Multidisciplinary simulation training for endotracheal intubation during COVID-19 in one Hong Kong regional hospital: strengthening of existing procedures and preparedness

Introduction

In early 2020, our hospital responded with high alertness when novel coronavirus SARS-CoV-2 appeared. A hospital-based training programme was rapidly arranged to prepare staff for the imminent threat.

Objective

We developed a hospital-wide multidisciplinary infection control training programme on endotracheal intubation for healthcare workers to minimise nosocomial spread of COVID-19 during this high-stress and time-sensitive risky procedure.

Methodology

Major stakeholders (Quality & Safety Department, Infection Control Team, Central Nursing Division, high-risk clinical departments and hospital training centre) formed a training programme task group. This group was tasked with developing high-fidelity scenario-based simulation training curriculum for COVID-19 endotracheal intubation with standard workflow and infection control practice. This group then implemented and evaluated the training programme for its effectiveness.

Results

101 training classes of 2-hour session were conducted from 5 February to 18 March 2020, involving 1415 hospital staff (~81% of target participants with training needs) either inside the hospital training centre or as in situ simulation training (intensive care unit or accident and emergency department). Learners’ satisfaction was reflected by overall positive response percentage at 90%. Opinions of participating staff were incorporated into the standard airway management and infection control practice for endotracheal intubation of adult patients with COVID-19. Thirty-five patients with COVID-19 were intubated with the current workflow and guideline without any nosocomial transmission.

Conclusion

An early planned and well-structured multidisciplinary hospital-wide simulation training programme was organised expeditiously to provide extensive staff coverage. The insight and experience gained from this project is valuable for future infectious disease challenges.

EMLA and amethocaine for reduction of children's pain associated with needle insertion

BACKGROUND

Children often experience pain from needle insertion procedures; therefore, several topical anaesthetics have been developed.

OBJECTIVES

To compare the topical anaesthetics amethocaine and an eutectic mixture of local anaesthetics (EMLA) in terms of anaesthetic efficacy, ease of needle insertion and adverse events when used for intravenous cannulation and venipuncture in children.

SEARCH STRATEGY

An exhaustive search that included over 30 databases and handsearching reference lists and journals. Language restrictions were not imposed.

SELECTION CRITERIA

Randomized controlled trials were selected that compared EMLA and amethocaine for relieving children's pain from intravenous cannulation or venipuncture.

DATA COLLECTION AND ANALYSIS

Two review authors independently determined eligibility for inclusion by assessing trial quality. Details of eligible studies were summarized. One author was contacted for additional information. Information about adverse events was obtained from the text of the trial reports. Review Manager 4.2 was used to perform a meta-analysis and compute relative risks (RR) with 95% confidence intervals.

MAIN RESULTS

Six trials consisting of 534 children, three months to 15 years of age, were included in this review. A meta-analysis was done comparing amethocaine with EMLA on anaesthetic efficacy, ease of needle procedure and resultant skin changes. For anaesthetic efficacy, amethocaine significantly reduced the risk of pain compared to EMLA when all pain data were combined into a common pain metric (RR 0.78, 95% CI 0.62 to 0.98); when pain was self-reported by children (RR 0.63, 95% CI 0.45 to 0.87); or when pain was observed by researchers (sensitivity analysis: RR 0.71, 95% CI 0.52 to 0.96). Compared to EMLA, amethocaine significantly reduced the risk of pain when drugs were applied for the following durations: for 30 to 60 minutes (RR 0.61, 95% CI 0.41 to 0.91); when applied according to manufacturer's instructions (sensitivity analysis: RR 0.64, 95% CI 0.46 to 0.89); and when applied for over 60 minutes (RR 0.70, 95% CI 0.51 to 0.96). Amethocaine was also significantly more efficacious than EMLA when used specifically for intravenous cannulation (RR 0.70, 95% CI 0.55 to 0.88). Insufficient data were available to compare anaesthetic efficacy for venipuncture.A comparison of amethocaine and EMLA for ease of a needle procedure was not significant; only one trial reported data that could be included. For skin changes, EMLA was favoured in the analysis of erythema (RR 14.83, 95% CI 2.28 to 96.36). Erythema was observed after use of amethocaine whereas blanching was observed after using EMLA. Adverse effects included itching and one case of conjunctival irritation.

