The Value of Preoperative Systemic Immune-Inflammation Index as a Predictor of Prolonged Hospital Stay in Orthopedic Surgery: A Retrospective Study

Introduction

Orthopedic procedures are commonly performed, focusing on the diagnosis and treatment of musculoskeletal disorders such as bone fractures, joint replacements, and spinal deformities.¹ While these procedures are generally performed safely, they can be associated with prolonged length of stay and various postoperative complications, such as infection, bleeding, and pain.²

There are several risk factors associated with postoperative infection following orthopedic surgeries, such as age, sex, body mass index (BMI), duration of surgery, and anesthetic technique.² While most of these risk factors are not modifiable, predictive biomarkers can be useful tools to predict postoperative outcomes.

Numerous metrics such as operative time, preoperative stay, length of hospitalization, blood loss, length of incision, and postoperative pain scores are employed to assess the degree of invasiveness of various treatments across a range of surgical specialties.^3–5 A defensive inflammatory response, on the other hand, can be brought on by tissue damage, infection, or ischemia and can result in an increase in platelets, which are the main players in this reaction.⁶ Additionally, connected to playing a predicative role in stress and trauma was an elevated leukocyte count. It is well known that neutrophils contribute to bone fracture healing because their levels rise in the early fracture hematoma, triggering the secondary immune response. They also play a significant role in the elimination of damage-associated molecular patterns (DAMPs) as well as microbe- or pathogen-associated molecular patterns (MAMPs) in unsterile skin wounds, which can improve healing.⁷ Furthermore, lymphopenia is linked to trauma, which increases mortality rates and lengthens hospital admissions.⁸

Several inflammation markers, such as the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and soluble urokinase plasminogen activating receptor, have been suggested to predict mortality and postoperative outcomes in orthopedic patients.^9–11 Complete blood counts (CBCs) are obtained routinely on all patients at admission and the first day following any surgical procedure. As a result, simple and inexpensive predictive hematologic biomarkers such as the NLR, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII) can be obtained.^12,13 These biomarkers have recently been linked to systemic inflammation that the human body experiences following various fracture types, polytraumatisms, carcinomas, and numerous musculoskeletal illnesses. Several fields relevant to surgery have been associated with their diagnostic and prognostic values.⁶

Clinicians are becoming more aware of the immunological responses that occur postoperatively and their possible harmful consequences. The SII, which is a comprehensive indicator integrating neutrophils, platelets, and lymphocytes, has been recently identified¹⁴ and has been shown to serve as a potential prognostic marker for different medical conditions such as cancer,^15,16 cardiac disorders,^17–19 and autoimmune diseases.²⁰ In non-orthopedic patients, Parmana et al showed that high preoperative SII levels (>878.05) after off-pump coronary artery bypass graft surgery can predict longer intensive care unit (ICU) stay and prolonged mechanical ventilation.²¹ Furthermore, Song et al demonstrated that SII (>650) can predict postoperative delirium in patients undergoing non-neurosurgery and non-cardiac surgery.²² In orthopedic surgical patients, SII value was elevated (934) among patients with postoperative cognitive decline compared to those without (388). The authors also demonstrated that preoperative SII was independently associated with the occurrence of postoperative cognitive decline in elderly patients undergoing orthopedic operation.²³ In another study, SII was independently associated with poor all-cause mortality in elderly patients undergoing hip fracture surgery.²⁴

Previous studies have highlighted that the SII is a simple, inexpensive, and effective tool for predicting postoperative complications and mortality. However, there is a paucity of research investigating the usefulness of the SII index in predicting the length of hospital stay following orthopedic surgery. In the previous literature, the criteria to assess long-term versus short-term hospitalization vary between studies. Hospitalization period that exceeds the average length of hospital stay (LOHS) is considered long-term admission.²⁵ In the current study, the median LOHS was measured, and the study cohort was divided into two groups according to their LOHS. This study aimed to explore the relationship between the SII and length of stay for patients undergoing orthopedic surgery.

