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Immune-nutritional indicators predict short-term mortality in older patients after emergency gastrointestinal surgery: a retrospective study

BMC Gastroenterology volume 25, Article number: 99 (2025) Cite this article

Abstract

Background

The aim of this study was to discover immune-nutritional indicators that can preoperatively predict short-term mortality in older patients undergoing emergency gastrointestinal surgery.

Methods

We retrospectively analyzed older patients older than 65 years of age who underwent various types of emergency gastrointestinal surgery under general anesthesia between January 2012 and December 2023. The immune-nutritional indicators were defined according to previous literature. The primary endpoint of this study was 90-day survival after surgery.

Results

A total of 4120 patients older than 65 years were included in this study. ROC curves and the decision curve analysis for eight factors predicting 90-day postoperative survival were well predicted by the mGPS (0.68, 95% CI: 0.66–0.70), PNI (0.68, 95% CI: 0.66–0.71) and CONUT score (0.68, 95% CI: 0.66–0.70). The models constructed by LASSO Cox and CoxBoost were used to score the risk for each patient, and the high LASSO Cox model risk score group had worse 90-day survival than the low score group, whereas patients in the low CoxBoost model score group had a worse prognosis. The AUC of the CoxBoost model was greater than that of the LASSO Cox model. A nomogram model was constructed using the variables screened by the LASSO Cox model with a C-index of 0.706.

Conclusions

Immune-nutritional factors could be a favorable predictor for older patients undergoing emergency gastrointestinal surgery.

Peer Review reports

Background

The growth of the older population has led to an increase in the incidence of gastrointestinal emergencies [1]. It is estimated that approximately 15% of people aged 65 years or older experience at least one gastrointestinal emergency [2], such as obstruction, perforation, or bleeding, each year. These conditions usually require urgent surgical intervention. Because older adults often have multiple chronic diseases and diminished physiologic function [3], they face additional challenges in the recovery process after surgery.

Older patients have a higher rate of complications following emergency gastrointestinal surgery [4], and studies have shown that more than 40% of patients in this population develop major complications [5], such as infections, cardiac complications, or pulmonary complications. These complications significantly increase the length of hospitalization and the risk of death. The risk of cardiac and pulmonary complications is especially high in the first few days after surgery, a critical period for increased mortality [6, 7].

Factors affecting mortality within 90 days after surgery in older patients include the patient’s overall health, the type and urgency of the procedure, and the type and severity of complications [8]. Prior to emergency gastrointestinal surgery, immune-nutritional indicators have a significant impact on complications and mortality after surgery in older patients. According to recent studies and expert consensus [9], preoperative nutritional status and inflammation levels, especially in older individuals, are strongly associated with the risk of postoperative complications [10, 11].

In coping with surgery for older patients with gastrointestinal emergencies, medical teams need to pay special attention to those patients with high-risk factors. Currently, commonly used inflammatory indices, such as the neutrophil-to-lymphocyte ratio (NLR) [12], neutrophil-to-platelet ratio (NPR) [13], platelet-to-lymphocyte ratio (PLR) [13], systemic inflammatory response index (SIRI) [14], systemic immune-inflammation index (SII) [14], and modified Glasgow Prognostic Score (mGPS) [15], have been used to assess the inflammatory state of patients and can be used as biomarkers to predict the risk of complications and death after surgery, while the Controlling Nutritional Status (CONUT) [16] score and the Naples Prognostic Score (NPS) [16] can be used to easily and quickly evaluate the nutritional status of patients. At present, these indices can better predict the short-term risk of patients with pulmonary embolism [17], myocardial infarction [15], and bone fracture [18], but they have not been reported in gastrointestinal emergency surgery.

Therefore, the aim of this study was to identify risk factors that can preoperatively predict short-term mortality in older patients undergoing emergency gastrointestinal surgery and establish a relevant model by analyzing these relevant indicators to understand and manage the systemic status of older patients as early as possible and to improve the survival rate and quality of life.

