BACKGROUND The serratus anterior plane block (SAPb) is a promising interfascial plane technique able to provide profound thoracic analgesia. As only a few studies with quite small patient samples are presently available, the analgesic efficacy of adding SAPb to general anaesthesia in video-assisted thoracoscopic surgery (VATS), compared with general anaesthesia only, remains unclear. OBJECTIVES Our primary aim was to assess the analgesic efficacy of SAPb for VATS peri-operative pain control. The secondary aims were to evaluate differences in postoperative opioid use, intra-operative hypotension, postoperative side-effects and complications, time to chest tube removal, length of hospital stay. DESIGN Systematic review of randomised controlled trials (RCTs) with meta-analyses. DATA SOURCES PubMed, Web of Science, Google Scholar and the Cochrane Library, searched up to 6 December 2019. ELIGIBILITY CRITERIA RCTs including adult patients undergoing VATS who received single shot SAPb (cases), compared with general anaesthesia (controls). RESULTS Seven RCTs, with a total of 489 patients were included. SAPb reduced pain scores peri-operatively, compared with controls: 6 h [mean difference −1.86, 95% confidence interval (CI) −2.35 to −1.37, P < 0.001]; 12 h (mean difference −1.45, 95% CI −1.66 to −1.25, P < 0.001); 24 h (mean difference −0.98, 95% CI −1.40 to −0.56, P < 0.001). SAPb also reduced the use of postoperative opioids (mean difference: −4.81 mg of intravenous morphine equivalent, 95% CI −8.41 to −1.22, P < 0.03) and decreased the incidence of nausea and vomiting (risk ratio 0.53, 95% CI 0.36 to 0.79, P < 0.002). CONCLUSION Compared with general anaesthesia only and if no other locoregional techniques are used, SAPb significantly reduces postoperative pain and nausea and vomiting in patients undergoing VATS. Grading of Recommendations Assessment, Development and Evaluation rating are, nonetheless, quite low, due to high heterogeneity. Well designed and properly powered RCTs are necessary to confirm these preliminary findings.

Background Pathophysiological features of coronavirus disease 2019-associated acute respiratory distress syndrome (COVID-19 ARDS) were indicated to be somewhat different from those described in nonCOVID-19 ARDS, because of relatively preserved compliance of the respiratory system despite marked hypoxemia. We aim ascertaining whether respiratory system static compliance (Crs), driving pressure (DP), and tidal volume normalized for ideal body weight (VT/kg IBW) at the 1st day of controlled mechanical ventilation are associated with intensive care unit (ICU) mortality in COVID-19 ARDS. Methods Observational multicenter cohort study. All consecutive COVID-19 adult patients admitted to 25 ICUs belonging to the COVID-19 VENETO ICU network (February 28th–April 28th, 2020), who received controlled mechanical ventilation, were screened. Only patients fulfilling ARDS criteria and with complete records of Crs, DP and VT/kg IBW within the 1st day of controlled mechanical ventilation were included. Crs, DP and VT/kg IBW were collected in sedated, paralyzed and supine patients. Results A total of 704 COVID-19 patients were screened and 241 enrolled. Seventy-one patients (29%) died in ICU. The logistic regression analysis showed that: (1) Crs was not linearly associated with ICU mortality (p value for nonlinearity = 0.01), with a greater risk of death for values < 48 ml/cmH2O; (2) the association between DP and ICU mortality was linear (p value for nonlinearity = 0.68), and increasing DP from 10 to 14 cmH2O caused significant higher odds of in-ICU death (OR 1.45, 95% CI 1.06–1.99); (3) VT/kg IBW was not associated with a significant increase of the risk of death (OR 0.92, 95% CI 0.55–1.52). Multivariable analysis confirmed these findings. Conclusions Crs < 48 ml/cmH2O was associated with ICU mortality, while DP was linearly associated with mortality. DP should be kept as low as possible, even in the case of relatively preserved Crs, irrespective of VT/kg IBW, to reduce the risk of death.

Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality.

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