Innovative use of Australian cancer registry data for early detection of the effects of epidemics and other mass disruptions on cancer incidence.

BACKGROUND: Predictive modelling using pre-epidemic data have long been used to guide public health responses to communicable disease outbreaks and other health disruptions. In this study, cancer registry and related health data available 2-3 months from diagnosis were used to predict changes in cancer detection that otherwise would not have been identified until full registry processing was completed about 18-24 months later. A key question was whether these earlier data could be used to predict cancer incidence ahead of full processing by the cancer registry as a guide to more timely health responses. The setting was the Australian State of New South Wales, covering 31 % of the Australian population. The study year was 2020, the year of emergence of the COVID-19 pandemic. METHODS: Cancer detection in 2020 was modelled using data available 2-3 months after diagnosis. This was compared with data from full registry processing available from 2022. Data from pre-pandemic 2018 were used for exploratory model building. Models were tested using pre-pandemic 2019 data. Candidate predictor variables included pathology, surgery and radiation therapy reports, numbers of breast screens, colonoscopies, PSA tests, and melanoma excisions recorded by the universal Medical Benefits Schedule (MBS). Data were analysed for all cancers collectively and 5 leading types. RESULTS: Compared with full registry processing, modelled data for 2020 had a >95 % accuracy overall, indicating key points of inflexion of cancer detection over the COVID-disrupted 2020 period. These findings highlight the potential of predictive modelling of cancer-related data soon after diagnosis to reveal changes in cancer detection during epidemics and other health disruptions. CONCLUSIONS: Data available 2-3 months from diagnosis in the pandemic year indicated changes in cancer detection that were ultimately confirmed by fully-processed cancer registry data about 24 months later. This indicates the potential utility of using these early data in an early-warning system.

What is the Accuracy of the ACS-NSQIP Surgical Risk Calculator in Emergency Abdominal Surgery? A Meta-Analysis.

BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator provides an estimation of 30-d post-operative complications including mortality. This tool has the potential to both aid in decision-making for patients and their families and also in optimizing the clinical management of high-risk patients. However, it's utility in patients requiring emergency abdominal surgery has shown to be inconsistent outside of NSQIP participating institutions. This study undertook a meta-analysis to assess the calculator's accuracy in predicting mortality in these patients. METHODS: A literature search of PubMed, Medline and Cochrane databases was conducted between October 2019 to April 2020. The PubMed, Medline and Cochrane Databases were searched for relevant studies. The search strategy included studies from January 2013 to April 2020. Studies including elective surgery were excluded. A random effects model was used and fitted using restricted maximum likelihood estimation. The O:E ratio was used to validate the calculator's accuracy in predicting mortality. RESULTS: Six studies were included in the meta-analysis, with a total of 1835 patients undergoing emergency intra-abdominal surgery. The summary estimate of the O:E ratio of the ACS-NSQIP surgical risk calculator in predicting 30-d post-operative mortality was 1.06 (95% CI 0.74-1.51). There was significant heterogeneity between studies with a Cochrane Q of 11.96 (P = 0.04) and I2 = 57.5%. CONCLUSIONS: The ACS-NSQIP surgical risk calculator is a reliable predictor of mortality in this external cohort and has potential to be utilised in the multi-disciplinary care of patients undergoing emergency abdominal surgery.

Utility of the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator in predicting mortality in an Australian acute surgical unit.

BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator provides an estimate preoperatively of operative risks including mortality; however, its utility is not known in Australian emergency general surgical patients. This study sought to determine accuracy of the calculator in predicting outcome of high-risk patients in an Australian acute surgical unit to establish if this calculator could be a useful tool to identify high-risk patients in an Australian setting. METHODS: Retrospective analysis of patients admitted to the acute surgical unit at a tertiary referral centre between 2018 and 2019 was conducted. High-risk patients were defined as those who underwent an emergency operation with an ACS-NSQIP surgical mortality score ≥5%. Post-operative outcomes assessed included mortality and return to operating theatre, readmission and discharge to nursing home. External validation of the calculator was performed using discrimination and calibration statistics. RESULTS: Over a 14-month period, 58 patients were high risk, with an average age of 75 years, 93% were classified as functionally independent/partially dependent and 91.4% underwent a laparotomy. Overall 30-day mortality rate was 20.7%. The ACS-NSQIP calculator was a reliable predictor of mortality, with c-statistic of 0.835 (0.654-0.977), Brier score of 0.125 (0.081-0.176) and Hosmer-Lemeshow statistic of 0.389. The calculator was less accurate in its prediction of other outcomes assessed. CONCLUSION: The ACS-NSQIP calculator accurately approximated mortality in high-risk Australian patients requiring emergency surgery. This study has demonstrated that in this patient population, the calculator could reliably be applied in the multidisciplinary care of emergency surgical patients.

Microwave spectrum, r(0) structure, dipole moment, barrier to internal rotation, and Ab initio calculations for fluoromethylsilane.

The microwave spectra of seven isotopomers of fluoromethylsilane, CH(2)FSiH(3), in the ground vibrational state were measured and analyzed in the frequency range 18-40 GHz. The rotational and centrifugal distortion constants were evaluated by the least-squares treatment of the observed frequencies of a- and b-type R- and b-type Q-transitions. The values for the components of the dipole moment were obtained from the measurements of Stark effects from both a- and b-type transitions and the determined values are: |mu(a)| = 1.041(5), |mu(b)| = 1.311(6), and |mu(t)| = 1.674(4) D. Structural parameters have been determined and the heavy atom distances (r(0)) in Angstroms are: Si-C = 1.8942(57) and C-F = 1.4035(55) and the angle in degree, angleSiCF = 109.58(14). A semi-experimental r(e) structure was also determined from experimental ground state rotational constants and vibration-rotation constants derived from ab initio force fields. The internal torsional fundamental, SiH(3), was observed at 149.2 cm(-1) with two accompanying hot bands at 138.8 and 127.5 cm(-1). The barrier to internal rotation was obtained as 717.3(16) cm(-1) (2.051(46) kcal mol(-1)) by combining the analysis of the microwave A and E splittings and the torsional fundamental and hot band frequencies. Ab initio calculations have been carried out with full electron correlation by the second-order perturbation method with several different basis sets up to MP2/6-311+G(d,p) to obtain geometrical parameters, barriers to internal rotation, and centrifugal distortion constants. Adjusted r(0) structural parameters have been obtained by combining the ab initio MP2/6-311+G(d,p) predicted values with the determined rotational constants for the fluoride as well as with the previously reported microwave data for the chloro- and bromo- compounds. These experimental results are compared to the corresponding parameters for the carbon analogues.

Production of plasmid DNA in industrial quantities according to cGMP guidelines.

Qiagen offers a unique technology for plasmid manufacturing, working reliably for every parent plasmid. The process steps such as strain and clone selection, and fermentation optimization ensure optimal plasmid DNA yield and quality in the starting material. Master Cell Bank and Working Cell Bank manufacturing is then performed under cGMP conditions. A high-yield, low mechanical stress alkaline lysis procedure, followed by a proprietary endotoxin-removal step and anion-exchange chromatography ensures consistently high plasmid DNA quality. The material undergoes stringent quality control tests and is accompanied by a comprehensive quality control report and a documentation package for regulatory filing. The following chapter describes the necessary steps such as host cell selection, growth conditions, downstream processing, and quality assurance and control.