Metabolic acid-base adaptation triggered by acute persistent hypercapnia in mechanically ventilated patients with acute respiratory distress syndrome

Mortality Due to Acute Respiratory Distress Syndrome in Latin America

OBJECTIVES

Mortality due to acute respiratory distress syndrome (ARDS) is a major global health problem. Knowledge of epidemiological data on ARDS is crucial to design management, treatment strategies, and optimize resources. There is ample data regarding mortality of ARDS from high-income countries; in this review, we evaluated mortality due to ARDS in Latin America.

DATA SOURCES

We searched in PubMed, Cochrane Central Register of Controlled Trials, Web of Science, and Latin American and Caribbean Health Science Literature databases from 1967 to March 2023.

STUDY SELECTION

We searched prospective or retrospective observational studies and randomized controlled trials conducted in Latin American countries reporting ARDS mortality.

DATA EXTRACTION

Three pairs of independent reviewers checked all studies for eligibility based on their titles and abstracts. We performed meta-analysis of proportions using a random-effects model. We performed sensitivity analyses including studies with low risk of bias and with diagnosis using the Berlin definition. Subgroup analysis comparing different study designs, time of publication (up to 2000 and from 2001 to present), and studies in which the diagnosis of ARDS was made using Pa o2 /F io2 less than or equal to 200 and regional variations. Subsequently, we performed meta-regression analyses. Finally, we graded the certainty of the evidence (Grading of Recommendations Assessment, Development, and Evaluation).

DATA SYNTHESIS

Of 3315 articles identified, 32 were included (3627 patients). Mortality was 52% in the pooled group (low certainty of evidence). In the sensitivity analysis (according to the Berlin definition), mortality was 46% (moderate certainty of evidence). In the subgroup analysis mortality was 53% (randomized controlled trials), 51% (observational studies), 66% (studies published up to 2000), 50% (studies after 2000), 44% (studies with Pa o2 /F io2 ≤ 200), 56% (studies from Argentina/Brazil), and 40% (others countries). No variables were associated with mortality in the meta-regression.

CONCLUSIONS

ARDS mortality in Latin America remains high, as in other regions. These results should constitute the basis for action planning to improve the prognosis of patients with ARDS (PROSPERO [CRD42022354035]).

Breath-holding as model for the evaluation of EEG signal during respiratory distress

PURPOSE

Research describes the existence of a relationship between cortical activity and the regulation of bulbar respiratory centers through the evaluation of the electroencephalographic (EEG) signal during respiratory challenges. For example, we found evidences of a reduction in the frequency of the EEG (alpha band) in both divers and non-divers during apnea tests. For instance, this reduction was more prominent in divers due to the greater physiological disturbance resulting from longer apnea time. However, little is known about EEG adaptations during tests of maximal apnea, a test that voluntarily stops breathing and induces dyspnea.

RESULTS

Through this mini-review, we verified that a protocol of successive apneas triggers a significant increase in the maximum apnea time and we hypothesized that successive maximal apnea test could be a powerful model for the study of cortical activity during respiratory distress.

CONCLUSION

Dyspnea is a multifactorial symptom and we believe that performing a successive maximal apnea protocol is possible to understand some factors that determine the sensation of dyspnea through the EEG signal, especially in people not trained in apnea.

Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach

The goal of this study was to develop a predictive machine learning model to predict the risk of prolonged mechanical ventilation (PMV) in patients admitted to the intensive care unit (ICU), with a focus on laboratory and Arterial Blood Gas (ABG) data. This retrospective cohort study included ICU patients admitted to Rajaei Hospital in Shiraz between 2016 and March 20, 2022. All adult patients requiring mechanical ventilation and seeking ICU admission had their data analyzed. Six models were created in this study using five machine learning models (PMV more than 3, 5, 7, 10, 14, and 23 days). Patients' demographic characteristics, Apache II, laboratory information, ABG, and comorbidity were predictors. This study used Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and C.5 decision tree (C.5 DT) to predict PMV. The study enrolled 1138 eligible patients, excluding brain-dead patients and those without mechanical ventilation or a tracheostomy. The model PMV > 14 days showed the best performance (Accuracy: 83.63-98.54). The essential ABG variables in our two optimal models (artificial neural network and decision tree) in the PMV > 14 models include FiO2, paCO2, and paO2. This study provides evidence that machine learning methods outperform traditional methods and offer a perspective for achieving a consensus definition of PMV. It also introduces ABG and laboratory information as the two most important variables for predicting PMV. Therefore, there is significant value in deploying such models in clinical practice and making them accessible to clinicians to support their decision-making.

