Predicting individualized treatment effects of corticosteroids in community-acquired-pneumonia: a data-driven analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Background Corticosteroids could improve outcomes in patients with community-acquired pneumonia (CAP). However, we hypothesize that corticosteroid effectiveness varies among individual patients, resulting in inconsistent outcomes and unclear clinical indication. Therefore, we developed and validated a predictive, causal model based on baseline characteristics to predict individualized treatment effects (ITEs) of corticosteroids on mortality in patients with CAP. Methods We obtained individual patient data from six randomized controlled trials comparing corticosteroid therapy to placebo in 1,869 adult CAP patients. The study endpoint was 30-day mortality. We performed effect modelling through logistic regression and evaluated the predicted ITEs in terms of discrimination and calibration for benefit. Our modelling procedure involved variable selection, missing value imputation, data normalization, encoding treatment variables, creating interaction terms, optimizing penalization strength, and training logistic regression models. We evaluated discriminative performance using the newly proposed ‘AUC-benefit’. Findings The model identified high levels of CRP and glucose, at baseline, as main predictors for benefit of corticosteroid treatment. Using a decision threshold of ITE=0, the model predicted harm in 1,004 patient and benefit in 864 patients. We observed benefit in patients where the model predicted benefit, with an odds ratio of 0.5 (95% CI: 0.3 to 0.9) and a mortality reduction of 3.2% (95% CI: 0.7 to 5.6), and no statistically significant benefit in the patients where the model predicted harm, with an odds ratio of 1.1 (95% CI: 0.7 to 1.8) and a negative mortality reduction (hence, increase) of −0.3% (95% CI: −2.6 to 1.8). The model yielded an AUC-benefit of 184.9 (28.6 to 347.6, 95% CI), underestimated ITEs in the lower ITE region and slightly overestimated ITEs in the higher ITE region. Interpretation Our model has potential to identify patients with CAP who benefit from corticosteroid treatment, and aid in the design of personalized clinical trials. We will prospectively validate the model in two recent CAP trials.
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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 | 0.270 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".