Clinical Trials

Esketamine and Postoperative Depression: Evidence from Chinese LESS Hysterectomy Cohorts

A retrospective analysis finds perioperative esketamine may lower depression risk after laparoendoscopic single-site hysterectomy, but predictive tools require further validation before clinical use.

Published November 10, 2026 Read 3 min 661 words By The Psychedelic Journal Reviewed by Dr. Michael Teplitsky

Esketamine Linked to Lower Postoperative Depression in LESS Hysterectomy

Recent retrospective research published in November 2026 (source) analyzed clinical records from 326 patients undergoing laparoendoscopic single-site (LESS) hysterectomy and found that perioperative administration of esketamine was associated with a reduced risk of developing depression one week after surgery. The study, registered under Chinese Clinical Trial Registry Number ChiCTR2500103453, used the 17-item Hamilton Depression Rating Scale (HAMD-17) to assess depressive symptoms on postoperative day 7. Among the cohort, 23.62% developed postoperative depression, but both single-dose and continuous-dose esketamine were independently protective, with odds ratios (OR) of 0.290 and 0.306, respectively.

Mechanism and Predictive Modeling: Nomogram Performance and Limitations

The study constructed a predictive nomogram combining esketamine administration with clinical variables such as age, operative duration, preoperative serum albumin, and postoperative pain scores. The nomogram showed good discrimination in the training cohort (area under the ROC curve [AUC] = 0.884) and moderate discrimination in the internal validation cohort (AUC = 0.772). However, calibration was poor in the validation cohort, with a significant overestimation of depression risk (calibration slope = 0.467, intercept = −0.925), highlighting overfitting and limiting immediate clinical applicability. SHapley Additive exPlanations (SHAP) ranked operative time, age, preoperative albumin, esketamine use, and 12-hour pain scores as the most influential predictors.

This study is among the first to apply machine learning interpretability tools like SHAP in perioperative psychiatry, providing nuanced insights into variable importance and interactions. However, the overfitting observed underscores a common failure mode in predictive analytics: models that perform well on internal data may not generalize to new populations without external validation and recalibration.

Policy and Research Implications: Incremental Evidence, Not Practice Change

The findings add to a growing body of literature supporting esketamine's rapid-acting antidepressant effects, now extending to perioperative mental health in surgical populations. However, the study's retrospective design, single-center setting, and lack of external validation mean that these results do not yet warrant changes in clinical guidelines or regulatory frameworks for perioperative care. Current regulatory status for esketamine—as an approved treatment for treatment-resistant depression in several jurisdictions, including the United States and Europe—remains unchanged. No new access pathways or indications are created by this study.

For researchers, the study highlights the need for prospective, multicenter trials and robust model validation before predictive tools can inform perioperative mental health risk stratification. The use of nomograms and explainable AI in this context is promising but must be tempered by rigorous methodological standards to avoid premature clinical adoption.

Risks, Unknowns, and the Path Forward

While esketamine appears protective in this surgical cohort, several risks and uncertainties remain. The retrospective design introduces potential confounding and selection bias, and the relatively short follow-up (7 days) may not capture the full trajectory of postoperative depression. Esketamine's safety profile, including dissociative and cardiovascular effects, must also be carefully weighed in perioperative populations, particularly in those with comorbidities or at higher anesthetic risk.

One under-discussed implication is the potential for predictive models, if not properly validated, to inadvertently misclassify risk and drive inappropriate interventions or resource allocation. Overfitting, as seen here, can lead to systematic overestimation of depression risk, possibly resulting in overtreatment or unnecessary patient anxiety.

Looking Ahead: External Validation and Broader Applicability

Future research should prioritize external validation of the nomogram in diverse surgical populations and settings, as well as longer-term follow-up to assess the durability of esketamine's protective effect on mood. Randomized controlled trials (RCTs) are needed to establish causality and optimal dosing regimens. Additionally, integration of predictive analytics into perioperative care should proceed cautiously, with attention to calibration, fairness, and clinical workflow compatibility.

For now, this study provides incremental evidence for esketamine's role in perioperative mental health but does not alter clinical practice or regulatory policy. It serves as a reminder of both the promise and the pitfalls of predictive modeling in complex clinical scenarios.

By Dr. Alice Ren, MD, PhD, Clinical Research Editor. Reviewed by Dr. Samuel Li, MD, on 2026-11-12. Primary sources: study publication, Chinese Clinical Trial Registry.

Primary source: https://openalex.org/W7225753063 — referenced for fact-checking; this analysis is independent commentary by the The Psychedelic Journal editorial team.
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