Identifying patients at high risk for antibiotic treatment following hospital admission: a predictive score to improve antimicrobial stewardship measures
Bibliographic record
Abstract
PURPOSE: Identifying patients for clinical studies evaluating strategies to reduce unnecessary antibiotic usage in hospitals is challenging. This study aimed to develop a predictive score to identify newly hospitalized patients with high likelihood of receiving antibiotics, thus improving patient inclusion in future studies focusing on antimicrobial stewardship (AMS) programs. METHODS: This retrospective analysis used data from the PILGRIM study (NCT03765528), which included 1,600 patients across ten international sites. Predictive variables for antibiotic treatment during hospitalization were computed, and an additive score model was developed using logistic regression and 10-fold cross-validation. The PILGRIM score was validated in an independent cohort (validation cohort), with performance metrics assessed. RESULTS: Data from 1,258 patients was included. In the development cohort 52.8% (n = 445) and in the validation cohort 42.4% (n = 134) of patients received antibiotics. Key predictors included hematologic malignancies, immunosuppressive medication, and past hospitalization. The logistic regression model demonstrated an area under the curve of 0.74 in the validation. The final additive score incorporated these predictors plus "planned elective surgery" achieving a specificity of 92%, a positive predictive value of 78%, a sensitivity of 41%, and a negative predictive value (NPV) of 69%in validation set. CONCLUSION: The PILGRIM score effectively identifies newly hospitalized patients likely to receive antibiotics, demonstrating high specificity and PPV. Its application can improve future AMS programs and trial recruitment by facilitating targeted inclusion of patients, especially in the hematological and oncological setting. Further -external and prospective- validation is needed to broaden the model's applicability.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".