A nomogram to predict pulmonary complications after gastrointestinal surgery: a retrospective study

Abstract

Objective

We aimed at developing a nomogram able to predict postoperative pulmonary complications (PPC) after gastrointestinal surgery.

Methods

We retrospectively analyzed the clinical data of patients who underwent gastrointestinal surgery at Jiangnan University Affiliated Hospital from December 2017 to May 2022. Patients were randomly divided into training cohort and validation cohort at a 7:3 ratio. The training cohort is divided into PPC group and Non-PPC group. The Least Absolute Shrinkage and Selection Operator (LASSO) method and logistic regression were used to determine the independent risk factors. The identified risk factors were used to construct a nomogram model for predicting the risk of PPC after gastrointestinal surgery. The nomogram model was validated by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

Results

A total of 563 patients were admitted. The incidence of PPC was 17.6% (99/563). In the training cohort, multiple logistic regression showed that age, hypertension, history of respiratory diseases, preoperative albumin, intraoperative blood loss, postoperative intensive care unit (ICU) time, postoperative arterial oxygen partial pressure (PaO2), and postoperative tracheal intubation time were identified as the influencing factors of PPC (P < 0.05). We constructed a nomogram model for predicting the PPC of the training cohort, with a C-index of 0.857 (95%CI 0.812–0.902). In the validation cohort, the C-index of the model is 0.936 (95%CI 0.890–0.982). The ROC curve of the training cohort is 0.875 (95%CI 0.832–0.918), similar with validation cohort 0.929 (0.876–0.982). The calibration curve indicates that the predicted results are correlated with the observed results.

Conclusions

The constructed nomogram model has certain predictive value, and can provide a scientific reference for predicting the occurrence of PPC after gastrointestinal surgery.

Introduction

Gastrointestinal surgery, including gastrectomy, colectomy and other operations, is one of the important methods for the treatment of gastrointestinal diseases [1]. Despite current advances, gastrointestinal surgery is still associated with serious postoperative adverse effects, such as PPC, including pulmonary infection and pneumonia [1,2,3]. Studies have shown that up to 10–20% of patients who undergo gastrointestinal surgery will develop PPC [3,4,5]. Which not only prolongs the hospitalization time, and increases medical expenses, but also impacts postoperative recovery of patients, and increases mortality rates [4, 5]. Therefore, identifying risk factors of PPC in patients after gastrointestinal surgery is crucial for optimizing clinical treatment and improving the prognosis [5, 6]. However, while several studies attempted to explore the risk factors of postoperative PPC after gastrointestinal surgery [7, 8], the research results are inconsistent and often controversial.

The purpose of this study was to explore risk factors of PPC after gastrointestinal surgery, and to construct the corresponding nomogram model to provide reference for the prevention and treatment of PPC in this population of patients.

Materials and methods

Quality control

Determine what information needs to be collected by reviewing extensive literature and consulting with gastroenterologists and respiratory physicians. Epidata (Version3.1) was used to establish a database to collect medical records of hospitalized patients retrospectively, and a follow-up cohort was established at the same time to follow up the patients included in the study.

Professionally trained clinicians in the hospital will collect data, fully understand the relevant processes of case reference and data entry. During data entry, double entry will be carried out in EpiData3.1 software according to the electronic medical record report form developed in advance, and consistency test will be conducted on the results of double entry. If the results are inconsistent, the original case data will be consulted to correct the data. To ensure the accuracy of data.

Patient selection: We retrospectively screened and included records of patients who underwent gastrointestinal surgery at Jiangnan University Affiliated Hospital from December 2017 to May 2022. The inclusion criteria were as follows: (1) The operation site was the gastrointestinal tract excluding anal canal and appendix; (2) Laparotomy, non-laparoscopic surgery; (3) Age ≥ 18 years old; (4) The clinical data were complete. The exclusion criteria were as follows: (1) Presence of other malignant tumors; (2) Patients with a history of abdominal surgery; (3) Patients with preoperative co-morbidities of serious medical diseases.

