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Record W4310096378 · doi:10.1182/blood-2022-157194

FLIPI24: An Improved International Prognostic Model Developed on Early Events in Follicular Lymphoma

2022· article· en· W4310096378 on OpenAlexaff
Matthew J. Maurer, Vít Procházka, Christopher R. Flowers, Lasse Jakobsen, Diego Villa, Caroline E. Weibull, Elliot Cahn, Alexandra N. Smith, Hervé Ghesquières, Robert Kridel, Maher K. Gandhi, Chan Y. Cheah, Eliza A. Hawkes, John F. Seymour, Ciara L. Freeman, Michael Roost Clausen, Björn E. Wahlin, Jonathan W. Friedberg, Carla Casulo, Thomas M. Habermann, Yucai Wang, Loretta J. Nastoupil, Peter de Nully Brown, David Belada, Andrea Janíková, Heidi Móciková, Tomáš Fürst, Richard Burack, Peter Martin, Jonathon B. Cohen, Izidore S. Lossos, Brad S. Kahl, Brian K. Link, Karin E. Smedby, Laurie H. Sehn, Marek Trněný, Tarec Christoffer Christoffer El-Galaly, James R. Cerhan

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health NetworkSpinal Cord Injury BC
Fundersnot available
KeywordsMedicineFollicular lymphomaBendamustineOncologyInternal medicineProportional hazards modelCohortRituximabLymphoma

Abstract

fetched live from OpenAlex

Background: Early events are associated with inferior outcomes in follicular lymphoma (FL) and increased risk of death due to refractory FL. Risk prediction of early events prior to the start of therapy may enhance clinical management and research strategies. We report the FL International Prognostic Index model developed specifically to predict the risk of event within 24 months (EFS24) of starting first-line immunochemotherapy (IC) (FLIPI24). Methods: Modeling was performed using individual patient data from 10 observational cohorts from Europe, North America, and Australia. Eligible patients were diagnosed with grades 1-3A FL and initiated frontline R-CHOP, R-CVP, R-bendamustine (B-R), or like therapies. Event-free survival (EFS) was defined as time from start of IC to progression, retreatment, transformation, or death due to any cause. Model development utilized a truncated Cox model stratified on IC type and cohort with a time-dependent adjustment for maintenance therapy. Multiple imputation was implemented to address missing data. Functional forms of variables were assessed using splines. Model selection was based on model performance, clinical relevance, and parsimony. Gradient boosting machines were evaluated as a complementary approach and used to inform model selection. Model building was performed on an 80% split sample with 20% held for internal testing. External validation occurred in an independent cohort. FLIPI24 risk scores assumed B-R or R-CHOP based chemotherapy and 50% likelihood of maintenance. Results: 4485 patients initiating frontline IC between 2002 and 2018 were utilized in the analysis; 3577 patients were utilized for model building and 908 for internal model testing. Median age at diagnosis in the model build dataset was 61 years (IQR 53-69) and 51% were male. FLIPI was 21%, 32%, and 48% for low, intermediate, and high risk, respectively. IC type was 1874 R-CHOP or like (52%), 730 B-R or like (20%), and 973 R-CVP (27%); 2164 received CD20 antibody maintenance (61%). At median follow-up of 77 months (IQR 48-115), EFS24 estimate was 81% (95% CI: 79-82) and 835 patients (23%) died. 5-year survival after an early (non-death) event was 46% (95% CI: 42-50). The FLIPI24 model utilizes 5 continuous variables: age (linear 60-90 years with inflection at age 75), hemoglobin (linear 8-17 g/dL), white blood cell count (linear 4-11 109/L), normalized lactate dehydrogenase (linear 0.5-5), and beta-2-microglobulin (linear 1-10 mg/L). Linear variables and valid ranges were utilized based on close examination of functional forms across potential models. Continuous FLIPI24 risk scores were grouped for visualization in K-M curves as follows: very low risk (0-0.10), low risk (0.10-0.15), average risk (0.15-0.20), high risk (0.20-0.40), very high risk (>0.40), Figure 1. Internal validation was performed on the 20% holdout set (N=908). The FLIPI24 had superior concordance (c-stat) for EFS24 (c-stat=0.666) compared to FLIPI (c-stat=0.630) and PRIMA-PI (c-stat=0.590) models, Table 1. Importantly, FLIPI24 also improved prognostic ability for 10-year OS following IC (c-stat = 0.672) vs FLIPI (c-stat=0.629) or PRIMA-PI (c-stat=0.595). External validation was performed in a prospective cohort of 1438 newly diagnosed patients with FL (558 treated with IC) enrolled in the multicenter US LEO Cohort between 2015 and 2020. The FLIPI24 had superior c-stats compared to the FLIPI and PRIMA-PI for EFS24 (0.671 vs 0.600 vs 0.602) and OS (0.732 vs. 0.652 vs 0.635) in IC treated patients and for OS (0.785 vs 0.703 vs 0.662) when assessed in all patients. Conclusion: The FLIPI24 model provides an individual risk score at diagnosis for the likelihood of experiencing an event within 24 months from starting IC. The FLIPI24 was rigorously developed, tested, and externally validated using large harmonized and pooled international observational cohorts from the IC era. The FLIPI24 utilizes objective variables easily and reliably measured at diagnosis. A novel feature of the model is the significance of established blood-based measurements in the model, which appears to capture underlying tumor and host biology. Model performance for FLIPI24 was superior to standard clinical models for prediction of both EFS24 and OS in internal and external validation sets. Elevated FLIPI24 risk represents a patient population for further biologic studies and who should be considered for novel frontline therapies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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)0.004
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.263
Teacher spread0.243 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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