 versus grades 1-2 (235 [157-353], p<00001), malignant versus benign or obstetric diagnosis (155 [101-239], p=0046), emergency versus elective surgery (167 [106-263], p=0026), and major versus minor surgery (152 [101-231], p=0047). Interpretation Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Surgical site infections after emergency hernia repair: substudy from the Management of Acutely Symptomatic Hernia (MASH) study

Introduction Acutely symptomatic abdominal wall and groin hernias (ASH) are a common acute surgical presentation. There are limited data to guide decisions related to surgical repair technique and use of antibiotics, which can be driven by increased risk of surgical site infection (SSI) in this group. This study aims to report rates of SSI following ASH repair and explore the use of patient-reported outcome measure reporting in this setting. Methods An 18-week, UK-based, multicentre prospective cohort study (NCT04197271) recruited adults with ASH. This study reports operatively managed patients. Data on patient characteristics, inpatient management, quality of life, complications, and wound healing (Bluebelle score) were collected. Descriptive analyses were performed to estimate event rates of SSI and regression analysis explored the relationship between Bluebelle scores and SSI. The 30 and 90-day follow-up visits assessed complications and quality of life. Results The MASH study recruited 273 patients, of whom 218 were eligible for this study, 87.2 per cent who underwent open repair. Mesh was used in 123 patients (50.8 per cent). Pre- and postoperative antibiotics were given in 163 (67.4 per cent) and 28 (11.5 per cent) patients respectively. There were 26 reported SSIs (11.9 per cent). Increased BMI, incisional, femoral, and umbilical hernia were associated with higher rates of SSI (P = 0.006). In 238 patients, there was a difference in healthy utility values at 90 days between patients with and without SSI (P = 0.025). Also, when analysing 191 patients with Bluebelle scores, those who developed an SSI had higher Bluebelle values (P < 0.001). Conclusion SSI is frequent in repair of acutely symptomatic hernia and correlates with BMI and site of hernia

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Machine learning product key performance indicators and alignment to model evaluation

Machine Learning has seen amazing progress the past years with increasing commercial use from industries across the business spectrum. Businesses strive for alignment of vision and mission statement to the actual products they sell. For that reason tools like the Key Performance Indicators exist in order to monitor such progress. Nevertheless, products that embed a machine learning component are being optimized with other objective functions and are being evaluated in a vacuum with specific performance evaluation metrics that often have nothing to do with the business vision. In this position paper, we highlight this gap in different instances of the machine learning life cycle, explore and critically evaluate the current available solutions in the literature and introduce Key Performance Indicators in the machine learning development process. The paper also discusses representative machine learning KPIs in the development and deployment process. © 2021 IEEE

Analytics and Blockchain for Data Integrity in the Pharmaceuticals Industry

The data quantity explosion that we witnessed during the last two decades has lead industrial organizations to exploit this sheer amount of data for tasks that previously would seem impossible. However, larger data volumes, draw together a series of drawbacks that affect data quality and integrity and this becomes more evident in government supervised industrial settings. In many of such cases, public authorities have defined sets of principles (e.g., ALCOA+) regarding data management that industrial organizations must abide to, and either deliberate or not violation of these standards most often comes with severe legal consequences. As a matter of fact, in an effort to follow as much as humanly possible to such principles, pharmaceuticals industries invest heavily in resources to maintain high quality standards in their data urging for automated methods for calculating, monitoring and predicting compliance.Also, in complex manufacturing and production lines, data analytics provide means for real-time and continuous monitoring of large numbers of sensor variables and categorical or numerical values where higher order conclusions can be derived and taken into account when business process optimizations are considered. In this work we present an easy-to-use integrated platform for real-time raw sensor data monitoring and pre-processing in pharmaceuticals production lines combining blockchain storage for data integrity and deep-learning capabilities for data analytics. Additionally, the platform is able to calculate, monitor and predict compliance to the ALCOA+ set of principles, reducing substantially the time and effort needed to maintain and calculate such complex parameters manually. © 2022 IEEE

An Ontology based Conceptualization of Data Integrity Regulatory Compliance in Pharmaceutical Industry: The SPuMoNI Case

Data integrity plays a pivotal role in the pharmaceutical regulatory landscape, where data must be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available, in other words, must be ALCOA compliant. Data in the pharmaceutical industry are complex and come in large amounts and various types, thus making the utilization of information technology systems in managing data integrity compliance, necessary. This study, has been conducted in the context of the SPuMoNI project, and it presents the conceptual design of a regulatory framework for the pharmaceutical domain, which is aligned with the data integrity ALCOA principles and based on ontology engineering technology. In doing so, it proposes the Data Integrity Ontology (DIOnt), which provides a solid basis for the semantic representation of the pharmaceutical manufacturing process and data, and the modeling and management of regulatory requirements. Moreover, a proof-of-concept prototype has been implemented and validated with real world pharmaceutical data in ALCOA principle violation scenarios. © 2021 ACM

An SQWRL-Based Method for Assessing Regulatory Compliance in the Pharmaceutical Industry

Nowadays, data integrity has become a critical issue in the pharmaceutical regulatory landscape, one that requires data to be compliant to ALCOA principles (i.e., data must be Attributable, Legible, Contemporaneous, Original, and Accurate). In this paper, we propose a method which exploits semantic web technologies to represent pharma manufacturing data in a unified manner and evaluate in a systematic manner their ALCOA compliance. To this purpose, in the context of a pharma manufacturing environment, a data integrity ontology (DIOnt) is proposed to be utilized as the basis for the semantic representation of pharma production data and the associated regulatory compliance management processes. We further show that semantic annotations can be used to represent the required ALCOA compliance information, and that semantic reasoning combined with SQWRL queries can be used to evaluate ALCOA compliance. The proposed approach has been implemented in a proof-of-concept prototype and validated with real world pharma manufacturing data, supporting the combined execution of SWRL rules and SQWRL queries with the aim to support the ALCOA compliance assessment and calculate non-compliance percentages per each ALCOA principle. © 2022 by the authors