Materials and Methods

Study Design

This retrospective study included patients who underwent orthopedic surgery at King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia, from 2016 to 2021. The study was approved by the Institutional Review Board of the King Abdullah International Medical Research Centre (KAIMRC) (IRB/1263/22). The ethics committee/Institutional Review Board of KAIMRC waived the need for informed consent since this is a retrospective study and all identifying information was removed to ensure patient confidentiality. The study was performed according to the Helsinki Declaration and local and institutional regulations.

Inclusion and Exclusion Criteria

Patients who were admitted to undergo orthopedic procedures from 2016 to 2021 were at the Department of Orthopedic Surgery in KAMC and were considered for inclusion in our retrospective study if they (1) were 18 years and over; (2) underwent elective orthopedic surgery; (3) has complete medical records. Patients were excluded from the study if they (1) are younger than 18 years old; (2) non-orthopedic surgeries or underwent emergency orthopedic surgery; (3) had prior malignancy; (4) had hematological disorder; (5) had incomplete medical records (Figure 1).

Figure 1 Flowchart of the study population. Abbreviations: LOHS, length of hospital stay; SII, systemic immune-inflammation index.

Data Collection

From the database, we identified 196 patients who underwent orthopedic surgery at our institution. From the database, the median length of stay of the identified patients in our

institution during the study period was 21 days. Therefore, the study cohort of 196 patients was divided into two groups according to their length of hospitalization. Patients whose length of hospitalization was 21 days and less were included in study group A, and patients hospitalized for more than 21 days were included in study group B.

The outcome variable, the LOHS, was obtained from the electronic medical records and examined in the current study. LOHS indicates the number of days from patient’s admission until discharge after surgery.

The independent variables were composed of demographic data (age, gender, BMI) and baseline data (ASA score and type of surgery). The name of surgery was recorded from the electronic medical records and classified as (1) spinal decompression; (2) close reduction and internal fixation of a fracture; (3) arthroplasty; (4) open reduction and internal fixation of a fracture; (5) segmental fixation of spine; (6) correction of spinal deformity; (7) spinal fusion; (8) arthroscopy; (9) others such as revision and nailing surgeries. Preoperative laboratory parameters from routine blood test were also obtained including neutrophil and lymphocyte counts, platelet count, white blood cell count, and hemoglobin level. The SII inflammatory marker was obtained using the following formula: SII = platelet × neutrophil/lymphocyte count, as previously defined.¹⁴ We also collected duration of surgery, postoperative admission to the ICU, and length of ICU stay. The duration of surgery was measured based on the time of surgery, excluding anesthesia. All outcome and predictor variables were obtained from the electronic medical records.

Statistical Analysis

All statistical analyses were performed using SPSS 28.0 (IBM, Armonk, NY, United States). Distribution of continuous variables was assessed using Shapiro–Wilk test, and clinical variables were expressed as median (interquartile range) as it was nonparametric in nature. Mann Whitney U-test was used for comparisons of continuous variables and chi-square test or Fisher’s exact test for categorical variables. Categorical parameters were expressed as frequencies and percentages and compared using the Chi-square test or Fisher’s exact test. The receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off value of SII, and the area under the curve (AUC) was calculated. The sensitivity and specificity values were calculated, and patients were divided into high- and low-SII value groups, based on the cutoff values. Univariate analysis was conducted to identify variables associated with the LOHS using simple linear regression analysis. Further multivariate analysis was conducted, which incorporated variables with significant differences in the univariate analysis, along with age, sex, and ASA classification. Variables in the SII formula, including neutrophils, lymphocytes, and platelets, were not included in multivariate analysis. The results of the linear regression analysis are expressed as standardized beta and 95% confidence intervals (CI). Statistical significance was set at P  < 0.05 (two-sided) was considered significant.

