Gastroesophageal reflux disease predicts 6-month readmission in acute exacerbation of chronic obstructive pulmonary disease: development and validation of a nomogram

Introduction

Chronic obstructive pulmonary disease (COPD) is the most common respiratory disease, characterized by progressive irreversible decline in lung function and extensive lung damage. According to statistics, there are 392 million COPD patients worldwide, causing over 3 million deaths annually, making it the third leading cause of death globally (1,2). In developing countries such as China, due to the high smoking rate among men and air pollution from early industrialization, the prevalence of COPD is higher than that in developed countries (3). Currently, there are approximately 100 million COPD patients in China, ranking as the third leading cause of death after stroke and ischemic heart disease (4). COPD patients are prone to recurrent acute exacerbations, primarily manifesting as dyspnea, cough, and sputum production (5). Approximately 6.2% of COPD patients experience severe acute exacerbations annually, requiring hospitalization (6). Acute exacerbations of COPD (AECOPD) can accelerate lung function decline, worsen quality of life, and increase mortality (7,8). COPD often coexists with multiple comorbidities, including cardiovascular diseases, diabetes mellitus, anemia, osteoporosis, mood disorders, and gastroesophageal reflux disease (GERD) (9). These comorbidities interact with COPD and exhibit a bidirectional causal relationship, significantly increasing the risk of acute exacerbations, hospitalization rates, and mortality in COPD patients. GERD is a common digestive system disorder defined as the reflux of gastric contents (gastric acid, food, bile, etc.) into the esophagus, causing symptoms and/or complications (10). GERD is one of the common comorbidities of COPD but has often been overlooked in the past (9). Microaspiration resulting from reflux of gastric contents can directly damage the airways and lung parenchyma and increase infection risk, thereby increasing the risk of asthma and pneumonia (11,12). However, studies on the impact of GERD on acute exacerbations of COPD remain limited. This study aimed to analyze the risk factors for readmission within 6 months after discharge in patients with AECOPD, to construct a logistic regression prediction model, and to internally validate the model’s predictive performance. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0849/rc).

Methods

Study design and patients

This study consecutively enrolled hospitalized patients with AECOPD admitted to the Department of Respiratory Medicine at Anhui No. 2 Provincial People’s Hospital from May 2023 to March 2025. Index hospitalization was defined as the first COPD-related hospitalization during the study period.

Inclusion criteria: (I) primary diagnosis met the diagnostic criteria for COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 report (13), with a primary diagnosis of AECOPD [International Classification of Diseases, Tenth Revision (ICD-10) codes J44.001 or J44.101]; (II) received standardized inpatient treatment (length of hospital stay ≥3 days) to ensure adequate clinical observation and completeness of follow-up data; (III) age ≥60 years, as AECOPD admissions in Anhui No. 2 Provincial People’s Hospital occur predominantly in this age group.

Exclusion criteria: (I) concurrent malignant tumors; (II) concurrent chronic kidney disease or chronic liver disease, because these conditions may independently affect B-type natriuretic peptide (BNP) levels and nutritional markers; (III) stroke with significant sequelae; (IV) severe pulmonary hypertension or severe cardiac insufficiency; (V) other neuromuscular diseases affecting swallowing function; (VI) incomplete data; (VII) death during hospitalization or follow-up.

This study adhered to the ethical principles of the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Anhui No. 2 Provincial People’s Hospital (approval No. 2024154). All patients provided written informed consent.

Data collection

The following data were collected: age, sex, body mass index (BMI), smoking status (never, previous, current), hypertension, diabetes, coronary heart disease (CHD), acute exacerbation in previous 1 year, GOLD stage, inhaled medications for COPD controller [bronchodilators, inhaled corticosteroids (ICS), anticholinergic drugs], GERD, obstructive sleep apnea-hypopnea syndrome (OSAHS), and laboratory testing parameters including PaO₂, PaCO₂, neutrophil count, lymphocyte count, hemoglobin, red blood cell distribution width (RDW), platelet count, BNP, C-reactive protein (CRP), albumin (Alb), uric acid, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Pulmonary function test data, including forced expiratory volume in 1 second (FEV₁; mL) and forced vital capacity (FVC; mL), were also collected.