PPC symptoms

PPC refers to various respiratory complications that occur after surgery, including pneumonia, respiratory failure, pleural effusion, pneumothorax, atelectasis, and acute respiratory distress syndrome.

PPC diagnostic criteria: Pneumonia: According to IDSA/ATS 2019 criteria (body temperature > 38℃+ imaging infiltration + WBC > 10 × 10⁹/L); Respiratory failure: meeting the Berlin standard (PaO2/FiO2 ≤ 300 mmHg); Pleural effusion: Imaging examination (X-ray /CT showing effusion) + pleural puncture confirmation; Pneumothorax: Imaging examination (X-ray /CT showing gas buildup) + sudden chest pain/shortness of breath; Atelectasis: Imaging (X-ray /CT showing increased density/decreased volume) + shortness of breath/cough; Acute respiratory distress syndrome: meeting the Berlin criteria (acute onset + infiltration of both lungs + PaO2/FiO2 ≤ 300 mmHg + exclusion of heart failure).

Data collection: Data were collected with reference to relevant literature on the risk factors for inducing PPC in patients after gastrointestinal surgery [6,7,8]. Data were collected based on hospital system information and follow-up information, including: (1) Basic clinical data such as gender, age, body mass index (BMI), smoking history. (2) Basic diseases such as heart disease, diabetes, hypertension, history of respiratory diseases; (3) Preoperative examination and intervention: including electrocardiogram (ECG), chest X-ray examination, and chemotherapy two weeks before operation; (4) Preoperative blood biochemical indicators: albumin, urea nitrogen, creatinine, total cholesterol, HDL-C; (5) Categories of primary diseases: stomach, duodenal ulcer, gastric cancer, colorectal cancer and others; (6) Surgical methods: subtotal gastrectomy, radical resection of gastric cancer, radical resection of colorectal cancer and others; (7) Intraoperative conditions: operation time, incision location, incision length, intraoperative blood loss, intraoperative intubation time; (8) Postoperative intervention: postoperative ICU time, postoperative eating time, postoperative tracheal intubation time, postoperative mechanical ventilation time and postoperative nasogastric tube indwelling time. (9) Postoperative PaCO2 level.

Indicator description

(1) Heart diseases, including angina pectoris, old myocardial infarction, and rheumatic heart disease. (2) History of respiratory diseases, including chronic bronchitis, obstructive pulmonary emphysema, old pulmonary tuberculosis, bronchial asthma, and bronchiectasis. (3) Abnormal electrocardiogram, including myocardial strain, low voltage in the limb leads, chronic coronary artery insufficiency, incomplete/complete right and left bundle branch block, left ventricular hypertrophy, sinus bradycardia/sinus tachycardia, atrioventricular block, paroxysmal supraventricular tachycardia, premature atrial/ventricular complexes, pre excitation syndrome, atrial fibrillation, and old myocardial infarction. (4) Abnormal chest X-ray, including chronic bronchitis, emphysema, pulmonary tuberculosis, lung cancer / metastatic lung cancer, and other conditions such as pleural thickening.

