A study on the influencing factors of early clinical stability in patients with acute exacerbation of chronic obstructive pulmonary disease complicated by pneumonia

Introduction

Chronic obstructive pulmonary disease (COPD), being a commonly prevalent chronic respiratory disorder, is characterized by persistent airflow obstruction. Its main symptoms include coughing, expectoration, shortness of breath, and dyspnea, all of which have a significantly adverse influence on patients’ quality of life and survival probabilities (1,2). According to the statistics provided by the World Health Organization, COPD has risen to become the fourth leading cause of death globally, just after ischaemic heart disease, coronavirus disease 2019 (COVID-19) and stroke, thus presenting an immense challenge to public health (3). Recent evidence also highlights the growing trend of early patient discharge for home management. A systematic review demonstrated that in severe cases of COVID-19 pneumonia, early discharge was safe as long as patient selection was optimized (4). This underscores the importance of accurately identifying patients who achieve early clinical stability to facilitate personalized treatment planning and efficient healthcare resource utilization.

The acute exacerbation stage of COPD constitutes a crucial turning point in the progression of the disease and it is often induced by factors such as infections, with pneumonia being one of the most frequent precipitating factors (5,6). When COPD is accompanied by pneumonia during an acute exacerbation, patients’ conditions usually deteriorate rapidly, along with a sharp decline in lung function and an enhanced inflammatory response (7,8). This not only results in a sudden increase in hospitalization rates and medical costs but also remarkably raises the mortality risk. Therefore, the early identification and effective management of patients with acute exacerbation of COPD and concurrent pneumonia are of the utmost importance.

Early clinical stability acts as a critical indicator for evaluating the severity and prognosis of COPD patients in such circumstances (9,10). The indicators of clinical stability cover multiple aspects, including vital signs, respiratory function, and mental state, which are capable of comprehensively reflecting patients’ physiological conditions during the acute phase and their reactions to treatment (10-12). Patients who achieve clinical stability generally display a more controllable condition and a relatively favorable prognosis, while those who do not may face increased risks of complications and death (10,12). However, the current research on the factors affecting the early clinical stability of these patients is still insufficient, lacking a systematic and comprehensive analysis.

This study aimed to explore the related factors that affected the early clinical stability of these patients by retrospectively analyzing the clinical data of a large number of patients with acute exacerbation of COPD accompanied by pneumonia. These data included demographic information, clinical manifestations, and laboratory test results. Through an in-depth analysis of these factors, the study intended to provide clinicians with a basis for the early identification of high-risk patients and the formulation of personalized treatment plans. This, in turn, was expected to improve patients’ clinical stability, enhance their prognosis, and reduce the mortality rate and medical burden. In addition, a multivariate Logistic regression model was constructed to evaluate the predictive effectiveness of the model, thereby laying a solid foundation for future research and clinical applications. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-156/rc).

Methods

Study design and sample selection

This was a retrospective study. Seven hundred thirteen patients with acute exacerbation of COPD complicated by pneumonia who were admitted to the Clinical Medical College & Affiliated Hospital of Chengdu University from December 2017 to December 2018 were included. The inclusion criteria were: meeting the diagnostic criteria of COPD and having an acute exacerbation accompanied by pneumonia during that hospitalization. The inclusion criteria were: a COPD diagnosis and an acute exacerbation with pneumonia during hospitalization. The exclusion criteria were: other respiratory diseases (e.g., bronchial asthma, allergic diseases, bronchiectasis, or tuberculosis), or needing supplemental oxygen at baseline. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of Clinical Medical College & Affiliated Hospital of Chengdu University (approval No. PJ2021-045-01) and informed consent was taken from the patients.

Grouping criteria

Patients need to meet the following clinical stability indicators (13) within 72 hours after treatment: (I) body temperature ≤37.8 ℃; (II) pulse <100 beats/min; (III) respiratory rate <24 breaths/min; (IV) systolic blood pressure (SBP) ≥90 mmHg; (V) arterial blood oxygen saturation (SpO₂) ≥90% or arterial partial pressure of oxygen (PO₂) ≥60 mmHg when breathing room air; (VI) being able to take food orally; (VII) normal mental state. Patients who could meet all the clinical stability indicators were included in the stable group (n=363); patients who could not meet any one of the clinical stability indicators or died within 72 hours after admission were included in the unstable group (n=350).

