Assessment of respiratory support decision and the outcome of invasive mechanical ventilation in severe COVID-19 with ARDS

Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments

The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90-0.95) for 4C and 0.903 (95% CI: 086-0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.

A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach

The global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern's unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country's performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape.

Comparison of limited driving pressure ventilation and low tidal volume strategies in adults with acute respiratory failure on mechanical ventilation: a randomized controlled trial

BACKGROUND

Ventilator-induced lung injury (VILI) presents a grave risk to acute respiratory failure patients undergoing mechanical ventilation. Low tidal volume (LTV) ventilation has been advocated as a protective strategy against VILI. However, the effectiveness of limited driving pressure (plateau pressure minus positive end-expiratory pressure) remains unclear.

OBJECTIVES

This study evaluated the efficacy of LTV against limited driving pressure in preventing VILI in adults with respiratory failure.

DESIGN

A single-centre, prospective, open-labelled, randomized controlled trial.

METHODS

This study was executed in medical intensive care units at Siriraj Hospital, Mahidol University, Bangkok, Thailand. We enrolled acute respiratory failure patients undergoing intubation and mechanical ventilation. They were randomized in a 1:1 allocation to limited driving pressure (LDP; ⩽15 cmH2O) or LTV (⩽8 mL/kg of predicted body weight). The primary outcome was the acute lung injury (ALI) score 7 days post-enrolment.

RESULTS

From July 2019 to December 2020, 126 patients participated, with 63 each in the LDP and LTV groups. The cohorts had the mean (standard deviation) ages of 60.5 (17.6) and 60.9 (17.9) years, respectively, and they exhibited comparable baseline characteristics. The primary reasons for intubation were acute hypoxic respiratory failure (LDP 49.2%, LTV 63.5%) and shock-related respiratory failure (LDP 39.7%, LTV 30.2%). No significant difference emerged in the primary outcome: the median (interquartile range) ALI scores for LDP and LTV were 1.75 (1.00-2.67) and 1.75 (1.25-2.25), respectively (p = 0.713). Twenty-eight-day mortality rates were comparable: LDP 34.9% (22/63), LTV 31.7% (20/63), relative risk (RR) 1.08, 95% confidence interval (CI) 0.74-1.57, p = 0.705. Incidences of newly developed acute respiratory distress syndrome also aligned: LDP 14.3% (9/63), LTV 20.6% (13/63), RR 0.81, 95% CI 0.55-1.22, p = 0.348.

CONCLUSIONS

In adults with acute respiratory failure, the efficacy of LDP and LTV in averting lung injury 7 days post-mechanical ventilation was indistinguishable.

CLINICAL TRIAL REGISTRATION

The study was registered with the ClinicalTrials.gov database (identification number NCT04035915).

Modeling the therapy system of noninvasive pressure support ventilation with the respiratory patient in COPD and ARDS

The noninvasive pressure support ventilation (NPSV) has been one of mechanical ventilation widely applied for the respiratory patients in chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), etc. To investigate and develop the technologies in NPSV conveniently and in low-cost, in this article, a therapy system model of NPSV was designed with developing the mathematical submodels of NPSV respirator and respiratory patient in COPD and ARDS. By simulating the respirator, breath circuit, mask and respiratory patients, a MATLAB-based virtual experimental platform was developed for virtual ventilations. And in order to test the authenticity and practicability of the therapy system model of NPSV, a lot of ASL5000-based physical experiments were carried out for comparative analysis with the simulated outputs: pressures, flows and volumes. The statistical conclusions demonstrate that the simulated results are consist with the results from the physical experiments (TTEST P > 0.39). The experimental results tell that the therapy system model of NPSV is effective and workable. The developed therapy system model of NPSV will be beneficial for clinician and researcher to explore the therapeutic methods and some potential measures in NPSV for saving the respiratory patient's health and life.