Cardiovascular Disease Risk in SMI Across Various Settings in a Semirural Area – a Study During COVID-19 Pandemic
Notice bibliographique
Résumé
Aims The risk for cardiovascular-related death is predicted to be higher in individuals with Serious Mental Illness (SMI) due to increased prevalence of common cardiac risk factors like smoking, physical inactivity, poor diet, substance use and hyperlipidemia among them. Methods The aim of this retrospective study was to evaluate the physical health of patients with SMI in various settings- acute inpatient, tertiary care hospital and community. We estimated the cardiovascular disease risk of schizophrenia patients with the aid of Framingham Risk Score (FRS) assessment tool, which can quantitatively predict both the heart age and 10-year CVD Risk percentage of patients aged ≥ 30 years. The clozapine to norclozapine ratio was compared with triglyceride levels, body weight, BMI, and fasting blood glucose in patients after treatment with clozapine. Southlake Regional Health Center's practice was compared with the national standards set by Diabetes Canada 2018 guidelines by conducting a clinical audit. 68 non-diabetic, patients aged ≥ 30 years with all the risk factor records for FRS assessment were selected from a cohort of 183 patients registered in the schizophrenia clinic of Southlake Regional Health Centre. The data were collected from patient records from the 75 patients registered with Assertive Community Treatment Team in Georgina, Ontario. The sample size of the study on inpatients was 49 participants from the acute psychiatry ward consisting of 28 females and 21 males during the month of November 2021. Results Males, on average, were found to have an intermediate 10-year CVD risk (~11.2%; FRS total points: 11.27) in comparison to females who, on average, had a low 10-year CVD risk (~7.3%; FRS total points: 11.19). 26% of the patients using FRS were calculated to be at high risk and 28% with intermediate risk of developing a CVD. The average heart age of the sample patients was 60 years, which was 9 years higher than the total average age (51 years). The investigated biomarkers of Hemoglobin A1C, triglycerides, and glucose serum concentration were examined graphically, separated into categories of the ratio measurements of 0–2, 2–3, and 3+. For all biomarkers, lower values were more desirable. Triglycerides were the lowest in the 3+ ratio category. Hemoglobin A1C and glucose serum concentration were lowest in the 0–2 ratio category.100% of patients with diabetes had their blood sugar levels measured and 66.67% were referred to an endocrinologist. In patients without diabetes, 91.30% had their blood sugar levels measured, 39.13% had their HbA1C levels measured, and 6.52% had neither their HbA1C, nor their blood sugar levels measured. Conclusion Cardiovascular complication can be one of the leading causes of death in the next 10 years among schizophrenia patients due to age, poor lifestyle choices, and current estimations via the FRS assessment tool. Further studies need to be conducted with a larger sample size and more recent data to examine any adverse lifestyle changes in schizophrenia patients during the pandemic, which could have negatively influenced their cardiovascular health. It is recommended that doctors weigh the risks vs benefits of prescribing clozapine to patients with high triglyceride levels.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».