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Enregistrement W4211072399 · doi:10.1002/14651858.cd012501.pub2

Cerivastatin for lowering lipids

2020· review· en· W4211072399 sur OpenAlexaff
Stephen P Adams, Nicholas Tiellet, Nima Alaeiilkhchi, James M Wright

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueLipoproteins and Cardiovascular Health
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésCerivastatinRosuvastatinAtorvastatinMedicineStatinFluvastatinPlaceboCholesterolPharmacologyAdverse effectInternal medicinePravastatinClinical trialSimvastatin

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Cerivastatin was the most potent statin until it was withdrawn from the market due to a number of fatalities due to rhabdomyolysis, however, the dose-related magnitude of effect of cerivastatin on blood lipids is not known. OBJECTIVES: Primary objective To quantify the effects of various doses of cerivastatin on the surrogate markers: LDL cholesterol, total cholesterol, HDL cholesterol and triglycerides in children and adults with and without cardiovascular disease. The aim of this review is to examine the pharmacology of cerivastatin by characterizing the dose-related effect and variability of the effect of cerivastatin on surrogate markers. Secondary objectives To quantify the effect of various doses of cerivastatin compared to placebo on withdrawals due to adverse effects. To compare the relative potency of cerivastatin with respect to fluvastatin, atorvastatin and rosuvastatin for LDL cholesterol, total cholesterol, HDL cholesterol and triglycerides. SEARCH METHODS: The Cochrane Hypertension Information Specialist searched the following databases for RCTs up to March 2019: CENTRAL (2019, Issue 3), Ovid MEDLINE, Ovid Embase, the WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov.We also searched the European Patent Office, FDA.gov, and ProQuest Dissertations & Theses, and contacted authors of relevant papers regarding further published and unpublished work. The searches had no language restrictions. SELECTION CRITERIA: RCTs and controlled before-and-after studies evaluating the dose response of different fixed doses of cerivastatin on blood lipids over a duration of three to 12 weeks in participants of any age with and without cardiovascular disease. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed eligibility criteria for trials to be included and extracted data. We entered data from RCTs and controlled before-and-after studies into Review Manager 5 as continuous and generic inverse variance data respectively. We collected information on withdrawals due to adverse effects from the RCTs. We assessed all trials using the 'Risk of bias' tool under the categories of sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and other potential biases. MAIN RESULTS: Fifty trials (19 RCTs and 31 before-and-after studies) evaluated the dose-related efficacy of cerivastatin in 12,877 participants who had their LDL cholesterol measured. The participants were of any age with and without cardiovascular disease and the trials studied cerivastatin effects within a treatment period of three to 12 weeks. Cerivastatin 0.025 mg/day to 0.8 mg/day caused LDL cholesterol decreases of 11.0% to 40.8%, total cholesterol decreases of 8.0% to 28.8% and triglyceride decreases of 9.0% to 21.4%. We judged the certainty of evidence for these effects to be high. Log dose-response data over doses of 2.5 mg to 80 mg revealed strong linear dose-related effects on LDL cholesterol, total cholesterol and triglycerides. When compared to fluvastatin, atorvastatin and rosuvastatin, cerivastatin was about 250-fold more potent than fluvastatin, 20-fold more potent than atorvastatin and 5.5-fold more potent than rosuvastatin at reducing LDL cholesterol; 233-fold more potent than fluvastatin, 18-fold more potent than atorvastatin and six-fold more potent than rosuvastatin at reducing total cholesterol; and 125-fold more potent than fluvastatin, 11-fold more potent than atorvastatin and 13-fold more potent than rosuvastatin at reducing triglycerides. There was no dose-related effect of cerivastatin on HDL cholesterol, but overall cerivastatin increased HDL cholesterol by 5%. There was a high risk of bias for the outcome withdrawals due to adverse effects, but a low risk of bias for the lipid measurements. Withdrawals due to adverse effects were not different between cerivastatin and placebo in 11 of 19 of these short-term trials (risk ratio 1.09, 95% confidence interval 0.68 to 1.74). AUTHORS' CONCLUSIONS: The LDL cholesterol, total cholesterol, and triglyceride lowering effect of cerivastatin was linearly dependent on dose. Cerivastatin log dose-response data were linear over the commonly prescribed dose range. Based on an informal comparison with fluvastatin, atorvastatin and rosuvastatin, cerivastatin was about 250-fold more potent than fluvastatin, 20-fold more potent than atorvastatin and 5.5-fold more potent than rosuvastatin in reducing LDL cholesterol, and 233-fold greater potency than fluvastatin, 18-fold greater potency than atorvastatin and six-fold greater potency than rosuvastatin at reducing total cholesterol. This review did not provide a good estimate of the incidence of harms associated with cerivastatin because of the short duration of the trials and the lack of reporting of adverse effects in 42% of the RCTs.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0040,003
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

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.

Tête enseignante Opus0,130
Tête enseignante GPT0,398
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations20
Publié2020
Routes d'admission1
Résumé présentoui

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