External validity of a model to predict postoperative atrial fibrillation after thoracic surgery
Bibliographic record
Abstract
OBJECTIVES: A prediction model developed by Passman et al. stratifies patients' risk of postoperative atrial fibrillation (POAF) after major non-cardiac thoracic surgery using 3 simple factors (sex, age and preoperative resting heart rate). The model has neither undergone external validation nor proven to be relevant in current thoracic surgery practice. METHODS: A retrospective single-centre analysis of all patients who underwent major non-cardiac thoracic surgery (2008-2017) with prospective documentation of incidence and severity of POAF was used for external validation of Passman's derivation sample (published in 2005 with 856 patients). The model calibration was assessed by evaluating the incidence of POAF and patients' risk scores (0-6). RESULTS: A total of 2054 patients were included. Among them, POAF occurred in 164 (7.9%), compared to 147 (17.2%) in Passman's study. Differences in our sample compared to Passman's sample included mean heart rate (75.7 vs 73.7 bpm, P < 0.001), proportion of patients with hypertension (46.1 vs 29.4%, P < 0.001), proportion of extensive lung resections, particularly pneumonectomy (6.1 vs 21%, P < 0.001) and proportion of minimally invasive surgeries (56.6% vs 0%). The model demonstrated a positive correlation between risk scores and POAF incidence (risk score 1.2% vs 6.16%). CONCLUSIONS: The POAF model demonstrated good calibration in our population, despite a lower overall incidence of POAF compared to the derivation study. POAF rates were higher among patients with a higher risk score and undergoing procedures with greater intrathoracic dissection. This tool may be useful in identifying patients who are at risk of POAF when undergoing major thoracic surgery and may, therefore, benefit from targeted prophylactic therapy.
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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".