This research originates from a previous well-consolidated experience (the ERASO collaborative study group) which has led to the FRAILESEL project and related reports (23). Following a similar methodology, a new collaborative research group was founded. The IGo-GIPS (Italian Group for Gastro-Intestinal Postoperative Surveillance) is a large, nationwide network created with the aim to undertake both prospective and/or retrospective studies investigating the perioperative outcomes of specific topics mainly concerning gastrointestinal surgery. Centers were included on a volunteer basis, and neither investigators nor participating hospitals were paid for their collaboration. Clinical decisions, including operative technique, were always based on the criteria of individual centers and staff surgeons or on specific guidelines in case of intestinal obstruction (24). Although procedures were not standardized per a study protocol, it is important to note that they were likely similar among participating hospitals, with some slight technical differences across institutions seldom taken into account because they were judged to not influence the outcome. The investigators were informed about the objectives of the project and asked for complete details about the surgical management of patients following standard methods and collection protocols as already described. Data regarding patients were prospectively collected from the FRAILESEL study participating centers from January 2017 to June 2018, while data regarding other patients were retrospectively retrieved from hospital electronic databanks. The FRAILESEL Study protocol was approved by the Ethics Committee of Sapienza University and of all the centers, while no formal approval was requested for any other retrospective un-interventional study except in case of specific indication deemed by a single center. However, signed consent for the treatment and the analysis of data for the scientific purpose was obtained from all patients before any surgical procedures. This study was conducted in accordance with the Declaration of Helsinki and its later amendments. All parts of the studies and the present manuscript have been checked and presented according to the checklist for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) (25).

Exclusion criteria were the following: age <65 years; lack of informed consent for the study participation, if requested; patients participating in other randomized or interventional clinical trial; with regard to emergency surgery, patients were excluded if already hospitalized and scheduled for the same procedure. Sole emergency endoscopic procedures or reoperations after elective surgery were excluded. Submissions made by unconfirmed participants, duplicate submissions, records with more than 5% of missing data, and data submitted by residents from dual or more residency programs were excluded. Although demographic information was collected on the patients, all data were anonymized before analysis even for center identification.

For the aim of the present study, we analyzed data of all patients with nonmalignant diseases requiring an emergency surgical procedure from January 2017 to December 2020. Cancer patients were excluded because of the possible concomitant role of the neoplasm in determining an inflammatory response and/or an adverse outcome. Data collected included patient demographic characteristics (age, gender, weight, height), medical and surgical history (comorbidities), common preoperative biochemical blood examination (including PCR, PCT, and arterial blood gas analysis), pathological features, and operative details. Comorbidity was recorded if the condition was being medically treated at the time of admission, or if previous treatment for the condition was described in the admission report. The age-adjusted Charlson comorbidity index (age-CACI) was calculated and a score ≥6 was used to categorize patients having a severe comorbid condition (26, 27). The preoperative risk was assessed by the anesthesiologist-assigned American Society of Anesthesiologists (ASA). Because serum triglycerides and cholesterol were not included in blood examination at admission and waist circumference was not recorded in the FRAILESEL database, the occurrence of MetS was identified using modified definition criteria used by the NCEP-ATP III Expert Panel: (1) obesity (BMI ≥30), (2) hypertension, (3) diabetes, or (4) if medication for high triglyceride or for low HDL cholesterol was taken (28). Diagnosis of MetS required at least the presence of two of the above findings. With regard to BMI, patients were also classified in four standard categories according to the US National Heart, Lung, and Blood Institute (29). Briefly, the following terminology has been used: underweight (BMI of <18.5), normal weight (BMI of 18.5 to <25), overweight (BMI of 25 to <30), and obesity (BMI of ≥30). Systemic inflammatory response syndrome (SIRS) was evaluated according to the original consensus study (Sepsis-1) (30). SIRS criteria ≥2 met the definition of SIRS. The frailty profile was investigated by the 5-modified Frailty Index (5-mFI) and the Emergency Surgery Frailty Index (EmSFI). According to other literature reports, a 5-mFI value ≥0.4 and an EmSFI value ≥4 were adopted as a cut-off to define a patient as frailty (31, 32), but when performing statistical analysis, to simplify and make the results comparable with literature, only the 5-mFI ≥0.4 score was used for considering the frailty positive population. Postoperative complications have been reported and categorized according to the Clavien–Dindo (C–D) classification system by the study leader in each of the participating centers (33). In the statistical comparison we excluded C-D 1 grade. Morbidity and mortality have been mainly considered as the 30-day standard period definition. However, adverse outcomes have been reported regardless of the time elapsed from the surgical procedure if reasonably related to it and occurred during the hospitalization following the main emergency procedure. The cut-off adopted for CACI, 5-mFI, and EmSFI, and the choice to consider C-D complication ≥2 derives from both literature and our previous already published statistical analysis (34). The primary aim was to critically appraise the influence of MetS with or without SIRS, and with or without frailty in the elderly undergoing emergency surgical procedure.

Statistical analysis was carried out using IBM Corp. Released 2019, IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. Dichotomous data and counts were presented in frequencies, whereas continuous data were presented as mean values ± standard deviations (SD) and/or median with 25–75 Interquartile Range (IQR), or minimum-maximum range. Differences between means were compared using the independent sample Student’s t-test or the Mann–Whitney U test when indicated. Fisher’s exact test or χ² test, with or without Yates correction, was used to compare differences in frequencies. The role of MetS stratified for the BMI class, and the presence of SIRS and/or frailty was also assessed using the estimated adjusted odds ratio. All tests were two-tailed, and a p-value ≤0.05 was considered statistically significant.

