Development of a novel model for accurate prediction of secondary pneumonia in patients with acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND

Given the increased risk factors such as the wide application of various dose forms of corticosteroids and broad-spectrum antibiotics in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in recent years, the incidence of invasive pneumonia secondary to AECOPD tends to increase. However, infections secondary to AECOPD are often neglected in clinical practice, or even misdiagnosed as bacterial infections, resulting in disease deterioration due to delayed diagnosis. Knowing that early diagnosis and timely treatment can obviously improve the prognosis of pulmonary candidiasis, improving the early diagnosis rate is the key to reduce the mortality of AECOPD-associated candidiasis. The present study was intended to develop a new model that can early and accurately predict the occurrence of infections secondary to AECOPD.

METHODS

Clinical data of 164 hospitalized patients with AECOPD who received treatment in the department of respiratory medicine of Yiwu Central Hospital between January 2022 and January 2024 were reviewed retrospectively, including the diagnosis, gender, age, BMI, use of inhaled corticosteroids, the duration of using antibiotics, use of carbapenem antibiotics, random blood glucose, albumin level, the presence or absence of cerebral infarction aspiration, cancer chemoradiotherapy, complicated cardiovascular disease, procalcitonin level, pulmonary function grade, and surviving time. Data were treated and analyzed by R language statistical software.

RESULTS

Of the 164 AECOPD patients, 87 were male and 77 were female, with a mean age of 77.28 ± 8.10 years. The model group consisted of 127 AECOPD patients, including 64 with candidiasis secondary to AECOPD and 63 with no candida infection; the validation group consisted of 37 patients, including 14 with secondary candidiasis and 23 with no infection. Single factor logistic regression analysis of the patients in the model group showed that BMI, use of antibiotics ≥2 weeks, cancer chemoradiotherapy and pulmonary function grade were four independent predictors for the occurrence of secondary infection. The weigh factor of the four risk factors was further determined by Multivariate logistic regression analysis as follows: Probability of infection () = EXP (-17.7063452 + 1.8265388pulmonary function grade + 1.8443357cancer chemoradiotherapy + 4. 1749059use of antibiotics ≥ 2 weeks + 0.4527216BMI), and > 0.5 suggests the probability of developing secondary candidiasis in the AECOPD patient.

CONCLUSION

The result demonstrated that this new model could accurately predict the occurrence of secondary candidiasis in AECOPD patients, with an accuracy rate of 84%, thus providing a simple and accurate tool for predicting the probability of secondary candidiasis in AECOPD patients, especially in cancer patients complicated with AECOPD. This model can only be used as an auxiliary assessment tool for the possibility of secondary infection and cannot be used as a diagnostic basis.