Establishment and validation of a risk prediction model for postoperative persistent cough in lung resection patients: a systematic review and meta-analysis-based approach

Introduction

Lung cancer remains the leading cause of both incidence and mortality among malignant tumors in China (1). The widespread use of early screening methods has notably increased the detection rate of pulmonary nodules, thereby driving a sustained rise in the demand for surgical resection as a definitive treatment for patients with lung cancer (2). Postoperative cough, a common complication following lung resection, is notably prevalent (3). Research indicates that approximately 25% to 50% of patients experience cough symptoms in the immediate postoperative period (3). However, a subset of these patients develop cough after pulmonary resection (CAP), a condition characterized by persistent dry cough lasting more than two weeks post-surgery. CAP persists despite the exclusion of potential confounders such as postnasal drip syndrome, bronchial asthma, and angiotensin-converting enzyme inhibitor use, and is not associated with any abnormalities on chest X-ray examinations (4). Previous research suggest that the incidence of CAP can exceed 50% within the first year following surgery and remains as high as 18% after 5 years (5).

Persistent cough not only aggravates postoperative wound pain but also severely disrupts patients’ sleep quality and communication, significantly diminishing their overall quality of life (5,6). Additionally, this symptom may lead to a wide array of complications affecting the cardiovascular, urogenital, musculoskeletal, and nervous systems, posing substantial health risks. More critically, persistent cough can cause patients to question the efficacy of their treatment, contributing to anxiety, depression, and even social isolation (3,5-9). These psychological and social ramifications underscore the critical need to address postoperative cough in patients with lung cancer.

Currently, no standardized, effective treatment regimen exists for CAP. Therefore, the early identification of high-risk patients and the implementation of targeted preventive strategies are crucial to reducing the frequency of cough, alleviating symptoms, promoting postoperative recovery, and improving patients’ quality of life (7). Although some researchers have attempted to develop risk prediction models for CAP (8,9), these models are often based on single-center studies with limited sample sizes and lack external validation. By integrating data from high-quality observational studies through meta-analysis, this study aimed to overcome these limitations, ensuring an adequate sample size and enhancing the stability and reliability of the predictive model. Ultimately, this effort seeks to establish a more accurate risk prediction model for persistent CAP based on its risk factors, with rigorous external validation, to provide healthcare providers with a more scientific approach for screening and managing patients with persistent CAP, thereby improving treatment outcomes and improving patient quality of life. We present this article in accordance with the PRISMA and TRIPOD reporting checklists (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-440/rc).

Methods

Literature screening

• PubMed, Web of Science, and The Cochrane Library were systematically searched for relevant publications from their earliest available date through August 10, 2024. Additionally, an updated literature search was conducted in January 2025 to incorporate the latest findings before proceeding with data processing. The search strategies combined both MeSH terms and free-text keywords. Detailed search strategies for each database are provided in Table S1.
• The inclusion criteria adhered to the principles of PECOs framework (P: participants, E: exposures, C: comparisons, O: outcomes, s: study design), as follows: P: adult patients (18 years or older) diagnosed with lung cancer and undergoing thoracoscopic surgery or open-assisted lobectomy; E: factors related to the patient and surgical procedure that may be associated with persistent cough following pulmonary resection; O: the primary outcome was the development of a persistent CAP, defined according to the diagnostic criteria for CAP; s: inclusion of cross-sectional, case-control, and cohort studies. The exclusion criteria were as follows: patients were excluded if they met any of the following conditions: (i) presence of acute respiratory illness, acute exacerbation of chronic respiratory disease, tuberculosis, or pulmonary infection; use of oral angiotensin-converting enzyme inhibitors; or any other preoperative condition associated with a significant cough; and (ii) evidence of respiratory failure, significant dysfunction or pathology in vital organs, or severe complications such as pulmonary infection or bronchopleural fistula; (iii) studies with incomplete data and no contact with the original author; (iv) publications in languages other than English or Chinese; and (v) inaccessibility of full-text articles.

