Introduction

Surgery has become more accessible for a broader range of diseases and patients due to developments in the field of anesthesia. As a result, more procedures are conducted worldwide each year [1]. It is estimated that 187–200 million cases of surgery are performed worldwide. In addition, it is estimated that one million people die annually within thirty days after surgery [2], and nearly 10% of patients toil from adverse events [3].

Safety and quality are significant matters while providing healthcare services. Since surgery plays a more noticeable role in healthcare globally, safety and quality of such care is receiving growing notice [3].

Reducing perioperative complications/mortality is crucial for patient safety and healthcare economics. It has been observed that approximately fifty percent of post-surgical complications can be prohibited, and advancements in anesthesia-related agents play a significant role in this prevention [4]. Numerous evaluation methods have been suggested to calculation the incidence of post-surgical outcomes/mortality [4]. By objectively assessing these factors, it becomes possible to predict the demand for additional care in intensive care or high-dependency settings and prioritize efforts to reduce surgical complications [5, 6].

Several scoring systems are employed to evaluate surgical patients, such as the American Society of Anesthesiology classification, Revised Cardiac Risk Index, Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity score, and the National Surgical Quality Improvement Program score. However, these systems of scoring come with limitations specific to them. These limitations include variability in interpretations between different observers, complexities in the calculation, and the need for biochemical investigations [2]. Gawande et al. first presented the SAS implication [7].

A prognostic metric called the SAS is used to forecast post-surgical morbidity/mortality for surgical procedures. This scoring system is straightforward with a range of 0 to 10 points that considers LHR, MAP and EBL during surgery. For first time created for those undergoing vascular and general procedures [8], the SAS has demonstrated its effectiveness in various surgical, containing urological, gynecologic, orthopedic, and neurosurgery [9]. The SAS is a comprehensive tool that provides a detailed assessment of the clinical and biological status of the patient, aiding in predicting mortality [10]. However, studies investigating the effectiveness of SAS have yielded conflicting results. While some studies support its value in predicting postoperative outcomes, others focusing on gastric, neurosurgery, and orthopedic patients have been unable to establish a consistent relationship [11,12,13]. According to Pittman et al., the SAS exhibits a modest postoperative morbidity discrimination level and mortality across a variety of surgical specialties [14].

Considering that no study has so far comprehensively focused on the effect of the Apgar score on the prediction of surgical results, the current review study investigated SAS results on the prediction of surgical outcomes.

Materials and methods

Data collection

The PRISMA checklist and flowchart were used to evaluate the retrieved studies and improve their quality to identify the relationship between SAS and surgical complications [15].

Search strategy

The relevant literature was thoroughly searched within the PubMed, Medline, Scopus, Web of Science, and Embase databases until June 2022 with no time or language limitation using keywords such as (‘Surgical Apgar score’ AND ‘Complication’ AND ‘Predict’). To conduct the study, three authors initially reviewed the sources of qualified article reports and subsequently evaluated the abstracts and titles of the identified articles. Irrelevant, duplicate, and non-original essays were excluded from further investigation. The relevant data, including the first author, publication year, demographic characteristics of countries, participants, and treatment options for each group, were extracted using a predefined standardized procedure. All these tasks were carried out independently by the authors.

Inclusion and exclusion criteria

The entry criteria included the correlation between SAS and any modified/adjusted SAS (m SAS,Footnote 1 eSAS,Footnote 2 M eSAS,Footnote 3 and SASA), and complications before, during, and after surgeries. On the other hand, letters to the editor, reviews, and case reports were excluded from the study.

Data extraction

The process of article selection involved three investigators (MM, MS, and MB), who independently carried out the screening. In case of disagreements, a third author was involved in reaching a consensus. The data extraction process utilized a datasheet that included year of publication, first author, country, study design, SAS, modified or adjusted SAS, complications occurring before, during, and after surgeries, and article quality.

