ABSTRACT
There has been a substantial increase in the use of extracorporeal membrane oxygenation (ECMO) support in critically ill adults. Understanding the complex changes that could affect drugs' pharmacokinetics (PK) and pharmacodynamics (PD) is of suitable need. Therefore, critically ill patients on ECMO represent a challenging clinical situation to manage pharmacotherapy. Thus, clinicians' ability to predict PK and PD alterations within this complex clinical context is fundamental to ensure further optimal and, sometimes, individualized therapeutic plans that balance clinical outcomes with the minimum drug adverse events. Although ECMO remains an irreplaceable extracorporeal technology, and despite the resurgence in its use for respiratory and cardiac failures, especially in the era of the COVID-19 pandemic, scarce data exist on both its effect on the most commonly used drugs and their relative management to achieve the best therapeutic outcomes. The goal of this review is to provide key information about some evidence-based PK alterations of the drugs used in an ECMO setting and their monitoring.
ABSTRACT
Introduction Carboxyhemoglobinemia is characterised by decreased oxygen delivery to tissues. In severe and critical coronavirus disease 2019 (COVID-19) illness with hypoxia, this can herald a grave and protracted course of illness. Patients with COVID-19 experience respiratory impairment, lowering the pace at which carbon monoxide (CO) is eliminated and raising the likelihood of carboxyhemoglobinemia. We set out to explore early arterial carboxyhemoglobin (COHb) and COVID-19 patient outcomes in non-smokers and its potential as a predictive tool for mortality. Methods Forty-five patients, non-smokers with severe/critical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection requiring admission in a North Indian 1200-bedded tertiary care hospital, were recruited prospectively from October 2020 to March 2021. Arterial COHb% was evaluated with arterial blood gases using an analyser, which were taken at the time of admission and then every alternate day for the first 10 days. Carboxyhemoglobinemia was defined as COHb% more than 1%. The primary outcome was defined as the patient's hospital outcome (survivor/non-survivor). Results Of the total 45 subjects, 51.1% (n=23) survived. Patients developed carboxyhemoglobinemia with an incidence of 51% during the course of their hospital stay. The mean ± SD of COHb% on admission was 1.0 ± 0.58 and 1.03 ± 0.8 in non-survivors and survivors, respectively (p=0.870). Maximal individual values of 5.3% and 6.1% were seen in survivors and non-survivors, respectively. On serial COHb measurement, non-survivors had significantly higher COHb% on days 6 and 10. No co-relation of COHb% with inflammatory markers was noted. Conclusion Arterial COHb levels in non-survivors were significantly higher than in survivors on days 6 and 10. Our study did not show a prognostic value of serial COHb measurement in patients with severe COVID-19. To establish COHb as a predictive marker in severely ill COVID-19 patients, additional research is required.
ABSTRACT
BACKGROUND AND AIMS: During endoscopy, droplets with the potential to transmit infectious diseases are known to emanate from a patient's mouth and anus, but they may also be expelled from the biopsy channel of the endoscope. The main goal of our study was to quantify droplets emerging from the biopsy channel during clinical endoscopy. METHODS: A novel light-scattering device was used to measure droplets emanating from the biopsy channel. An endoscopy model was created, and in vitro measurements were carried out during air insufflation, air and water suctioning, and the performance of biopsy sampling. Similar measurements were then made on patients undergoing endoscopy, with all measurements taking place over 2 days to minimize variation. RESULTS: During in vitro testing, no droplets were observed at the biopsy channel during air insufflation or air and water suctioning. In 3 of 5 cases, droplets were observed during biopsy sampling, mostly when the forceps were being removed from the endoscope. In the 22 patients undergoing routine endoscopy, no droplets were observed during air insufflation and water suctioning. Droplets were detected in 1 of 11 patients during air suctioning. In 9 of 18 patients undergoing biopsy sampling and 5 of 6 patients undergoing snare polypectomies, droplets were observed at the biopsy channel, mostly when instruments were being removed from the endoscope. CONCLUSIONS: We found that the biopsy channel may be a source of infectious droplets, especially during the removal of instruments from the biopsy channel. When compared with droplets reported from the mouth and anus, these droplets were larger in size and therefore potentially more infectious.
Subject(s)
Communicable Diseases , Endoscopes , Humans , Endoscopy, Gastrointestinal , Biopsy , Endoscopy , WaterABSTRACT
BACKGROUND: The SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate. OBJECTIVE: In this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc. METHOD: The size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE). RESULTS: Experimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%. CONCLUSION: This percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.
ABSTRACT
Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients' sensory data is performed to obtain highly accurate predictions of the patients' sensory data (patients' vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.