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Record W4385727542 · doi:10.1371/journal.pone.0289828

A new cost-utility analysis assessing risk factor-guided prophylaxis with palivizumab for the prevention of severe respiratory syncytial virus infection in Italian infants born at 29–35 weeks’ gestational age

2023· article· en· W4385727542 on OpenAlexaff
Ian P. Keary, Roberto Ravasio, John Fullarton, Paolo Manzoni, Marcello Lanari, Bosco Paes, Xavier Carbonell‐Estrany, Eugenio Baraldi, Jean‐Éric Tarride, Barry Rodgers‐Gray

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsSt. Joseph’s Healthcare HamiltonPrograms for Assessment of Technology in Health Research InstituteMcMaster University
FundersSanofiAstraZenecaPfizer
KeywordsPalivizumabMedicinePediatricsGestational agePopulationNeonatal intensive care unitCost-effectiveness analysisCost effectivenessRespiratory systemPregnancyInternal medicineBiology

Abstract

fetched live from OpenAlex

Since the last Italian cost-utility assessment of palivizumab in 2009, new data on the burden of respiratory syncytial virus (RSV) and an International Risk Scoring Tool (IRST) have become available. The objective of this study was to provide an up-to-date cost-utility assessment of palivizumab versus no prophylaxis for the prevention of severe RSV infection in otherwise healthy Italian infants born at 29-31 weeks' gestational age (wGA) infants and those 32-35wGA infants categorized as either moderate- or high-risk of RSV-hospitalization (RSVH) by the IRST. A decision tree was constructed in which infants received palivizumab or no prophylaxis and then could experience: i) RSVH; ii) emergency room medically-attended RSV-infection (MARI); or, iii) remain uninfected/non-medically attended. RSVH cases that required intensive care unit admission could die (0.43%). Respiratory morbidity was considered in all surviving infants up to 18 years of age. Hospitalization rates were derived from Italian data combined with efficacy from the IMpact-RSV trial. Palivizumab costs were calculated from vial prices (50mg: €490.37 100mg: €814.34) and Italian birth statistics combined with a growth algorithm. A lifetime horizon and healthcare and societal costs were included. The incremental cost-utility ratio (ICUR) was €14814 per quality-adjusted life year (QALY) gained in the whole population (mean: €15430; probability of ICUR being <€40000: 0.90). The equivalent ICURs were €15139 per QALY gained (€15915; 0.89) for 29-31wGA infants and €14719 per QALY gained (€15230; 0.89) for 32-35wGA infants. The model was most sensitive to rates of long-term sequelae, utility scores, palivizumab cost, and palivizumab efficacy. Palivizumab remained cost-effective in all scenario analyses, including a scenario wherein RSVH infants received palivizumab without a reduction in long-term sequelae and experienced a 6-year duration of respiratory morbidity (ICUR: €27948 per QALY gained). In conclusion, palivizumab remains cost-effective versus no prophylaxis in otherwise healthy Italian preterm infants born 29-35wGA. The IRST can help guide cost-effective use of palivizumab in 32-35wGA infants.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.152
GPT teacher head0.386
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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