Quality assessment

The methodological quality of the articles was assessed following the guidelines provided by the Newcastle and Ottawa statements. These guidelines were used as a framework for evaluating the quality of the included studies during the review process [16]. In this guideline, criteria were considered to check the selection of subjects under study, their comparability, exposure, and outcome, and at most nine stars were assigned to each study. Studies with seven or more stars and six stars or less were classified as high and low-quality, respectively. The potential for bias in the study results was examined independently by two researchers. In cases where disagreements arose, the researchers resolved them through discussion and negotiation. This process ensured a comprehensive assessment of potential bias in the study findings.

Results

The SAS is a straightforward scaling model that uses easily computed and recorded. It provides surgeons and anesthesiologists a tool to recognize patients at a higher risk for adverse outcomes or complications. By utilizing the SAS, healthcare professionals can effectively assess the risk profile of patients and make informed decisions regarding their care and management. Therefore, the present study investigated and searched for information about SAS this species. To achieve this goal, PubMed, Scopus, Embase, Web of Science, and Google Scholar databases were searched using keywords ((surgical Apgar score) AND (complication)) AND (predict). In the initial search, 882 articles were found from four databases, the Google Scalar search engine and nine additional records were identified through the other sources. Overall, 660 duplicates were excluded with the help of EndNote X8 software, and 114 articles remained, among which 36 articles were excluded after reviewing their full texts, and 78 articles remained for full-text screening (Fig. 1 The remaining 78 articles included 28, 26, and 22 retrospectives, cohort, and prospective studies, respectively, and two studies were randomized control trials (Tables 1, 2, 3, and 4).

Fig. 1
figure 1

Flow diagram of the study selection for the review process

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Table 1 (General-Vascular- Oncologic- Neuro)Surgery
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Table 2 (Orthopedic-Urologic-Gynecologic-Thoracic) Surgery
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Table 3 All kind of surgeries
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Table 4 Main characteristics of included studies
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Our research showed contradictory results regarding the use of SAS and modified SAS in different surgeries; for example, six studies represented that SAS was not an estimated factor for complications following surgery, but modified SAS was considered a valuable predictor for surgery complications (Tables 1, 2, 3, and 4).

In addition, a study found that the eSAS may not be correlated during 30-day significant malady following surgery. Nevertheless, the modified eSAS demonstrated a significant association with major morbidity. (Table 4).

From another point of view, in the review of 63 studies, it was revealed that the SAS could predict cardiovascular, respiratory, digestive, urogenital, neurological, systemic, and infectious [91] complications, the duration of hospitalization in the intensive care unit (ICU), mortality, and the survival rate in various surgeries (Tables 1, 2, 3, and 4). Additionally, two other studies demonstrated SAS weak differences for major and minor complications after surgeries (Table 1).

On the contrary, in 11 studies, it was found that SAS was not correlated with complications after surgeries (Tables 1, 2, 3, and 4). The obtained results are discussed in the following sections.

General and vascular surgery

Regarding general and vascular surgery, there were a total of 14 studies in which SAS predicted complications after surgery (n = 12), death after surgery (n = 6), and the requirement to stay in the ICU (n = 2).

Emergency surgery

In the field of emergency surgery, six studies were found in which SAS could predict complications after surgery (n = 5), died after surgery (n = 4), and the requirement to stay in ICU (n = 1), along with one non-prediction case.

Thoracic surgery

Considering thoracic surgery, a total of 10 studies were obtained in which SAS predicted complications after surgery (n = 9), death after surgery (n = 1), and length of hospital stay (n = 2) in addition to one non-prediction case.

Cancer surgery

Overall, 12 studies were found regarding cancer surgery, in which SAS could predict complications after surgery (n = 10), died after surgery (n = 3), and the requirement to stay in ICU (n = 1).

Gynecological surgery

In the Gynecosurgery field, three studies were achieved, in which SAS predicted complications after surgery (n = 3), death after surgery (n = 1), and the requirement to stay in the ICU (n = 1).

Liver and pancreas surgery

There were seven studies in emergency surgery [92] in which SAS could predict complications after surgery (n = 7) and death after surgery (n = 4).

Orthopedic surgery

With regard to orthopedic surgery, there were a total of six studies in which SAS anticipated complications after surgery (n = 4), death after surgery (n = 1), and the requirement to stay in the ICU (n = 1), along with two non-prediction cases.