Definitions of the variables

GOLD stage: COPD severity was classified into stages 1 to 4 based on the percentage of predicted FEV₁ obtained from pulmonary function testing (14). The classification criteria were as follows: GOLD stage 1 (mild): FEV₁ ≥80% predicted; GOLD stage 2 (moderate): FEV₁ 50–79% predicted; GOLD stage 3 (severe): FEV₁ 30–49% predicted; GOLD stage 4 (very severe): FEV₁ <30% predicted. Higher stages indicate more severe airflow limitation.

GERD: the diagnosis of GERD was assessed using the GERD Questionnaire (GerdQ), with a cumulative score of ≥8 used as the cutoff value for diagnosis (15).

Readmission and follow-up

During the follow-up period of 6 months ±15 days, any readmission was recorded, along with its frequency and cause. Readmission due to AECOPD was defined as the endpoint of this study. Hospitalizations for any reason other than COPD were excluded.

Statistical analysis

All statistical analyses were performed using R software (version 4.5.2) and SPSS 27.0. A two-sided P value <0.05 was considered statistically significant. Continuous variables were first tested for normality using the Shapiro-Wilk test. Normally distributed continuous variables were presented as mean ± standard deviation, and comparisons between groups were performed using the independent samples t-test. Non-normally distributed continuous variables were presented as median with interquartile range, and comparisons between groups were performed using the Mann-Whitney U rank-sum test. Categorical variables were presented as frequencies and percentages (n, %), and comparisons between groups were performed using the Chi-squared test.

The patient cohort was randomly assigned to the training group (n=478) and validation group (n=205) in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to preliminarily screen predictors of readmission risk. Given the large number of candidate variables included, to avoid overfitting, the optimal regularization parameter λ was determined using 10-fold cross-validation. Two criteria were applied to select predictors with non-zero coefficients: λ_min (the λ value with the minimum cross-validation error) and λ_1se (the λ value corresponding to the most parsimonious model within one standard error of the minimum error). Subsequently, the potential predictors identified by LASSO regression were incorporated into multivariate logistic regression analysis, and a predictive model was established using backward stepwise regression. A nomogram was then constructed to predict the risk of readmission within 6 months after discharge in patients with AECOPD.

Model discrimination was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration curves. Calibration was quantitatively assessed using calibration intercept, calibration slope, and Brier score. An intercept of 0 and a slope of 1 indicate perfect calibration; a slope <1 suggests overfitting. The Brier score ranges from 0 to 1, with lower values indicating better accuracy. Decision curve analysis (DCA) was employed to evaluate the clinical net benefit of the nomogram model. The lower x-axis (cost: benefit ratio) expresses the threshold probability as the relative weight of false positives (harm) to true positives (benefit): harm : benefit = 1 : c, and threshold probability = 1/(1 + c). Internal validation was performed using the validation set to assess the generalizability of the model.

Results

Patient enrollment

A total of 878 patients with AECOPD were initially enrolled during the study period. After exclusions, 15 patients with malignant tumors, 35 with chronic kidney disease or chronic liver disease, 39 with stroke and significant sequelae, 45 with severe pulmonary hypertension or severe cardiac insufficiency, 19 with other neuromuscular diseases affecting swallowing function, and 42 with incomplete data or death during hospitalization or follow-up were excluded. Ultimately, 683 patients were included in the final analysis, comprising 478 patients in the training group (140 in the readmission group and 338 in the non-readmission group) and 205 patients in the validation group (Figure 1).

