Development and Validation of a Mortality Risk Prediction Index Score for Adults Living with HIV and Multiple Chronic Comorbidities
Bibliographic record
Abstract
IntroductionAging while living with HIV poses new challenges in clinical management, mainly due to the onset of multiple chronic comorbidities. Population-specific risk prediction indices considering comorbidities and other risk factors are essential to comprehensively characterise disease burden among PLWH. We developed and validated a mortality risk prediction index i to predict the risk of one-year all-cause mortality among people living with HIV (PLWH). MethodsParticipants were ≥18 years and had initiated antiretroviral therapy (ART) between 01/2001 and 12/2018, in British Columbia, Canada. The index date was randomly selected between one-year post-ART initiation and the end of the follow-up. Participants were followed for at least one year from the index date until 12/2019, the last contact date, or the date of death (all-cause), whichever came first. The MRPi included 18 physical/mental comorbidities, demographic and clinical variables, and ranged from 0 (no risk) to 100 (highest risk). ResultsThe final model demonstrated the highest discrimination (c-statistic 0.8355, 95% CI: 0.8187-0.8523 in the training dataset and 0.7965, 95% CI: 0.7664-0.8266 in the test dataset). The comorbidities with the highest weights in the MRPi were substance use disorders, metastatic solid tumors and non-AIDs defining cancers. For example, for an MRPi of 30, the predicted one-year all-cause mortality was 0.2%, while an MRPi of 50 had a predicted mortality of 2.3%. ConclusionsThe MRPi provides a promising tool to assess the risk of short-term mortality among PLWH in the modern ART era that can inform clinical practice and health policy decisions.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".