Comparative Prognostic Accuracy of Risk Prediction Models for Cardiogenic Shock
Bibliographic record
Abstract
Objectives: Despite advances in medical therapy, reperfusion, and mechanical support, cardiogenic shock remains associated with excess morbidity and mortality. Accurate risk stratification may improve patient management. We compared the accuracy of established risk scores for cardiogenic shock. Methods: Patients admitted to tertiary care center cardiac care units in the province of Alberta in 2015 were assessed for cardiogenic shock. The Acute Physiology and Chronic Health Evaluation-II (APACHE-II), CardShock, intra-aortic balloon pump (IABP) Shock II, and sepsis-related organ failure assessment (SOFA) risk scores were compared. Receiver operating characteristic curves were used to assess discrimination of in-hospital mortality and compared using DeLong’s method. Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Results: The study included 3021 patients, among whom 510 (16.9%) had cardiogenic shock. Patients with cardiogenic shock had longer median hospital stays (median 11.0 vs 4.1 days, P < .001) and were more likely to die (29.0% vs 2.5%, P < .001). All risk scores were adequately calibrated for predicting hospital morality except for the APACHE-II score (Hosmer-Lemeshow P < .001). Discrimination of in-hospital mortality with the APACHE-II (area under the curve [AUC]: 0.72, 95% confidence interval [CI]: 0.66-0.76) and IABP-Shock II (AUC: 0.73, 95% CI: 0.68-0.77) scores were similar, while the CardShock (AUC: 0.76, 95% CI: 0.72-0.81) and SOFA (AUC: 0.76, 95%CI: 0.72-0.81) scores had better discrimination for predicting in-hospital mortality. Conclusions: In a real-world population of patients with cardiogenic shock, existing risk scores had modest prognostic accuracy, with no clear superior score. Further investigation is required to improve the discriminative abilities of existing models or establish novel methods.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".