Data accessibility and quality evaluation

Data extraction from the included studies was performed independently by two researchers. In cases of disagreement, consensus was reached through discussion or consultation with a third reviewer. All extracted data were stored in a Microsoft Excel file for further analysis.

Case-control and cohort studies were assessed using the Newcastle-Ottawa Scale (NOS) (10). Scores ranged from 0 to 9, with 0–3 indicating low quality, 4–6 indicating moderate quality, and 7–9 indicating high quality. Cross-sectional studies were evaluated based on the quality assessment criteria recommended by the Agency for Healthcare Research and Quality (AHRQ) (11). A maximum score of 11 was assigned, with scores of ≥8 indicating high quality, 6–7 indicating medium quality, and ≤5 indicating low quality.

Validation population

Prospective enrollment was conducted for patients undergoing lung resection surgery in the Thoracic Surgery Department of the Affiliated Nanjing Brain Hospital, Nanjing Medical University between October and December 2024. The inclusion and exclusion criteria were as follows:

Inclusion criteria were as follows: (I) patients aged 18 years or older who underwent video-assisted thoracoscopic surgery (VATS) lung resection; (II) postoperative chest X-ray examinations revealed no signs of lung inflammation, excluding other potential chronic cough factors such as pneumonia or pleural effusion; (III) multiple preoperative examinations confirmed the absence of distant metastasis; (IV) patients provided informed consent, voluntarily participated, and had no language communication barriers.

Exclusion criteria were as follows: (I) preoperative conditions likely to induce chronic cough, such as chronic bronchitis, bronchial asthma, or bronchiectasis; (II) postoperative severe pulmonary complications, including empyema, chylothorax, bronchopleural fistula, or pulmonary embolism; (III) patients who refused to participate in the study, failed to complete follow-up surveys, or were lost to follow-up.

Ethics

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Affiliated Nanjing Brain Hospital, Nanjing Medical University (No. 2023-KY077-01). All participants provided informed consent before participating in the study.

Data collection

The diagnostic criterion for chronic cough following lung resection was established as a cough lasting 8 weeks or more, based on prior research (12). The severity of persistent cough was assessed using the Numerical Rating Scale (NRS) (12), which categorizes cough severity as follows: 0, no cough; 1–3, mild cough with no impact on sleep; 4–6, moderate cough affecting sleep but allowing rest; 7–9, severe cough preventing sleep or waking the patient; 10, patients with intense cough with an NRS score of 4 or higher were classified into the postoperative persistent cough group. Online assessments were conducted by case managers at 8 weeks postoperatively, with patients completing the questionnaire themselves. Using the established risk prediction model, a data collection form was designed, and surgical details were retrieved from electronic medical records. During the 8-week follow-up assessment for chronic cough, patients were also queried about the presence of postoperative gastroesophageal reflux.

Statistical analysis

Meta-analysis

Odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated for risk factors for CAP. Heterogeneity was assessed using the I-squared statistic (I²). If low heterogeneity was observed (P>0.10, I²≤50%), a fixed effects model was used. If heterogeneity was significant, sensitivity and subgroup analyses were performed using a one-by-one elimination protocol to explore potential causes of heterogeneity. In the presence of high heterogeneity, a random effects model was applied. Descriptive analysis was used to identify risk factors that could not be aggregated due to limited data or insufficient studies (13). Sensitivity analyses were conducted using change-effects modeling. A P value of <0.05 was considered statistically significant (Figures S1-S10).

Model development

A comprehensive meta-analysis was initially conducted to identify significant risk factors associated with persistent cough following lung resection. These risk factors were treated as independent variables, with persistent cough as the dependent variable. ORs and their corresponding 95% CIs for each risk factor were extracted and pooled (14,15). The regression coefficient (β) for each risk factor was calculated by taking the natural logarithm of its OR, using the formula β = ln(OR). For scoring purposes, β was multiplied by 10 and rounded to one decimal place (14,15).

