Advancing ICU patient care with a Real-Time predictive model for mechanical Power to mitigate VILI.

BACKGROUND: Invasive Mechanical Ventilation (IMV) in Intensive Care Units (ICU) significantly increases the risk of Ventilator-Induced Lung Injury (VILI), necessitating careful management of mechanical power (MP). This study aims to develop a real-time predictive model of MP utilizing Artificial Intelligence to mitigate VILI. METHODOLOGY: A retrospective observational study was conducted, extracting patient data from Clinical Information Systems from 2018 to 2022. Patients over 18 years old with more than 6 h of IMV were selected. Continuous data on IMV variables, laboratory data, monitoring, procedures, demographic data, type of admission, reason for admission, and APACHE II at admission were extracted. The variables with the highest correlation to MP were used for prediction and IMV data was grouped in 15-minute intervals using the mean. A mixed neural network model was developed to forecast MP 15 min in advance, using IMV data from 6 h before the prediction and current patient status. The model's ability to predict future MP was analyzed and compared to a baseline model predicting the future value of MP as equal to the current value. RESULTS: The cohort consisted of 1967 patients after applying inclusion criteria, with a median age of 63 years and 66.9 % male. The deep learning model achieved a mean squared error of 2.79 in the test set, indicating a 20 % improvement over the baseline model. It demonstrated high accuracy (94 %) in predicting whether MP would exceed a critical threshold of 18 J/min, which correlates with increased mortality. The integration of this model into a web platform allows clinicians real-time access to MP predictions, facilitating timely adjustments to ventilation settings. CONCLUSIONS: The study successfully developed and integrated in clinical practice a predictive model for MP. This model will assist clinicians allowing for the adjustment of ventilatory parameters before lung damage occurs.

A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients.

Objective: To determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes. Design: A secondary analysis derived from multicenter, observational study. Setting: Critical Care Units. Patients: Adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain. Interventions: Corticosteroids vs. no corticosteroids. Main variables of interest: Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). We performed a multivariate analysis after propensity optimal full matching (PS) for whole population and weighted Cox regression (HR) and Fine-Gray analysis (sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes. Results: A total of 2017 patients were analyzed, 1171 (58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR: 1.0; 95% CI: 0.98-1.15). Corticosteroids were administered in 298/537 (55.5%) patients of "A" phenotype and their use was not associated with ICU mortality (HR = 0.85 [0.55-1.33]). A total of 338/623 (54.2%) patients in "B" phenotype received corticosteroids. No effect of corticosteroids on ICU mortality was observed when HR was performed (0.72 [0.49-1.05]). Finally, 535/857 (62.4%) patients in "C" phenotype received corticosteroids. In this phenotype HR (0.75 [0.58-0.98]) and sHR (0.79 [0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality. Conclusion: Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate dose. Only patients with the highest inflammatory levels could benefit from steroid treatment.

Objetivo: Evaluar si el uso de corticoesteroides (CC) se asocia con la mortalidad en la unidad de cuidados intensivos (UCI) en la población global y dentro de los fenotipos clínicos predeterminados. Diseño: Análisis secundario de estudio multicéntrico observacional. Ámbito: UCI. Pacientes: Pacientes adultos con COVID-19 confirmado ingresados en 63 UCI de España. Intervención: Corticoides vs. no corticoides. Variables de interés principales: A partir del análisis no supervisado de grupos, 3 fenotipos clínicos fueron derivados y clasificados como: A grave, B crítico y C potencialmente mortal. Se efectuó un análisis multivariado después de un propensity optimal full matching (PS) y una regresión ponderada de Cox (HR) y análisis de Fine-Gray (sHR) para evaluar el impacto del tratamiento con CC sobre la mortalidad en la población general y en cada fenotipo clínico. Resultados: Un total de 2.017 pacientes fueron analizados, 1.171 (58%) con CC. Después del PS, el uso de CC no se relacionó significativamente con la mortalidad en UCI (OR: 1,0; IC 95%: 0,98-1,15). Los CC fueron administrados en 298/537 (55,5%) pacientes del fenotipo A y no se observó asociación significativa con la mortalidad (HR = 0,85; 0,55-1,33). Un total de 338/623 (54,2%) pacientes del fenotipo B recibieron CC sin efecto significativo sobre la mortalidad (HR = 0,72; 0,49-1,05). Por último, 535/857 (62,4%) pacientes del fenotipo C recibieron CC. En este fenotipo, se evidenció un efecto protector de los CC sobre la mortalidad HR (0,75; 0,58-0,98). Conclusión: Nuestros hallazgos alertan sobre el uso indiscriminado de CC a dosis moderadas en todos los pacientes críticos con COVID-19. Solamente pacientes con elevado estado de inflamación podrían beneficiarse con el tratamiento con CC.