Data collection

We collected comprehensive data on patients’ demographic characteristics, clinical manifestations, laboratory test indicators, and clinical assessment scores. Demographic data included gender, age, and smoking history. Clinical manifestations covered vital signs such as body temperature, pulse, respiratory rate, SBP, diastolic blood pressure (DBP), and SpO₂. Laboratory tests assessed white blood cell count (WBC), lymphocyte count (LYM), monocyte count (MON), eosinophil count (EOS), basophil count (BAS), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), high-sensitivity C-reactive protein (HS-CRP), procalcitonin (PCT), alanine aminotransferase (ALT), total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CREA), pH, partial pressure of carbon dioxide (PCO₂), PO₂, and hydrogen carbonate ion (HCO₃), along with lactate (LAC). Clinical assessments used the comprehensive respiratory symptoms and quality of life assessment scale total score (CRSQLASTS) (14), modified Medical Research Council (mMRC) dyspnea score (15), cough grade, sputum grade, chest tightness grade, dyspnea grade, impact of labor score, impact of outdoor activity score, impact of sleep score, and impact of energy score (16).

Statistical analysis

SPSS 26.0 and R software was used for statistical analysis. Continuous variables were expressed as mean ± standard deviation (x¯±s), and comparisons between two groups were performed using independent sample t-tests. Non-parametric data were presented as median and interquartile range (IQR) and analyzed using the Mann-Whitney U test. Categorical variables were described using numbers and percentages, and Chi-squared tests were used for univariate analysis. Multivariate logistic regression was employed to analyze factors affecting the clinical stability of COPD. A multicollinearity test was conducted to check for collinearity among influencing factors, with no collinearity indicated if tolerance was >0.1 and the variance inflation factor (VIF) was <10. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the multivariate logistic regression model. Additionally, Kaplan-Meier curves were used to assess the relationship between clinical stability indicators and hospitalization risk, with the log-rank method employed for comparisons, and the survminer R package was used to generate these curves (17). P<0.05 was considered statistically significant.

Results

Characteristics of COPD patients

This study included 713 patients with acute exacerbation of COPD and pneumonia. Of these, 363 were in the stable group and 350 in the unstable group. No significant differences were found in gender, smoking history, SBP, diastolic blood pressure, or presence of tumor, liver disease, neuropsychiatric disease, kidney disease, asthma, and bronchiectasis between the two groups (all P>0.05). However, significant differences were observed in age, comorbidities, cardiovascular disease, and malnutrition (P<0.05). In terms of age, the median age of patients in the unstable group was 78 (IQR, 71–84) years, which was higher than that in the stable group (76 years, IQR, 69–83 years), with a P value of 0.04. Regarding comorbidities, 292 patients (83.43%) in the unstable group had comorbidities, significantly higher than 265 patients (73.00%) in the stable group (P<0.001). Further analysis of specific comorbidities revealed that cardiovascular disease was more prevalent in the unstable group. In the unstable group, 259 patients (74.00%) had cardiovascular disease, compared to 231 patients (63.64%) in the stable group (χ²=8.90, P=0.003). Similarly, malnutrition was also more common in the unstable group. A total of 45 patients (12.86%) in the unstable group were malnourished, compared to 12 patients (3.31%) in the stable group (χ²=22.10, P<0.001).

These results indicated that patients with older age, more comorbidities, particularly cardiovascular disease and malnutrition, were associated with poorer early clinical stability (Table 1).