Subsequently, a multifactorial logistic regression analysis was conducted to establish a prediction model and generate a nomogram. Receiver operating characteristic (ROC) curve was plotted to assess the predictive performance of the model. Sensitivity, specifity, and the area under the curve (AUC) were calculated at different cutoff values, and the calculations were used to identify the optimal cutoff point (15). According to the optimal cut-off point, patients were categorized into four risk levels, including low, moderate, high, and very high risk. Additionally, the model’s goodness-of-fit was assessed using the Hosmer-Lemeshow test. A difference was considered nonsignificant if P>0.05.

Results

Literature screening and Study features

An initial search yielded 1,335 studies, of which 13 were ultimately selected for inclusion in this review (4,8,9,16-25). Figure 1 presents a flowchart illustrating the literature screening process. Among the selected studies, 11 were case-control studies, one was a cohort study, and one was a cross-sectional study.

Figure 1 Flow diagram depicting the selection process for included studies.

The combined sample size across all studies was 3,669 participants, with 1,115 individuals in the case group. The incidence of CAP reported in research published between 2005 and 2024 ranged from 21.1% to 50.0%. These studies were primarily conducted in China (n=12), with one study from Japan. Regarding the definition of post-lung surgery cough, most studies defined it as a cough lasting at least two weeks following lung resection (4,8,9,18,19,20,24,25). However, some studies extended the duration to eight weeks (17,22), while others defined it as three weeks (16) or did not specify the duration at all (21,23). In terms of assessment methods, the Leicester Cough Questionnaire (LCQ) was the most commonly used tool (17,18,21,23,24,25), followed by the Visual Analog Scale (VAS) (4,17,20,22,25). The Cough Symptom Score (CSS) was employed in three studies (18,20,24). Notably, four studies did not specify the assessment method used (8,9,16,19). A summary of the risk factors, number of studies, sample size, and pooled OR (95% CI) is provided in Table S2.

Quality of included studies

Quality assessment was performed using the AHRQ criteria for one study (4), while 12 studies (8,9,16-25) were assessed using the NOS. Of the studies, two (18,22) were categorized as moderate quality, while the remaining 11 were rated as high quality with low risk of bias. Detailed quality assessment results are available in Tables S3-S5.

Validation population

For model validation, a total of 294 patients who underwent lung resection were recruited. Among them, 102 developed persistent cough, resulting in an incidence rate of 34.7%, while 192 patients did not develop persistent cough. The distribution of relevant factors in the validation cohort is presented in Table 1.

Model development and validation

Among the nine risk factors identified through systematic review and meta-analysis, seven were associated with persistent postoperative cough: lobectomy, right-side operation, right upper lobectomy, subcarinal lymph node dissection, lymph node dissection around the bronchial tree, tracheal intubation time (≥172 min), and postoperative acid reflux (Table 2). The β value for each factor was derived from the natural logarithm (ln) of the OR in the meta-analysis, calculated as β = ln(OR). The resulting predictive model formula is: logit(P) = 1.556 × lobectomy + 0.802 × right-side operation + 0.718 × right upper lobectomy + 1.459 × subcarinal lymph node dissection + 1.356 × lymph node dissection around the bronchial tree + 0.846 × tracheal intubation time (≥172 mins) + 1.524 × postoperative acid reflux.

To generate the risk score for each factor, the regression coefficients were multiplied by 10 and rounded to the nearest integer. The total score for the Logistic Risk Prediction Model ranged from 0 to 82; patients with more risk factors accumulated higher scores and had an increased likelihood of developing chronic postoperative cough (Table 2). Based on these predictors, a nomogram risk prediction model was established, incorporating the independent risk factors (Figure 2).

Figure 2 Risk factors of lobectomy, right-side operation, right upper lobectomy, subcarinal lymph node dissection, lymph node dissection around the bronchial tree, tracheal intubation (≥172 min), and postoperative acid reflux for the nomogram model.