Statistical method: All statistical analyses and data were conducted using SPSS 22.0 (IBM Corp, Armonk, NY, USA) and R software 4.0.2 (R Statistical Calculation Foundation, Vienna, Austria) (https://www.r-project.org). The measurement data that met the normal distribution were expressed as (\(\bar \chi \pm s\)). The independent sample t test was used for the comparison between the two groups. Data that did not meet the normal distribution were expressed by M (IQR). Mann-Whitney U test was used for the comparison between the two groups. Count data were expressed in n (%), and Chi square test was used for comparison between groups. In order to construct a nomogram model and check its external validity, patients were divided into a training cohort and a validation cohort in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to cross-verify the optimal penalty coefficient by 10 folds, compress the coefficient of irrelevant variables to zero, and eliminate collinearity. Ultimately, independent predictors highly associated with PPC after GI surgery were retained. Then the predictor variables selected by Lasso regression were merged into the logistic regression analysis. R (R 4.0.2) software package and RMS program package were used to construct nomogram model. In the model, scores were calculated for each predictive factor. Nomogram model visualizes the model. Then, the discriminative power of the model was evaluated through ROC curve analysis. A curve area AUC of 0.75 or greater indicates good discriminative power. The prediction accuracy was evaluated through calibration, and the clinical utility was evaluated through decision curve analysis (DCA). The nomogram model was validated through a validation cohort. All tests were double tailed tests, with p ≤ 0.05 indicating statistical significance.

Results

Finally, 563 patients who underwent gastrointestinal surgery were included; Among them, there were 380 males and 183females; Age ranged from 47 to 82 years old, the median age is 64 (59–68) years old; Among them, 99 cases (17.6%) experienced PPC. PPC categories are shown in Table 1. We randomly divided 563 patients into a training cohort (n = 393) and a validation cohort (n = 170) in a 7:3 ratio. There were 70 cases of PPC in the training cohort (17.8%) and 29 cases of PPC in the validation cohort (17.1%), with no statistically significant difference between the two groups (P > 0.05). The baseline demographic and clinical pathological characteristics of the training cohort and validation cohort are shown in Table 2.

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Firstly, we preliminarily selected the predictive factors for PPC occurrence through LASSO regression. The variables were centralized and normalized through 10 fold cross validation (Fig. 1). The selected predictive factors were age, hypertension, history of respiratory disease, preoperative albumin, intraoperative blood loss, postoperative ICU time, postoperative PaO2, and postoperative tracheal intubation time. Secondly, eight predictive factors were included as independent risk variables, and a predictive model was constructed using multivariate logistic regression (Table 3): Age (OR: 1.085; 95% CI: 1.024 ~ 1.149), hypertension (2.241; 1.095 ~ 4.587), history of respiratory disease (2.901; 1.366 ~ 6.158), preoperative albumin (0.949; 0.901 ~ 0.999), intraoperative blood loss (1.004; 1.001 ~ 1.007), postoperative ICU time (1.035; 1.004 ~ 1.067), postoperative PaO2 (0.877; 0.816 ~ 0.944), and postoperative tracheal intubation time (1.187; 1.092 ~ 1.291).

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Based on the results of the multivariate logistic regression analysis, the nomogram model of PPC risk after gastrointestinal surgery was constructed by using R software 4.0.2 and its RMS package (Fig. 2). A sum score could be calculated as the total scores of related predictors and referred to the probability of advanced PPC in the basal axis. For example, if a 65 year old patient with concomitant hypertension has preoperative albumin of 25 g/L, intraoperative bleeding of 400 ml, postoperative ICU time of 20 h, postoperative PaO2 was 12 kPa, and postoperative tracheal intubation time of 15 h, the corresponding total score is 22 + 13 + 18 + 21 + 15 + 34 + 40 = 163 points, and the probability of PPC occurrence is about 83%. Internal validation of the nomogram model was performed using Bootstrap method (after 1000 repeated samplings of raw data), while external validation was conducted through a validation cohort. The results showed that the C-indices of the training set and validation set were 0.857 (95% CI: 0.812–0.902) and 0.936 (95% CI: 0.890–0.982), respectively. The calibration curves of the two sets were closer to the diagonal (ideal curve) (Fig. 3). The area under the ROC curve (AUC) for internal validation was 0.875 (95% CI: 0.832–0.902), with a sensitivity of 40.0% and a specificity of 96.3%; The external validation AUC was 0.929 (95% CI: 0.876–0.982), with a sensitivity of 58.6% and a specificity of 97.9% (Fig. 4); The P-values of the Hosmer Lemeshow goodness of fit test for the modeling group and validation group were 0.534 and 0.651, respectively, indicating good model calibration. When the threshold probabilities of the training cohort and the validation cohort are 3 -83% and 5 -100%, respectively, clinical intervention may benefit patients (Fig. 5).

