Review Individualised prediction of postoperative cardiorespiratory complications after upper abdominal surgery Ornella Piazza a, * , Viviana Miccich  e a , Ciro Esposito b , Gianmarco Romano c , Edoardo De Robertis c a Department of Medicine, University of Salerno, Via Allende, Baronissi, SA, 84081, Italy b Liver Transplant Unit, Cardarelli Hospital, Via A. Cardarelli 9, Napoli, 80131, Italy c Department of Neurosciences, Reproductive and Odontostomatologic Sciences, Via S. Pansini 5, Napoli, 80131, Italy article info Article history: Received 23 October 2015 Received in revised form 24 February 2016 Accepted 29 February 2016 Keywords: CPET 6MWT Preoperative evaluation Cardiac risk Pulmonary failure abstract Risk evaluation for the development of postoperative complications is essential to select the patients that are able to face major abdominal surgery, to strengthen the process of informed consent and to optimize perioperative diagnostic strategies in patients at higher risk. The estimation of functional reserve is particularly important, since a poor cardiopulmonary reserve determines the inability to cope with the stress induced by surgery and thus contributes to the development of postoperative complications. Cardiopulmonary exercise testing (CPET) derived variables are helpful for patients' stratification; 6 min walk test (6MWT) could also be of use, by itself or in association with other risk predictors (clinical scores and biomarkers). A hierarchy of tests may be used to describe risk: simple clinical risk scores may be used to screen out low risk patients, whereas patients at high or uncertain risk could be evaluated by a more complex battery of tests including CPET and biomarkers. © 2016 Elsevier Ltd. All rights reserved. Contents 1. Introduction ....................................................................................................................... 11 2. Classification of post-operative complications ................................................ ......................................... 14 3. Scores ................................................................... ......................................................... 14 3.1. Respiratory scores ............................................................................................................ 15 3.2. Cardiac scores ................................................................................................................ 15 3.3. Generic risk scores ........................................................................................................... 16 4. Biomarkers ........................................................................................................................ 16 5. Functional reserve studies ........................................................................................................... 16 6. Conclusion ........................................................................................................................ 18 References ......................................................................................................................... 18 1. Introduction Several factors might influence patient perioperative outcome. The prediction of postoperative complications [1e4] is based pri- marily on the identification of preoperative risk factors represented by:  patient-related risk factors: comorbidities as chronic obstructive pulmonary disease (COPD), bronchial asthma not controlled by medication, active cardiac conditions and/or chronic ischemic heart failure, diabetes, obesity, American Society of Anesthesi- ologists (ASA) class, advanced age, functional status (partially or totally dependent), impaired sensorium, weight loss, smoking habits, alcohol use. * Corresponding author. E-mail address: opiazza@unisa.it (O. Piazza). Contents lists available at ScienceDirect Trends in Anaesthesia and Critical Care journal homepage: www.elsevier.com/locate/tacc http://dx.doi.org/10.1016/j.tacc.2016.02.003 2210-8440/© 2016 Elsevier Ltd. All rights reserved. Trends in Anaesthesia and Critical Care 6 (2016) 11e19