Clinical Features for ECT Response Prediction in Adolescent MDD Patients

503 adolescent patients with major depressive disorder were retrospectively enrolled to identify baseline clinical factors associated with poor response to electroconvulsive therapy. The dataset was created by Bingyang Zha and last updated on May 7, 2026. It likely contains clinical variables used to build a machine learning model, including the neutrophil-to-platelet ratio and Hamilton Depression Scale scores.

Use Cases

• Predict poor response to electroconvulsive therapy based on baseline neutrophil-to-platelet ratio and HAMD score.
• Analyze the relationship between the number of ECT sessions completed and treatment response groups.
• Identify high-risk adolescent MDD patients for targeted clinical management using routine admission data.

Strengths

• Dataset is based on a cohort of 503 adolescent MDD patients.
• The derived predictive model using two features achieved an AUC of 0.731 on the testing set.
• Data is shared under a CC-BY-4.0 license, permitting reuse with attribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 9.3 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Retrospective clinical study.

Freshness

Last updated 2026-05-07 05:43:59; freshness should be verified.