Predictive performance and efficacy of the model

The predictive model demonstrated an area under the ROC curve of 0.87 (95% CI: 0.82–0.91), indicating strong predictive performance (Figure 3A). Given that the aim of the model development was an early detection of patients with lung resection at high-risk for CAP, 23.25 was selected as the optimal cutoff risk score with a higher sensitivity of 0.735 and a specificity of 0.849. Sensitivity and specificity at different cutoff risk score are shown Table S6. Based on the obtained frequencies of CAP using different risk scores, the 294 patients with lung resection were further categorized into four risk-level groups: low (n=124), moderate (n=66), high (n=30), and very-high (n=74) risk, corresponding to risk scores of <8.0, 8.0–23.2, 23.3–36.3, ≥36.4, respectively. The numbers of patients who developed CAP were 8 (6.5%), 19 (28.8%), 20 (66.7%), and 55 (74.3%) in these four groups, respectively (Figure 3B). The Hosmer-Lemeshow goodness-of-fit test confirmed consistency between the model’s predictions and actual outcomes, suggesting robust calibration (P=0.063, χ²=14.788) (Figure 3C).

Figure 3 Overview of performance and risk stratification for a recurrence risk prediction model in CAP patients. (A) ROC curve of the risk prediction model for recurrence in patients with CAP. (B) Prevalence of CAP across four risk groups. (C) Calibration curve of the risk prediction model for patients with CAP in the validation cohort. AUC, area under the curve; CAP, cough after pulmonary resection; CI, confidence interval; ROC, receiver operating characteristic.

Discussion

The concept of chronic CAP, first introduced by Sawabata et al. in 2005 (4), has received growing attention in the era of enhanced postoperative recovery, particularly due to its impact on postoperative quality of life. Many patients continue to experience persistent cough symptoms post-discharge, which can hinder recovery, delay the return to normal daily activities and work, and ultimately diminish overall postoperative well-being (3). Current evidence highlights the multifactorial nature of CAP, emphasizing the importance of early identification of high-risk individuals, timely intervention, and the development of effective intraoperative and postoperative preventive strategies to optimize patient outcomes (6). In this context, our meta-analysis identified several significant risk factors associated with postoperative cough and used them to construct the first predictive model specifically targeting CAP. This model is designed to facilitate the early identification of high-risk patients and guide clinical decision-making for prevention and management.

Key surgical factors contributing to CAP include lobectomy—particularly right upper lobectomy—mediastinal and peribronchial lymph node dissection, and prolonged tracheal intubation (3). Lobectomy, due to the significant volume of pulmonary tissue removed, can lead to postoperative bronchial deformity, distortion, or obstruction, increasing airway sensitivity and predisposing patients to chronic cough (19). The upper lobes, especially the right upper lobe, leave a larger residual cavity post-resection, which may further exacerbate airway irritation and elevate the risk of CAP. Segmentectomy, which offers comparable therapeutic efficacy to lobectomy in patients with stage IA non-small cell pulmonary cancer (26), is increasingly recommended as a less invasive alternative to reduce surgical trauma and improve postoperative quality of life. During pulmonary resection, the removal of subcarinal lymph nodes for pathological evaluation may inadvertently damage the left vagus nerve (VN) and recurrent laryngeal nerve (RLN), potentially triggering CAP (3). Preserving the pulmonary branches of the VN—by limiting lymph node dissection to targeted sampling on the surgical side—has been shown to significantly reduce CAP incidence (25). Furthermore, lymph node dissection may induce a localized inflammatory response, releasing inflammatory mediators that activate the transient receptor potential vanilloid 1 (TRPV1) pathway, a known contributor to cough hypersensitivity (25). Right-sided pneumonectomy, which typically involves denser lymphatic tissue and greater tracheal exposure, carries a higher risk of CAP than left-sided procedures (27).