AUTHORS' CONCLUSIONS

Although EMLA is an effective topical anaesthetic for children, amethocaine is superior in preventing pain associated with needle procedures.

Stereochemical control factors in the Hantzsch thiazole synthesis: a Hammett substitution correlation analysis

[reaction--see text] It is possible to correlate the distribution of stereochemical products produced during a Hantzsch thiazole synthesis according to the Hammett free-energy equation. This analysis confirms the presumed control of the rate of epimerization during thiazole formation due to stabilization of a cationic transition state intermediate during dehydration of the thiazoline ring system. In the chemical system under study, the stereochemical outcome of the reaction also appears to occur according to a kinetically controlled protonation of a thiazoline tautomer.

Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility

A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was developed for fast evaluation of aqueous solubility. The model was able to predict the molar solubility of a diverse set of 1312 organic compounds with an overall correlation coefficient of 0.92 and a standard deviation of 0.72 log unit between the calculated and experimental data. Considering the fact that the estimated uncertainty of the experimental data is no less than 0.5 log unit, the results demonstrate that carefully chosen physically meaningful 1D and 2D descriptors encode sufficient molecular information for fast and reasonably reliable prediction of aqueous solubility with a simple neural network. As a comparison, we calculated the solubility of a test set of 258 compounds, ranging from simple hydrocarbons to more complex multifunctional organic molecules, with a commercial program (QMPR+ version 2.0.1 of SimulationPlus Inc.) and compared the results with predictions from our model. Statistical parameters indicate that for small and simple organic compounds, QMPR+ outperforms our model. However for more complex multifunctional molecules, our model is superior.

Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 2. Blood-brain barrier penetration

A new molecular lipoaffinity descriptor was introduced in this paper to account for the effect of molecular hydrophobicity on blood-brain barrier penetration. The descriptor was defined based on Kier and Hall's atom-type electrotopological state indices. Its evaluation requires 2-D molecular bonding information only. A multiple linear regression equation using this descriptor and molecular weight reproduces the experimental logBB values of 55 training set compounds and 11 test set compounds satisfactorily with statistical parameters nearly identical to the best models based on polar surface area and ClogP. The results indicate that the lipoaffinity descriptor defined in this paper may be a significant descriptor for molecular transport properties across lipid bilayers.

Evaluation of designed ligands by a multiple screening method: application to glycogen phosphorylase inhibitors constructed with a variety of approaches

Glycogen phosphorylase (GP) is an important enzyme that regulates blood glucose level and a key therapeutic target for the treatment of type II diabetes. In this study, a number of potential GP inhibitors are designed with a variety of computational approaches. They include the applications of MCSS, LUDI and CoMFA to identify additional fragments that can be attached to existing lead molecules; the use of 2D and 3D similarity-based QSAR models (HQSAR and SMGNN) and of the LUDI program to identify novel molecules that may bind to the glucose binding site. The designed ligands are evaluated by a multiple screening method, which is a combination of commercial and in-house ligand-receptor binding affinity prediction programs used in a previous study (So and Karplus, J. Comp.-Aid. Mol. Des., 13 (1999), 243-258). Each method is used at an appropriate point in the screening, as determined by both the accuracy of the calculations and the computational cost. A comparison of the strengths and weaknesses of the ligand design approaches is made.

Quantitative structure-activity relationship studies of progesterone receptor binding steroids

The selection of appropriate descriptors is an important step in the successful formulation of quantitative structure-activity relationships (QSARs). This paper compares a number of feature selection routines and mapping methods that are in current use. They include forward stepping regression (FSR), genetic function approximation (GFA), generalized simulated annealing (GSA), and genetic neural network (GNN). On the basis of a data set of steroids of known in vitro binding affinity to the progsterone receptor, a number of QSAR models are constructed. A comparison of the predictive qualities for both training and test compounds demonstrates that the GNN protocol achieves the best results among the 2D QSAR that are considered. Analysis of the choice of descriptors by the GNN method shows that the results are consistent with established SARs on this series of compounds.

