Risk assessment in pulmonary hypertension
Bibliographic record
Abstract
Pulmonary hypertension (PH) is a disease of the pulmonary circulation that, if untreated, can progress to right ventricular failure and death. Assessment of the risk of mortality guides treatment strategies, such as the selection and modification of targeted pulmonary vasodilator therapy and consideration of lung transplantation. Risk stratification models employ multi-modality metrics which include an assessment of patient’s functional disability, exercise capacity and right ventricular impairment. Currently used models in clinical practice require face-to-face assessment to allow objective risk stratification. In addition, risk stratification is required at regular intervals which consequently results in frequent outpatient appointments. There has been increased interest in telehealth within PH, which was further accelerated by the 2019 coronavirus pandemic, however robust methods to acquire virtual risk metrics are lacking. The first work package in this thesis aimed to describe the current challenges to the implementation of the remote assessment of risk in PH and to develop exploratory knowledge into how exercise capacity and the measurement of biomarkers could be remotely obtained. The results demonstrate that remote risk assessment in PH is feasible, although there were important caveats to the validity of such testing. The second work package focused on the refinement of risk assessment models in PH. The first study herein demonstrates the validity of the novel 4-strata risk assessment model in a cohort of patients with Chronic Thromboembolic Pulmonary Hypertension (CTEPH). The 4-strata model is limited by the inclusion of the World Health Organisation Functional Class and the MRC Dyspnoea Scale was studied an alternative as a measure of functional limitation, with results suggesting that incorporating this may allow further refinement of risk strata. Finally, two pre-test probability algorithms (the H2FPEF and OPTICS scores) were validated to assess whether patients with pre- and post-capillary PH can be differentiated non-invasively, without the need for invasive haemodynamic studies. The results demonstrated that these non-invasive scoring systems do not have sufficient specificity to recommend their routine clinical use.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".