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Objective To explore the risk factors for medium- and long-term mortality in patients with severe community-acquired pneumonia (SCAP) based on the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ), construct a prognostic model and evaluate its predictive efficacy. Methods In this retrospective cohort study, 1943 SCAP patients from the U.S. MIMIC-Ⅳ database (2008-2019) were randomly divided into training (n=1363) and validation (n=580) sets (7:3 ratio). Primary and secondary endpoints were 1-year and 30-/90-day all-cause mortality, respectively. Prognostic factors were selected using LASSO regression and multivariable Cox proportional hazards modeling, and a visual nomogram model was built. Model performance was assessed via C-index, receiver operator characteristic (ROC) curves, and calibration curves, and compared with the CURB-65 score. Risk stratification was validated using Kaplan-Meier analysis. Results The 30-day, 90-day, and 1-year all-cause mortality rates for SCAP patients were 25.9%, 34.5%, and 42.6%, respectively. Seven independent risk factors were identified: age (HR=1.037), heart rate (HR=1.007), red blood cell distribution width (RDW, HR=1.092), Acute Physiology Score Ⅲ (APS-Ⅲ, HR=1.013), cerebrovascular disease (HR=1.453), liver disease (HR=1.272), and malignancy (HR=2.007). Based on these factors, Cox regression model was constructed and nomogram was drawn, C-indices of training set and validation set were 0.710 and 0.688, respectively. For 1-year mortality prediction, the model achieved superior area under the ROC curve (AUC) values (training set: 0.768; validation set: 0.738) compared with CURB-65 score (training set: 0.648; validation set: 0.616). Kaplan-Meier survival analysis revealed significantly worse survival in high-risk group than low-risk group (P<0.0001). Conclusions Age, heart rate, RDW, APS-Ⅲ, cerebrovascular disease, liver disease, and malignant tumor were medium- and long-term mortality risk factors in SCAP patients. The prognostic model constructed based on these factors has high predictive power and provides an important clinical diagnosis and treatment reference.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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