Age Demographics of Subjects Enrolled in Global, Interventional Phase 3 Melanoma Clinical Trials

Background Melanoma is a skin cancer with a rising worldwide incidence of just over 280,000 individuals with the greatest burden of illness in European, New Zealander, and Australian populations. Patients are diagnosed with melanoma with the mean and median ages being 65 and 59 years old, respectively. Phase 3 trials not only provide a wide representation of the target population but also study the efficacy for a certain intervention. Objective The objective of this literature review is to analyze patient demographics of phase 3 trials for melanoma and identify if there is a true disparity between the clinical trial age demographics and the natural epidemiological age demographics. Data Sources The authors conducted a search on clinicaltrials.gov, a publicly available resource that lists clinical trials and their data. The reported mean and median ages for each trial were extracted after determining if each trial meets our inclusion criteria. Weighted mean and median ages were calculated using an online calculator. Data Summary Data from 35 trials were evaluated with 30 trials reporting a weighted mean age of 55.85 years and 5 trials reporting a weighted median age of 55.14 years. Conclusion Based on the results, melanoma clinical trials enroll patients who are younger than the epidemiological mean and median ages. Due to this underrepresentation of the elderly patients with melanoma, clinical trials may provide limited application for the use of their results.

Evaluation of the Implementation of the 4C Mortality Score in United Kingdom hospitals during the second pandemic wave

ABSTRACT The 4C Mortality Score (4C Score) was designed to risk stratify hospitalised patients with COVID-19. We assessed inclusion of 4C Score in COVID-19 management guidance and its documentation in patients' case notes in January 2021 in UK hospitals. 4C Score was included within guidance by 50% of sites, though score documentation in case notes was highly variable. Higher documentation of 4C Score was associated with score integration within admissions proformas, inclusion of 4C Score variables or link to online calculator, and management decisions. Integration of 4C Score within clinical pathways may encourage more widespread use.

Validation of an Online Calculator to Predict Short-term Postoperative Outcomes in Patients with Metastatic Brain Tumors

Introduction: Estimating the risk of extended length of stay (LOS) or non-routine discharge disposition is helpful in surgical decision-making for patients with brain metastases (BM). In 2020, an online calculator was introduced by Khalafallah et al. that stratified the risk of patients with brain tumors based on poor surgical outcomes. We applied the calculator to our population of BM patients to determine its generalizability and validity. Methods: We included BM patients who underwent a cranial procedure between 2015 and 2018 at a single academic institution. Patient age, race, marital status, admission status, KPS score, and medical co-morbidities (5-point modified frailty index (mFI-5)) were included in the analysis. We calculated the areas under the Receiver Operating Characteristics (ROC) curves to determine the validity of the model proposed in predicting extended LOS (>7 days) and need for specialty care at discharge (non-routine discharge disposition). Results: We analyzed 244 patients (mean age 61.2 years (SD 11.1), 57.0% female, and 78.1% Caucasian). The areas under the ROC curves were 0.8427 and 0.8422 for extended LOS and non-routine discharge disposition, suggesting high accuracy of the models for these outcomes. However, the (mFI-5) was not a significant predictor of either outcome in our multivariate analyses. Conclusions: We validated Khalafallah et al.’s predictive models of extended LOS and non-routine discharge disposition in our patient population, which included a broader range of surgical procedures. Further investigation of this model could clarify how the type of neurosurgical procedure influences outcomes, the role of the mFI-5, and its overall generalizability.

P-EGS04 Assessment of Malnutrition in emergency laparotomy patients. A QIP highlighting simple measures can improve early recognition and optimisation of high-risk patients

