Background

In the era of enhanced recovery after surgery, there is significant discussion regarding the impact of intraoperative anesthetic management on short-term outcomes following liver transplantation (LT), with no clear consensus in the literature.

Objectives

To identify whether or not intraoperative anesthetic management affects short-term outcomes after liver transplantation.

Data sources

Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central.

Methods

A systematic review following PRISMA guidelines was undertaken. The systematic review was registered on PROSPERO (CRD42021239758). An international expert panel made recommendations for clinical practice using the GRADE approach.

Results

After screening, 14 studies were eligible for inclusion in this systematic review. Six were prospective randomized clinical trials, three were prospective nonrandomized clinical trials, and five were retrospective studies. These manuscripts were reviewed to look at five questions regarding anesthetic care and its impact on short term outcomes following liver transplant. After review of the literature, the quality of evidence according to the following outcomes was as follows: intraoperative and postoperative morbidity and mortality (low), early allograft dysfunction (low), and hospital and ICU length of stay (moderate).

Conclusions

For optimal short term outcomes after liver transplantation, the panel recommends the use of volatile anesthetics in preference to total intravenous anesthesia (TIVA) (Level of Evidence: Very low; Strength of Recommendation: Weak) and minimum alveolar concentration (MAC) versus bispectral index (BIS) for depth of anesthesia monitoring (Level of Evidence: Very low; Strength of Recommendation: Weak). Regarding ventilation and oxygenation, the panel recommends a restrictive oxygenation strategy targeting a PaO₂ of 70-120 mmHg (10-14 kPa), a tidal volume of 6-8 ml/kg ideal body weight (IBW), administration of positive end expiratory pressure (PEEP) tailored to patient intraoperative physiology, and recruitment maneuvers. (Level of evidence: Very low; Strength of Recommendation: Strong). Finally, the panel recommends the routine use of antiemetic prophylaxis. (Level of evidence: low; Strength of Recommendation: Strong).

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.