| Summary: | Sebastian Kazimierczak,1 Anita Rybicka,2 Jochen Strauss,1 Malgorzata Schram,3 Arkadiusz Kazimierczak,4 Elżbieta Grochans2 1Anesthesiology, Perioperative Care and Pain Therapy Department, HELIOS Hospital, Berlin-Buch, Germany; 2Department of Nursing, Faculty of Health Sciences, Pomeranian Medical University, Szczecin, Poland; 3Department of Anesthesiology and Intensive Care Medicine, Evangelisch-Freikirchliches Krankenhaus Bernau, Bernau Bei Berlin, Germany; 4Vascular Surgery Department of Pomeranian Medical University, Szczecin, PolandCorrespondence: Anita RybickaDepartment of Nursing, Faculty of Health Sciences, Pomeranian Medical University, ul. Żołnierska 48, Szczecin 71-210, PolandTel +48 91 48 00 910Fax +48 91 48 00 905Email anita.rybicka@pum.edu.plBackground: Preoperative risk assessment is a key issue in the process of patient preparation for surgery and the control of quality improvement in health care and certification programs. Hence, there is a need for a prognostic tool, whose usefulness can be assessed only after validation in the center other than the home one. The aim of the study was to validate the Surgical Mortality Probability Model (S-MPM) for detecting deaths and complications in patients undergoing non-cardiac surgery and to assess its suitability for various surgical disciplines.Methods: This retrospective study involved 38,555 adult patients undergoing non-cardiac surgery in a single center in 2012–2015. The observation period concerned in-hospital mortality.Results: In-hospital mortality for the total population was 0.89%. Mortality in the S-MPM I class amounted to 0.26%, S-MPM II 2.51%, and in the S-MPM III class 22.14%. This result was in line with those obtained by the authors. The discriminatory power for in-hospital mortality was good (area under curve (AUC) = 0.852, 95% CI: 0.834–0.869, p = 0.0000). The scale was the most accurate in general surgery (AUC = 0.89, 95% CI: 0.858–0.922) and trauma (AUC = 0.89; 95% CI: 0.87–0.915). In the logistic regression analysis, the scale showed a perfect fit/goodness of fit in the cross-validation method (v-fold cross-validation): Hosmer–Lemeshow (HL) = 7.945; p = 0.159. This result was confirmed by the traditional derivation and validation data set method (1:3; 9712 vs 22.748 cases): HL test = 3.073 (p = 0.546) in the teaching derivation data set and 10.77 (p = 0.029) in the test sample (validation data set).Conclusion: The S-MPM scale by Glance et al has proven to be a useful tool to assess the risk of in-hospital death and can be taken into account when considering treatment indications, patient information, planning post-operative care, and quality control.Keywords: S-MPM, early mortality, in-hospital mortality, ASA, RCRI
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