Background: Venlafaxine is an antidepressive drug with the special characteristic of inhibiting both synaptic serotonin and norepinephrine reuptake. This double action is dosage dependent, with the relatively weaker inhibition of norepinephrine becoming clinically relevant only at higher dosages. This allows treatment to be tailored towards the needs of individual patients through differential dosing. It is unknown, however, how physicians use this unique feature in prescribing venlafaxine in routine treatment.
Method: Data from a drug utilization observation (DUO) study, including 6706 patients, are used to investigate which patient and setting variables predict dosage of venlafaxine as prescribed by psychiatrists in inpatient and outpatient settings. Treatment outcome and adverse drug reactions (ADR) were analyzed for different dosage groups.
Results: Treatment setting is the most important factor in predicting high (> 75 mg/day) or low (up to 75 mg/day) dosage of venlafaxine, with inpatients receiving higher dosages. Severity of illness and a history of previous treatment with major antidepressives are also related to higher dosages. Although the total rate of ADR did not increase with increased dosage, the profile of drug reactions changed. Response to therapy was better in cases of non-chronic, major depression with no treatment history of antidepressives. Additionally, increased dosage increased the likelihood of response in outpatients. In both settings, very high dosages predicted better response to venlafaxine among severely ill patients.
Conclusion: Venlafaxine at a dosage of 75 mg/day is sufficient for the majority of cases. In extremely ill patients, higher dosages are associated with additional benefits. Therefore, a stepwise dosage regimen is suggested, with an increase of dosage to upper limits in cases of non-response before discontinuation of treatment with venlafaxine.
Key words
Depression - antidepressives - venlafaxine - dosage - drug utilization observation - treatment-resistant depression - treatment algorithm
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Prof. Dr. M. Linden
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