Urological surgery

In the field of urological surgery, four studies were found in which SAS predicted complications after surgery (n = 2) and the requirement to stay in the ICU (n = 1), as well as one non-prediction case.

Neurosurgery

Generally, six studies were related to neurosurgery, in which SAS could predict complications after surgery (n = 6) and death after surgery (n = 3).

Head and neck surgery

Two studies were about head and neck surgery, in which SAS anticipated complications after surgery (n = 1), along with one non-prediction case.

Other surgeries

As regards the other surgeries, eight studies were obtained in which SAS predicted complications after surgery (n = 4), died after surgery (n = 4), and the requirement to stay in ICU (n = 2) in addition to one non-prediction case.

Hence, after this study, it can be concluded that the "SAS,” “Postoperative complications," "Surgery," "Morbidity," “requirement to stay in ICU," and "Mortality" used SAS as a predictor instrument to identify the correlation with early and late postoperative outcomes. In addition, modifications in SAS (Modified SAS) or the combination of SAS with ASA criteria can help identify patients who require incessant monitoring and follow-up while going through the postoperative period.

Discussion

Virginia Apgar initially developed the Apgar score in 1953 for assessing neonatal health and predicting morbidity and mortality shortly after birth. It was primarily designed for use in obstetrics and pediatrics to quickly evaluate the newborn's overall condition based on specific criteria such as heart rate, respiratory effort, muscle tone, reflex irritability, and color. The Apgar score has since become a widely used and standardized method for evaluating the immediate health status of newborns. Giugliano et al., by modifying and applying some changes in this score, designed the SAS in a way that it can predict complications and mortality during surgery [64]. The SAS is a straightforward scoring system from 0 to 10. it is derived from three during-procedure variables collected during surgery, which include LHR, EBL and MAP. Variables are used to Compute the SAS, concisely assessing a patient's physiological status during the surgical procedure [38]. Several studies have examined many data regarding SAS prospectively and retrospectively. The following section will discuss the results of these studies in detail.

The results obtained from these studies were categorized into four tables, including SAS results in general, vascular, oncological, and neurological (Table 1), as well as orthopedics, urology, gynecology/obstetrics, and thoracic (Table 2) surgeries, respectively. In addition, Tables 3 and 4 present SAS results in different surgeries and modified SAS, respectively. Many of these studies have demonstrated that SAS alone can be a valuable model for estimating complications after a variety of surgical specialties such as general [12, 32, 52], colorectal [17], gynecology [28], orthopedics [24], and neurosurgery [27] ones. Mastalerz et al. confirmed SAS < 8 for the prediction of thirty-day complications after surgery [93]. Likewise, Haynes et al. confirmed SAS globally in a multicenter clinical study in eight countries [76]. Some evidence indicated that SAS, combined with other criteria, has a better diagnostic ability to estimate complications after surgeries. Pinho et al. [50] examined Possum and SAS for their utility in determining whether to admit patients to the ICU right away following colorectal surgery; they found that Possum had greater sensitivity and specificity, but the drawback is that it needs a wide variety of clinical and laboratory data. In addition to the initial SAS, various iterations of the SAS were created by researchers to more accurately pinpoint the hazards related to particular patients or surgical groups.

In comparison to SAS and ASA used separately, evidence shows SASA (a compound of SAS and ASA) and came to the conclusion that this new version is more accurate at predicting postoperative problems [1]. In the investigation of Kotera et al., another combination of SAS was used in patients with femoral neck fracture; their results revealed that the combination of SAS with ASA class = 3 improves the capability to predict post-surgical complications [94]. In their study, Miki et al. analyzed the files of 328 people undergoing gastrectomy. They simultaneously used the original and modified SAS criteria to predict surgical results. It was found that mSAS (modified Surgical Apgar Score) was reported to be a valuable predictor for drastic outcomes succeeding gastrectomy. At the same time, oSAS (original Surgical Apgar Score) did not demonstrate the same predictive value [12]. Another version of SAS used in esophageal surgeries is called SAS. The results regarding eSAS are also contradictory. Janowak et al. reported that SAS ≤ 6 strongly predicts postoperative complications [90]. Xing et al.'s findings, indicated a powerful relation between the eSAS and the hospitalization period. However, they did not see an association between eSAS and the stay length in the ICU or the mortality rate [87]. Aoki et al. demonstrated that eSAS was not significantly related to major 30-day complications after esophagectomy [80]. The findings confirmed that eSAS is not an available universal score and seems to vary based on the type of the performed surgical technique; therefore, it needs re-evaluation.

