Engaging Patients in Clinical Trial Planning: Exploring the Concept of a Minimal Clinically Important Difference Among Patients with Obstetrical Antiphospholipid Syndrome
Notice bibliographique
Résumé
Abstract INTRODUCTION: Women with antiphospholipid syndrome are at increased risk of pregnancy loss. A large randomized controlled trial is still needed to confirm or refute the efficacy of low-molecular-weight heparin (LMWH) and aspirin (ASA) prophylaxis during pregnancy in women with antiphospholipid syndrome (APS) and past pregnancy loss. The minimal clinically important difference (MCID) is a statistical concept that quantifies the smallest amount of benefit from a treatment that a patient would identify as important, and is used to adequately power clinical trials. When planning a clinical trial, the MCID is most often determined by specialists in the field. How patients make sense of MCID as a concept, and how patients' perceptions of MCID compare to physician responses is unknown. In a challenging and high stakes situation, understanding different patients' perspectives must be considered when planning a large clinical trial. METHODS: Consecutive patients were approached from a specialty Thrombosis clinic and from the TIPPS (Thrombophilia in Pregnancy Prophylaxis Study) screening logs between January 2017 and March 2018 in Ottawa, Canada to complete a survey and interview. Patients were eligible if they met the revised Sapporo/Sydney laboratory criteria for antiphospholipid syndrome and had at least 1 late pregnancy loss or 2 early losses (<10 weeks gestation). A similar survey was distributed to 350 specialists identified through 5 professional organizations between May-Sept 2016. Both patient and physician groups had access to an introductory video. The mean MCID between the patient group and physician group was compared using an independent t-test, with p<0.05 being significant. Patient interview data were collected and analysed iteratively, in keeping with constructivist grounded theory methodology. RESULTS: There were 22 (33%) patients who completed surveys and 10 patients who completed semi-structured interviews. Among the physician group, 72 (20.6%) responded to the survey, including obstetricians (34.7%), hematologists (26.4%), general internists (18%), maternal fetal medicine specialists (6.9%) and other specialties (12.6%). Assuming a live birth rate of 60% in the ASA alone arm, the mean MCID answered by physicians was 11.6% (range 5-25%, SD 5.2), compared to the mean MCID answered by patients was 10.0% (range 5-20%, SD 6.4) (p=0.28). The mean number needed to treat (NNT) answered by both physicians and patients was 16. In the majority of cases from both groups, the related concepts of MCID and NNT did not match statistically (i.e. an MCID of 15% is a NNT of 7). An overarching theme from the patient survey comments and interviews was that statistics were somewhat irrelevant. The majority of women thought that "any increase in a chance of live birth" was worth it. However, there was variation in patient responses where some women would only accept a certain success rate of LMWH to justify its use because of drug cost or side effects. Some women acknowledged that they would not use LMWH themselves, but still advocated for other patients to have the option of LMWH use for a chance of a live birth. CONCLUSION: The mean MCID and NNT were similar between patient and physician groups. The low mean MCID and NNT values likely reflect the importance of the outcome studied (avoiding pregnancy loss) and the perceived safety of the proposed interventions of LMWH and/or ASA, which is supported by qualitative data from patient interviews. While the MCID and NNT responses were similar among physicians and patients, the understanding of statistics was somewhat limited in both groups. By better understanding patients' rationale and beliefs about LMWH use, we can better engage them in clinical trial planning and patient care and ensure that we are performing research that is relevant to patients. Disclosures Skeith: Leo Pharma: Honoraria; CSL Behring: Research Funding. Rodger:Biomerieux: Research Funding.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,171 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».