Methods and materials

Patients

We retrospectively analyzed older patients older than 65 years of age who underwent various types of emergency gastrointestinal surgery under general anesthesia between January 2012 and December 2023 at the Department of Gastrointestinal Surgery, West China Hospital, Sichuan University. We excluded patients whose clinical data were incomplete. The study protocol was reviewed and approved by the Ethics Committee of West China Hospital, Sichuan University. The patient’s diagnosis included intestinal obstruction, perforation, gastrointestinal bleeding and other types of acute abdomen.

Data collection

The following clinical and surgical data were collected from the electronic medical records: age, sex, surgical procedure, and duration of surgery, and the following data were collected from the testing system: total neutrophils (10^9/L), total monocytes (10^9/L), total lymphocytes (10^9/L), platelet count (10^9/L), serum albumin (ALB, g/L), serum total cholesterol (mg/dL) and serum C-reactive protein (CRP, mg/L).

Systemic Immune-inflammation Index(SII) is defined as platelet count x neutrophil count/lymphocyte count.

Systemic Inflammation Response Index(SIRI) is the monocyte count × neutrophil count/lymphocyte count ratio.

The prognostic nutritional index (PNI) was defined as the serum ALB concentration (g/L) + 5 × lymphocyte count (10^9/L).

The pan-immune-inflammation value (PIV) was calculated with the formula neutrophil count× platelet count × monocyte count/lymphocyte count.

NLR = neutrophils/lymphocytes, LMR = lymphocytes/monocytes. Based on previous study definitions [16], the NPS was calculated based on the following 4 parameters: serum ALB < 40 g/L, total cholesterol ≤ 180 mg/dL, NLR > 2.96, and LMR < 4.44, which were each assigned a score of 1, and the sum of the scores for each of the 4 parameters was the NPS. Patients with a score of 0 were considered to be at no immune-nutritional risk (Group 0), those with a score of 1 or 2 were considered to have a mild immune-nutritional risk (Group 1), and those with a score of 3 or 4 were considered to have a severe immune-nutritional risk (Group 2).

The CONUT score was defined as the sum of three scores based on the serum ALB concentration, lymphocyte count and total cholesterol concentration. Serum ALB concentration was categorized into ≥ 35, 30-34.9, 25-29.9, and < 25 (g/L) groups, with scores of 0, 2, 4, and 6, respectively, while lymphocyte count was categorized into ≥ 1.6, 1.2–1.5, 0.8–1.1, and < 0.8 (× 10^9) groups, and total cholesterol concentration was categorized into ≥ 180, 140–179, 100–139, and < 100 mg/dL with scores of 0, 1, 2 and 3 for the four groups, respectively [16].

The mGPS score was defined according to previous literature as 2 points for CRP > 10 mg/L and Alb < 35 g/L, 1 point for CRP > 10 mg/L and Alb ≥ 35 g/L only, and 0 points whenever CRP ≤ 10 mg/L [15].

The selection of these inflammatory-nutritional indicators was based on previous literature reporting on their value in predicting surgery-related risk [9].

The follow-up endpoint of this study was 90 days after surgery.

Statistical analysis

Statistical analyses were performed using R software (version 4.3.3). Continuous variables are expressed as medians (interquartile spacing), and categorical variables are expressed as numbers and percentages. The selection of cutoff values was based on previous literature while utilizing ROC curve analysis to determine these thresholds in our cohort to meet the optimal thresholds for predicting 90-day mortality.

The survminer package (version 0.4.9) was utilized to calculate the critical PNI, SII, SIRI, PIV and CONUT scores to divide patients into two groups: PNI > 34.5, SII < 2472.94, SIRI < 5.98, PIV < 350.5 and CONUT < 6. In this study, we first used the LASSO Cox regression method to extract meaningful indicators from the study cohort and plotted a nomogram of the relevant indicators, derived the C index and calibration curves based on Cox regression analysis, calculated the risk score of each patient based on the LASSO (least absolute shrinkage and selection operator) regression results, and plotted Kaplan‒Meier (K‒M) curves based on the median number of patients divided into two groups. The log-rank test was used to compare the prognostic differences between the two groups. Similarly, we used the CoxBoost package (version 1.4) to fit the Cox proportional risk model, scored the patients according to the model, divided them into two groups again, plotted the K‒M curves and compared the prognostic differences.

