Medicina Intensiva最新文献

Objective

To describe changes in pulmonary mechanics when changing from supine position (SP) to prone position (PP) in mechanically ventilated (MV) patients with Acute Respiratory Distress Syndrome (ARDS) due to severe COVID-19.

Design

Retrospective cohort.

Setting

Intensive Care Unit of the National Institute of Respiratory Diseases (Mexico City).

Patients

COVID-19 patients on MV due to ARDS, with criteria for PP.

Intervention

Measurement of pulmonary mechanics in patients on SP to PP, using esophageal manometry.

Main variables of interest

Changes in lung and thoracic wall mechanics in SP and PP

Results

Nineteen patients were included. Changes during first prone positioning were reported. Reductions in lung stress (10.6 vs 7.7, p = 0.02), lung strain (0.74 vs 0.57, p = 0.02), lung elastance (p = 0.01), chest wall elastance (p = 0.003) and relation of respiratory system elastances (p = 0.001) were observed between patients when changing from SP to PP. No differences were observed in driving pressure (p = 0.19) and transpulmonary pressure during inspiration (p = 0.70).

Conclusions

Changes in pulmonary mechanics were observed when patients were comparing values of supine position with measurements obtained 24 h after prone positioning. Esophageal pressure monitoring may facilitate ventilator management despite patient positioning.

Objectives

Analizing associated factors with vasoplegic shock in the postoperative period of cardiac surgery. Analizing the influence of vasopressin as rescue therapy to first-line treatment with norepinephrine.

Design

Cohort, prospective and observational study.

Setting

Main hospital postoperative cardiac ICU.

Patients

Patients undergoing cardiac surgery with subsequent ICU admission from January 2021 to December 2022.

Interventions

Record of presurgical, perioperative and ICU discharge clinical variables.

Main variables of interest

Chronic treatment, presence of vasoplegic shock, need for vasopressin, cardiopulmonary bypass time, mortality.

Results

773 patients met the inclusion criteria. The average age was 67.3, with predominance of males (65.7%). Post-CPB vasoplegia was documented in 94 patients (12.2%). In multivariate analysis, vasoplegia was associated with age, female sex, presurgical creatinine levels, cardiopulmonary bypass time, lactate level upon admission to the ICU, and need for prothrombin complex transfusion. Of the patients who developed vasoplegia, 18 (19%) required rescue vasopressin, associated with pre-surgical intake of ACEIs/ARBs, worse EuroScore score and longer cardiopulmonary bypass time. Refractory vasoplegia with vasopressin requirement was associated with increased morbidity and mortality.

Conclusions

Postcardiopulmonary bypass vasoplegia is associated with increased mortality and morbidity. Shortening cardiopulmonary bypass times and minimizing products blood transfusion could reduce its development. Removing ACEIs and ARBs prior to surgery could reduce the incidence of refractory vasoplegia requiring rescue with vasopressin. The first-line treatment is norepinephrine and rescue treatment with vasopressin is a good choice in refractory situations. The first-line treatment of this syndrome is norepinephrine, although rescue with vasopressin is a good complement in refractory situations.

The impairment of the right ventricle is a common condition in critically ill patients. Right ventricular dysfunction is associated with increased mortality, and its diagnosis remains challenging. In this review, we aim to outline the potential mechanisms underlying abnormal biomechanics of the right ventricle and different phenotypes of injury. A comprehensive understanding of the pathophysiology and natural history of right ventricular injury may be informative for the intensivist in the diagnosis and management of this condition, as well as contributing to the guidance of individualized treatment strategies.

We describe the main recommended parameters to assess right ventricular systolic and diastolic function. We also define how to evaluate cardiac output and pressures of the pulmonary circulation with echocardiography, with a focus on the diagnosis of acute cor pulmonale and relevant applications in critical illnesses such as distress, septic shock, and right ventricular infarction.

Objective

Study and Evaluation of Two Scores: Shock Index (SI) and Physiological Stress Index (PSI) as discriminators for proactive treatment (reperfusion before decompensated shock) in a population of intermediate-high risk pulmonary embolism (PE).

Design

Using a database from a retrospective cohort with clinical variables and the outcome variable of “proactive treatment”, a comparison of the populations was conducted. Optimal cut-off for “proactive treatment” points were obtained according to the SI and PSI. Comparisons were carried out based on the cut-off points of both indices.

Setting

Patients admitted to a mixed ICU for PE.

Participants

Patients >18 years old admitted to the ICU with intermediate-high risk PE recruited from January 2015 to October 2022.

Interventions

None.

Main variables of interest

Population comparison and metrics regarding predictive capacity when determining proactive treatment.

Results

SI and PSI independently have a substandard predictive capacity for discriminating patients who may benefit from an early reperfusion therapy. However, their combined use improves detection of sicker intermediate-high risk PE patients (Sensitivity = 0.66) in whom an early reperfusion therapy may improve outcomes (Specificity = 0.9).

Conclusions

The use of the SI and PSI in patients with intermediate-high risk PE could be useful for selecting patients who would benefit from proactive treatment.

Objective

To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves.

Design

Observational, retrospective, multicentre study.

Setting

Intensive Care Unit (ICU).

Patients

Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves.

Interventions

None.

Main variables of interest

Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model’s performance was measured using accuracy test and area under curve (AUC) ROC.

Results

A total of 2330 patients (mean age 63 [53–82] years, 1643 (70.5%) male, median APACHE II score (12 [9–16]) and SOFA score (4 [3–6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was −0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC −0.08).

Conclusion

Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.