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Discussion

The results of this study showed that of 563 patients who underwent gastrointestinal surgery, 99 had developed PPC, with the incidence rate of 17.6%, which is lower than the 26.0%~41.3% reported in the previous studies [5, 9, 10]. This discrepancy may be related to the small sample size.

In addition, our multivariate binary logistic regression analysis showed that age, hypertension, history of respiratory disease, preoperative albumin, intraoperative blood loss, postoperative ICU time, postoperative PaO2, and postoperative tracheal intubation time were all independent influencing factors of PPC. With age, lung capacity and respiratory strength may decrease, and lung ventilation function may become limited [11, 12]. This makes elderly patients more prone to postoperative PPC, such as atelectasis and pulmonary infection [13]. In addition, higher age is associated with the decline in the function of the immune system, including lower cellular and humoral immunity. This makes elderly population of patients more prone to postoperative pulmonary infection and other complications [14]. At the same time, aging may also be accompanied by the increase in the rate of cardiovascular and metabolic diseases, including heart disease, diabetes and hypertension [13, 14]. Our study confirmed that hypertension is associated with the increased risk of postoperative PPC, which is consistent with the research of Chen et al. [15] We may speculate that hypertension is indicative of the impaired circulatory system. Hyperbaric blood may lead to abnormal hemodynamics and affect pulmonary ventilation and oxygenation function, while unstable hemodynamic state may lead to pulmonary edema, pulmonary hypertension and other PPC [15, 16].

The results of our study showed that history of respiratory disease was an independent risk factor for PPC in patients after gastrointestinal surgery. Endo et al. [17] found in a study of elderly patients over 80 years old that a history of respiratory disease is a risk factor for pneumonia after total gastrectomy. In addition, Xiang et al. [18] reported that chronic obstructive pulmonary disease (COPD) is an independent risk factor for postoperative PPC. The main reason may be that long-term chronic respiratory system diseases cause damage to the bronchial and alveolar mucosa, resulting in delayed discharge of respiratory secretions, which may easily cause respiratory obstruction. Moreover, COPD mostly affects elderly people with decreased physical function, postoperative incision pain, and inability to cough up sputum [11, 12, 17, 18].

This study also showed that preoperative albumin level was risk factors for PPC after gastrointestinal surgery. The common causes of lower than normal preoperative albumin level are simple serum albumin reduction, malnutrition, liver and kidney dysfunction, and chronic heart failure [19]. Xiang et al. [18] showed that albumin reduction was an independent risk factor for postoperative pneumonia. At the same time, Lin et al. [20] showed that early enteral nutrition support can effectively improve the nutritional status of elderly patients after thoracoscopic and laparoscopic radical resection of esophageal cancer, enhance immune function and reduce complications. In addition, excessive intraoperative bleeding can also lead to a decrease in blood volume, thereby affecting the body’s nutritional supply. Zhang et al. [21] also showed that laparoscopic surgery with less bleeding during duodenectomy can significantly reduce the risk of postoperative PPC and abdominal infection. This study also confirmed that excessive intraoperative bleeding is an independent risk factor for PPC. The main reason is that excessive blood loss can lead to hypotensive shock, multiple organ failure, and decreased resistance.

The results of this study showed that the long postoperative ICU hospitalization time was also one of the risk factors for PPC in patients after gastrointestinal surgery. The patients in ICU are all critically ill patients, and their immunity is lower than that of patients in ordinary wards, which increases the probability of nosocomial infection [22].

