Does the Surgical Apgar Score predict serious complications after elective major cancer surgery?

Ability to predict surgical outcomes by surgical Apgar score: a systematic review

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.

The Surgical Apgar Score: A Systematic Review of Its Discriminatory Performance

To review the current literature evaluating the performance of the Surgical Apgar Score (SAS).

Background

The SAS is a simple metric calculated at the end of surgery that provides clinicians with information about a patient's postoperative risk of morbidity and mortality. The SAS differs from other prognostic models in that it is calculated from intraoperative rather than preoperative parameters. The SAS was originally derived and validated in a general and vascular surgery population. Since its inception, it has been evaluated in many other surgical disciplines, large heterogeneous surgical populations, and various countries.

Methods

A database and gray literature search was performed on March 3, 2020. Identified articles were reviewed for applicability and study quality with prespecified inclusion criteria, exclusion criteria, and quality requirements. Thirty-six observational studies are included for review. Data were systematically extracted and tabulated independently and in duplicate by two investigators with differences resolved by consensus.

Results

All 36 included studies reported metrics of discrimination. When using the SAS to correctly identify postoperative morbidity, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.59 in a general orthopedic surgery population to 0.872 in an orthopedic spine surgery population. When using the SAS to identify mortality, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.63 in a combined surgical population to 0.92 in a general and vascular surgery population.

Conclusions

The SAS provides a moderate and consistent degree of discrimination for postoperative morbidity and mortality across multiple surgical disciplines.

The utility of surgical Apgar score in predicting postoperative morbidity and mortality in general surgery

Objectives

Many surgical scoring systems are used to predict operative risk but most are complicated. The aim of the study was to determine the utility of the Surgical Apgar Score (SAS) in predicting post operative mortality and morbidity in general surgical cases.

Material and Methods

This was a prospective observational study. All adult patients for emergency and elective general surgical procedures were included. Intraoperative data was collected, and post operative outcomes were followed up till 30 days. SAS was calculated from intraoperative lowest heart rate, lowest MAP and blood loss.

Results

A total of 220 patients were included in the study. All consecutive general surgical procedures were included. Sixty of the 220 cases were emergency and the rest were elective. Forty-five (20.5%) of the patients developed complication. Mortality rate was 3.2% (7 out of 220). The cases were divided into high risk (0-4), moderate risk (5-8) and low risk (9-10) based on SAS. Complication and mortality rates were 50% and 8.3% in the high risk group, 23% and 3.7% in the moderate risk and 4.2% and 0 in the low risk group, respectively.

Conclusion

The surgical Apgar score is a simple and valid predictor of postoperative morbidity and 30-day mortality among patients undergoing general surgeries. It is applicable to all types of surgeries for emergency and elective cases and irrespective of the patient general condition and type of anesthesia and surgery planned.

Surgical Apgar Score predictive value for early postoperative organ dysfunction in cancer patients

BACKGROUND

Surgical APGAR Score (SAS) is based only on intraoperative data and has the advantage of being easy to calculate. Low SAS was associated with an increased risk for postoperative complications, but its utility for specific outcomes prediction, such as postoperative cardiovascular, renal, or metabolic dysfunction is less investigated. Our study aimed to investigate SAS predictive value for early postoperative organ dysfunction in a surgical oncological population.

METHODS

This is a prospective observational study that enrolled all consecutive patients submitted to oncologic surgery over 20-days. Registered parameters included demographics, comorbidities, diagnosis and surgery data, SAS score, postoperative complications, organ dysfunction and in-hospital mortality. SAS predictive value for postoperative organ dysfunction was assessed using logistic regression and ROC curves.

RESULTS

The study included 205 oncological patients with a mean age (standard deviation) of 60 (12.8) years. SAS was between 8 and 10 in 60% of patients and between 0 and 7 in 40% of patients. Postoperative complications developed in 33 patients (16.1%) and organ dysfunction in 26 patients (12.7%). The rates of postoperative complications, organ dysfunction and mortality, were significantly higher in patients with a low SAS (0-7) than high SAS (8-10). SAS had a low discrimination capacity to distinguish between patients who will develop postoperative complications and those who will not (AUROC 0.65) but was more accurate in identifying surgical oncological patients at risk for cardiovascular and metabolic dysfunction (AUROC 0.83 and 0.85 respectively).

CONCLUSION

SAS may be a useful tool to identify cancer surgery patients at risk for postoperative cardiovascular and metabolic dysfunction.