Exploration of prognostic factors for prediction of mortality in elderly CAP population using a nomogram model

Abstract

Background: The 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.

Methods: We 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.

Results: LASSO 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.

Conclusion: Our 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.

Keywords: CAP in elderly patients; CURB-65; nomogram model; prognosis; qSOFA.

FIGURE 1

Study flowchart.

FIGURE 2

Selection of variables for mortality were performed using the LASSO analysis. (A) Model coefficient trendlines of the 28 variables for mortality. The profile graph was plotted by coefficients against the L1 norm. (B) Tuning parameter λ in the LASSO model. The parameter (λ = 0.068) was selected under the minimum criteria. The vertical line was drawn at the value selected by 10-fold cross-validation, including optimized 8 non-zero coefficients.

FIGURE 3

Establishment of a nomogram model for predicting the mortality of elderly patients with CAP. (A) Forest map showing multivariate analyses. (B) The nomogram model for predicting the mortality of elderly patients with CAP. The nomogram factors included age, pulse, NLR, albumin, BUN, and D-dimer. The nomogram summed the scores for each scale and variable. The total score on each scale indicated the risk of mortality. NLR (neutrophil-lymphocyte ratio), BUN (blood urea nitrogen).

FIGURE 4

Performance of the nomogram model in training cohort. (A) Calibration curves for predicting the mortality in the training set. (B) Decision curve analysis for the predict-ed-nomogram model of mortality in the training set. (C) The receiver operator characteristic curves (ROC) of the nomogram model, PSI, CURB-65 and qSOFA in training cohort for evaluating the risk of in-hospital mortality in older patients with CAP. CURB-65: confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years; qSOFA, quick Sequential Organ Failure Assessment; PSI, Pneumonia Severity Index.

FIGURE 5

Validation of the nomogram in validation cohort set. (A) Calibration curves for predicting the mortality in the validation set. (B) Decision curve analysis for the nomogram model in the validation set. (C) The receiver operator characteristic curves (ROC) of the nomogram model, PSI, CURB-65 and qSOFA in validation cohort for predicting the risk of mortality in the elderly with CAP. CURB-65: confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years; qSOFA, quick Sequential Organ Failure Assessment; PSI, Pneumonia Severity Index.

FIGURE 6

Validation of the nomogram in external validation cohort. (A) Calibration curves for predicting the mortality in the external validation set. (B) Decision curve analysis for the nomogram model in the external validation set. (C) The receiver operator characteristic curves (ROC) of the nomogram model, PSI, CURB-65 and qSOFA in external validation cohort for predicting the risk of mortality in the elderly with CAP. CURB-65: confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years; qSOFA, quick Sequential Organ Failure Assessment; PSI, Pneumonia Severity Index.