From another point of view, the review of the conducted studies showed that the SAS has the ability to predict cardiovascular, respiratory, digestive, urogenital, neurological, systemic, and infectious complications, the duration of hospitalization in the ICU, mortality, and the survival rate. The SAS was found to be directly related to the development of pancreatic fistula following the operation in the paper by Asifi et al., which covered 553 people who underwent pancreaticoduodenectomy surgery. The 30-day mortality for these individuals was not significantly predicted by the SAS, though [36]. In addition, Reynolds et al., investigating the data of 123,864 surgical procedures that included all surgical specialties, concluded that the correlation between SAS and postoperative mortality on days 7, 30, and 90 varied based on the type of surgery [8]. This issue can be justified by considering the difference in co-morbidities and potential causes of death in each type of surgical specialty; for instance, SAS is more likely to predict major cardiac events in vascular patients than sepsis in burn patients. A different study conducted by Buzincu et al. noted that the SAS (Surgical Apgar Score) had limited ability to differentiate between patients who would experience complications following an operation and those who would not. However, despite this limitation, the SAS proved valuable in diagnosing patients at risk of prompt postoperative dysfunction of the organ.

Additionally, it was effective in predicting early postoperative cardiovascular complications that require inotrope/vasopressor therapy and metabolic disorders characterized by elevated serum lactate levels. These findings suggest that while the SAS may not excel in overall complication prediction, it can be valuable in identifying specific postoperative issues such as organ dysfunction, cardiovascular complications, and metabolic disturbances [37]. According to the study conducted by Glass et al. [78], an SAS value of less than eight was a valuable criterion for predicting the requirement of special care in those who underwent general procedures. In another survey involving 399 patients who underwent esophagectomy surgery, it was observed that the SAS had a significant correlation with the incidence of pulmonary complications, anastomosis leakage, and surgical site infection [63]. Finally, some evidence indicated that SAS was unable to predict outcomes in those undergoing knee arthroplasty [55], malignant hysterectomy [69], spine surgery for metastasis [11], gastrectomy [12], and cervical vascular reconstruction [95]. These studies discussed several reasons for the limited capability of the SAS to predict surgical outcomes. Some of the reasons are highlighted in the following paragraph.

Most of the studies were retrospective analyses in a single institution; therefore, there was a possibility for several biases. Hence, to evaluate the usefulness of this score, a prospective study with follow-ups on the potential effect of the score on the results is necessary. Furthermore, minor complications such as urinary tract infections may not be recorded in the discharge summary and electronic file. The evidence suggests that EBL has a high grade of mistake and varies depending on the performance of each center or person and the type of surgery; it can also increase the possibility of errors in the study results. Moreover, SAS is not extensively used in surgical specialties and may be considered more as an instrument to compare the research. Moreover, it is not known whether the proper control of these three variables (LHR, EBL & MAP) can improve patient outcomes. Considering the findings, it appears that the SAS should be changed in the future for improved prediction among each surgical subspecialty, even if it has already been validated in an expansive variety of surgical subspecialties.

Conclusion

SAS is a straightforward system of scoring, which is easy to compute and record. SAS is independent of the kind of surgery (elective, urgent, or emergency) and does not require biochemical analyses, clinical assessments, or the classification of a disease as acute or chronic. Low SAS patients may experience difficulties following surgery for thirty days. Surgeons and anesthesiologists can recognize patients who are in danger thanks to the analysis of SAS. Furthermore, by modifying the SAS or combining it with ASA (American Society of Anesthesiologists) criteria, healthcare professionals can better identify patients who require to be continuously monitored and followed up in the postoperative period. This can help ensure timely interventions and appropriate care for patients with raised complications.