This study found that low postoperative PaO2 was an independent risk factor for postoperative PPC. Low PaO2 is indicative of hypoxemia, which may stimulate pulmonary artery contraction and spasm. At the same time, it will cause acid-base balance disorder, reduce digestive enzyme activity, and lead to carbon dioxide retention in the human body, thus causing PPC [23]. However, our study included only relatively small number of patients with pulmonary function examination and blood gas analysis. Therefore, their inclusion is bound to cause a sharp reduction in the total number of cases, thus affecting the analysis of other factors. Consequently, other blood gas indexes were not included in the analysis, which is one of the limitations of this study.

Studies have shown that endotracheal intubation destroys the natural barrier of the normal respiratory tract and has the potential to increase the risk of respiratory secretions and bronchospasm [24, 25]. High concentration of oxygen can also damage the bronchial ciliary epithelium, blocking sputum discharge, and leading to lung infection. In addition, endotracheal operation also increases the chance of infection [25]. The results of our study confirmed that too long tracheal intubation time was significantly associated with higher rates of postoperative PPC. It is plausible that the secretion retained around the balloon of endotracheal tube leaks down, causing the bacteria to enter the lower respiratory tract, leading to ventilator-associated lung injury. It has been reported that the incidence of the ventilator-associated lung injury.is as high as 5 -40% [26]. Contamination of respiratory equipment, especially contamination of atomizer, humidification bottle, oxygen delivery tube, as well as the impact of ward environment and the lax aseptic operation of medical staff can also lead to the occurrence of pulmonary infection [27, 28].

Gastrointestinal surgery includes various surgeries related to the digestive tract and related organs. Previous studies have focused on using nomogram models to predict PPC after surgery on a specific organ, but there has been little research on surgery on the entire gastrointestinal system. We used the identified independent risk factors of PPC to construct a nomogram model, and showed that the predictive value of the model was good. Our results indicate that the predictive value could be obtained from a combination of several different types of indicators. Incidence of PPC in surgical patients may be predicted by the score of each item, high-risk patients may be identified in a timely manner, which allows to address controllable risk factors to reduce the risk of postoperative PPC.

In order to avoid overfitting of the model and ensure its accuracy, this study conducted multiple verifications on the constructed Nomogram model. Using multiple validation methods such as C-index, calibration curve, ROC curve, and decision curve is more reliable. The verification results showed that the C-indices of the training and validation cohorts were 0.857 (95%CI: 0.812–0.902) and 0.936 (95%CI: 0.890–0.982), respectively. The calibration curves of the two cohorts were close to the diagonal (ideal curve); The AUC values under the ROC curve were 0.875 (95% CI: 0.832–0.918) and 0.929 (95% CI: 0.876–0.982), respectively; The decision curve shows a higher net benefit value. Further demonstrate that the nomogram model has good predictive accuracy for predicting PPC. Nomogram can be used as a rapid bedside risk assessment tool to provide technical support for clinicians to quantify individual patient risk. First, doctors can calculate the total score and probability of PPC occurrence according to the clinical data of patients, and define the risk threshold combined with clinical resources and patient benefits, such as the occurrence rate of PPC > 30% to start preventive respiratory support. Based on the real-time data, PaO2 and intubation time, the Nomogram score was updated to adjust the intervention intensity. This model can guide secondary prevention and evaluate postoperative intervention decisions.

Our study has some limitations. First of all, the sample size of this study is small, and all patients are from the same hospital, so there may be a selection bias. Secondly, small number of biochemical indicators were included. Third, the repeatability and robustness of the nomogram need to be verified in prospective multicenter studies with larger data sets. Further studies are needed to optimize and verify the model by expanding the sample size, enriching the risk variables and improving the experimental design.

Conclusion

This study found that age, hypertension, history of respiratory disease, preoperative albumin, intraoperative blood loss, postoperative ICU time, postoperative PaO2, and postoperative tracheal intubation time, were independent risk factors for PPC in patients undergoing gastrointestinal surgery. The nomogram model constructed by this method has certain predictive value.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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