Patient-ventilator interaction using autoencoder derived magnitude of asynchrony breathing

The occurrence of asynchronous breathing (AB) is prevalent during mechanical ventilation (MV) treatment. Despite studies being carried out to elucidate the impact of AB on MV patients, the asynchrony index, a metric to describe the patient-ventilator interaction, may not be sufficient to quantify the severity of each AB fully in MV patients. This research investigates the feasibility of using a machine learning-derived metric, the ventilator interaction index, to describe a patient’s interaction with a mechanical ventilator. VI is derived using the magnitude of a breath’s asynchrony to measure how well patient is interacting with the ventilator. 1,188 hours of hourly and for 13 MV patients were computed using a convolution neural network and an autoencoder. Pearson’s correlation analysis between patients’ and versus their levels of partial pressure oxygen (PaO2) and partial pressure of carbon dioxide (PaCO2) was carried out. In this patient cohort, the patients’ median is 38.4% [Interquartile range (IQR): 25.9-48.8], and the median is 86.0% [IQR: 76.5-91.7]. Results show that high AI does not necessarily predispose to low. This difference suggests that every AB poses a different magnitude of asynchrony that may affect patient’s PaO2 and PaCO2. Quantifying hourly along with during MV could be beneficial in explicating the aetiology of AB

Predicting mechanically ventilated patients future respiratory system elastance – A stochastic modelling approach

Background and objective: Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment. Methods: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves. Results: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th – 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range. Conclusion: The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment

Practical guide in using insulin degludec/insulin aspart: A multidisciplinary approach in Malaysia

Insulin degludec/insulin aspart (IDegAsp) co-formulation provides both basal and mealtime glycaemic control in a single injection. The glucose level-lowering efficacy of IDegAsp is reported to be superior or non-inferior to that of the currently available insulin therapies with a lower rate of overall hypoglycaemia and nocturnal hypoglycaemia. An expert panel from Malaysia aims to provide insights into the utilisation of IDegAsp across a broad range of patients with type 2 diabetes mellitus (i.e. treatment-naïve or insulin-naïve patients or patients receiving treatment intensification from basal-only regimens, premixed insulin and basal–bolus insulin therapy). IDegAsp can be initiated as once-daily dosing for the main meal with the largest carbohydrate content with weekly dose adjustments based on patient response. A lower starting dose is recommended for patients with cardiac or renal comorbidities. Dose intensification with IDegAsp may warrant splitting into twice-daily dosing. IDegAsp twice-daily dosing does not need to be split at a 50:50 ratio but should be adjusted to match the carbohydrate content of meals. The treatment of patients choosing to fast during Ramadan should be switched to IDegAsp early before Ramadan, as a longer duration of titration leads to better glycated haemoglobin level reductions. The pre-Ramadan breakfast/lunch insulin dose can be reduced by 30%–50% and taken during sahur, while the pre-Ramadan dinner dose can be taken without any change during iftar. Education on the main meal concept is important, as carbohydrates are present in almost all meals. Patients should not have a misconception of consuming more carbohydrates while taking IDegAsp

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes

Fiscal reforms in Thailand

The fiscal crisis facing most developing countries and the corresponding problems of resource mobilization, external debt and widening savings-investment gap have directed new attention to the importance of sound fiscal policies over the past few years. Like many other developing countries, Thailand was unable to escape from the aftermath of the fiscal crisis. Hence, to cure the country of its economic ills, the Thai government shifted more emphasis towards restructuring its fiscal system, with the aim of achieving economic stability. As result, Thailand’s economy improved significantly and its growth rate began to increase rapidly. Despite these seemingly favourable developments, Thailand was once again forced with severe economic problems due to occurrences of various external events. Major structural weaknesses in the fiscal system surfaced in the light of these problems, thereby signalling the urgent need for further reform.BUSINES

Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders

10.1016/j.cmpb.2021.106601Computer Methods and Programs in Biomedicine215106601-10660

Stochasticity of the respiratory mechanics during mechanical ventilation treatment

Stochastic models have been used to predict dynamic intra-patient respiratory system elastance (Ers) in mechanically ventilated (MV) patients. However, existing Ers stochastic models were developed using small cohorts, potentially showing bias and overestimation during prediction. Thus, there is a need to improve the stochastic model's performance. This research investigates the effect of the kernel density estimator (KDE) parameter tuned with a constant, c on the performance of a 30-min interval Ers stochastic model. Thirteen variations of a stochastic model were developed using varying KDE parameters. Model bias and overestimation were evaluated by the percentage of actual data captured within the 25th – 75th and 5th – 95th percentile lines (Pass50 and Pass90). The optimum range of c was chosen to tune the KDE parameter and minimise the temporal variations of model-predicted 25th – 75th and 5th – 95th percentile values of Ers (ΔRange50 and ΔRange90) in an independent retrospective clinical cohort of 14 patients. In this cohort, the values of ΔRange50 and ΔRange90 exhibit a converging behaviour, resulting in a cohort-optimised value of c = 0.4. Compared to c = 1.0 (benchmark study model), c = 0.4 significantly reduces model overestimation by up to 25.08% in the 25th – 75th percentile values of Ers. Overall, c = 0.3–1.0 presents as a generalised range of optimum c values, considering the trade-off between data overfitting and model overestimation. Optimisation of the KDE parameter enables more accurate and robust Ers stochastic models in cases of limited training data availability

Social Frailty Is Independently Associated with Mood, Nutrition, Physical Performance, and Physical Activity: Insights from a Theory-Guided Approach

Notwithstanding the increasing body of evidence that links social determinants to health outcomes, social frailty is arguably the least explored among the various dimensions of frailty. Using available items from previous studies to derive a social frailty scale as guided by the Bunt social frailty theoretical framework, we aimed to examine the association of social frailty, independently of physical frailty, with salient outcomes of mood, nutrition, physical performance, physical activity, and life&ndash;space mobility. We studied 229 community-dwelling older adults (mean age 67.22 years; 72.6% females) who were non-frail (defined by the FRAIL criteria). Using exploratory factor analysis, the resultant 8-item Social Frailty Scale (SFS-8) yielded a three-factor structure comprising social resources, social activities and financial resource, and social need fulfilment (score range: 0&ndash;8 points). Social non-frailty (SNF), social pre-frailty (SPF), and social frailty (SF) were defined based on optimal cutoffs, with corresponding prevalence of 63.8%, 28.8%, and 7.4%, respectively. In logistic regression adjusted for significant covariates and physical frailty (Modified Fried criteria), there is an association of SPF with poor physical performance and low physical activity (odds ratio, OR range: 3.10 to 6.22), and SF with depressive symptoms, malnutrition risk, poor physical performance, and low physical activity (OR range: 3.58 to 13.97) compared to SNF. There was no significant association of SPF or SF with life&ndash;space mobility. In summary, through a theory-guided approach, our study demonstrates the independent association of social frailty with a comprehensive range of intermediary health outcomes in more robust older adults. A holistic preventative approach to frailty should include upstream interventions that target social frailty to address social gradient and inequalities