Comparison of laboratory and clinical indicators between groups

Significant differences in vital signs (body temperature, pulse, respiratory rate, SpO₂) were found between the groups (P<0.05). The stable group had lower body temperature (P=0.001), lower pulse (82.00 vs. 98.00, P<0.001), lower respiratory rate (20.00 vs. 24.00, P<0.001), and higher SpO₂ (96.00 vs. 92.00, P<0.001). In hematological indicators, the stable group showed lower WBC, LYM, EOS, and higher Hb (P<0.05). No significant differences were observed in MON, BAS, RBC, and PLT (P>0.05). For inflammation and infection markers, the stable group had lower HS-CRP (9.24 vs. 17.26, P=0.006) and PCT (0.04 vs. 0.07, P<0.001). In liver function, ALB was higher in the stable group (36.12±4.54 vs. 34.61±5.01, P<0.001), while no differences were found in ALT and TP (P>0.05). For renal function, BUN was lower in the stable group (5.13 vs. 5.59, P<0.001), but CREA showed no significant difference (P=0.28). In blood gas indices, the stable group had lower PCO₂ and higher HCO₃ (P<0.05), with no differences in pH, PO₂, and lactic acid (LAC) (P>0.05). The CRSQLASTS were significantly lower in the stable group (P<0.001), indicating better symptom control and quality of life (Table 2).

Correlation analysis of laboratory indicators with early clinical stability

Correlation analysis showed significant associations between various indicators and early clinical stability (Table S1). Age had a weak positive correlation (r=0.077, P=0.04), meaning instability slightly increased with age. Comorbidities, temperature, pulse, respiratory rate, WBC, HS-CRP, PCT, BUN, PCO₂, and HCO₃ were positively correlated with instability (all P<0.001 or P=0.006). In contrast, SpO₂, LYM, EOS, Hb, and ALB were negatively correlated with instability (all P<0.001). The “CRSQLASTS”, “dyspnea score (mMRC)”, “cough grade”, “sputum grade”, “chest tightness grade”, “dyspnea score”, “impact of labor score”, “impact of outdoor activity score”, “impact of sleep score”, “impact of energy score”, “comorbidity”, “critical illness”, “cardiovascular disease”, “malnutrition”, and “diabetes” also showed significant correlations with early clinical stability (all P<0.001 or P=0.003, P=0.009).

Results of logistic regression analyses

The study conducted a univariate logistics regression analysis to identify factors influencing patient stability. The results showed that patient stability was significantly affected by numerous factors. Age, WBC, Hb, CRSQLASTS, mMRC dyspnea score, as well as scores for the impact of labor, cough, sputum, chest tightness, dyspnea, outdoor activity, sleep, and energy, along with PCO₂, HCO₃, comorbidity, cardiovascular disease, malnutrition, and diabetes, were all significantly associated with patient stability (P<0.05). Notably, critical illness was linked to a lower likelihood of increased patient stability. In contrast, LYM, PCT, and BUN showed no significant relationship with patient stability (P>0.05) (Table 3).

Multivariate analysis revealed that WBC [β=0.069, P=0.009, odds ratio (OR) =1.071, 95% confidence interval (CI): 1.017–1.129], Hb (β=−0.012, P=0.009, OR =0.988, 95% CI: 0.979–0.997), sleep impact score (β=0.369, P=0.002, OR =1.447, 95% CI: 1.147–1.825), HCO₃ (β=0.056, P=0.002, OR =1.058, 95% CI: 1.021–1.095), malnutrition (β=1.165, P=0.007, OR =3.205, 95% CI: 1.376–7.464), and non-critical illness (β=−0.746, P=0.007, OR =0.474, 95% CI: 0.277–0.814) were independent factors influencing patient stability (Table 4). The multivariate logistic regression model for predicting patient stability can be represented as follows:

log(ρ1−ρ)=−0.421+0.069×WBC+(−0.012)×HGB+0.369×ImpactofSleepScore+0.056×HCO3+1.165×Malnutrition+(−0.746)×Non-CriticalIllness[1]

(ρ denotes the probability that the patient is stable).

These factors were derived from the multivariate logistic regression analysis and are graphically summarized in a forest plot, which illustrates their respective ORs and CIs (Figure 1).

Figure 1 Forest plot of multivariate logistic regression analysis of factors influencing patient stability. CI, confidence interval; HCO₃, hydrogen carbonate ion.

Kaplan-Meier analysis of hospitalization time

The Kaplan-Meier curve demonstrates the relationship between clinical stability and hospitalization time, revealing a significant difference between the unstable and stable groups (log-rank P<0.001). Patients with unstable conditions showed a longer hospitalization time, with a hazard ratio (HR) of 0.574 (95% CI: 0.487–0.676). This indicates that early clinical stability is a strong predictor for estimating hospitalization duration in patients with acute exacerbation of COPD complicated by pneumonia (Figure 2).