A total of 2,318 patients fulfilling the inclusion criteria were evaluated. Table 1 reports the organ site of the procedure. The overall mean age was 77.7 ± 7.7 (range, 65–100 years), n = 1,175 (50.7%) were male. The overall BMI was 25.7 ± 4.5 (range, 13.3–61.2). Of the total patients included in this study, n = 1,010 (43.6%) fulfilled the criteria for MetS (MetS Group). Of these, n = 414 (38.1%) were under weight, n = 22 (2.0%) were normal weight, n = 376 (34.6%) over weight, and n = 275 (25.3%) obese. A 5-mFI ≥0.4 was observed in n = 1,203 (52.0%) patients and an EmsFI ≥4 in n = 887 (38.0%). A CACI >6 was observed in n = 870 (37.5%) patients (Table 2). The mean length of hospital stay was 10.9 ± 10.7 days and SIRS occurred in 720 (31.1%) patients. The overall morbidity rate was 31.8% (n = 738 pts) and a Clavien–Dindo ≥2 was present in 505 (22%) patients. (Table 2). Data about demographics, clinical features and patient’s frailty stratified by the presence of MetS are reported in Table 2.

No difference in terms of age and sex were identified between the two groups. Regarding frailty, both 5-mFI score and EmsFI showed greater fragility in patients with MetS. A 5-mFI score ≥0.4 was more frequent in the MetS Group [n = 744 pts (73.7%)] vs. the n-MetS Group [n = 459 pts (35.1%)] (p < 0.001); an EmSFI ≥ 4 was detected more frequently in MetS Group [n = 437 pts (43.2%)] vs. n-MetS Group [n = 446 pts (34.1%)] (p < 0.001). Similarly, all patients with MetS showed more frequently a CACI index ≥6 (MetS Group n = 451 pts (44.6%) vs n-MetS Group n = 419 (32.0%) p < 0.001). The occurrence of SIRS was higher in the MetS Group [n = 349 (34.5%) vs n = 371 (28.4%)] with a statistically significant difference (p = 0.001). We then evaluated the surgical outcomes according to the presence or absence of MetS (Table 2). LOS was longer in patients with MetS (MetS Group 11.4 ± 12 days vs. n-MetS Group 10.5 ± 10.2 days p = 0.046).

Patients with MetS have a significantly higher rate of morbidity (n = 353 (35.0%) vs. n = 385 (29.4%), p = 0.005). In particular, CD ≥2 was detected in n = 245 (24.3%) patients with MetS and in n = 260 (20.0%) patients without it, being the difference statistically significant (p = 0.011). We then conducted a sub-analysis considering separately the morbidity risk according to the Clavien–Dindo grading classification showing no association with the presence of MetS, SIRS, or frailty (Table 3). The overall mortality rate was 9.8% (n = 227 patients). The mortality rate in patients with MetS was similar to the mortality rate in patients without it (Table 2). Considering patients with MetS that developed SIRS and those that had Frailty or both, we found that the occurrence of these conditions was associated with a higher rate of morbidity and mortality (Table 4). The stratified estimated adjusted odds ratio analysis is reported in Table 5 and is drawn in Figure 1. Patients with MetS in which SIRS occurred and with frailty were at increased risk of mortality (SIRS odds ratio [OR] 1.643; 95% CI 1.170 to 2.308; p = 0.003; frailty odds ratio [OR] 1.443; 95% CI 1.088 to 1,953; p = 0.010; SIRS + frailty odds ratio [OR] 1.936; 95% CI 1.354 to 2.788; p < 0.001). Regarding the overall morbidity (CD I-IV), patients with MetS and normal weight or overweight were at increased risk (normal weight odds ratio [OR] 1.472; 95% CI 1.117 to 1,940; p = 0.005; overweight odds ratio [OR] 1.379; 95% CI 1.038 to 1.831; p = 0.025). Again, patients with MetS in which SIRS occurred and with frailty were at increased risk of overall morbidity (SIRS odds ratio [OR] 1.514; 95% CI 1.196 to 1,916; p < 0.001; frailty odds ratio [OR] 1.334; 95% CI 1.109 to 1.605; p = 0.002; SIRS + frailty odds ratio [OR] 1.555; 95% CI 1.200 to 2.015; p < 0.001).

This study has several potential limitations. First, although the FRAILSESL is a rich clinical registry, we had to adapt the NCEP-ATP III definition of MetS to the data recorded in the FRAILESEL database. In the light of this, our definition of Mets is rather deficient due to the data collection which concerns emergency conditions whose often not all data are recorded, this may have led to an uncorrected estimation of the actual incidence of Mets. Some patients with the MetS may have been “missed” because we did not include the lipid profile in our definition—and because central obesity is not always captured by a high BMI, while others could have arbitrarily included. However, our results report an incidence of MetS comparable to the literature. Furthermore, the results of this study empirically demonstrate that this syndrome, as defined here, is associated with significant morbidity. A further limitation is the design of the study with a mixed perspective and retrospective component as well as the inclusion of all diseases except malignancy; in our opinion, further studies would be useful to specifically analyze certain diseases. However, one of the primary strengths of this study is that the number of elderly patients with MetS was sufficiently large to explore the impact of this syndrome on 30-day mortality and on individual postoperative complications. Another important strength of this study is the richness of the database on which it is based. Because of the large number of clinical variables collected on the patients in the FRAILESEL database, we were able to control for many important confounders and to evaluate the impact of Mets when it is associated with both SIRS and frailty. This feature is particularly important given the fact that patients with the MetS have many comorbidities.