Prolonged tracheal intubation (≥172 min) has been identified as an independent risk factor for CAP, as it can cause sustained trauma to sensitive airway tissues and promote airway inflammation (3). Intraoperative interventions—such as elevated cuff pressure and frequent suctioning of secretions—may further exacerbate mucosal injury, thereby exacerbating postoperative cough symptoms (6). Additionally, the presence of residual muscle relaxants can impair coordination of the pharyngeal muscle coordination, diminish pulmonary function, and weaken protective airway reflexes, all of which contribute to the development of chronic cough (5). Postoperative gastroesophageal reflux is another recognized independent risk factor for CAP. Physiological changes following pulmonary resection—such as diaphragmatic elevation and reduced pulmonary volume—may predispose patients to reflux (3). Gastric acid reflux can stimulate the VN, which innervates both the esophagus and bronchial tree, supporting a neurogenic mechanism for the onset of CAP in these patients (3,4). For patients with CAP linked to reflux post-pulmonary resection, treatment with proton pump inhibitors and prokinetic drugs is recommended to manage symptoms and reduce recurrence risk (28).

This study presents an innovative approach by integrating regression coefficients with meta-analysis results to develop a predictive model for CAP. The model incorporates key risk factors, including lobectomy, right pulmonary resection, right upper lobectomy, mediastinal and peribronchial lymph node dissection, prolonged tracheal intubation (≥172 min), and postoperative gastroesophageal reflux. It demonstrated strong predictive performance, effectively identifying high-risk patients for CAP following pulmonary resection. The result enables early nursing interventions for those at elevated risk while minimizing unnecessary testing for low-risk individuals, thereby enhancing the efficiency of medical resource utilization. The model’s clinical utility is underscored by its excellent discrimination (area under the ROC curve =0.87, 95% CI: 0.82–0.91) and good calibration (Hosmer-Lemeshow test: P=0.063, χ²=14.788), indicating strong agreement between predicted and observed outcomes. Additionally, the use of a visual nomogram transforms complex regression outputs into an intuitive, user-friendly tool, improving accessibility for clinical decision-making. In resource-constrained healthcare environments, this model serves as a practical and cost-effective method to prioritize high-risk patients for further evaluation and timely intervention, ultimately optimizing patient care and resource allocation.

This study has several limitations. Firstly, due to differences in study design and varying standards for assessing postoperative cough, systematic reviews and meta-analyses inherently exhibit heterogeneity. Secondly, the majority of participants in this study were from China, where lung cancer is the leading cause of cancer incidence and mortality. This may limit the broader applicability of the findings to other populations or regions. Lastly, the validation cohort used in this study was drawn exclusively from a single institution, which may limit the predictive model’s external validity. Specifically, data from a single center may not fully reflect the diversity of patient populations across different geographic regions and healthcare settings. Furthermore, the relatively narrow time frame of the validation cohort may contribute to homogeneity in patient characteristics, surgical practices, and postoperative care protocols, thereby potentially limiting the generalizability of the findings. To address these limitations, we plan to conduct a multicenter validation study involving healthcare facilities of varying levels and from diverse regions to ensure the heterogeneity and representativeness of the study population. In addition, we will expand the data collection period to encompass a broader temporal range, allowing for a more comprehensive evaluation of the model’s stability, performance, and reliability over time.

Conclusions

The meta-analysis conducted has significantly contributed to the development of a reliable and effective predictive model specifically designed to assess the risk of chronic CAP. By incorporating multiple key factors—including lobectomy, right pneumonectomy, right upper lobectomy, mediastinal lymph node dissection, peribronchial lymph node dissection, duration of tracheal intubation (≥172 min), and postoperative gastroesophageal reflux—the model has been validated using an external cohort, ensuring its robustness and accuracy. This model serves as a valuable tool for assessing the risk of persistent postoperative cough in these patients, facilitating stratified management and improving the efficiency of prevention and management strategies.

Acknowledgments

We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Footnote

Reporting Checklist: The authors have completed the PRISMA and TRIPOD reporting checklists. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-440/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-440/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-440/prf

Funding: This work was supported by Special Fund Project for the Development of Health Science and Technology in Nanjing (grant No. YKK23148), and Nanjing Medical University Science and Technology Development Fund Project (NMUB20240191).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-440/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Affiliated Nanjing Brain Hospital, Nanjing Medical University (No. 2023-KY077-01). All participants provided informed consent before participating in the study.

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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