Risk factors for hyponatremia in acute exacerbation chronic obstructive pulmonary disease (AECOPD): a multicenter cross-sectional study

Abstract

Background Hyponatremia is an independent predictor of poor prognosis, including increased mortality and readmission, in COPD patients. Identifying modifiable etiologies of hyponatremia may help reduce adverse events in patients with AECOPD. Therefore, the aim of this study was to explore the risk factors and underlying etiologies of hyponatremia in AECOPD patients. Methods A total of 586 AECOPD patients were enrolled in this multicenter cross-sectional study. Finally, 323 had normonatremia, and 90 had hyponatremia. Demographics, underlying diseases, comorbidities, symptoms, and laboratory data were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to select potential risk factors, which were substituted into binary logistic regression to identify independent risk factors. Nomogram was built to visualize and validate binary logistics regression model. Results Nine potential hyponatremia-associated variables were selected by LASSO regression. Subsequently, a binary logistic regression model identified that smoking status, rate of community-acquired pneumonia (CAP), anion gap (AG), erythrocyte sedimentation rate (ESR), and serum magnesium (Mg2+) were independent variables of hyponatremia in AECOPD patients. The AUC of ROC curve of nomogram was 0.756. The DCA curve revealed that the nomogram could yielded more clinical benefits if the threshold was between 10% and 52%. Conclusions Collectively, our results showed that smoking status, CAP, AG, ESR, and serum Mg2+ were independently associated with hyponatremia in AECOPD patients. Then, these findings indicate that pneumonia, metabolic acidosis, and hypomagnesemia were the underlying etiologies of hyponatremia in AECOPD patients. However, their internal connections need further exploration.

The Impact of Different Smoking Behavior on Pulmonary Function and Pulmonary Hypertension Among Chinese Male Patients with Chronic Obstructive Pulmonary Disease

“… 8 , 33 Inadequate dietary intake and malnutrition were prevalent in COPD patients. 34 BMI ≥18.5 was a potential protective factor of lung volume in late-smoking subjects of our study. BMI <18.5 indicated airway disease-predominant pattern and great increase in residual volume, suggesting severe small airways obstruction and gas trapping.…”

Logistic regression analysis of the value of biomarkers, clinical symptoms, and imaging examinations in COVID-19 for SARS-CoV-2 nucleic acid detection

“…Therefore, LASSO is used for screening disease risk factors and establishing predictive models, often combined with binary logistic regression. [14][15][16][17] All the data were processed with SPSS 22.0 and SPSSPRO for statistical analysis. The preliminary screening of variables was conducted using LASSO regression analysis.…”

“…Therefore, LASSO is used for screening disease risk factors and establishing predictive models, often combined with binary logistic regression. [14–17]…”

Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study

“…Meanwhile, due to safety concerns and cooperation of the spirometer test, the lung function test was not performed in AECOPD patients with respiratory failure. Ex-smoker was defined as quitting smoking more than 6 months before data collection ( 21 , 22 ). Biomass fuel exposure was defined as using an indoor open fire with coal, coke, wood, pellet, agricultural residue, or animal dung at home as a primary means of cooking or heating for ≥ 1 year ( 23 , 24 ).…”

“…A threshold of p < 0.05 was considered to be significant. In addition, the nomogram, calibration curve, ROC curve, and DCA curve were established by R version 4.1.2 ( 22 ).…”