We used the timeROC package (version 0.4) to plot each of the individual metrics and plotted receiver operating characteristic (ROC) curves based on the models obtained from LASSO-Cox and CoxBoost while calculating the area under the curve (AUC) values to assess the differences in the ability of the different metrics to predict survival. Decision curves were plotted using the ggDCA package (version 1.2). p < .05 was considered to indicate a statistically significant difference. We constructed nomograms using variables screened by the LASSO Cox model. The LASSO Cox model allowed us to efficiently identify the most relevant variables for predicting 90-day mortality, while its regularization technique helped to deal with covariance among predictors. The coefficients generated by the CoxBoost model cannot be directly used to construct nomograms due to their nonlinear nature.

Results

Clinical characteristics of patients

A total of 4120 patients older than 65 years were included in this study. As shown in Table 1, most of the patients were male (61.2% vs. 38.8%), and the median [IQR] patient age was 71 [65,78] years. In addition, the majority of patients had small bowel and appendix disease (73.1%), followed by colon (12.7%), upper gastrointestinal (8.2%), and perianal rectal disease (6%). Bowel obstruction was diagnosed in 30.2% of patients, GI perforation in 19.6%, bleeding in 1.4%, and other diseases in 48.8% of patients. Of the total patients, 10.2% died within 90 days due to septic shock (68.8%), multi-organ failure (11.6%), acute thrombotic events (13.2%), and other factors (6.4%). Among the hematologic indices, the neutrophil count was 7.71 [4.89, 11.29] × 109/L, the lymphocyte count was 0.81 [0.51, 1.19] × 109/L, the monocyte count was 0.45 [0.28, 0.67] × 109/L, the platelet count was 169.00 [119.00, 230.00] × 109/L, the serum ALB level was 35.20 [28.00, 40.90] g/L, and the TC concentration was 125.60 [90.10, 160.80] mg/dL. The calculated NLR was 9.60 [5.28, 16.93], and the LMR was 1.80 [1.13, 3.00]. Table 1 also describes the inflammatory indices and immune-nutrition system scores of all patients with different clinical characteristics.

Table 1 Patients clinical characteristics
Full size table

A single indicator predicts the 90-day risk of death

ROC curves for eight factors predicting 90-day postoperative survival were plotted after grouping patients according to previously defined inflammatory indices or nutritional scores (Fig. 1A). The mGPS (0.68, 95% CI: 0.66–0.70), PNI (0.68, 95% CI: 0.66–0.71) and CONUT score (0.68, 95% CI: 0.66–0.70) had the largest areas under the curve. This was followed in descending order by NPS (0.55, 95% CI: 0.53–0.57), SII (0.54, 95% CI: 0.52–0.57), PIV (0.53, 95% CI: 0.51–0.56) and SIRI (0.52. 95% CI: 0.49–0.54).

The results of plotting the decision curves for the 7 indicators were similar to those described above (Fig. 1B). All metrics were superior to the all-patient death scenario or the no-patient death scenario in predicting 90-day mortality if the patient threshold probability was between 5% and 20%. In addition, the mGPS, PNI, and CONUT had comparable net benefits and were significantly better than the NPS, SII, PIV, and SIRI. When the patient threshold probability was between 10% and 20%, the use of the PNI was more advantageous for predicting patient death at 90 days.

Fig. 1
figure 1

(A) ROC curve analysis for a single indicator to predict 90-day postoperative survival. (B) Decision curve analysis for a single indicator to predict 90-day postoperative survival

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Selection of prognostic indicators

After these seven indicators were subjected to one-way Cox regression analysis (Table 2), the mGPS, PNI, CONUT, SII, and PIV were found to be significantly correlated with 90-day mortality. The mGPS, PNI, SII and PIV were found to be independent risk factors affecting patient prognosis via multifactorial regression.

