Training in cardiothoracic surgery: The role for simulation.

Adequate cardiothoracic surgical training is essential for provision of quality care to patients. In recent years, simulation-based training has been advocated as an adjunct to traditional surgical training. Advances in simulation technology has resulted in many low- and highfidelity simulators being employed in cardiothoracic surgical training. Such models allow trainees to practice an array of realistic full-length procedures in a safe and controlled environment, with the window to make mistakes and consider them learning points. There is significant evidence to demonstrate the effectiveness of cardiothoracic surgery simulation in improving surgical skills and operating room performances in addition to building confidence among trainees. However, owing to the high financial cost of arranging it, simulation-based training is not widespread in low- and middle-income countries, including Pakistan. More work is warranted on the cost effectiveness of implementing simulation-based learning, which, in turn, would increase the uptake of simulation to enhance cardiothoracic surgical training in Pakistan.

Impact of Drain Placement on Postoperative Complications after Thyroidectomy for Substernal Goiter.

Introduction Despite the evidence against drain placement after thyroidectomy, there is a lack of consensus on drain use in patients with substernal goiter. Objective To assess the factors that increase the likelihood of drain placement and its impact on postoperative hematoma and other 30-day complications among adult patients undergoing thyroidectomy for substernal goiter. Methods A retrospective cohort study that used data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). Adult patients (aged ≥ 18 years) who underwent elective thyroidectomy for substernal goiter from 2016 to 2020 were included. Cases with closed suction neck drains placed upon completion of surgery were included in the drain group, and the remaining cases formed the nondrain group. Results A total of 1,229 patients were included (46.5% with drain placement). The factors that increased the likelihood of drain placement included body mass index (BMI) ≥ 30 kg/m 2 , score between 3 and 5 on the American Society of Anesthesiologists (ASA) physical status classification, sternal split/transthoracic surgical approach, operative time ≥ 90 minutes, and surgery conducted by otolaryngologists. Patients with clean-contaminated or contaminated wound classifications were less likely to be submitted to drain placement. In addition, drain use had no impact on postoperative hematoma formation but was found to independently increase the risk of prolonged length of hospital stay. Conclusion Thyroidectomy without drain placement might be safe for substernal goiter. However, this decision should be individualized for each patient. Level Of Evidence: 3.

Perceptions, challenges and experiences of frontline healthcare providers in Emergency Departments regarding Workplace Violence during the COVID-19 pandemic: A protocol for an exploratory qualitative study from an LMIC.

INTRODUCTION: Workplace violence (WPV) against Healthcare Workers (HCWs) has emerged as a global issue. Emergency Department (ED) HCWs as front liners are more vulnerable to it due to the nature of their work and exposure to unique medical and social situations. COVID-19 pandemic has led to a surge in the number of cases of WPV against HCWs, especially against ED HCWs. In most cases, the perpetrators of these acts of violence are the patients and their attendants as families. The causes of this rise are multifactorial; these include the inaccurate spread of information and rumours through social media, certain religious perspectives, propaganda and increasing anger and frustration among the general public,ED overcrowding, staff shortages etc. We aim to conduct a qualitative exploratory study among the ED frontline care providers at the two major EDs of Karachi city. The purpose of this study is to determine the perceptions, challenges and experiences regarding WPV faced by ED healthcare providers during the COVID-19 pandemic. METHODS AND ANALYSIS: For this research study, a qualitative exploratory research design will be employed using in-depth interviews and a purposive sampling approach. Data will be collected using in-depth interviews from study participants working at the EDs of Jinnah Postgraduate Medical Centre (JPMC) and the Aga Khan University Hospital(AKUH) Karachi, Pakistan. Thestudy data will be analysed thematically using NVivo V.12 Plus software. ETHICS AND DISSEMINATION: The ethical approval for this study was obtained from the Aga Khan University Ethical Review Committee and from Jinnah postgraduate Medical Center (JPMC). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study.The findings of this study will help to explore the perceptions of ED healthcare providers regarding WPV during the COVID-19 pandemic and provide a better understanding of study participant's' challenges concerning WPV during the COVID-19 pandemic.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.

BACKGROUND: The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. METHODS: We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. RESULTS: After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5. CONCLUSION: Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

Clinical characteristics and outcomes of patients presenting with hip fractures at a tertiary care hospital in Pakistan.

Osteoporosis remains under-recognized and sub-optimally managed in Pakistan, with a lack of awareness that minimal impact hip fracture is a manifestation of low bone mineral density (BMD). PURPOSE: Hip fracture is often the first clinical presentation of osteoporosis and an opportunity to intervene and reduce future fracture risk. Our aim was to understand the current practices in Pakistan related to bone health in patients presenting with a hip fracture. METHODS: This is a retrospective study at a tertiary care center in Pakistan of patients admitted with a hip fracture. Data collected includes previous fracture history, known preceding diagnosis of low BMD medication details, comorbidities, and DXA results. RESULTS: Two hundred ten patients were studied. The mean age of patients was 73.1 years, with 112 (53.3%) women. Most (195 (92.9%)) had presented with a low-impact hip fracture, with 17 (8.1%) reporting previous history of fracture. None had been treated with osteoporosis medications prior to fracture. Nineteen (9%) were on calcium and vitamin D supplements prior to fracture; of the minority who were screened, all were vitamin D deficient and subsequently discharged on vitamin D supplements. No one was prescribed medications to reduce fracture risk at discharge. CONCLUSION: This study reveals that patients admitted with minimal impact hip fractures in Pakistan are rarely evaluated for low BMD and not started on osteoporosis medications even after presenting with a typical osteoporosis-related fracture. This underscores the need for health provider education about osteoporosis as a major cause for hip fractures and the need to intervene for future fracture risk reduction.

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.

BACKGROUND: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. METHODS: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). RESULTS: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. CONCLUSION: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.