Figure 1 Enrollment flowchart. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

Comparison of demographic, clinical, and laboratory characteristics between the training group and validation group

The results showed no statistically significant differences between the two groups in baseline characteristics, including readmission rate (29.29% vs. 30.24%, P=0.87), age (median 77 years in both groups, P=0.69), sex (male proportion 64.23% vs. 60.49%, P=0.40), and BMI (24.40 vs. 24.50 kg/m², P=0.94). In addition, smoking status, hypertension, diabetes, CHD, history of acute exacerbation in the previous year, GOLD stage, use of inhaled medications, comorbidities (GERD, OSAHS), and all laboratory parameters were similarly distributed between the two groups (all P>0.05). These findings indicate that the training group and validation group were well balanced in terms of demographic, clinical, and laboratory characteristics, satisfying the baseline comparability requirements for subsequent model development and validation (Table 1).

Comparison of demographic, clinical, and laboratory characteristics between the readmission group and non-readmission group in the training group

Compared with the non-readmission group, patients in the readmission group exhibited the following characteristics: significantly lower BMI (22.75 vs. 24.80 kg/m², P<0.001), significantly higher BNP level (172.44 vs. 162.44, P<0.001), higher proportion of patients with a history of acute exacerbation in the previous year (37.86% vs. 23.67%, P<0.001), and higher prevalence of GERD (39.29% vs. 19.23%, P<0.001). The distribution of GOLD stage differed significantly between the two groups (P=0.01), with a higher proportion of patients with GOLD stages 3–4 in the readmission group (41.43% vs. 26.63%). No significant differences were observed between the two groups in terms of age, sex, smoking status, other comorbidities (hypertension, diabetes, CHD, OSAHS), use of inhaled medications, or other laboratory parameters (all P>0.05) (Table 2).

Construction of the prediction model

LASSO regression analysis for variable screening

LASSO regression was used to screen risk factors for readmission in the training group, with 28 clinical and laboratory parameters included as independent variables. The results showed that under the λ_min criterion, GERD (β =0.77), history of acute exacerbation (β =0.35), GOLD stage (β =0.22), HDL (β =0.67), RDW (β =0.07), PaCO₂ (β =0.01), BNP (β =0.01), BMI (β =−0.11), and lymphocyte count (β =−0.17) were included in the final model. Under the more conservative λ_1se criterion, only GERD (β =0.50), history of acute exacerbation (β =0.10), GOLD stage (β =0.09), BMI (β =−0.06), and BNP (β =0.01) retained non-zero coefficients. The coefficients of the remaining variables shrank to zero under both λ selection criteria, indicating that they contributed little to the prediction of readmission (Figure 2A,2B).

Figure 2 Variable selection via LASSO regression: (A) Distribution of LASSO regression coefficients for candidate predictor variables. (B) Selection of the optimal penalty coefficient in the LASSO regression. LASSO, least absolute shrinkage and selection operator.

Model development and visualization

The five variables screened by LASSO regression were incorporated into multivariate logistic regression analysis. The results showed that all five predictors were significantly associated with the risk of readmission in patients with AECOPD (all P<0.05). Among them, GERD [odds ratio (OR) =2.553; P<0.001] and history of acute exacerbation in the previous year (OR =1.629; P=0.03) significantly increased the risk of readmission. The risk also increased with each increment in GOLD stage (OR =1.365; P=0.005) and each unit increase in BNP level (OR =1.008; P=0.006), while BMI (OR =0.875; P<0.001) was the only protective factor (Table 3).

A nomogram predictive model was constructed based on the above five indicators. The total score was calculated by summing the scores corresponding to each factor, and the risk value on the corresponding axis indicated the probability of readmission within 6 months in patients with AECOPD. A higher total score suggested a greater risk of readmission within 6 months after discharge (Figure 3).

Figure 3 Nomogram for predicting 6-month readmission in AECOPD. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; BMI, body mass index; BNP, B-type natriuretic peptide; GERD, gastroesophageal reflux disease; GOLD, Global Initiative for Chronic Obstructive Lung Disease; Prior AE 1 year, acute exacerbation in previous 1 year.