Table 2 COX regression analysis of 90-day mortality
Full size table

LASSO-Cox regression was performed on the above seven indicators, and all of them were significantly correlated with the 90-day mortality (Fig. 2A). Further disciplinary regression included the mGPS, PNI and CONUT as independent risk factors for patient prognosis.

Afterwards, the relationships between these seven indicators and prognosis were analyzed using CoxBoost machine learning, and we selected all predictors to construct the CoxBoost model (Fig. 2B).

Fig. 2
figure 2

(A) Risk factors selected using LASSO Cox regression analysis. The two vertical lines were drawn at the optimal scores by minimum criteria (lsft) and 1-s.e. criteria (right). (B) Risk factors selected using CoxBoost analysis

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Construction of the prediction model

The models constructed by LASSO-Cox and CoxBoost were used to score the risk for each patient, after which the median was used to categorize the patients into high and low groups.

K‒M analysis was used to analyze the associations between the LASSO Cox and CoxBoost models and patient prognosis. The results of the analysis showed that patients in the high LASSO Cox model risk score group had a greater 90-day risk of death than did those in the low LASSO model risk score group (Fig. 3A, P < .0001), whereas CoxBoost constructed a model in which patients in the low score group had a worse prognosis (Fig. 3B, P < .0001). Moreover, the ROC curve showed that the area under the curve (AUC) of the CoxBoost model was greater than that of the LASSO Cox model (Fig. 3C, 0.76 > 0.68).

Fig. 3
figure 3

(A) Kaplan‒Meier survival curves of the LASSO Cox model. (B) Kaplan‒Meier survival curves of the CoxBoost model. (C) ROC curve analysis of the ability of the LASSO Cox model and CoxBoost model to predict 90-day postoperative survival

Full size image

A nomogram model was constructed using the variables screened by the LASSO Cox model (Fig. 4a), with a C-index of 0.706, and the probability calibration plot of 90-day mortality showed that the predictions of the nomogram were in good agreement with the actual observations (Fig. 4b).

Fig. 4
figure 4

(A) Nomograms including the mGPS, CONUT, and PNI for 30-, 60-, and 90-day postoperative survival. (B) Calibration curve of the nomogram

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Discussion

With the increase in modern life expectancy and the gradual increase in the aging population, the incidence of acute abdominal diseases in older people has gradually increased [19]. Older patients with acute abdomen often have atypical clinical symptoms [20] and more rapid changes in condition, and the prognosis of older patients is often poorer due to their own functional deterioration, complications and comorbidities [21]. Many studies have shown that nutritional status and disease-induced inflammatory response are important predictors of perioperative complications and short-term mortality [15, 16, 22]. Older patients with acute abdomen often suffer from poor nutritional status [23] and an increased incidence of sepsis [24] due to decreased intake of nutrients caused by primary diseases and treatments and decreased resistance to infections [25] due to decreased immune function.

With the increasing popularity of the concept of enhanced recovery after surgery (ERAS), the concepts and strategies for surgical patient care have also changed. The implementation of holistic management to maximize the efficacy of surgical treatment has become an important concept [26]. Therefore, it is important to evaluate the immune-nutritional parameters of older patients who need to undergo acute surgery. By evaluating the relationship between the immune system and short-term mortality after gastrointestinal surgery in older patients, appropriate therapeutic strategies can be developed. In this study, eight immune-nutritional systems were analyzed based on the calculation of inflammatory cells in routine blood tests and nutritional indicators such as serum ALB and cholesterol. The results showed that the mGPS, PNI, and CONUT could more accurately predict 90-day mortality after gastrointestinal surgery in older patients. These four scoring systems had similar predictive ability, with AUC values of 0.68, and had similar net benefits on the decision curve. The predictive ability of the risk model constructed using LASSO Cox and CoxBoost was superior to that of a single indicator. The nomogram constructed using LASSO regression had a C-index of 0.706, and the probability calibration curves showed good homogeneity between the model predictions and actual observations, while the CoxBoost model had an AUC value of 0.76. The above results showed that the model was more accurate in determining the 90-day mortality of patients. In clinical practice, we can use this model to identify patients with systemic malnutrition and high inflammatory response on the one hand, so as to provide them with comprehensive treatment as early as possible and wait for the indicators to improve before surgery in non-life-threatening cases. After surgery, we can also focus on the dynamic changes in these patients’ indicators and provide early prevention of surgical site infections and high nutritional support.