A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors

Finding an accurate method for estimating the affinity of protein ligands activity is the most challenging task in computer-aided molecular design. In this study we investigate and compare seven different prediction methods for a set of 30 glycogen phosphorylase (GP) inhibitors with known crystal structures. Five of the methods involve quantitative structure-activity relationships (QSAR) based on the 2D or 3D structures of the GP ligands alone. They are hologram QSAR (HQSAR), receptor surface model (RSM), comparative molecular field analysis (CoMFA), and applications of genetic neural network to similarity matrix (SM/GNN) or conventional descriptors (C2GNN). All five QSAR-based models have good predictivity and yield q2 values ranging from 0.60 to 0.82. The other two methods, LUDI and a structure-based binding energy predictor (SBEP) system, make use of the structures of the ligand-receptor complexes. The weak correlation between biological activities and the LUDI scores of this set of inhibitors suggests that the LUDI scoring function, by itself, may not be a general method for reliable ranking of a congeneric series of compounds. The SBEP system is derived from a set of physical properties that characterizes ligand-receptor interactions. The final neural network model, which yields a q2 value of 0.60, employs four descriptors. A jury method that combines the predictions of the five QSAR-based models leads to an increase in predictivity. A multi-layer scoring system that utilizes all seven prediction methods is proposed for the evaluation of novel GP ligands.

Use of quantitative structure-property relationships to predict the folding ability of model proteins

We investigate the folding of a 125-bead heteropolymer model for proteins subject to Monte Carlo dynamics on a simple cubic lattice. Detailed study of a few sequences revealed a folding mechanism consisting of a rapid collapse followed by a slow search for a stable core that served as the transition state for folding to a near-native intermediate. Rearrangement from the intermediate to the native state slowed folding further because it required breaking native-like local structure between surface monomers so that those residues could condense onto the core. We demonstrate here the generality of this mechanism by a statistical analysis of a 200 sequence database using a method that employs a genetic algorithm to pick the sequence attributes that are most important for folding and an artificial neural network to derive the corresponding functional dependence of folding ability on the chosen sequence attributes [quantitative structure-property relationships (QSPRs)]. QSPRs that use three sequence attributes yielded substantially more accurate predictions than those that use only one. The results suggest that efficient search for the core is dependent on both the native state's overall stability and its amount of kinetically accessible, cooperative structure, whereas rearrangement from the intermediate is facilitated by destabilization of contacts between surface monomers. Implications for folding and design are discussed.

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications

Validation of a method that uses a genetic neural network with electrostatic and steric similarity matrices (SM/GNN) to obtain quantitative structure-activity relationships (QSARs) is performed with eight data sets. Biological and physicochemical properties from a broad range of chemical classes are correlated and predicted using this technique. Quantitatively the results compare favorably with the benchmarks obtained by a number of well-established QSAR methods; qualitatively the models are consistent with the published descriptions on the relative contribution of steric and electrostatic factors. The results demonstrate the general utility of this method in deriving QSARs. The implication of the importance of molecular alignment and possible methodological improvements are discussed.

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations

The utility of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) from molecular similarity matrices is described. In this application, the corticosteroid-binding globulin (CBG) binding affinity of the well-known steroid data set is examined. Excellent predictivity can be obtained through the use of either electrostatic or shape properties alone. Statistical validation using a standard randomization test indicates that the results are not due to chance correlations. Application of GNN on the combined electrostatic and shape matrix produces a six-descriptor model with a cross-validated r2 value of 0.94. The model is superior to those obtained from partial least-squares and genetic regressions, and it also compares favorably with the results for the same data set from other established 3D QSAR methods. The theoretical basis for the use of molecular similarity in QSAR is discussed.

Potential effect of cyclosporin A in formation of cholesterol gallstones in pediatric liver transplant recipients

Recent advancements in liver transplantation have resulted in extended survival both for grafts and recipients. Such improvement, together with the shortage of donor organs has prompted expansion of the donor pool to include less than ideal donors, especially in life-threatening situations. The use of older liver donors has been associated with lower long-term survival. However, potential morbidity such as gallstone formation has not been explored. We analyzed bile composition in a child who developed cholesterol gallstones in the proximal bile duct two years after undergoing emergency liver transplantation with a liver from a 78-year-old donor. Oral administration of ursodeoxycholic acid (ursodiol) shifted the cholesterol composition of the bile from a supersaturated, potentially crystallized state to a liquid (micellar) state. Unlike cyclosporin A, FK506 showed an increase in the proportion of chenodeoxycholic acid and a decrease in the proportion of cholic acid, and thus may exhibit minimal or no hepatotoxic effect. Thus, in donor livers with factors known to be associated with cholesterol gallstone formation (such as age, sex, or obesity), one may consider analyzing the bile composition at the time of procurement. Depending on cholesterol and bile acid composition the use of FK506 with or without addition of ursodeoxycholic acid may be warranted.

Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors

A novel tool, called a genetic neural network (GNN), has been developed for obtaining quantitative structure-activity relationships (QSAR) for high-dimensional data sets (J. Med. Chem. 1996, 39, 1521-1530). The GNN method uses a neural network to correlate activity with descriptors that are preselected by a genetic algorithm. To provide an extended test of the GNN method, the data on 57 benzodiazepines given by Maddalena and Johnston (MJ; J. Med. Chem. 1995, 38, 715-724) have been examined with an enhanced version of GNN, and the results are compared with the excellent QSAR of MJ. The problematic steepest descent training has been replaced by the scaled conjugate gradient algorithm. This leads to a substantial gain in performance in both robustness of prediction and speed of computation. The cross-validation GNN simulation and the subsequent run based on an unbiased and more efficient protocol led to the discovery of other 10-descriptor QSARs that are superior to the best model of MJ based on backward elimination selection and neural network training. Results from a series of GNNs with a different number of inputs showed that a neural network with fewer inputs can produce QSARs as good as or even better than those with higher dimensions. The top-ranking models from a GNN simulation using only six input descriptors are presented, and the chemical significance of the chosen descriptors is discussed. The statistical significance of these GNN QSARs is validated. The best QSARs are used to provide a graphical tool that aids the design of new drug analogues. By replacing functional groups at the 7- and 2'-positions with ones that have optimal substituent parameters, a number of new benzodiazepines with high potency are predicted.

Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks

A new hybrid method (GNN) combining a genetic algorithm and an artificial neural network has been developed for quantitative structure-activity relationship (QSAR) studies. A suitable set of molecular descriptors are selected by a genetic algorithm. This set serves as input to a neural network, in which model-free mapping of multivariate data is performed. Multiple predictors are generated that are superior to results obtained from previous studies of the Selwood data set, which is used to test the method. The neural network technique provides a graphical description of the functional form of the descriptors that play an important role in determining drug activity. This can serve as an aid in future design of drug analogues. The effectiveness of GNN is tested by comparing its results with a benchmark obtained by exhaustive enumeration. Different fitness strategies that tune the evolution of genetic models are examined, and QSARs with higher predictiveness are found. From these results, a composite model is constructed by averaging predictions from several high-ranking models. The predictions of the resulting QSAR should be more reliable than those derived from a single predictor because it makes greater use of information and also permits error estimation. An analysis of the sets of descriptors selected by GNN shows that it is essential to have one each for the steric, electrostatic, and hydrophobic attributes of a drug candidate to obtain a satisfactory QSAR for this data set. This type of result is expected to be of general utility in designing and understanding QSAR.

Structure-activity relationships from molecular similarity matrices

An alternative method for determining structure-activity correlations is presented. Ligand molecules are described using data matrices derived from the results of N by N (each molecule compared to every other) molecular similarity calculations. The matrices were analyzed using a neural network pattern recognition technique and partial least squares statistics, with the results obtained compared to those achieved using comparative molecular field analysis (CoMFA). The molecular series used in the study comprised 31 steroids. The resultant pattern recognition analysis showed clustering of compounds with high, intermediate, and low affinity into separate regions of the neuron output plots. The cross-validated correlation coefficients obtained from statistical analyses of the matrices against steroid binding data compared well with those achieved using CoMFA. These results show that data matrices derived from molecular similarity calculations can provide the basis for rapid elucidation of both qualitative and quantitative structure-activity relationships.

Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors

A comparative study of quantitative structure-activity relationships involving diaminopyrimidines as DHFR inhibitors using regression analysis and the neural-network approach suggests that the neural network can outperform traditional methods. The technique permits the highlighting the functional form of those parameters which have an influence on the biological activity.