Dataset Information

Serotonin Syndrome Triggered by Increasing the Dose of Quetiapine.

ABSTRACT:

Case presentation

An 85-year-old woman with a history of depression treated with polypharmacy including selective serotonin reuptake inhibitor presented to the emergency department with head, and upper and lower limb tremors four hours after increasing the dose of quetiapine from 12.5 milligrams (mg) per day to 25 mg/day. She was diagnosed with serotonin syndrome (SS), and all medications except clotiazepam were discontinued. The symptoms subsided within 48 hours.

Discussion

The use of atypical antipsychotics alone seldom increases the risk of SS. However, combining atypical antipsychotics with serotonergic agents increases the risk of SS because the activity of serotonin receptor subtype 1A is relatively enhanced. This report suggests that physicians should be aware that even a small increase in quetiapine could pose a risk of developing SS.

SUBMITTER: Miyamatsu Y

PROVIDER: S-EPMC8373192 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Serotonin Syndrome Triggered by Increasing the Dose of Quetiapine.

Miyamatsu Yayoi Y Tanizaki Ryutaro R

Clinical practice and cases in emergency medicine 20210801 3

<h4>Case presentation</h4>An 85-year-old woman with a history of depression treated with polypharmacy including selective serotonin reuptake inhibitor presented to the emergency department with head, and upper and lower limb tremors four hours after increasing the dose of quetiapine from 12.5 milligrams (mg) per day to 25 mg/day. She was diagnosed with serotonin syndrome (SS), and all medications except clotiazepam were discontinued. The symptoms subsided within 48 hours.<h4>Discussion</h4>The u ...[more]

PMID: 34437050

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Project description:ImportanceQuetiapine has been associated with increased risk of type 2 diabetes when used in medium or high doses for the treatment of severe mental disorders. It is not known whether low doses, commonly used off-label for sedative-hypnotic purposes, are also associated with increased risk of type 2 diabetes.ObjectiveTo investigate whether there is an association between prescription of low-dose quetiapine and the risk of type 2 diabetes.Design, setting, and participantsThis cohort study examined nationwide Danish health registers for data regarding new users of quetiapine (n = 185 938) or selective serotonin reuptake inhibitors (SSRIs) (n = 1 031 920) who were aged 18 years or older between January 1, 1998, and December 31, 2018. Individuals with schizophrenia or bipolar disorder were excluded. Quetiapine-initiators were matched 1:1 with initiators of SSRIs, using a high-dimensional propensity score (hdPS). Maximum follow-up was 5 years. Association with cumulative dose was investigated, using a case-control approach nested among quetiapine users. Data analysis was performed from May to September 2020.ExposuresDispensing of quetiapine or SSRIs. Quetiapine prescriptions were limited to tablet strengths of 25 mg and 50 mg to focus on low-dose use.Main outcomes and measuresIncident type 2 diabetes was defined as first filling of an antidiabetic medication, first register diagnosis of type 2 diabetes or first hemoglobin A1C measurement greater than or equal to 6.4% (≥48 mmol/mol). Incidence rates (IRs), incidence rate ratios (IRRs), and number-needed-to-harm (NNH) were calculated for full and matched cohorts using as-treated and intention-to-treat approaches. Odds ratios (ORs) were calculated for the association with cumulative quetiapine dose.ResultsAltogether, 896 285 patients were included in the full cohort; 538 164 (60%) were female and the median (interquartile range) age was 47 (33-67) years. There were 57 701 low-dose quetiapine initiators and 838 584 SSRI initiators. The matched cohort consisted of 54 616 pairs. In as-treated analyses, the incidence of type 2 diabetes during treatment with low-dose quetiapine (425 cases) was 9.59 cases/1000 person-years (PY) (95% CI, 8.72-10.5/1000 PY), which was slightly higher than for SSRI users (8462 cases; IR, 8.13/1000 PY; 95% CI, 7.96-8.30/1000 PY), resulting in a significant IRR of 1.18 (95% CI, 1.07-1.30) and NNH of 684 (95% CI, 418-1873). However, the between-group difference was nonsignificant in the hdPS-matched cohort (IR, 9.49 vs IR, 9.58; IRR, 0.99; 95% CI, 0.87-1.13). The case-control analysis found no dose-response association of low-dose quetiapine with diabetes (OR for doubling of the cumulative dose: 1.02; 95% CI, 0.95-1.09; P = .54), but in sensitivity analyses higher daily doses were associated with diabetes (all tablet strengths: OR, 1.08; 95% CI, 1.03-1.13).Conclusions and relevanceIn this cohort study, use of low-dose quetiapine was not associated with excess risk of type 2 diabetes in comparison with SSRIs.

Project description:BackgroundBeing one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens.MethodsThe study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model.ResultsFour predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively.ConclusionsIn this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.

Dataset Information

Serotonin Syndrome Triggered by Increasing the Dose of Quetiapine.

Case presentation

Discussion

Publications

Serotonin Syndrome Triggered by Increasing the Dose of Quetiapine.

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