Results

Baseline Characteristics

A total of 196 patients who underwent orthopedic surgery were enrolled in this study. The median age of all patients was 60 years, and the majority of patients were male (59.7%). Clinical features, laboratory parameters, and patient outcomes are shown in Table 1. In summary, most patients were classified as ASA III (46.9%) followed by ASA II (34.7%). The most common type of surgical procedures that were performed among all patients was spinal decompression (27.6%) followed by closed reduction and internal fixation (21.9%) and arthroplasty (16.8%). The median length of ICU stay among all patients was two days. The patients were divided into two groups according to the median LOHS (Table 1). In the subgroup analysis, significant differences were observed in ASA score between both groups. In addition, we observed that those who has longer hospitalization had lower hemoglobin levels, higher duration of surgery, ICU admission requirements and increased stay in the ICU.

Table 1 Baseline Demographic and Clinical Characteristics of the Studied Population Grouped According to LOHS

ROC Analysis for Cut-Off Value of SII

We investigated the ideal cut-off value for the SII, determined by ROC analysis. The SII had area under the curve (AUC) of 0.568. The optimal SII cut-off value was 799.86 with a sensitivity of 0.597 and specificity of 0.448.

Subgroup Analysis by SII Cut-Off Value

Based on the cut-off value of SII, patients were divided into a high-SII group (SII ≥799.86) and a low-SII group (SII <799.86). Results indicated that significant differences exist on the type of surgery between both groups. Furthermore, patients in the high-SII group had higher neutrophil, platelet, and WBC counts (P < 0.001 for all). They also had lower lymphocyte count (P < 0.001). Moreover, patients in the high-SII group had higher median LOHS (22 vs 17 days; P = 0.006). No significant differences were observed in age, gender, ASA score, duration of surgery, requirement of ICU admission and length of ICU stay (P>0.05) (Table 2).

Table 2 Baseline Demographic and Clinical Characteristics of the Studied Population Grouped According to SII Cut-Off Value

Linear Regression Analyses

In univariate linear regression analysis, SII was significantly associated with LOHS (β = 0.246, 95% confidence interval [CI] 0.002–0.006, P < 0.001). In addition, LOHS was associated with ASA classification, platelet count, hemoglobin level, and duration of surgery (Table 3).

Table 3 Univariate Linear Regression Analysis of Factors Associated with LOHS

In the multivariable linear regression analysis, the SII was significantly related to LOHS after adjusting for confounders [β = 0.143, 95% CI 0.000–0.005, P = 0.031]. ASA (β = 0.262, 95% CI 3.882–12.672, P < 0.001), hemoglobin level (β = -0.285, 95% CI −0.448– −0.166, P < 0.001), and duration of surgery (β = 0.260, 95% CI 0.024–0.078, P < 0.001) were associated with LOHS but independent of the SII (Table 4).

Table 4 Multivariate Linear Regression Analysis of Factors Associated with LOHS

Discussion

Numerous disorders have shown that the systemic immune-inflammation index (SII), a newly identified composite indicator incorporating platelet, neutrophil, and lymphocyte counts, is a promising prognostic predictor.^14,24 These involve cardiac disorders,^17–19 malignant tumors,¹⁵ critical illness,^26,27 and patients undergoing surgical interventions.^22,28,29 In this study, the laboratory indicator SII was collected to determine whether it was associated with LOHS in patients who underwent major orthopedic surgery. Multivariate linear regression analysis showed that SII was independently associated with LOHS.

A local inflammatory response, characterized by the recruitment of polymorphonuclear neutrophils (PMN) and monocytes, facilitates the faster healing of surgical wounds by limiting tissue damage and clearing cell debris. The increased production of several proinflammatory mediators, such as interleukin (IL)-1 and tumor necrosis factor (TNF-), as well as other cytokines, such as IL-6, has been linked to postoperative complications following orthopedic surgery and has also been used in several studies to characterize this postoperative inflammatory reaction.³⁰ However, previous studies have demonstrated that surgery causes immunological suppression, in contrast to reports of heightened proinflammatory responses following surgery.³¹

