Construction and validation of a meta-analysis-based risk prediction model for nosocomial infections in adult patients undergoing extracorporeal membrane oxygenation
Bibliographic record
Abstract
Objective To construct a meta-analysis-based risk prediction model for nosocomial infections in adult patients undergoing extracorporeal membrane oxygenation (ECMO), thereby providing an assessment tool to evaluate and reduce the risk of nosocomial infections in this population. Methods Databases were systematically searched from inception to June 20, 2025, for relevant literature on risk factors associated with nosocomial infections in adult ECMO patients. The quality of included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed with Review Manager 5.4, and integrated risk values of identified factors were used to construct a logistic regression prediction model. Patients who underwent ECMO treatment in a tertiary care hospital from January 2023 to August 2024 were enrolled as the model validation cohort. The model performance was assessed using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). Results Twenty-six literatures, involving 3, 872 patients were included, and the overall incidence of nosocomial infections in adult ECMO patients was 34.19%. The logistic regression model was constructed as follows: Logit (P)=ɑ-0.02×age+0.09×BMI+0.08×duration of ECMO support+ 0.27×duration of mechanical ventilation+0.02×duration of central venous catheterization+2.06×SOFA score+ 1.07×CRRT use+1.78×IABP use. The sensitivity and specificity of the model were 80.0% and 68.9%, respectively. The area under the ROC curve (AUC) was 0.777 (95% CI: 0.659-0.894), indicating good discrimination. The Hosmer-Lemeshow test showed satisfactory model calibration (χ2=8.325, P=0.402). Calibration curve analysis revealed a prediction error between the predictive model and the actual observations was 0.013, indicating high accuracy and consistency. DCA demonstrated a positive net benefit, suggesting favorable clinical utility. Conclusion The meta-analysis-based risk prediction model for nosocomial infections in adult ECMO patients demonstrates strong predictive performance. It serves as a useful tool for early identification of patients at high risk for nosocomial infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".