Background Early recognition of high-risk malnourished patients is important for optimisation of nutritional status leading to better outcomes. The accurate recording of malnutrition universal screening tool (MUST) results is vital in this regard. This quality improvement project (QIP) aimed to review the quality of nutritional assessment of emergency laparotomy patients against the National Institute for Health and Care Excellence (NICE) guidelines and outline area of improvement. Methods The QIP was conducted at Royal Oldham Hospital in 2019-2020 over a seven-month period. Fifty random patients were included in the first audit cycle over a 4-month period, followed by implementation of recommended changes and a re-audit of 30 patients over a 2-month period. The initial MUST scores which were calculated and documented by nursing staff were identified as the nursing staff MUST score (NSMS). To assess the accuracy of NSMS, we developed a MUST rescoring method which was performed by a senior member of the medical team and was identified as the medical team MUST rescore (MTMR). Results The initial audit showed a significant difference between NSMS and MTMR scores. According to MTMR, 23 patients (46%) had an inaccurate MUST score assessment by the nursing staff. A multidisciplinary approach using a standard online calculator were recommended. The second phase of the QIP showed an obvious improvement in the accuracy of MUST assessment. Our interventions improved the accuracy rate of MUST scores significantly (27, 54% vs 29, 96.6%, P = 0.00005). Conclusions A multidisciplinary team approach and online calculator are useful in improving the accuracy of MUST assessment in emergency laparotomy patients. This helped early involvement of the dietitian leading to improvement in morbidity and mortality.

MIMO Using Silicon Patch Antenna

The growth of mass communication system results the need for the high gain compactable multi-antenna base station by employing the recently introduced Multi-Input Multi-Output (MIMO) concept. A dual-band silicon patch antenna for MIMO operating at 2.4GHz and 5GHz has been designed and stimulated results are presented in this paper. The antenna designed with the dielectric constant 2.2 of material RT-duroid results in high efficiency and low substrate loss. ADS software is used for the stimulation of antenna RF-properties and other parameters. With the use of online calculator, the antenna has been designed for 33mm of length and 35mm of width.

Machine Learning Based Algorithms to Impute PaO 2 from SpO2 Values and Development of an Online Calculator

We created an online calculator using machine learning algorithms to impute the partial pressure of oxygen (PaO2)/fraction of delivered oxygen (FiO2) ratio using the non-invasive peripheral saturation of oxygen (SpO2) and compared the accuracy of the machine learning models we developed to previously published equations. We generated three machine learning algorithms (neural network, regression, and kernel-based methods) using 7 clinical variable features (N=9,900 ICU events) and subsequently 3 features (N=20,198 ICU events) as input into the models. Data from mechanically ventilated ICU patients were obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC III) database and used for analysis. Compared to seven features, three features (SpO2, FiO2 and PEEP) were sufficient to impute PaO2 from the SpO2. Any of the tested machine learning models enabled imputation of PaO2 from the SpO2 with lower error and showed greater accuracy in predicting PaO2/FiO2 < 150 compared to the previously published log-linear and non-linear equations. Imputation using data from an independent validation cohort of ICU patients (N = 133) from 2 hospitals within the University of Pittsburgh Medical Center (UPMC) showed greater accuracy with the neural network and kernel-based machine learning models compared to the previously published non-linear equation.

Development and validation of a novel online calculator for estimating survival benefit of adjuvant transcatheter arterial chemoembolization in patients undergoing surgery for hepatocellular carcinoma

Background and aims Although adjuvant transcatheter arterial chemoembolization (TACE) for resected hepatocellular carcinoma (HCC) may improve survival for some patients, identifying which patients can benefit remains challenging. The present study aimed to construct a survival prediction calculator for individualized estimating the net survival benefit of adjuvant TACE for patients with resected HCC. Methods From a multicenter database, consecutive patients undergoing curative resection for HCC were enrolled and divided into the developing and validation cohorts. Using the independent survival predictors in the developing cohort, two nomogram models were constructed for patients with and without adjuvant TACE, respectively, which predictive performance was validated internally and externally by measuring concordance index (C-index) and calibration. The difference between two estimates of the prediction models was the expected survival benefit of adjuvant TACE. Results A total of 2514 patients met the inclusion criteria for the study. The nomogram prediction models for patients with and without adjuvant TACE were, respectively, built by incorporating the same eight independent survival predictors, including portal hypertension, Child–Pugh score, alpha-fetoprotein level, tumor size and number, macrovascular and microvascular invasion, and resection margin. These two prediction models demonstrated good calibration and discrimination, with all the C-indexes of greater than 0.75 in the developing and validation cohorts. A browser-based calculator was generated for individualized estimating the net survival benefit of adjuvant TACE. Conclusions Based on large-scale real-world data, an easy-to-use online calculator can be adopted as a decision aid to predict which patients with resected HCC can benefit from adjuvant TACE.