Serum electrolytes as prognostic markers: a new frontier in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) management

Introduction

Chronic obstructive pulmonary disease (COPD) is a globally prevalent chronic progressive respiratory disease characterized by chronic inflammation and not fully reversible airflow limitation (1). An acute exacerbation of COPD (AECOPD) is characterized by a temporary increase in shortness of breath, cough, and sputum production lasting less than 14 days. It may be accompanied by shortness of breath and/or tachycardia, usually related to increased local or systemic inflammation caused by infection, pollution, or other pulmonary damage (2). Currently, AECOPD is the most common reason for hospitalization among patients with COPD (3). However, the emergence of coronavirus disease 2019 (COVID-19) has increased the frequency and severity of AECOPD, imposing a heavy economic burden on the families of patients and society (1). Surveys indicate that COPD patients experience an average of 0.5 to 3.5 acute exacerbations per year, with each hospitalization for an acute exacerbation incurring an average cost of up to 11,598 yuan per person (4). Each acute exacerbation not only creates an economic burden for patients but may cause irreversible damage to their lung function, with some scholars even referring to this acute exacerbation as a “pulmonary stroke” (5). COPD readmission is defined as patients who need readmission for AECOPD after being discharged from treatment for COPD (6). The 1-year readmission rate for COPD patients worldwide is 25% to 87% (7). COPD is the fourth leading cause of mortality globally and was responsible for 3.5 million deaths in 2021, accounting for approximately 5% of worldwide deaths, as reported by the World Health Organization. In high-income countries, smoking accounts for more than 70% of COPD cases, whereas in low- and middle-income countries, household air pollution is the predominant risk factor (8). The World Health Organization predicts that the annual number of deaths from COPD and related diseases will exceed 5.4 million by 2060 (8). Therefore, investigating risk factors that affect the prognosis of AECOPD patients is crucial for reducing the mortality rate and readmission rate. Current research indicates that age, sex, smoking status, body mass index (BMI), electrolytes, liver and kidney function, and comorbidities might influence patient outcomes (9,10). Furthermore, electrolyte imbalances are prevalent among AECOPD patients, with the incidence of hyponatremia exceeding 20% (11,12), and sodium and calcium ions directly participate in the inflammatory response (13), respiratory muscle function (14) and fluid metabolism (15) of COPD patients to affect their pathological process, given their high prevalence, established pathophysiological roles, and underexplored prognostic value, this study focused on serum sodium and calcium, and serum electrolyte levels might serve as predictive factors for poor prognosis in AECOPD patients (11,16). Therefore, this study is the first to assess the levels of serum sodium (Na) and calcium (Ca) ions in AECOPD patients and their independent associations with 1-year readmission risk. This study will help clinicians optimize electrolyte and fluid management, adjust diuretics, and provide an evidence-based basis for individualized treatment strategies. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-709/rc).

Methods

Study design and patient selection

This single-center, retrospective, observational cohort study selected eligible subjects from among 601 AECOPD patients hospitalized at The First Hospital of Lanzhou University from October 2021 to May 2023. The inclusion criteria were as follows: (I) aged 18 years or older and (II) met the diagnostic criteria for AECOPD (1). The exclusion criteria were as follows: (I) received Na or Ca ion therapy prior to hospitalization; (II) had cachexia, gastrointestinal tumors, or gastrointestinal bleeding; (III) had underlying pulmonary diseases other than COPD (such as pulmonary tumors, pulmonary embolism, pulmonary vasculitis, etc.); (IV) had immunodeficiency diseases or malignant tumors; (V) had a major surgical history within the past 3 months; (VI) had severe liver, kidney, or heart failure; and (VII) were lost to follow-up and had significant data deficiencies. A total of 558 patients were followed up through the hospital’s electronic medical record system and telephone follow-ups to track readmission within 1 year after discharge. The follow-up was successfully completed by 535 patients, while 23 patients did not complete the follow-up, resulting in a follow-up rate of 95.9%. For the data of this group of people who were lost to follow-up, this study clearly reported the loss rate in the results section and repeated the analysis after excluding patients with hypoproteinemia/anemia. The follow-up time was calculated from the date of patient discharge, with a maximum follow-up time of 12 months. The average follow-up was 10.5 months, with a median follow-up time of 12 months (interquartile range of 11–12 months). Ultimately, 535 patients were included in the final analysis of the study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethical Committee of The First Hospital of Lanzhou University (Approval No. LDYYLL2024-487) and written informed consent from the study subjects was waived because of the retrospective design.