A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients.

OBJECTIVE: To determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes. DESIGN: A secondary analysis derived from multicenter, observational study. SETTING: Critical Care Units. PATIENTS: Adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain. INTERVENTIONS: Corticosteroids vs. no corticosteroids. MAIN VARIABLES OF INTEREST: Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). We performed a multivariate analysis after propensity optimal full matching (PS) for whole population and weighted Cox regression (HR) and Fine-Gray analysis (sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes. RESULTS: A total of 2017 patients were analyzed, 1171 (58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR: 1.0; 95% CI: 0.98-1.15). Corticosteroids were administered in 298/537 (55.5%) patients of "A" phenotype and their use was not associated with ICU mortality (HR=0.85 [0.55-1.33]). A total of 338/623 (54.2%) patients in "B" phenotype received corticosteroids. No effect of corticosteroids on ICU mortality was observed when HR was performed (0.72 [0.49-1.05]). Finally, 535/857 (62.4%) patients in "C" phenotype received corticosteroids. In this phenotype HR (0.75 [0.58-0.98]) and sHR (0.79 [0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality. CONCLUSION: Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate dose. Only patients with the highest inflammatory levels could benefit from steroid treatment.

Secondary use of data extracted from a clinical information system to assess the adherence of tidal volume and its impact on outcomes.

OBJECTIVES: To extract data from clinical information systems to automatically calculate high-resolution quality indicators to assess adherence to recommendations for low tidal volume. DESIGN: We devised two indicators: the percentage of time under mechanical ventilation with excessive tidal volume (>8mL/kg predicted body weight) and the percentage of patients who received appropriate tidal volume (≤8mL/kg PBW) at least 80% of the time under mechanical ventilation. We developed an algorithm to automatically calculate these indicators from clinical information system data and analyzed associations between them and patients' characteristics and outcomes. SETTINGS: This study has been carried out in our 30-bed polyvalent intensive care unit between January 1, 2014 and November 30, 2019. PATIENTS: All patients admitted to intensive care unit ventilated >72h were included. INTERVENTION: Use data collected automatically from the clinical information systems to assess adherence to tidal volume recommendations and its outcomes. MAIN VARIABLES OF INTEREST: Mechanical ventilation days, ICU length of stay and mortality. RESULTS: Of all admitted patients, 340 met the inclusion criteria. Median percentage of time under mechanical ventilation with excessive tidal volume was 70% (23%-93%); only 22.3% of patients received appropriate tidal volume at least 80% of the time. Receiving appropriate tidal volume was associated with shorter duration of mechanical ventilation and intensive care unit stay. Patients receiving appropriate tidal volume were mostly male, younger, taller, and less severely ill. Adjusted intensive care unit mortality did not differ according to percentage of time with excessive tidal volume or to receiving appropriate tidal volume at least 80% of the time. CONCLUSIONS: Automatic calculation of process-of-care indicators from clinical information systems high-resolution data can provide an accurate and continuous measure of adherence to recommendations. Adherence to tidal volume recommendations was associated with shorter duration of mechanical ventilation and intensive care unit stay.