Evaluation and validation of the predictive model

The AUC was 0.708 [95% confidence interval (CI): 0.653–0.758] in the training group, indicating good discriminative ability. The Youden index was 0.330, with a sensitivity of 66.4% and a specificity of 66.6%. The AUC in the validation group was 0.731 (95% CI: 0.648–0.784), which was similar to and slightly higher than that in the training group, suggesting that the model did not exhibit overfitting and had good generalizability and stability (Figure 4A,4B).

Figure 4 Receiver operating characteristic curves of the nomogram for predicting 6-month readmission in AECOPD patients in (A) training group and (B) validation group. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; AUC, area under the curve.

The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.708 in the training group and 0.738 in the validation group (both P>0.05), indicating a good model fit. The calibration curve of the training group showed that the actual curve closely matched the bias-corrected curve, with an intercept close to 0 and a slope close to 1. The Brier score was 0.18, indicating high consistency between the model predictions and observed outcomes, reflecting good calibration. The calibration curve of the validation group also demonstrated a good fit (Figure 5A,5B).

Figure 5 Calibration curves of the nomogram for predicting 6-month readmission in AECOPD patients in (A) training group and (B) validation group. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

DCA was used to evaluate the clinical utility of the model. The results showed that within a reasonable high-risk threshold range of 0.2–0.6, the nomogram model provided a higher net benefit than the “treat all” or “treat none” strategies.

The cost: benefit ratio displayed on the lower x-axis represents the threshold probability used to calculate the net benefit. For example, applying this model at a threshold probability of 0.2 (cost-benefit ratio of 1:4) implies that physicians consider the cost of “misidentifying a healthy individual as high-risk” to be merely one-fourth of the benefit derived from “correctly identifying a genuine patient”. Consistent trends were observed in both the training group and the validation group (Figure 6A,6B). This finding indicates that the model has good clinical utility and can serve as a reference for risk stratification of readmission within 6 months after discharge and for intervention decision-making in patients with AECOPD.

Figure 6 Decision curve analysis of the nomogram for predicting 6-month readmission in AECOPD patients in the (A) training group and (B) validation group. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

Discussion

This prospective cohort study enrolled 683 hospitalized patients with AECOPD and identified BMI, Acute exacerbation in the previous year, GERD, GOLD stage, and BNP as independent predictors of 6month readmission. The model demonstrated acceptable discrimination and good calibration in both the training group (AUC =0.708) and the validation group (AUC =0.731). Calibration curves showed good agreement between predicted probabilities and observed outcomes (Hosmer-Lemeshow test P>0.05). Nomogram models can quantify, visualize, and graphically present the results of logistic regression analyses. They provide an intuitive display of variable values and present predicted outcomes as continuous probabilities, and have therefore been widely applied in clinical practice (16). DCA showed that within a threshold probability range of 0.2–0.6, our nomogram provided a higher net benefit than the “treat all” or “treat none” strategies. Clinicians can use the cost: benefit ratio on the lower x-axis to intuitively understand the trade-off between the cost of “misclassifying a healthy individual as high-risk” and the benefit of “correctly identifying a genuine patient”. Nevertheless, the AUC values remain below 0.8, suggesting that clinically meaningful improvements in predictive accuracy could be achieved by incorporating additional biomarkers or clinical parameters in future studies.

Our model showed comparable predictive performance to that of Zhu et al. (17), who developed a 1-year COPD readmission prediction model incorporating white blood cell (WBC) count, disease duration >10 years, the number of acute exacerbations in the past year, and concurrent respiratory failure (AUC: 0.719 in the training set and 0.676 in the validation set), but the generalizability of their model was clearly inferior to ours. Goto et al. (18) reported a 30-day readmission prediction model for COPD patients using the XGBoost machine learning approach; their simplified model with 11 predictors achieved an AUC of 0.746, which is higher than that of our model. However, their model incorporated more predictors and had a shorter prediction window. Our model predicts over a longer time window, which involves more confounding factors and greater prediction difficulty, yet requires only five routine clinical indicators (BMI, history of acute exacerbations, GERD, GOLD stage, BNP), making it simpler and more user-friendly. None of these existing models included GERD as a predictor, whereas our study identifies GERD as an important but easily overlooked predictor in COPD readmission prediction.

