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Estimating Changes in Weight and Metabolic Parameters Before and After Treatment With Cariprazine: A Retrospective Study of Electronic Health Records

2023· article· en· W4389210383 on OpenAlexaff
Prakash S. Masand, Roger S. McIntyre, Andrew J. Cutler, Michael L. Ganz, Andrea L. Lorden, Kiren Patel, Ken Kramer, Amanda Harrington, Huy‐Binh Nguyen

Bibliographic record

VenueClinical Therapeutics · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
FundersAbbVie
KeywordsMedicineDiscontinuationInternal medicineRetrospective cohort studyWeight changeWeight lossWeight gainBody weightObesity

Abstract

fetched live from OpenAlex

PURPOSE: Weight gain and associated negative cardiometabolic effects can occur as a result of mental illness or treatment with second-generation antipsychotics (SGAs), leading to increased rates of morbidity and mortality. In this analysis, we evaluated the effect of the SGA cariprazine on weight and metabolic parameters in a real-world, retrospective, observational dataset. METHODS: Electronic health records from the Optum Humedica database (October 1, 2014-December 31, 2020) were analyzed during the 12-month period before starting cariprazine (baseline) and for up to 12 months following cariprazine initiation; approved and off-label indications were included. Body weight trajectories were estimated in the overall patient cohort and at 3-, 6-, and 12-month timepoints (primary objective). Changes in hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides were also evaluated (secondary objectives). Percentages of patients with clinically relevant shifts in body weight, total cholesterol, and fasting triglycerides were also determined. Discontinuation rates for metabolic regulating medications were calculated. Average predicted values were estimated by linear mixed-effects regression models. FINDINGS: A total of 2,301 patients were included; average duration of follow-up was 133.7 days. Average predicted weight change for patients during the cariprazine overall follow-up period was +2.4 kg, with predicted weight changes of +0.8 kg (n = 811), +1.1 kg (n = 350), and +1.4 kg (n = 107) at months 3, 6, and 12, respectively. Overall, the majority of patients did not experience clinically significant (≥7%) weight gain (82.8%) or loss (90.5%) after starting cariprazine. Average predicted HbA1c levels (n = 189) increased during baseline (0.15%/year) and decreased during cariprazine treatment (-0.2%/year). Average predicted triglyceride levels (n = 257) increased during baseline (15.0 mg/dL/year) and decreased during cariprazine treatment (-0.7 mg/dL/year). Predicted LDL (n = 247) and HDL (n = 255) values decreased during baseline (-7.3 and -1.1 mg/dL/year, respectively); during cariprazine treatment, LDL increased by 5.6 mg/dL/year and HDL decreased by -0.6 mg/dL/year. During follow-up, most patients did not shift from normal/borderline to high total cholesterol (<240 to ≥240 mg/dL; 522 [90.2%]) or fasting triglyceride (<200 to ≥200 mg/dL; 143 [88.8%] patients) levels; shifts from high to normal/borderline levels occurred in 44 (61.1%) patients for total cholesterol and 38 (57.6%) patients for fasting triglycerides. After starting cariprazine, the discontinuation rate per 100 patient-years was 60.4 for antihyperglycemic medication and 87.4 for hyperlipidemia medication. IMPLICATIONS: These real-world results support short-term clinical trial findings describing a neutral weight and metabolic profile associated with cariprazine treatment and they expand the dataset to include long-term follow-up.

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 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 score0.185
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.065
GPT teacher head0.406
Teacher spread0.341 · 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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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