Evidence map›Paper›PMID 42115852›Full record

ArticleBMC psychiatry2026

Predicting the final response and remission of electroconvulsive therapy in treating major depressive disorder: exploring the earliest predictive factors for early improvement.

Nanxue Duan, Jie Hu, Jian Guan, Yang Ji, Wanling Huang, Rui Qian, Hao Zheng, Yuhui Wan, Kai Wang, Guixian Xiao and 2 more

Abstract read
In one paragraph

Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nanxue Duan *Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230022, China.
Jie Hu *Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Jian Guan *Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230022, China.
Yang JiDepartment of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Wanling HuangDepartment of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230022, China.
Rui QianDepartment of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Hao ZhengDepartment of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Yuhui WanCenter for Big Data and Population Health of IHM (Anhui Medical University), School of Public Health, Anhui Medical University, Hefei, 230032, China.
Kai WangDepartment of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230022, China.
Guixian XiaoDepartment of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China. ayxiaogx@fy.ahmu.edu.cn.
Dai ZhangDepartment of Radiology, the Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China. dzhang@fy.ahmu.edu.cn.
Yanghua TianDepartment of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230022, China. tianyh@ahmu.edu.cn.

Funding

National Natural Science Foundation of China 32071054the Major Program of the National Natural Science Foundation of China 82090034
6 · The paper itself

Abstract

backgroundTo date, no study has investigated the earliest predictors of early improvement during electroconvulsive therapy (ECT) in patients with major depressive disorder (MDD). The primary objective of this study was to compare the predictive ability of the improvement rate in the 17-item Hamilton Depression Rating Scale (HAMD-17) scores after each ECT session for final treatment response and remission, thereby identifying the earliest early improvement predictors for ECT efficacy in MDD.

methodsHAMD-17 scores were collected from 143 patients with MDD after each ECT session. Treatment response was defined as a ≥ 50% reduction in HAMD-17 scores from baseline, and remission was defined as a HAMD-17 score ≤ 7. Predictive factors identified as significant in univariate analyses (p < 0.05) were further evaluated using receiver operating characteristic (ROC) curve analysis. An area under the ROC curve (AUC) value > 0.8 was considered to indicate good discriminative ability. The earliest time point at which the AUC first exceeded 0.8 was determined as the earliest early improvement predictor for treatment outcomes. The optimal threshold of early improvement for predicting response and remission was determined using the Youden index.

resultsOf the 143 patients who received ECT, 76.92% achieved treatment response and 58.04% achieved remission. An improvement rate of ≥ 42.21% in HAMD-17 scores after three ECT sessions optimally predicted final response, while an improvement rate of ≥ 47.05% after four sessions optimally predicted final remission.

conclusionEarly improvement during the course of ECT has predictive value for treatment outcomes. Close monitoring of treatment efficacy in the early stages of ECT may facilitate timely adjustment of treatment strategies and improve overall outcomes.

Indexed as

Electroconvulsive TherapyMajor Depressive DisorderAdultFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisPsychiatric Status Rating ScalesRemission InductionROC CurveTreatment OutcomeEarly improvementElectroconvulsive therapyMajor depressive disorderPredictRemissionResponse

Identifiers

PMID42115852
PMCPMC13330294

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.