A study using the U.S. National Inpatient Sample database, which included 7,159,694 adult hospitalizations, demonstrated that the prevalence of GERD was significantly higher in patients with COPD than in those without COPD (27.8% vs. 14.1%) (19). Reduced lung compliance in COPD patients leads to mediastinal structural deformation and esophageal traction, resulting in lower esophageal sphincter dysfunction (20-22). GERD accelerates lung function decline in COPD patients (with an additional annual decrease in FEV₁ of 2–5 mL) and exacerbates airway trapping (23). These studies demonstrate the bidirectional interaction between GERD and COPD. Proton pump inhibitor treatment in COPD patients with comorbid GERD significantly reduces the frequency of acute exacerbations without increasing the risk of pneumonia (24). These findings suggest that controlling GERD symptoms can effectively reduce the risk of COPD exacerbations, and that proton pump inhibitors and gastrointestinal prokinetic agents should be actively used in COPD patients with comorbid GERD.

Our study also found that BMI, Acute exacerbation in previous 1 year, GOLD stage, and BNP were independent predictors of 6-month readmission in patients with AECOPD. Low BMI often reflects malnutrition, which not only impairs respiratory muscle strength and reduces sputum clearance but also leads to overall decreased immune function, making patients more susceptible to infections or other triggers leading to readmission (25,26). Acute exacerbation in previous 1 year is a risk factor for readmission in COPD patients. Previous studies have shown that a history of exacerbations increases the likelihood of subsequent exacerbations and shortens the interval between them (27,28). A large cohort study based on a Chinese population confirmed that patients in GOLD group A1 (one prior exacerbation) had a 1.704-fold higher risk of hospitalization compared with those without a history of exacerbations (7). GOLD stage is an important reference for assessing the severity of COPD; higher GOLD stages are associated with increased rates of readmission and mortality (29). In COPD patients, even in the absence of a history of heart failure, plasma concentrations of BNP and N-terminal proBNP (NT-proBNP) increase proportionally with the severity of right ventricular diastolic dysfunction (30,31). BNP testing in AECOPD patients can increase the detection rate of newly diagnosed or previously unrecognized heart failure by approximately 20% (31).

Limitation

This study has several limitations. First, GERD was assessed using the GerdQ questionnaire rather than gold-standard methods such as pH monitoring or endoscopy, which may introduce subjective bias. Second, this was a single-center study with a limited sample size; multicenter external validation with larger samples is needed in the future. Third, the model achieved only moderate AUC values (0.708–0.731), indicating room for improvement in predictive accuracy. Fourth, the exclusion criteria (e.g., exclusion of patients with chronic kidney disease, chronic liver disease, severe pulmonary hypertension, or severe cardiac insufficiency) may introduce selection bias and affect the generalizability of the findings. Fifth, we did not record patients’ post-discharge medication adherence (particularly for GERD therapy and COPD preventive medications), which may have an impact on readmission outcomes. In addition, our study only included patients aged ≥60 years. Therefore, our findings may not be generalizable to younger COPD populations. Future multicenter studies should validate our model in younger cohorts.

Conclusions

This study selected five easily accessible clinical variables to construct a risk prediction model for 6-month readmission after discharge in patients with AECOPD, and presented the model as a visualized nomogram, facilitating clinical promotion and application. In particular, GERD was identified as an important predictor. For AECOPD patients with comorbid GERD, a more stringent post-discharge follow-up plan should be considered, and proton pump inhibitors and gastrointestinal prokinetic agents should be actively used to control GERD symptoms in order to reduce the risk of readmission.

Acknowledgments

The authors thank the staff of the Department of Respiratory Medicine, Anhui No. 2 Provincial People’s Hospital, for their assistance with data collection.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0849/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0849/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0849/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0849/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study adhered to the ethical principles of the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Anhui No. 2 Provincial People’s Hospital (approval No. 2024154). All patients provided written informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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