Which antidepressants work most effectively, and which barely beat placebo?
The largest meta-analysis (Cipriani et al., 2018) compared 21 antidepressants in 116,477 patients, revealing striking differences in efficacy and tolerability.
Here’s how this data can transform your prescribing practice 🧵👇
Most ‘Effective’ Antidepressants (Head-to-Head)
In the largest network meta-analysis to date, the following antidepressants consistently outperformed others in head-to-head comparisons for efficacy (odds ratio range: 1.19–1.96):
Caution:
● Least efficacy— Fluoxetine, Fluvoxamine, Reboxetine, Trazodone.
● High dropout— Amitriptyline, Clomipramine, Duloxetine, Fluvoxamine, Reboxetine, Trazodone, Venlafaxine.
Why This Study Stands Out
This network meta-analysis was groundbreaking, including:
● Data from 522 trials (published + unpublished).
● Direct and indirect comparisons of 21 drugs.
Strengths: Largest antidepressant dataset, reduced publication bias.
Limitations: Short trial durations (<12 weeks) and exclusion of treatment-resistant/psychotic depression.
Practical Prescribing Tips
Use these insights to refine your practice:
● Prioritise efficacy leaders when maximising response.
● Choose tolerability leaders for side-effect-sensitive patients.
● Use shared decision-making to align choice with patient needs.
Personalised Treatment Improves Outcomes
STAR*D showed that after first-line antidepressant treatment:
●~47% respond (~1 in 2)
●~27–37% remit (~1 in 3, scale-dependent)
Balancing efficacy, tolerability, and patient preferences is key to improving outcomes.
Learn more about antidepressant medications and delve deep into their intricate mechanisms of action below 👇
Refine Your Prescribing Expertise
Our course, ‘Antidepressant Mechanisms & Management: Advanced Training for Psychiatrists’, equips you with the tools to refine your decision-making and improve patient outcomes.
Females present with a specific neurobehavioural profile that may contribute to an underdiagnosis and subsequent under-treatment.
Here’s what clinicians need to assess and look out for
🧵👇
1/ Under-recognised, different profile.
Girls/women with ADHD often present with internalising symptoms (low mood, anxiety, emotional lability), so they’re mislabelled with mood/personality disorders and referred late.
💡 Psych Scene Tip:
If chronic anxiety/low mood rides alongside lifelong disorganisation, time-blindness, and procrastination across settings (since <12), screen for ADHD before defaulting to mood/BPD labels.
2/ Masking + compensation delay diagnosis.
Compliance, resilience, perfectionism, and high structure (supportive family/school) can temporarily “hide” impairment, until demands rise.
Expect later recognition at transitions (primary→secondary school, university, new job, parenthood). 
76% of ADHD patients achieved >50% symptom reduction when neurofeedback protocols were tailored to individual EEG profiles,
Using QEEG, researchers matched each patient to a protocol based on their brainwave patterns — producing significant improvements in inattention and hyperactivity.
Here’s what you need to know about EEG subtypes in ADHD, and how they can guide treatment when standard approaches fail.
1/15 🧵
QEEG can reveal brain-activity heterogeneity in ADHD that checklists miss.
Important: major guidelines don’t recommend QEEG to diagnose ADHD. Think of it as a potential stratification aid when progress stalls.
2/15 🧵
What QEEG does: records theta/alpha/beta rhythms and compares them to normative databases.
Deviations create a profile that may help plan treatment.