For instance, during the postoperative period, PMN, monocyte, and macrophage phagocytic activity are lowered, potentially resulting in a window for infection susceptibility.³² Other reports have shown that during prosthetic joint infection, a heterogeneous population of immature monocytes and granulocytes known as myeloid-derived suppressor cells (MDSCs) are recruited to tissues where they have an anti-inflammatory effect.^33,34 Furthermore, during the postoperative period, higher levels of anti-inflammatory molecules can also be produced, such as IL-4, IL-10, soluble tumor necrosis factor receptor 1 (sTNFR1), IL-1 receptor antagonist (IL-1Ra), and transforming growth factor (TGF), which may further bias the anti-inflammatory properties of wound-associated leukocytes.^35,36

Recently, several indicators such as NLR, PLR, and MLR have been shown to be important in various disorders. However, these indicators integrate only two cells. The SII, which is based on peripheral lymphocyte, neutrophil, and platelet counts, has recently been studied as a prognostic marker for multiple disorders. In the present study, we observed that the SII was independently associated with prolonged LOHS in patients undergoing orthopedic surgery. The findings of this study are consistent with previous research that has shown that inflammation is a significant risk factor for prolonged LOS in patients undergoing orthopedic surgery.³⁷ Inflammation can lead to a number of complications such as infection, sepsis, and organ failure, all of which can contribute to a longer LOS. In line with our findings, we also observed that the median LOHS was significantly higher in patients with SII value ≥ 799.86 compared to those with lower SII value (<799.86). These findings suggest that an elevated preoperative SII, which reflects systemic inflammation, may contribute to more complicated recovery and longer hospitalization in orthopedic patients. In support of this theory, Parmana et al reported the effectiveness of SII as a predictor of prolonged mechanical ventilation and ICU stay and its positive correlation with LOHS after off-pump coronary artery bypass grafting.²¹ Furthermore, Zinellu et al showed that SII and other immune-inflammatory ratios that were measured on admission positively correlate with LOHS in COVID patients. They also demonstrated in multiple regression analysis that only the SII was significantly associated with prolonged LOHS.³⁸

Furthermore, our study showed that ASA classification, hemoglobin level, and duration of surgery were independently associated with a prolonged length of hospital stay. ASA class and duration of surgery are indicators of the severity of surgical intervention and patient health status, and their associations with prolonged LOHS have been proven in previous studies.^39–41 In this study, we observed no significant relationship in terms of laboratory values with longer hospital stay. However, other reports have shown no direct evidence that elevated neutrophil counts are associated with poor prognosis. In addition, an elevated NLR was found to be an independent risk factor for postoperative myocardial injury, in-hospital mortality, and 1-year mortality after hip fracture surgery.^10,42 In contrast, a high platelet count is considered a risk factor for the development of postoperative pressure ulcers after hip fracture surgery.⁴³ In addition, a study reported that a high PLR (≥189) is associated with increased 1-year all-cause mortality in older adults with hip fractures.⁴⁴

This study has some limitations. Due to the retrospective nature of the study, which makes it dependent on data from medical records, which might be more prone to human error, as well as the relatively modest patient sample size, further confirmation is necessary in the future. The current study does not focus on a specific type of orthopedic procedure, and therefore our findings may not guide orthopedic surgeons with specialized practices. Nonetheless, these limitations should be addressed in a future work that investigates individual procedure types to broaden understanding of the SII, with a longer follow-up and larger patient sample, allowing the findings to be generalized to the local population. However, a significant strength of the current work is that the SII has demonstrated potential utility in clinical practice to identify patients at risk of prolonged hospitalization after orthopedic surgery.

Conclusion

Higher preoperative SII levels were independently associated with a higher risk of extended hospital stay. Thus, measuring simple/inexpensive inflammatory markers may help identify patients at risk of longer hospitalization. Based on observations from the current study, the SII has advantages regarding cost and time and may have a role in the field of orthopedic surgery, but further prospective studies with bigger samples of patients are necessary to corroborate our findings before their application in clinical practice.