Enhanced Recovery After Surgery (ERAS) is a multimodal, multidisciplinary approach to surgical patients with the aim of enhancing the quality of recovery after surgery (1,2). This strategy translates into faster post-operative recovery and improvements of outcomes. All the ERAS Society guidelines (freely available at www.erassociety.org) take into consideration the perioperative management of analgesia. The role of pain management in ERAS pathways is fundamental, considering the importance of containing surgical stress, reducing pain-related complications and speeding recovery (2-5). Correspondence to: Carlo Del Naja, MD. Casa Sollievo della Sofferenza Hospital, viale Cappuccini, 1, 71013 San Giovanni Rotondo (FG), Italy.Email: c.delnaja@operapadrepio.it.Abstract: Video-assisted thoracoscopic surgery (VATS) is a minimally invasive technique that allows a faster recovery after thoracic surgery. Although enhanced recovery after surgery (ERAS) principles seem reasonably applicable to thoracic surgery, there is little literature on the application of such a strategy in this context. In regard to pain management, ERAS pathways promote the adoption of a multimodal strategy, tailored to the patients. This approach is based on combining systemic and loco-regional analgesia to favour opioid-sparing strategies. Thoracic paravertebral block is considered the first-line loco-regional technique for VATS. Other techniques include intercostal nerve block and serratus anterior plane block. Nonsteroidal anti-inflammatory drugs and paracetamol are essential part of the multimodal treatment of pain. Also, adjuvant drugs can be useful as opioid-sparing agents. Nevertheless, the treatment of postoperative pain must take into account opioid agents too, if necessary. All above is useful for careful planning and execution of a multimodal analgesic treatment to enhance the recovery of patients. This article summarizes the most recent evidences from literature and authors' experiences on perioperative multimodal analgesia principles for implementing an ERAS program after VATS lobectomy.

In thoracic surgery, the introduction of video-assisted thoracoscopic techniques has allowed the development of fast-track protocols, with shorter hospital lengths of stay and improved outcomes. The perioperative management needs to be optimized accordingly, with the goal of reducing postoperative complications and speeding recovery times. Premedication performed in the operative room should be wisely administered because often linked to late discharge from the post-anesthesia care unit (PACU). Inhalatory anesthesia, when possible, should be preferred based on protective effects on postoperative lung inflammation. Deep neuromuscular blockade should be pursued and carefully monitored, and an appropriate reversal administered before extubation. Management of one-lung ventilation (OLV) needs to be optimized to prevent not only intraoperative hypoxemia but also postoperative acute lung injury (ALI): protective ventilation strategies are therefore to be implemented. Locoregional techniques should be favored over intravenous analgesia: the thoracic epidural, the paravertebral block (PVB), the intercostal nerve block (ICNB), and the serratus anterior plane block (SAPB) are thoroughly reviewed and the most common dosages are reported. Fluid therapy needs to be administered critically, to avoid both overload and cardiovascular compromisation. All these practices are analyzed singularly with the aid of the most recent evidences aimed at the best patient care. Finally, a few notes on some of the latest trends in research are presented, such as non-intubated video-assisted thoracoscopic surgery (VATS) and intravenous lidocaine.

Background Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessible online. In order to evaluate its clinical value, the AKIpredictor was compared to physicians’ predictions. Methods Prospective observational study in five ICUs of a tertiary academic center. Critically ill adults without end-stage renal disease or AKI upon admission were considered for enrollment. Using structured questionnaires, physicians were asked upon admission, on the first morning, and after 24 h to predict the development of AKI stages 2 or 3 (AKI-23) during the first week of ICU stay. Discrimination, calibration, and net benefit of physicians’ predictions were compared against the ones by the AKIpredictor. Results Two hundred fifty-two patients were included, 30 (12%) developed AKI-23. In the cohort of patients with predictions by physicians and AKIpredictor, the performance of physicians and AKIpredictor were respectively upon ICU admission, area under the receiver operating characteristic curve (AUROC) 0.80 [0.69–0.92] versus 0.75 [0.62–0.88] ( n = 120, P = 0.25) with net benefit in ranges 0–26% versus 0–74%; on the first morning, AUROC 0.94 [0.89–0.98] versus 0.89 [0.82–0.97] ( n = 187, P = 0.27) with main net benefit in ranges 0–10% versus 0–48%; after 24 h, AUROC 0.95 [0.89–1.00] versus 0.89 [0.79–0.99] ( n = 89, P = 0.09) with main net benefit in ranges 0–67% versus 0–50%. Conclusions The machine-learning-based AKIpredictor achieved similar discriminative performance as physicians for prediction of AKI-23, and higher net benefit overall, because physicians overestimated the risk of AKI. This suggests an added value of the systematic risk stratification by the AKIpredictor to physicians’ predictions, in particular to select high-risk patients or reduce false positives in studies evaluating new and potentially harmful therapies. Due to the low event rate, future studies are needed to validate these findings. Trial registration ClinicalTrials.gov, NCT03574896 registration date: July 2nd, 2018 Electronic supplementary material The online version of this article (10.1186/s13054-019-2563-x) contains supplementary material, which is available to authorized users.

Background: The extracorporeal removal of mediators is a rescue strategy for septic shock patients, which is still under investigation. Several techniques are available: coupled plasma filtration and adsorption (CPFA) combines plasma processing with renal replacement therapy. Methods: The study aimed to elucidate the role of both timing of initiation and intensity of treatment on the outcome, for which we retrospectively studied 52 patients. We collected the overall pre-CPFA time interval, starting from the first episode of hypotension in the wards and the volume of processed plasma (Vp), which we used as a proxy for intensity of treatment. Results: Timing of initiation did not significantly differ between survivors and non-survivors (25 vs. 27 h), while the Vp did (0.25 vs. 0.17 L/kg/session, p < 0.05). The significance of Vp was confirmed by a multiple logistic regression model. Conclusion: Our study confirms that intensity of CPFA, but not its timing of initiation, correlates with survival of septic shock patients.

Purpose of review The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality. Recent findings Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge. Summary Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients’ care.

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