Malnutrition increases the incidence of postoperative complications and mortality [27]. Reasonable nutritional support can correct the abnormal metabolism of nutrients, promote wound healing, reduce tissue oxidative stress, regulate the body’s inflammatory immune response, and enhance the mucosal barrier function of the intestine [28]. Almost all nutritional prognostic scoring systems include serum ALB levels because of the importance of the ALB concentration to patient prognosis [29].

The GPS was first proposed as a prognostic indicator for cancer patients by Forrest et al. [30] and then revised to the mGPS by McMillan et al. [31]. However, the ability of the mGPS to predict poor prognosis was limited because only a small number of patients with CRP > 10 mg/l received an mGPS. For this reason, subsequent studies have further reduced the mGPS to a CRP threshold of 3 mg/l to obtain the HS-mGPS (high-sensitive mGPS) [32] to improve the predictive ability of an inflammation-based prognostic system for cancer patients. In addition to their ability to predict the prognosis of patients with cancer, the GPS and mGPS have also been used to predict survival risk in studies on percutaneous coronary intervention [22]. Our findings showed that the mGPS could predicate 90-day mortality in older patients who underwent acute gastrointestinal surgery. However there may be potential benefits to using HS-mGPS, The increase in CRP will be more rapid and common than the decrease in ALB due to the shorter duration of the disease. Moreover, since the immune system response is reduced in older patients, even mild elevations of validated markers may reflect severe infections in the organism [33], so the use of a lower CRP cutoff value may better identify those patients at high risk. In addition, unlike the activation of lymphocytes [34] and monocyte-macrophages in tumorigenesis [35], in response to infections caused by intestinal diseases, the increase in leukocytes [36] is often dominated by neutrophils, with a relative decrease in the number of lymphocytes as well as monocytes, which may be related to the fact that the SII, SIRI, and PIV do not predict the risk of death in older patients or the risk of death.

In this study, the PNI, CONUT score and NPS of the patients were calculated according to the receiver operating characteristic (ROC) curve, which showed that both the PNI and CONUT scoring systems showed good predictive results. The PNI is a metric related to the serum ALB concentration and lymphocyte count and has been used to predict the risk of postoperative complications and OS in patients with a wide range of cancers [37]. The CONUT score, on the other hand, consists of the ALB concentration, lymphocyte count, and TC concentration. Although the specific values of the PNI and CONUT score have varied among different studies, the cutoff values obtained using statistical methods were chosen in this study to be 34.5 and 6, respectively. NPS, an index associated with serum ALB concentration, cholesterol concentration, LMR and NLR, was previously demonstrated to be an independent risk factor affecting the 30-day all-cause mortality rate in patients with pulmonary embolism [17] but was not found to be an independent risk factor for 30-day all-cause mortality in our study, possibly because the four indicators used for NPS, NLR, LMR and total cholesterol had cutoff values of 2.96, 4.4 and 180 mg/dl, respectively, whereas their corresponding median values in our cohort were 9.60, 1.80, and 125.60, respectively, and were therefore poorly discriminatory for patients.

Clinical studies on the use of immune-nutritional scoring systems for predicting the risk of postoperative death in older patients with gastrointestinal-related acute abdomen are still lacking. The PNI, CONUT, and mGPS are readily available comprehensive predictive assessments. They reflect systemic inflammation and the nutritional status of older patients in several ways. Subsequent nomograms and CoxBoost models obtained using LASSO Cox regression further improved the accuracy of the prediction.

