Key result
A risk nomogram model predicted poor prognosis in patients with severe pulmonary infection and respiratory failure with an AUC of 0.813 (95% CI 0.778-0.895).
Why the study?
To investigate the prognosis and analyze the influencing factors of prognosis in patients with severe pulmonary infection combined with respiratory failure.
Observational (n=218)
Effect estimate: AUC 0.813 (95% CI 0.778-0.895)
A risk nomogram incorporating comorbidities, APACHE II, MODS, PSI scores, multi-drug resistant infection, and albumin levels can accurately predict prognosis in patients with severe pulmonary infection and respiratory failure.
Nomogram may stratify risk in severe pulmonary infection with respiratory failure; leaves open external validation before clinical use.
Objective: To investigate the prognosis of patients with severe pulmonary infection combined with respiratory failure and analyze the influencing factors of prognosis. Methods: The clinical data of 218 patients with severe pneumonia complicated with respiratory failure were retrospectively analyzed. The risk factors were analyzed by univariate and multivariate logistic regression analyses. The risk nomogram and Bootstrap self-sampling method were used for internal inspection. Calibration curves and receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the model. Results: Among 218 patients, 118 (54.13%) cases had a good prognosis and 100 (45.87%) cases had a poor prognosis. Multivariate logistic regression analysis showed that the number of complicated basic diseases ≥ 5, APACHE II score > 20, MODS score > 10, PSI score > 90, and multi-drug resistant bacterial infection were independent risk factors affecting the prognosis (P< 0.05), and the level of Alb was an independent protective factor (P< 0.05). The consistency index (C-index) was 0.775, and the Hosmer Lemeshow goodness-of-fit test showed that the model was not significant ( P > 0.05). The area under the curve (AUC) was 0.813 (95% CI: 0.778~0.895), with the sensitivity of 83.20%, and the specificity of 77.00%. Conclusion: The risk nomograph model had good discrimination and accuracy in predicting the prognosis of patients with severe pulmonary infection combined with respiratory failure, which may provide a basis for early identification and intervention of patients at clinical risk and improve the prognosis.
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Liu et al. (2023) conducted an observational in Severe pulmonary infection combined with respiratory failure (n=218). Risk nomogram was evaluated on Prognosis prediction (AUC 0.813, 95% CI 0.778-0.895). A risk nomogram model predicted poor prognosis in patients with severe pulmonary infection and respiratory failure with an AUC of 0.813 (95% CI 0.778-0.895).
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