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Although a number of clinical predictive models were developed to predict postoperative pulmonary infection, few predictive models have been used in elderly patients. In this study, the researchers aim to compare different algorithms to predict postoperative pulmonary infection in elderly patients and to assess the risk of postoperative pulmonary infection in elderly patients.
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| Measure | Description | Time Frame |
|---|---|---|
| the incidence of postoperative pulmonary infection during hospitalization | the incidence of postoperative pulmonary infection during hospitalization | through study completion, an average of 30 days |
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Inclusion Criteria:
Exclusion Criteria:
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Elderly patients underwent surgery at the tertiary hospital Hospital from January 2014 to December 2019.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Union Hospital, Tongji Medical College, Huazhong University of Science and Technology | Wuhan | Hubei | 430022 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40108569 | Derived | Liu J, Li X, Wang Y, Xu Z, Lv Y, He Y, Chen L, Feng Y, Liu G, Bai Y, Xie W, Wu Q. Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models. BMC Pulm Med. 2025 Mar 19;25(1):128. doi: 10.1186/s12890-025-03582-4. |
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