A prediction model for hospital mortality in patients with severe community-acquired pneumonia and chronic obstructive pulmonary disease

Abstract

Background No personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients. Methods We performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts. Results Finally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809–0.872) in training cohort and 0.830 (95% CI 0.781–0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend < 0.001). The restricted cubic splines suggested that estimated associations between these predictors and hospital mortality were all linear relationships. Conclusion We developed a prediction model including seven risk factors for hospital mortality in patients with SCAP and COPD. It can be used for early risk stratification in clinical practice after more external validation. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-022-02181-9.

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

“…1 , 48 The influence of comorbidities on the prognoses of patients with AECOPD were inconsistent due to differences in the study population, comorbidities analyzed, and the relatively small sample sizes. 18 , 24 , 25 , 49 For example, Huang D et al found that in patients with AECOPD complicated with pneumonia (sample size: 873), diabetes and chronic kidney disease increased the risk of in-hospital mortality, while cardiovascular diseases did not. 49 Peng JC et al found that hypertension, diabetes, cardiovascular disease, chronic kidney disease, and malignancies did not significantly increase the 30-day risk of death in patients with AECOPD in the ICU (sample size: 494).…”

“… 18 , 24 , 25 , 49 For example, Huang D et al found that in patients with AECOPD complicated with pneumonia (sample size: 873), diabetes and chronic kidney disease increased the risk of in-hospital mortality, while cardiovascular diseases did not. 49 Peng JC et al found that hypertension, diabetes, cardiovascular disease, chronic kidney disease, and malignancies did not significantly increase the 30-day risk of death in patients with AECOPD in the ICU (sample size: 494). 24 In the present study, eight common and important comorbidities, including hypertension, ischemic heart disease, diabetes, cerebrovascular disease, bronchiectasis, interstitial lung disease, chronic hepatic insufficiency, and chronic kidney disease were analyzed ( Figure 2 ).…”

Development and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units

“…Huang et al established a nomogram to assess the risks of hospital mortality among 611 patients who had both SCAP and chronic obstructive pulmonary disease (COPD). This nomogram included various factors such as chronic renal diseases, diabetes, systolic blood pressure, interleukin-6 (IL-6), fibrinogen, and BUN (AUC: 0.840, 95% CI: 0.809–0.872) ( 36 ). In a recent study by Song et al ( 37 ) a similar nomogram was developed to predict the 28-day mortality among elderly SCAP patients.…”

Development and validation of a scoring system to predict the mortality of hospitalized patients with SARS-CoV-2 Omicron: a nationwide, multicentre study

“…One study reported that after correcting for renal function, a high BUN concentration on admission was still closely related to the adverse outcomes of critically ill patients in the ICU [ 24 ]. In addition, the BUN-to-serum albumin ratio is an important prognostic factor for mortality and severity in patients with aspiration pneumonia, hospital-acquired pneumonia, and community-acquired pneumonia [ 25 , 26 ]. LDH is an enzyme present in the cytoplasm that is involved in lactate metabolism.…”