Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis

Accurate prognostic tools for mortality in patients with healthcare-associated pneumonia (HCAP) are needed to provide appropriate medical care, but the efficacy for mortality prediction of tools like PSI, A-DROP, I-ROAD, and CURB-65, widely used for predicting mortality in community-acquired and hospital-acquired pneumonia cases, remains controversial. In this study, we conducted a systematic review and meta-analysis using PubMed, Cochrane Library (trials), and Ichushi web database (accessed on August 22, 2022). We identified articles evaluating either PSI, A-DROP, I-ROAD, or CURB-65 and the mortality outcome in patients with HCAP, and calculated the pooled sensitivities, specificities, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the summary area under the curves (AUCs) for mortality prediction. Additionally, the differences in predicting prognosis among these four assessment tools were evaluated using overall AUCs pooled from AUC values reported in included studies. Eventually, 21 articles were included and these quality assessments were evaluated by QUADAS-2. Using a cut-off value of moderate in patients with HCAP, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were found to be 0.91-0.97, 0.15-0.44, 1.14-1.66, 0.18-0.33, and 3.86-9.32, respectively. Upon using a cut-off value of severe in those patients, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were 0.63-0.70, 0.54-0.66, 1.50-2.03, 0.47-0.58, and 2.66-4.32, respectively. Overall AUCs were 0.70 (0.68-0.72), 0.70 (0.63-0.76), 0.68 (0.64-0.73), and 0.67 (0.63-0.71), respectively, for PSI, A-DROP, I-ROAD, and CURB-65 (p = 0.66). In conclusion, these severity assessment tools do not have enough ability to predict mortality in HCAP patients. Furthermore, there are no significant differences in predictive performance among these four severity assessment tools.

Investigation of predictors for in-hospital death or long-term hospitalization in community-acquired pneumonia with risk factors for aspiration

Background

It is known that the mortality of pneumonia in patients with risk factors for aspiration is worse than that in those without these risk factors. However, it is still unknown which risk factors for aspiration predict prognosis. Therefore, we aimed to determine which risk factors for aspiration are associated with death or prolonged hospitalization.

Methods

We prospectively followed patients with community-acquired pneumonia at a single hospital providing acute to chronic care in Japan until they died or were discharged. Patients at any risk of aspiration were included. The associations between pneumonia severity, individual risk factors for aspiration, and in-hospital death or prolonged hospitalization were investigated. Overall survival was estimated by the Kaplan - Meier method, and the factors associated with in-hospital death or prolonged hospitalization were investigated by multivariate analysis using factors selected by a stepwise method.

Results

In total, 765 patients with pneumonia and risk factors for aspiration were recruited. One hundred and ten patients deceased, and 259 patients were hospitalized over 27 days. In-hospital death increased as the number of risk factors for aspiration increased. In the multivariate analysis, male, impaired consciousness, acidemia, elevated blood urea nitrogen, and bedridden status before the onset of pneumonia were associated with in-hospital death (odds ratio [OR]: 2.5, 2.5, 3.6, 3.1, and 2.6; 95% confidence interval [CI]: 1.6-4.1, 1.4-4.2, 1.6-8.0, 1.9-5.0, and 1.6-4.2 respectively). In the Cox regression analysis, these factors were also associated with in-hospital death. None of the vital signs at admission were associated. Tachycardia, elevated blood urea nitrogen, hyponatremia, and bedridden status were associated with hospitalization for >27 days (OR: 4.1, 2.3, 4.3, and 2.9; 95% CI: 1.3-12.9, 1.5-3.4, 2.0-9.4, and 2.0-4.0, respectively).

Conclusions

Blood sampling findings and bedridden status are useful for predicting in-hospital mortality and long-term hospitalization in patients with pneumonia and any risk factor for aspiration.

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

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

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.