Abstract Background The Surgical Apgar score (SAS) is a straightforward and unbiased measure to assess the probability of experiencing complications after surgery. It is calculated upon completion of the surgical procedure and provides valuable predictive information. The SAS evaluates three specific factors during surgery: the estimated amount of blood loss (EBL), the lowest recorded mean arterial pressure (MAP), and the lowest heart rate (LHR) observed. Considering these factors, the SAS offers insights into the probability of encountering postoperative complications. Methods Three authors independently searched the Medline, PubMed, Web of Science, Scopus, and Embase databases until June 2022. This search was conducted without any language or timeframe restrictions, and it aimed to cover relevant literature on the subject. The inclusion criteria were the correlation between SAS and any modified/adjusted SAS (m SAS, (Modified SAS). eSAS, M eSAS, and SASA), and complications before, during, and after surgeries. Nevertheless, the study excluded letters to the editor, reviews, and case reports. Additionally, the researchers employed Begg and Egger's regression model to evaluate publication bias. Results In this systematic study, a total of 78 studies \were examined. The findings exposed that SAS was effective in anticipating short-term complications and served as factor for a long-term prognostic following multiple surgeries. While the SAS has been validated across various surgical subspecialties, based on the available evidence, the algorithm's modifications may be necessary to enhance its predictive accuracy within each specific subspecialty. Conclusions The SAS enables surgeons and anesthesiologists to recognize patients at a higher risk for certain complications or adverse events. By either modifying the SAS (Modified SAS) or combining it with ASA criteria, healthcare professionals can enhance their ability to identify patients who require continuous observation and follow-up as they go through the postoperative period. This approach would improve the accuracy of identifying individuals at risk and ensure appropriate measures to provide necessary care and support.
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