Data collection

This study included demographic data such as age, sex, smoking status (defined as at least 1 cigarette per day for a minimum of 1 year), and drinking status (defined as consuming more than 30 grams of liquor or more than 150 milliliters of beer on a daily basis for at least 1 year). Additional comorbid conditions included diabetes, hypertension, pleural effusion, pulmonary heart disease, and respiratory failure (categorized into three groups: nonrespiratory failure group, type I respiratory failure group, and type II respiratory failure group), as well as hypoxemia (classified into four categories: no hypoxemia, mild hypoxemia, moderate hypoxemia, and severe hypoxemia). These conditions were diagnosed according to the International Classification of Diseases, 11th Revision (ICD-11) (17). Biomarkers included BMI, blood pressure, and hematological indices. Hematological indices are obtained by collecting 5 mL of antecubital venous blood from all patients within 24 hours after their first hospital admission, under fasting conditions and before any treatment. The sample was placed in an anticoagulant-coated vacuum tube and sent to the hospital laboratory within 2 hours. A fully automated hematology analyzer measures the white blood cell count (WBC), hemoglobin (Hb), blood platelet count (BPC), lymphocyte percentage (LY%), neutrophil percentage (NEUT%), red cell distribution width-coefficient of variation (RDW-CV), red cell distribution width-standard deviation (RDW-SD), and immature granulocyte count (IG). A fully automated chemiluminescent immunoassay measures procalcitonin (PCT). A fully automated biochemical analyzer measures aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TBIL), albumin (ALB), and prealbumin (PA) levels. The levels of creatinine (CR), uric acid (UA), potassium (K), Na, Ca, inorganic phosphate (IP), glucose (GLu), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein A, and homocysteine (Hcy) and the level of glycated hemoglobin (HbA1C) were measured via high-performance liquid chromatography. Prothrombin time (PT), fibrinogen (FIB), and activated partial thromboplastin time (APTT) were measured via coagulation methods; D-dimer (DD) and fibrin(ogen) degradation products (FDP) were detected via immunoturbidimetric assays. In this study, Na and Ca were used as exposure variables to investigate the potential effects of different electrolyte levels on the prognosis of AECOPD patients. Na levels were stratified by quartiles: Q1 was <139 mmol/L (137 patients), Q2 was 139–140.7 mmol/L (134 patients), Q3 was 140.8–142.2 mmol/L (135 patients), and Q4 was >142.2 mmol/L (129 patients). Ca levels were also stratified by quartiles: Q1 was <2.03 mmol/L (141 cases), Q2 was 2.03–2.11 mmol/L (129 cases), Q3 was 2.12–2.20 mmol/L (138 cases), and Q4 was >2.20 mmol/L (127 cases). Pulmonary function was measured via a pulmonary function analyzer (CareFusion, Yorba Linda, CA, USA). Patients were instructed to firmly place the disposable mouthpiece in their mouth to minimize air leakage, inhale slowly and fully, and then exhale forcefully and quickly. The procedure was repeated at least twice, and the most cooperative test with the most standard measurement was selected as the final pulmonary function test report. Although retrospective studies cannot fully control for procedural variability, all tests adhered to institutional protocols: hematological assays used automated analyzers, and pulmonary function tests followed American Thoracic Society/European Respiratory Society (ATS/ERS) guidelines (18). The recorded pulmonary function indicators included the forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), the percentage of the normal value of forced expiratory volume in 1 second (FEV1% pred), the ratio of FEV1/FVC, the peak expiratory flow (PEF), the residual volume measured via the single-breath method (RV-SB), the ratio of residual volume to total lung capacity measured via the single-breath method (RV/TLC-SB), and the total lung capacity measured via the single-breath method (TLC-SB). The groups were classified on the basis of FEV1% value ranges: FEV1% ≥80% as the mild group, 80%> FEV1% ≥50% as the moderate group, 50%> FEV1% ≥30% as the severe group, and FEV1% <30% as the very severe group. Two methods were employed for follow-up in this study: first, patients’ readmission status was tracked within 1 year after discharge through the hospital’s electronic medical record system; second, telephone follow-ups were conducted to inquire whether they had been readmitted within 1 year after discharge. The follow-up period lasted from the time of discharge until the first readmission or for 1 year, whichever came first. AECOPD patients were divided into two groups on the basis of whether they were readmitted within 1 year: the non-readmitted within 1 year group (374 patients) and the readmitted within 1 year group (161 patients).

