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BackgroundThe incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. The assessment tools including CURB-65 and qSOFA have been applied in early detection of high-risk patients with CAP. However, several disadvantages exist to limit the efficiency of these tools for accurate assessment in elderly CAP. Therefore, we aimed to explore a more comprehensive tool to predict mortality in elderly CAP population by establishing a nomogram model.MethodsWe retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. The least absolute shrinkage and selection operator (LASSO) logistic regression combined with multivariate analyses were used to select independent predictive factors and established nomogram models via R software. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.ResultsLASSO and multiple logistic regression analyses showed the age, pulse, NLR, albumin, BUN, and D-dimer were independent risk predictors. A nomogram model (NB-DAPA model) was established for predicting mortality of CAP in elderly patients. In both training and validation set, the area under the curve (AUC) of the NB-DAPA model showed superiority than CURB-65 and qSOFA. Meanwhile, DCA revealed that the predictive model had significant net benefits for most threshold probabilities.ConclusionOur established NB-DAPA nomogram model is a simple and accurate tool for predicting in-hospital mortality of CAP, adapted for patients aged 65 years and above. The predictive performance of the NB-DAPA model was better than PSI, CURB-65 and qSOFA.

Purpose The study explores a clinical model based on aging-care parameters to predict the mortality of hospitalized patients aged 80-year and above with community-acquired pneumonia (CAP). Patients and methods In this study, four hundred and thirty-five CAP patients aged 80-years and above were enrolled in the Central Hospital of Minhang District, Shanghai during 01,01,2018–31,12,2021. The clinical data were collected, including aging-care relevant factors (ALB, FRAIL, Barthel Index and age-adjusted Charlson Comorbidity Index) and other commonly used factors. The prognostic factors were screened by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to predict the mortality risk. Results Univariate analysis demonstrated that several factors, including gender, platelet distribution width, NLR, ALB, CRP, pct, pre-albumin, CURB-65, low-density, lipoprotein, Barthel Index, FRAIL, leucocyte count, neutrophil count, lymphocyte count and aCCI, were associated with the prognosis of CAP. Multivariate model analyses further identified that CURB-65 (p < 0.0001, OR = 5.44, 95% CI = 3.021–10.700), FRAIL (p < 0.0001, OR = 5.441, 95% CI = 2.611–12.25) and aCCI (p = 0.003, OR = 1.551, 95% CI = 1.165–2.099) were independent risk factors, whereas ALB (p = 0.005, OR = 0.871, 95% CI = 0.788–0.957) and Barthel Index (p = 0.0007, OR = 0.958, 95% CI = 0.933–0.981) were independent protective factors. ROC curves were plotted to further predict the in-hospital mortality and revealed that combination of three parameters (Barthel Index+ FRAI +CURB-65) showed the best performance. Conclusion This study showed that CURB-65, frailty and aCCI were independent risk factors influencing prognosis. In addition, ALB and Barthel Index were protective factors for in CAP patients over 80-years old. AUC was calculated and revealed that combination of three parameters (Barthel Index+ FRAI +CURB-65) showed the best performance.