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Byline: C. Surekha, Venkata. Eadara, M. Satish Kumar Background and Aims: Changes in the sympathetic nervous system by pain can impact smooth muscle tone and can alter perfusion. This can [...]

Background: Laparoscopic procedures have gained upper hand over open surgeries in reducing hospital stay. With the advent of minimally invasive techniques there is potential to bypass the abdominal wall altogether for visceral access and resection reducing the need for analgesia, though post-operative symptoms like nausea and vomiting may increase the duration. Objectives:-1.To compare the effect of pulmonary recruitment maneuver and Sub diaphragmatic drain on incidence of PONV. 2. To evaluate the haemodynamic changes during pulmonary recruitment maneuver. Methods and study design:- A total of 60 patients belonging to ASA I and ASA II, planned for laparoscopic abdominal surgeries were randomly divided into two groups. In one group patients were placed in Trendelenburg position and CO2 was removed by means of PRM with trocar sleeve fully open. In another group a subdiaphragmatic drain was placed planned to evacuate the residual CO2. Reduction in Post-operative nausea and vomiting(0-3 scale) was observed as primary outcome and hemodynamic parametersduring PRM was also noted. Results: Study included 60 patients. Demographic data was comparable between both the groups .Post-operative nausea and vomiting was significantly lower in pulmonary recruitment maneuver group (P<0.05) at 6, 12, 24and 48 hours compared to subdiaphragmatic group. Hemodynamic parameters remained stable throughout the recruitment maneuver Interpretation and conclusion : Pulmonary recruitment maneuver offers a benefit in reducing postoperative nausea and vomiting with no adverse events in laparoscopic abdominal surgeries. [ABSTRACT FROM AUTHOR]

Background and Aims: The goal of this study was to develop a deep-learning artificial intelligence algorithm to identify the top predictors amongst the large array of clinical variables at admission to predict the likelihood of mortality in coronavirus disease 19 (COVID-19) patients. Methods: Data such as complete blood count, RFT, serum electrolytes, liver function tests, interleukin-6 (IL-6), C-reactive protein, procalcitonin, ferritin, FiO2, mode of ventilation, age, sex, and co-morbidities were collected from 250 patients admitted to the ICU. The variables missing for more than 20% of the people were removed. Chi-Square and Pearson's correlation coefficient were used to remove the clinical variables having less importance with respect to the probability of being discharged or mortality. The LightGBM model was used for classification. The data were split into train and test in the ratio of 0.8:0.2. This gave an output of survival and mortality probability. Results: The model gave a result with an accuracy of 0.64, a precision of 0.73, a recall of 0.64, an F1 score of 0.66, and an area under the curve of 0.79. The model had a specificity of 66.6% and a sensitivity of 61.5%. Ferritin and IL-6 values were the top predictors of mortality. Conclusion: This approach has the potential to provide frontline physicians with a simple and objective tool to stratify patients based on risks so that they can triage COVID-19 patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments. [ABSTRACT FROM AUTHOR]