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PS1244 REAL‐WORLD HEALTHCARE RESOURCE UTILIZATION (HRU) OF PATIENTS DIAGNOSED WITH CLASSICAL HODGKIN LYMPHOMA (CHL) TREATED WITH ANTI‐PD1 CHECKPOINT INHIBITORS IN THE UNITED STATES (US)

2019· article· en· W2949315927 on OpenAlexaff
François Laliberté, Monika Raut, M.S. Duh, Xiaoqin Yang, Guillaume Germain, Saugata Sen, Sean D. MacKnight, Kaushal Desai, Philippe Armand

Bibliographic record

VenueHemaSphere · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicinePembrolizumabNivolumabPediatricsInternal medicineRetrospective cohort studyCancerImmunotherapy

Abstract

fetched live from OpenAlex

Background: cHL patients with relapsed/refractory (RR) disease who relapse after or are ineligible for autologous stem cell transplantation have a poor prognosis. Recently, the anti‐PD1 monoclonal antibodies nivolumab and pembrolizumab were approved by the FDA (May 2016 and March 2017, respectively) as treatment options for RR cHL patients. Aims: This study aims to describe real‐world patient characteristics and HRU (hospitalizations and outpatient [OP] visits) among patients with RR cHL receiving pembrolizumab or nivolumab in the US. Methods: A retrospective database analysis was conducted using Symphony Health's Patient Integrated Dataverse ® (07/2014–06/2018). The date of the first dispensing or administration of pembrolizumab or nivolumab was termed the index date. Patients with ≥12 months of clinical activity prior to the index date, ≥1 hospitalization or ≥2 OP encounters with an ICD‐9/10‐CM diagnosis of cHL prior to the index date, no diagnosis of nodular lymphocyte‐predominant HL, and ≥18 years of age were included. Baseline patient characteristics were assessed in the 12 months prior to the index date. HRU was evaluated over the entire follow‐up period, from the index date to the end of clinical activity or data availability. Crude rates of hospitalizations and OP visits were calculated as number of events divided by person‐time of observation, expressed as rate per person per year (PPPY), to account for varying durations of observation across patients. Mean and median hospital length of stay (LOS) were reported. Results: Among cHL patients, 92 received pembrolizumab and 225 received nivolumab. The mean age was 59 and 53 years among those treated with pembrolizumab and nivolumab, of whom 40% and 44% were female, respectively. Corresponding median (IQR) follow‐up periods were 214 (92–325) and 249 (126–443) days. Mean baseline Quan‐Charlson comorbidity index score for pembrolizumab and nivolumab patients was 4.9 and 4.0; 18% and 14% had depressive disorders, and 16% and 8% had substance‐related and addictive disorders, respectively. Of pembrolizumab patients, 7% had received nivolumab and 26% brentuximab vedotin (BV). Of nivolumab patients, none had received pembrolizumab, 40% received BV, and 2% received ibrutinib. Pembrolizumab and nivolumab patients had an average of 1.5 and 1.4 all‐cause hospitalizations during the baseline period, respectively, while the corresponding rate of all‐cause hospitalizations during follow‐up was 0.9 and 1.3 PPPY with an associated mean [median] LOS of 3.1 [1.5] and 4.2 [2] days ( Figure ). The rate of all‐cause OP visits during follow‐up was 36.4 and 35.5 PPPY for pembrolizumab and nivolumab patients, respectively. The rate of cHL‐related hospitalizations during follow‐up was 0.1 PPPY for pembrolizumab patients, with a mean [median] LOS of 4.7 [1] days, and 0.4 PPPY for nivolumab patients, with a mean [median] LOS of 7.4 [4] days. Summary/Conclusion: This real‐world descriptive study attempts to provide an early assessment of nivolumab and pembrolizumab user profiles and resource utilization outcomes since their market approval in the US. cHL patients treated with pembrolizumab are found to be older at treatment initiation, with greater comorbidity burden and baseline hospitalization rates than the nivolumab group. image

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.257
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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