Statistical analysis

Statistical analysis was conducted via SPSS 26.0 software (IBM Corp., Armonk, NY, USA), with a two-sided P value <0.05 considered statistically significant. Continuous variables that conformed to a normal distribution were expressed as the mean ± standard deviation, with between-group differences compared via independent sample t-tests. Continuous variables that did not conform to a normal distribution were expressed as medians (quartiles), with between-group differences compared via the Mann-Whitney U test. Categorical variables were presented as frequencies and percentages (%), with between-group analysis conducted via the chi-squared (χ²) test or Fisher’s exact test. Univariable logistic and Cox regression analyses were subsequently utilized to assess risk factors for readmission within 1 year, and adjusted models were subsequently constructed for multivariable logistic regression analysis on the basis of variables with P<0.05. Cox regression analysis was used to evaluate the independent associations of Na and Ca with the risk of readmission within 1 year. The associations of Na and Ca with this risk across 14 subgroups were subsequently analyzed on the basis of sex (male/female), age (≤60 or >60 years), smoking status (yes/no), hypertension status (yes/no), diabetes status (yes/no), pulmonary heart disease status (yes/no), and COPD severity (mild-moderate/severe-very severe). Two sensitivity analyses were conducted to revalidate the robustness of the association between Na and Ca and the risk of readmission within 1 year. Finally, Kaplan-Meier survival curves were used to assess differences in the cumulative incidence of readmission within 1 year among the different Na and Ca groups. A two-sided P value <0.05 was considered statistically significant. This study used standardized tools to collect data to reduce information bias, multivariate analysis controlled confounding variables to reduce confounding bias.

Results

Baseline characteristics

A total of 535 patients with AECOPD were included in this study. According to whether they were readmitted within 1 year, they were divided into a non-readmitted group (374 cases) and a readmitted group (161 cases), the clinical characteristics of patients readmitted within one year are shown in Table 1. There was no significant difference in age between the two groups (P=0.09), sex distribution, or smoking or drinking rates between the two groups. The readmitted group had a significantly greater proportion of patients with diabetes (P<0.001), hypertension (P=0.001) and pulmonary heart disease (P=0.01). The readmitted group had significantly greater WBC (P=0.03), NEUT% (P=0.01), CR levels (P=0.03), UA levels (P=0.004), calcium levels (P=0.01), blood glucose levels (P<0.001) and PT (P=0.01). The serum sodium level (P=0.03), FEV1 (P=0.03) and FEV1% predicted value (P=0.01) in the readmission group were significantly reduced. There were no significant differences in BMI, blood pressure, hemoglobin, liver function (AST, ALT), blood lipids (TC, TG) or coagulation function (FIB, APTT) between the two groups (P>0.05). In general, the readmitted patients presented more significant inflammatory activation, renal damage, electrolyte imbalance (low sodium, high calcium) and decreased lung function.

Logistic and Cox regression analyses of readmissions within 1 year

In Table 2, univariate logistic and Cox regression analyses both indicated that diabetes, hypertension, cor pulmonale, WBC, NEUT%, CR, UA, Ca, GLu, and PT were positively correlated with the risk of readmission (P<0.05), whereas LY% and Na were negatively correlated with risk; after multivariate analysis, Na and Ca were still independently associated. As shown in Table 3, multivariate logistic and Cox regression analyses consistently revealed that, after adjusting for confounding factors, low sodium [odds ratio (OR) =0.937, P=0.03] and high calcium (OR =5.446, P=0.01) levels independently predicted the risk of readmission, and calcium levels showed a dose-response trend (P trend =0.01). For every unit increase in Na level, the risk of readmission decreased by 4.7%, and the risk of readmission in the Na-Q3 group was 0.609 times that in the Na-Q1 group [hazard ratio (HR) =0.609, 95% confidence interval (CI): 0.380–0.976, P=0.04]. For every unit increase in Ca level, the risk of readmission increased by 265.1%, and the risk of readmission in the Ca: Q4 group was 1.754 times that in the Ca: Q1 group (HR =1.754, 95% CI: 1.150–2.676, P=0.01).

Stratified associations of Na and Ca with the risk of readmission within 1 year

Tables 4,5 show that multivariate logistic regression stratified analysis revealed that low sodium levels in female patients and high calcium levels in elderly patients (>60 years old) with hypertension were independently associated with the risk of readmission. Additionally, in multivariate Cox regression analysis, Na was independently associated with the risk of readmission within 1 year in females, whereas Ca was independently associated with this risk in males, those aged >60 years, those with hypertension, and those with mild to moderate COPD severity (P<0.05). Among male patients, the risk of re-hospitalization within one year in the Q4 group was 1.846 times that in the Q1 group, and this risk was further increased in the subgroups of age >60 years, diabetes, no cor pulmonale, and mild-to-moderate COPD, with hazard ratios of 2.278, 2.270, 1.910, and 2.836, respectively (all P<0.05). Among hypertensive patients, the risk of re-hospitalization within one year in the Q2 and Q4 groups was 2.098 times and 2.674 times that in the Q1 group, respectively (all P<0.05). Among patients with cor pulmonale, the risk of re-hospitalization within one year in the Q2 group was 2.170 times that in the Q1 group (P<0.05).

