Developing and Validating an Inclusive and Cost-Effective Prediction Algorithm for Survival and Death Among People Living With HIV in Sub-Saharan Africa: Protocol for a Meta-Analysis and Case-Control and Cost-Effectiveness Study
Notice bibliographique
Résumé
BACKGROUND: Premature death in people with HIV in sub-Saharan Africa (SSA) is highly preventable. However, the lack of inclusive, cost-effective prognostic tools remains challenging. Most prognostic tools have been developed in high-income economies. The distinct cultural dynamics in HIV-related death epidemiology makes them unsuitable for the region. Additionally, the models lack systematic stratification of death determinants based on clinical relevance, and some included factors are too expensive for people with HIV in SSA. OBJECTIVE: We aimed to create a tailored predictive model that considers the unique context of SSA, including cultural dynamics, cost-effectiveness, and clinical relevance. METHODS: This is a 2-phase study. In the development phase, we will use a combination of evidence synthesis, namely meta-analysis, application epidemiology, biostatistical, and economic paradigms, to develop a prognostic model for people living with HIV in SSA. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol will be followed in the structuring of the meta-analysis. From their creation to the present, we will search African journals (Sabinet) and the PubMed, Scopus, MEDLINE, Academic Search Complete, Directory of Open Access Repository, Cochrane Library, Web of Science, EMBASE, and Cumulative Index for Nursing and Allied Health Literature databases. Only cohort studies with moderate to high quality will be included. The primary outcome variables include the predictors of HIV-related death and their corresponding effect sizes (adjusted relative risk). A random-effect meta-analysis model will be used to synthesize the unbiased estimate of risk (relative risk) per predictor. Epidemiological metrics such as risk responsiveness, geotemporal trend, risk weight (Rw), clinical minimum important difference (CMID), predictors interaction density (PID), critical risk points, and potential cost implication will be computed. A combination of Rw and CMID will be used for risk stratification. The model's constituent items will be selected based on the combination of Rw, CMID, PID and cost implication. In the validation phase, we will apply the emergent model to classify participants using a secondary data obtained from a cohort of people living with HIV in East and West Africa, with outcomes including sensitivity, specificity, calibration, and area under the receiver operating characteristic curve (AUC). RESULTS: The study is projected to commence in October 2025 and end in September 2026. The expected result will be published in November 2026. The result will be presented using narrative and quantitative synthesis. Indices of causality namely as strength of association, temporality, consistency, biological gradient, and specificity of the predictor-outcome association will be presented in a tabular format. TheAUC will be used to decide the optimal critical risk point for the emergent predictive algorithm. CONCLUSIONS: Effective prognostication coupled with intense monitoring and evaluation, and prioritizing of therapeutic targets could positively turn around the fate of millions of people living with HIV at risk of premature death in SSA. TRIAL REGISTRATION: PROSPERO CRD42023430437; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023430437. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63783.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,112 | 0,184 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,014 | 0,033 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».