The Association between Nutritional Status and In-Hospital Mortality of COVID-19 in Critically-Ill Patients in the ICU

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become one of the leading causes of death worldwide. The impact of poor nutritional status on increased mortality and prolonged ICU (intensive care unit) stay in critically ill patients is well-documented. This study aims to assess how nutritional status and BMI (body mass index) affected in-hospital mortality in critically ill COVID-19 patients Methods: We conducted a retrospective study and analysed medical records of 286 COVID-19 patients admitted to the intensive care unit of the University Clinical Hospital in Wroclaw (Poland).

RESULTS

A total of 286 patients were analysed. In the sample group, 8% of patients who died had a BMI within the normal range, 46% were overweight, and 46% were obese. There was a statistically significantly higher death rate in men (73%) and those with BMIs between 25.0-29.9 (p = 0.011). Nonsurvivors had a statistically significantly higher HF (Heart Failure) rate (p = 0.037) and HT (hypertension) rate (p < 0.001). Furthermore, nonsurvivors were statistically significantly older (p < 0.001). The risk of death was higher in overweight patients (HR = 2.13; p = 0.038). Mortality was influenced by higher scores in parameters such as age (HR = 1.03; p = 0.001), NRS2002 (nutritional risk score, HR = 1.18; p = 0.019), PCT (procalcitonin, HR = 1.10; p < 0.001) and potassium level (HR = 1.40; p = 0.023).

CONCLUSIONS

Being overweight in critically ill COVID-19 patients requiring invasive mechanical ventilation increases their risk of death significantly. Additional factors indicating a higher risk of death include the patient's age, high PCT, potassium levels, and NRS ≥ 3 measured at the time of admission to the ICU.

Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome

Heterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate.

Association Between Average Plasma Potassium Levels and 30-day Mortality During Hospitalization in Patients with COVID-19 in Wuhan, China

Background: Coronavirus disease 2019 (COVID-19) has resulted in more than 610,000 deaths worldwide since December 2019. Given the rapid deterioration of patients' condition before death, markers with efficient prognostic values are urgently required. During the treatment process, notable changes in plasma potassium levels have been observed among severely ill patients. We aimed to evaluate the association between average plasma potassium (K_a ⁺) levels during hospitalization and 30-day mortality in patients with COVID-19. Methods: Consecutive patients with COVID-19 hospitalized in the Zhongfaxincheng branch of Tongji Hospital in Wuhan, China from February 8 to 28, 2020 were enrolled in this study. We followed patients up to 30 days after admission. Results: A total of 136 patients were included in the study. The average age was 62.1±14.6 years and 51.5% of patients were male. The median baseline potassium level was 4.3 (3.9-4.6) mmol/L and K_a ⁺ level during hospitalization was 4.4 (4.2-4.7) mmol/L; the median number of times that we measured potassium was 4 (3-5). The 30-day mortality was 19.1%. A J-shaped association was observed between K_a ⁺ and 30-day mortality. Multivariate Cox regression showed that compared with the reference group (K_a ⁺ 4.0 to <4.5 mmol/L), 30-day mortality was 1.99 (95% confidence interval [CI]=0.54-7.35, P=0.300), 1.14 (95% CI=0.39-3.32, P=0.810), and 4.14 (95% CI=1.29-13.29, P=0.017) times higher in patients with COVID-19 who had K_a ⁺ <4.0, 4.5 to <5.0, and ≥5.0 mmol/L, respectively. Conclusion: Patients with COVID-19 who had a K_a ⁺ level ≥5.0 mmol/L had a significantly increased 30-day mortality compared with those who had a K_a ⁺ level 4.0 to <4.5 mmol/L. Plasma potassium levels should be monitored routinely and maintained within appropriate ranges in patients with COVID-19.