This study has several limitations. First, this study was retrospective. Only data from one institution were included, the source of the patient sample was biased, and selection bias was inevitable. The modeling relied on internal validation but lacked external validation, so the generalizability of the conclusions is limited. Second, the two-point thresholds for some indicators were obtained from previous studies, such as the NPS, and new cutoff values should be established according to the characteristics of the cohort, which may weaken the predictive ability of some indicators. In addition, this study was a single-center study lacking external validation, limiting its applicability to other populations and the need for external validation in different populations to strengthen the generalizability of our findings. Finally, this study used 90-day mortality as the study endpoint and lacked analysis of the incidence of other nonfatal postoperative complications. Inflammatory nutritional indicators not only reflect the risk of postoperative death, but are also strongly associated with postoperative infection [38], readmission [39] and surgical tolerance. Therefore, using the risk of death as a study endpoint and combining these indicators can provide important information for the postoperative management of elderly patients. However, this paper mainly explores the use of multiple prediction models to screen the indicators and does not do a sufficient inclusion of the definition of the ending event, the subsequent direction can be based on this research. Nevertheless, overall, the predictive significance of the immune-nutritional system for the risk of postoperative mortality in older patients with gastrointestinal-related acute abdomen remains of considerable value.

Conclusion

In summary, the mGPS, PNI, and CONUT could be comparable prognostic models that include immune-nutritional factors with favorable predictive performance for older patients undergoing emergency gastrointestinal surgery. In view of this, early detection and improvement of the nutritional and inflammatory status of older patients has the potential to improve patient short-term survival.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

NLR:

Neutrophil-to-lymphocyte ratio

NPR:

Neutrophil-to-platelet ratio

PLR:

Platelet-to-lymphocyte ratio

SIRI:

systemic inflammatory response index

SII:

Systemic immune-inflammation index

mGPS:

Modified Glasgow Prognostic Score

CONUT:

Controlling nutritional status score

NPS:

Naples Prognostic Score

ALB:

Serum albumin

CRP:

C-reactive protein

PIV:

The pan-mmune-inflammation value

NLR:

Neutrophils to lymphocytes ratio

LMR:

Lymphocytes to monocytes ratio

NEUT:

Neutrophil

LYMPH:

Lymphocyte

MOMO:

Monocyte

PLT:

Platelet

ALB:

Albumin

CHOL:

Cholesterol

LASSO:

Least absolute shrinkage and selection operator

K‒M curves:

Kaplan‒Meier curves

ROC:

Receiver operating characteristic

AUC:

Area under the curve

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Acknowledgements

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Funding

This work was supported, the 1·3·5 project for disciplines of excellence-Clinical Research Incubation Project, and West China Hospital, Sichuan University (No. 22HXFH001), the Department of Science and Technology of Sichuan Province (No. 2023NSFSC1834).

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Authors and Affiliations

  1. Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Guo Xue Xiang No. 37, Chengdu, 610041, China

    Zechuan Jin, Tinghan Yang & Ziqiang Wang

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  1. Zechuan Jin
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  2. Tinghan Yang
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  3. Ziqiang Wang
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Contributions

Zechuan Jin conducted a literature search, data extraction, and paper writing. Tinghan Yang conducted a literature search, data analysis. Ziqiang Wang conducted research design and paper revision. Ziqiang Wang confirmed the authenticity of the raw data.

Corresponding author

Correspondence to Ziqiang Wang.

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Because this study was a clinical retrospective analysis, the Ethics Committee of West China Hospital of Sichuan University was exempted from ethical approval and patient informed consent in accordance with national laws and institutional requirements.

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The authors declare no competing interests.

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Jin, Z., Yang, T. & Wang, Z. Immune-nutritional indicators predict short-term mortality in older patients after emergency gastrointestinal surgery: a retrospective study. BMC Gastroenterol 25, 99 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12876-024-03583-3

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12876-024-03583-3

Keywords

BMC Gastroenterology

ISSN: 1471-230X

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