Novel models for early prediction and prevention of acute respiratory distress syndrome in patients following hepatectomy: A clinical translational study based on 1,032 patients

Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications.

METHODS

A search on PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, Chinese Biomedical Literature (CBM), and VIP databases was systematically conducted, from their establishment to November 2023, to obtain eligible studies for the analysis and evaluation of the predictive effect of AI on ARDS. The retrieved literature was screened according to inclusion and exclusion criteria, the quality of the included studies was assessed using QUADAS-2, and the included studies were statistically analyzed.

RESULTS

Among the 2, 996 studies, 33 were included in this meta-analysis, which showed that the pooled sensitivity of AI in predicting ARDS was 0.81 (0.76-0.85), the pooled specificity was 0.88 (0.84-0.91), and the area under the receiver operating characteristic curve (AUC) was 0.91 (0.88-0.93). The analyzed studies included 28 models, with a pooled sensitivity of 0.79 (0.76-0.82), a pooled specificity of 0.85 (0.83-0.88), and an AUC of 0.89 (0.86-0.91). In the subgroup analysis, the pooled AUC of the AI models ANN, CNN, LR, RF, SVM, and XGB were 0.86 (0.83-0.89), 0.91 (0.88-0.93), 0.86 (0.83-0.89), and 0.89 (0.86-0.91), 0.90 (0.87-0.92), 0.93 (0.90-0.95), respectively. In an additional subgroup analysis, we evaluated the predictive performance of the AI models trained using different predictors. This meta-analysis was registered in PROSPERO (CRD42023491546).

CONCLUSION

AI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value. Algorithmic models such as CNN, SVM, and XGB have improved prediction performance. The subgroup analysis revealed that the model trained using images combined with other predictors had the best predictive performance.

Incidence, mortality, and predictive factors associated with acute respiratory distress syndrome in multiple trauma patients living in high-altitude areas: a retrospective study in Shigatse

Background

Acute respiratory distress syndrome (ARDS) is a severe complication that can lead to fatalities in multiple trauma patients. Nevertheless, the incidence rate and early prediction of ARDS among multiple trauma patients residing in high-altitude areas remain unknown.

Methods

This study included a total of 168 multiple trauma patients who received treatment at Shigatse People's Hospital Intensive Care Unit (ICU) between January 1, 2019 and December 31, 2021. The clinical characteristics of the patients and the incidence rate of ARDS were assessed. Univariable and multivariable logistic regression models were employed to identify potential risk factors for ARDS, and the predictive effects of these risk factors were analyzed.

Results

In the high-altitude area, the incidence of ARDS among multiple trauma patients was 37.5% (63/168), with a hospital mortality rate of 16.1% (27/168). Injury Severity Score (ISS) and thoracic injuries were identified as significant predictors for ARDS using the logistic regression model, with an area under the curve (AUC) of 0.75 and 0.75, respectively. Furthermore, a novel predictive risk score combining ISS and thoracic injuries demonstrated improved predictive ability, achieving an AUC of 0.82.

Conclusions

This study presents the incidence of ARDS in multiple trauma patients residing in the Tibetan region, and identifies two critical predictive factors along with a risk score for early prediction of ARDS. These findings have the potential to enhance clinicians' ability to accurately assess the risk of ARDS and proactively prevent its onset.

Post-operative sepsis-induced acute respiratory distress syndrome: risk factors for a life-threatening complication

Introduction

Prevalence and mortality of the acute respiratory distress syndrome (ARDS) in intensive care units (ICU) are unacceptably high. There is scarce literature on post-operative sepsis-induced ARDS despite that sepsis and major surgery are conditions associated with ARDS. We aimed to examine the impact of post-operative sepsis-induced ARDS on 60-day mortality.

Methods

We performed a secondary analysis of a prospective observational study in 454 patients who underwent major surgery admitted into a single ICU. Patients were stratified in two groups depending on whether they met criteria for ARDS. Primary outcome was 60-day mortality of post-operative sepsis-induced ARDS. Secondary outcome measures were potential risk factors for post-operative sepsis-induced ARDS, and for 60-day mortality.

Results

Higher SOFA score (OR 1.1, 95% CI 1.0-1.3, p = 0.020) and higher lactate (OR 1.9, 95% CI 1.2-2.7, p = 0.004) at study inclusion were independently associated with ARDS. ARDS patients (n = 45) had higher ICU stay [14 (18) vs. 5 (11) days, p < 0.001] and longer need for mechanical ventilation [6 (14) vs. 1 (5) days, p < 0.001] than non-ARDS patients (n = 409). Sixty-day mortality was higher in ARDS patients (OR 2.7, 95% CI 1.1-6.3, p = 0.024). Chronic renal failure (OR 4.0, 95% CI 1.2-13.7, p = 0.026), elevated lactate dehydrogenase (OR 1.7, 95% CI 1.1-2.7, p = 0.015) and higher APACHE II score (OR 2.7, 95% CI 1.3-5.4, p = 0.006) were independently associated with 60-day mortality.

Conclusion

Post-operative sepsis-induced ARDS is associated with higher 60-day mortality compared to non-ARDS post-operative septic patients. Post-operative septic patients with higher severity of illness have a greater risk of ARDS and worse outcomes. Further investigation is needed in post-operative sepsis-induced ARDS to prevent ARDS.