Two sensitivity analyses of serum Na and Ca ions and readmission within 1 year

The sensitivity analysis (Table 6) revealed that after patients with hypoalbuminemia or anemia were excluded, Na and Ca remained independently associated with the risk of readmission within 1 year (P<0.05). Moreover, after further adjusting for variables identified in the univariate regression analysis with a significance level between 0.05 and 0.1, the associations between Na and Ca levels and the risk of readmission within 1 year remained robust (P<0.05). For every unit increase in Ca level, the risk of readmission increased by 286%; and the risk of readmission in the Ca: Q4 group was 1.944 times that in the Ca: Q1 group (HR =1.944, 95% CI: 1.187–3.182, P=0.01). After further adjustment of variables with 0.05<P<0.1 in univariate regression analysis, the association between Na and Ca and the risk of readmission within one year remained robust (P<0.05). For every unit increase in Na level, the risk of readmission decreased by 4.7%, while for every unit increase in Ca level, the risk of readmission increased by 293.6%; and the risk of readmission in the Ca: Q4 group was 1.871 times that in the Ca: Q1 group (HR =1.871, 95% CI: 1.179–2.969, P=0.01).

Kaplan-Meier survival curves

Statistical differences were observed in the cumulative incidence of 1-year readmission risk across groups stratified by Na and Ca levels (P=0.01 and 0.02). Patients in the low-sodium group (Q1) and the high-calcium group (Q4) had the highest risk of readmission (Figure 1).

Figure 1 Kaplan-Meier survival curves for the cumulative incidence of readmission at 1 year in different Na (A) and Ca (B) groups. Na: Q1: <139 mmol/L; Q2: 139–140.7 mmol/L; Q3: 140.8–142.2 mmol/L; Q4: >142.2 mmol/L. Ca: Q1: <2.03 mmol/L; Q2: 2.03–2.11 mmol/L; Q3: 2.12–2.20 mmol/L; Q4: >2.20 mmol/L.

Discussion

This study revealed that COPD patients with hyponatremia and hypercalcemia have a significantly increased risk of hospital readmission within 1 year compared with those without readmission. Logistic and Cox regression analyses confirmed the independent associations between Na and Ca ions and the risk of readmission within 1 year in AECOPD patients. Subgroup and sensitivity analyses further validated the stability of these associations in diverse populations. These data indicate that serum electrolyte levels should be systematically incorporated into the prognostic management of AECOPD patients.

COPD continues to be a significant public health concern, and electrolyte imbalances represent a prevalent issue among patients with COPD, with both medical and nonmedical factors often interrelated, contributing to disturbances in the levels of electrolytes, such as Na and Ca ions (19). For example, in a retrospective case-control study conducted by Akash Deep et al. involving 41 patients with AECOPD and 34 patients with stable COPD, individuals with AECOPD presented lower average serum sodium and calcium levels and elevated average serum potassium levels. These patients are also more prone to complications, have a poorer prognosis, and experience prolonged hospitalization (11). Additionally, a study by Acharya et al. has reported that 57% of patients experiencing acute exacerbations of COPD presented with electrolyte imbalances, with over half of these cases involving hyponatremia (20). A retrospective analysis by Jae Kyeom Sim et al., published in 2020, examined 1,342 patients with initially normal sodium levels and reported that 217 (16.2%) developed ICU-acquired hyponatremia, suggesting that critically ill patients are at increased risk of developing hyponatremia, potentially necessitating renal replacement therapy. This finding may explain the increased prevalence of hyponatremia observed in patients with AECOPD (21).

