ABSTRACT
Objective:To construct and verify the nomogram prediction model based on inflammatory indicators, underlying diseases, etiology and the British Thoracic Society modified pneumonia score (CURB-65 score) in adults with severe community acquired pneumonia (CAP).Methods:The clinical data of 172 adult inpatients first diagnosed as CAP at Taikang Xianlin Drum Tower Hospital from January 2018 to December 2021 were divided into severe and non-severe diseases groups according to the severity of their conditions. The baseline conditions (including gender, age, past history, comorbidities and family history), clinical data (including chief symptoms, onset time, CURB-65 score), first laboratory results on admission (including whole blood cell count, liver and kidney function, blood biochemistry, coagulation function, microbiological culture results) and whether the antimicrobial therapy was adjusted according to the microbiological culture results were recorded in both groups. Univariate analysis was used to screen for differential indicators between severe and non-severe patients. After covariate analysis, multi-factor Logistic regression analysis was performed based on the Aakaike information criterion (AIC) forward stepwise regression method to rigorously search for risk factors for constructing the model. Based on the results of the multi-factor analysis, a nomogram prediction model was constructed, and the discriminatory degree and calibration degree of the model were assessed using the receiver operator characteristic curve (ROC curve) and calibration curve.Results:A total of 172 adult CAP patients were included, 48 in severe group and 124 in non-severe group. The median age was 74 (57, 83) years old, onset time was 5.0 (3.0, 10.0) days, total number of comorbidities was 3 (2, 5), including 58 cases (33.7%) with hypertension and 17 (9.9%) with heart failure, 113 (65.7%) with CURB-65 score≤1, 34 cases (19.8%) had a CURB-65 score = 2 and 25 cases (14.5%) had a CURB-65 score≥3. Univariate analysis showed that there were statistically significant differences between the two groups in age, smoking history, CURB-65 score, heart rate, onset time, total comorbidity, pathogenic microorganisms, fibrinogen (FIB), D-dimer, C-reactive protein (CRP), procalcitonin (PCT), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Multi-factor Logistic regression analysis showed that hypertension [odds ratio ( OR) = 3.749, 95% confidence interval (95% CI) 1.411 to 9.962], heart failure ( OR = 4.616, 95% CI was 1.116 to 19.093), co-infection ( OR = 2.886, 95% CI was 1.073 to 7.760), history of smoking ( OR = 8.268, 95% CI was 2.314 to 29.537), moderate to high CURB-65 score ( OR = 4.833, 95% CI was 1.892 to 12.346), CRP ( OR = 1.012, 95% CI was 1.002 to 1.022), AST ( OR = 1.015, 95% CI was 1.001 to 1.030) were risk factors for severe CAP (all P < 0.05). The filtered indicators were included in the nomogram model, and the results showed that the area under the ROC curve (AUC) for the model to identify patients with severe adult CAP was 0.896, 95% CI was 0.840 to 0.937 ( P < 0.05), and the calibration curve showed that the predicted probability of severe CAP was in good agreement with the observed probability (Hosmer-Lemeshow test: χ2 = 6.088, P = 0.665). Conclusions:The nomogram model has a good ability to identify patients with severe adult CAP and can be used as a comprehensive and reliable clinical diagnostic tool to provide a evidence for timely intervention in the treatment of adults with severe CAP.