Figure 2 Kaplan-Meier analysis of hospitalization time in relation to clinical stability. CI, confidence interval; HR, hazard ratio.

Model evaluation

In the multivariate Logistic regression analysis of this study, the constructed model had been comprehensively evaluated by various methods, showing good performance and reliability.

Model fit assessment

The Hosmer-Lemeshow test serves as a crucial method for assessing the goodness of fit of the model. In this analysis, the test yielded a significance value of 0.869, a Chi-squared value of 3.87, and 8 degrees of freedom. These results indicate that the model exhibits a satisfactory goodness of fit, meaning it can effectively accommodate the actual data.

Multicollinearity test for model stability and accuracy

To ensure the stability and accuracy of the model, a multicollinearity test was performed on each indicator (Table S2). It is generally believed that when the tolerance is greater than 0.1 and the VIF is less than 10, there is no serious multicollinearity problem. From the test results, the tolerance of most indicators is greater than 0.1, and the VIF is less than 10, indicating that there is no serious multicollinearity among the independent variables in the model, and the model has good stability and reliability.

Model predictive efficacy

The predictive efficacy of the logistic regression model for patients’ clinical stability was assessed via an ROC curve (Figure 3). The model’s area under the curve (AUC) was 0.764 (95% CI: 0.721–0.808), indicating moderate predictive accuracy. At the optimal cutoff value of 0.471, determined by the Youden index of 0.423, the model demonstrated a sensitivity of 0.726 (95% CI: 0.667–0.784) and a specificity of 0.697 (95% CI: 0.638–0.757). Additionally, the model’s accuracy, PPV, and NPV were 0.711 (95% CI: 0.667–0.753), 0.704 (95% CI: 0.645–0.762), and 0.719 (95% CI: 0.660–0.779), respectively. These results suggest that the model has an acceptable ability to predict patient stability.

Figure 3 ROC curves for model prediction of patient stability efficacy. AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

Discussion

Early clinical stability is crucial for managing COPD. A 2024 multicenter study by Fried et al. found that the median time to emergency department (ED) treatment for AECOPD was 59 minutes, with delays over 1 hour linked to older age and lack of a COPD diagnosis code in hospital records (4). This shows room for improvement in ED-based COPD identification and management. Similarly, our study found that early clinical stability is tied to multiple clinical variables, such as WBC, Hb, sleep impact score, HCO₃⁻, malnutrition, and critical illness. These factors can jointly impact early clinical stability and, in turn, affect hospitalization time and prognosis.

Firstly, WBC was identified as a significant factor influencing clinical stability. Elevated WBC levels typically indicate an enhanced inflammatory response in the body (18). In patients with acute exacerbation of COPD and pneumonia, inflammation plays a crucial role in disease progression (19). A study has shown that increased WBC levels were associated with more severe inflammation and poorer clinical outcomes in these patients (20). Identifying patients with elevated WBC levels early can help clinicians initiate timely anti-inflammatory and anti-infective treatments, thereby improving clinical stability and prognosis.

Secondly, the sleep impact score was identified as another significant factor. Poor sleep quality can negatively affect the immune system, mood, and overall quality of life in patients with COPD (21,22). During acute exacerbations, sleep disturbances may further aggravate the burden on the respiratory system and delay recovery (23). A prospective study highlighted that patients with poor sleep quality had higher levels of inflammatory markers and worse clinical outcomes (20). Interventions aimed at improving sleep quality, such as optimizing treatment regimens to alleviate nighttime symptoms and creating a comfortable sleep environment, may help enhance clinical stability and promote better outcomes.

Moreover, the significant association between HCO₃ levels and clinical stability underscores the importance of acid-base balance in these patients. Imbalances in acid-base levels can affect respiratory function and overall physiological stability (24). Monitoring and correcting acid-base imbalances through appropriate treatments, such as adjusting ventilation support or administering medications, can contribute to improved clinical stability. A large multicenter cohort study demonstrated that patients with metabolic acidosis had a higher risk of respiratory failure and mortality during acute exacerbations (25). Timely correction of acid-base imbalances is therefore critical for stabilizing patients’ conditions.