AECOPD, a chronic wasting disease, is more prone to electrolyte level disturbances (22). This finding is consistent with the conclusions of this study. This may be attributed to the following factors: (I) AECOPD often accompanies diseases affecting various systems, such as pulmonary infections and cardiorespiratory failure, which can lead to activation of the renin-angiotensin system and the development of the syndrome of inappropriate antidiuretic hormone (ADH) secretion, leading to water and Na retention (23). (II) Chronic infection and hypoxia induce a severe inflammatory response in the lungs, with inflammatory factors interfering with the endocrine regulatory axis, leading to parathyroid dysfunction and abnormal blood calcium levels (24). (III) The combination of hypoxia, acid-base imbalance, and hypokalemia affects cellular permeability, and Na-potassium pump dysfunction results in abnormal intra- and extracellular ion levels (25). (IV) Long-term inappropriate use of diuretics and glucocorticoids and chronic renal failure can reduce the glomerular filtration rate and decrease tubular reabsorption, further leading to electrolyte disturbances (12). (V) Treatment with β2 agonists and corticosteroids can lower Na and potassium ion levels, whereas aminophylline can increase renal excretion, leading to reduced levels of magnesium, calcium, and Na ions (26). (VI) AECOPD patients with ionic metabolism disturbances may experience compromised immune and metabolic functions and increased levels of inflammatory factors (such as IL-6 and TNF-α), increasing the risk of infection and creating a vicious cycle (27). Furthermore, electrolyte imbalances are intricately linked with other complications. Hypercalcemia elevates the risk of arterial calcification and cardiovascular events, whereas hyponatremia serves as a prognostic marker for heart failure. These conditions synergistically exacerbate overall health status, not only indirectly increasing the likelihood of hospital readmission but also adversely impacting the long-term prognosis of patients with AECOPD (28).

Our research indicates that the correlation between hyponatremia and AECOPD is more pronounced in female patients. From a hormonal standpoint, estrogen plays a role in the regulation of water and sodium metabolism through various mechanisms. It can inhibit the release of ADH by the hypothalamus-posterior pituitary axis, decrease water reabsorption in the renal tubules, and thereby mitigate the risk of hyponatremia. Additionally, estrogen facilitates sodium ion retention to sustain blood volume by enhancing the activity of the renin-angiotensin-aldosterone system (RAAS). Moreover, estrogen can augment the regulatory influence of atrial natriuretic peptide (ANP) on blood volume, indirectly affecting sodium excretion. The reduction in estrogen levels in postmenopausal women may result in diminished inhibition of ADH and an imbalance in the RAAS (29). These findings suggest that fluctuations in estrogen may constitute a significant underlying factor contributing to sex differences. Furthermore, this study revealed an increased correlation between calcium levels and prognosis in elderly patients. We hypothesize that this may be attributed to disruptions in calcium homeostasis resulting from age-related alterations in bone metabolism and chronic disease (30). Additionally, the use of diuretics and the presence of inflammatory states may further exacerbate calcium regulation disorders, collectively increasing the risk of readmission. Further research is warranted to substantiate these speculations.

However, there are several limitations in this study. First, in the quartile stratification analysis of this study, Na and Ca ion levels did not show a completely consistent linear trend, which may be influenced by the limitations of the sample size and the nonlinear relationship between electrolyte levels and the risk of acute exacerbation. Future research could consider the use of more refined analytical methods to further explore the nonlinear relationship between electrolyte levels and AECOPD prognosis. Second, we only analyzed electrolyte levels at the time of admission, without dynamic monitoring data throughout the year, limiting our comprehensive evaluation of fluctuations in electrolyte levels over time and their potential impact on prognosis, and the prognostic value for 1-year readmission needs to be validated with serial measurements. Future research should incorporate dynamic monitoring of electrolyte levels, analyze whether electrolyte abnormalities are related to the long-term survival rate of patients, add inflammatory markers to construct a multifactorial prognostic model, and accurately reflect the impact of electrolyte imbalances on the long-term prognosis of AECOPD patients. Furthermore, given that this is a retrospective, single-center study with a limited sample size, further prospective, multicenter studies with larger sample sizes are needed for additional exploration. Although stratification or dichotomization by etiology has certain clinical appeal, this study aims to initially explore the association between electrolytes and readmission for all causes. Future prospective studies can prospectively collect the etiology of readmission and verify the predictive power of specific cutoff values based on larger samples.

Conclusions

Our findings indicate that the serum Na and Ca ion levels are independently associated with the risk of readmission within one year for AECOPD patients. These findings suggest that further investigation into the role of serum Na and Ca ion levels in patient management is warranted. Consequently, the inclusion of electrolyte levels should be considered in the prognostic management of AECOPD patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-709/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-709/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-709/prf

Funding: This research was supported by the Key Research and Development Project of Gansu Province (grant No. 20YF8FA082).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-709/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 Ethical Committee of The First Hospital of Lanzhou University (approval No. LDYYLL2024-487) and written informed consent from the study subjects was waived because of the retrospective design.

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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