COPD is a prevalent and preventable disease contributing significantly to global morbidity and mortality and is targeted in the Healthy China Action Plan (2019–2030)[9]. A national cross-sectional study in 2018 investigated the lung health status of adults > 20 years old in 10 provinces of China, and showed that the prevalence of COPD in adults aged ≥ 40 was as high as 13.7% [2, 10]. COPD is currently the third leading cause of both death and loss of disability-adjusted life years, accounting for over 3.23 million deaths in 2019[11–13], has become a worldwide public health problem and induces serious economic burden on healthcare systems due to its high prevalence and related disability and mortality [14–16]. Acute exacerbation of COPD refers to the aggravation of respiratory symptoms in patients, which is the main reason for hospitalization and medical expenditure of COPD patients[17]. Epidemiological data indicate that the global prevalence of one-year unplanned readmissions for COPD ranges from 25.0–87.0%, with studies suggesting that up to 40.8% of these readmissions are preventable [17–19]. Consequently, early identification of patients with high risk of AECOPD and timely interventions to reduce the incidence of AECOPD and readmission are of great significance for reducing the rate of unplanned readmission of COPD and delaying the progression of the disease [2].
Considering treatment impact, individual parameters, and laboratory tests, it is crucial to construct an effective, convenient, and intuitive clinical predictive model enables healthcare professionals to estimate the probability of unplanned readmission based on specific patient conditions [20]. The nomogram model quantifies, visualizes, and graphically represents the logistic regression results, enabling inference of variable values by graph and displaying continuous prediction probabilities thus can provide reference for the medical staff to take preventive treatment for high-risk patients, has been widely used in clinical practice [20–21]. A previous study develop a nomogram model based on education level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal, the model has good prediction effect for acute exacerbation readmission risk within 30 days in elderly patients with COPD [22].Another predictive model constructed by the number of acute exacerbation hospitalizations in the previous year, increased GOLD grade and systemic use of glucocorticoids during hospitalization can predict the readmission risk of AECOPD patients within 1 year, providing a basis for clinical identification of high-risk readmission COPD patients [23].
Based on screening data on 607 patients with COPD, the current study successfully developed a predictive nomogram model by integrating independent influencing factors of unplanned readmission in patients with COPD. The results of multivariate-logistic-regression analysis showed that the WBC count, the course of disease more than 10 years, the number of acute exacerbation in the past 1 year and concomitant respiratory failure were independent risk factors for unplanned readmission in patients with COPD within one year. The AUC of the ROC curve of the nomogram model constructed based on the above risk factors was 0.719, indicating that the model has moderately predictive ability. This helps doctors to more intuitively assess the readmission risk of COPD and thus develop personalized intervention measures.
COPD is a chronic wasting disease, chronic progression of COPD often results in exacerbated lung damage, characterized by an imbalance in the alveolar ventilation/blood flow ratio, which can escalate the risks of respiratory failure and further acute exacerbations, potentially leading to readmission [24]. The present study found that the number of WBC was a risk factor for unplanned readmission in patients with COPD. Previous studies also shown that inflammatory indicators were significantly elevated in patients with AECOPD [25]. Inflammatory markers play a pivotal role in the pathophysiology of the disease by directly damaging the airway structures and contributing to systemic inflammation. This inflammation can intensify airway mucus production, induce bronchospasm, exacerbate airway narrowing, and impair pulmonary function, ultimately perpetuating the cycle of exacerbation and hospitalization [26–28].
Strengths and limitations
The current study successfully developed a predictive nomogram model by integrating independent influencing factors of unplanned readmission in patients with COPD. Although most of the risk factors are similar to previous studies [29–31], this study differs from the perspective of statistical research methods in that the previous studies used only logistic regression analysis. Our study screened variables using the LASSO regression analysis, and a traditional logistic regression analysis was also performed, and the internal validation and clinical predictive performance evaluation have shown that the model has good predictive value.
However, several limitations of this study should be noted. First, although strict inclusion and exclusion criteria were set in this study, due to its single-center design and small sample size, which might restrict the generalizability of the finding. Second, this study has not been externally validated, which may affect the reliability and generalizability of the conclusions. Finally, the majority of research subjects being rural residents, poor medical and economic conditions may lead to the inability to early diagnosis and treatment, and affect the incidence of readmission. Therefore, future research can construct a risk prediction models for readmission of COPD in different regions, economic conditions, or medical environments to guide precise clinical prevention and treatment.