In addition, malnutrition was found to be significantly associated with poor clinical stability. Nutritional status plays a critical role in maintaining the body’s immune function, muscle strength, and overall resilience (26,27). COPD patients, especially those in acute exacerbation phases, often experience increased energy consumption and reduced nutritional intake due to symptoms such as coughing and dyspnea (17,28). This can lead to malnutrition, which in turn weakens the body’s ability to combat diseases and respond to treatments. Studies showed that malnourished patients had longer hospital stays and higher mortality rates (29,30). Comprehensive nutritional assessments and timely nutritional support, such as oral nutritional supplements or enteral feeding, are essential to improve clinical stability and accelerate recovery.

Finally, the correlation analysis in this study indicates that critical illness has a negative correlation with early clinical stability. This finding aligns with expectations, as critically ill patients often face severe physiological instability and complex clinical challenges (31). Their conditions typically involve multiple organ dysfunctions, requiring intensive care and advanced life support measures such as mechanical ventilation and vasopressor therapy. For instance, patients with COPD and pneumonia who develop respiratory failure or septic shock need continuous monitoring and aggressive interventions to manage their unstable conditions (32). The presence of these severe complications and the need for multiple treatments can delay the achievement of clinical stability. This underscores the importance of specialized care and close monitoring for critically ill patients. In clinical practice, it highlights the need for early intervention and proactive management of complications to improve outcomes. Clinicians must recognize the heightened risk of instability in these patients and employ comprehensive strategies to stabilize their conditions as early as possible.

Furthermore, the Kaplan-Meier analysis in this study revealed a significant relationship between clinical stability and hospitalization duration. Patients who achieved early clinical stability had shorter hospital stays compared to those who did not. This finding underscores the importance of early clinical stability as a predictor of hospitalization time. By identifying patients at risk of instability early and implementing targeted interventions, clinicians may not only improve clinical outcomes but also reduce hospitalization duration and associated healthcare costs. This is particularly relevant in the context of healthcare resource utilization and efficient patient management.

The logistic regression model constructed in this study demonstrated good performance and reliability in predicting clinical stability, with an AUC of 0.764. This suggests that the model can serve as a useful tool for clinicians to assess the risk of instability in patients with acute exacerbation of COPD and pneumonia. Early identification of high-risk patients based on this model can facilitate timely interventions and personalized treatment plans, potentially improving clinical outcomes and reducing mortality and medical burden.

However, it is important to note that this study has some limitations. First, the model may not capture all potential factors affecting early clinical stability, and unmeasured confounding factors might exist. Second, the sample limitations could affect the model’s universality. Future research should expand the sample size and include more relevant factors to enhance the model’s accuracy and generalizability. Additionally, the retrospective study design introduces potential biases, such as selection and information bias, which might limit the results’ broad applicability. To improve research quality and clinical utility, future work should consider large-scale prospective studies, incorporate emerging factors like gene polymorphisms and microbiome characteristics, and utilize advanced statistical and machine learning methods to develop more accurate and universal predictive models. This will strengthen the evidence base for clinical decision-making and improve patient outcomes.

Conclusions

In conclusion, this study delved into the factors impacting early clinical stability in patients with acute COPD exacerbation and pneumonia, pinpointing several key indicators like WBC, Hb, sleep impact score, HCO₃, malnutrition, and critical illness. The predictive model exhibits robust performance, and the Kaplan-Meier analysis corroborates the link between early clinical stability and shorter hospitalization. These discoveries not only enhance our understanding of the condition but also bear significant clinical implications. They enable the early identification of high-risk patients, prompt interventions, and personalized treatment. However, the study’s retrospective and single-center nature means its results need further validation through larger, multi-center studies. This will help refine predictive models and advance clinical management strategies, ultimately improving patients’ quality of life.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-156/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-156/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-156/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-2025-156/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 Medical Ethics Committee of Clinical Medical College & Affiliated Hospital of Chengdu University (approval No. PJ2021-045-01) and informed consent was taken from the patients.

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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