Deans' stroke musings: Early prediction of post-stroke depression using polysomnography parameters: a prospective cohort study

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Wednesday, September 9, 2026

Early prediction of post-stroke depression using polysomnography parameters: a prospective cohort study

Competent persons would research preventing depression by have EXACT 100% RECOVERY PROTOCOLS! But there seems to be no functioning brains anywhere in stroke! Not solving stroke is the absolute stupidity out there! Your comeuppance/screaming when you are the 1 in 4 per WHO that has a stroke will be soul satisfying.

Early prediction of post-stroke depression using polysomnography parameters: a prospective cohort study

L
Lan Chen 1
B
Bin Wang 1
D
Dan Kuang 1
Lei Chen 2*

1. Department of Child Psychiatry, Shaoxing Seventh People’s Hospital, Affiliated Mental Health Center, Shaoxing University, Shaoxing, Zhejiang, China
2. Department of Neurology, Shaoxing People’s Hospital, The First Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, China

Abstract

Background:

Post-stroke depression (PSD) affects approximately one-third of stroke survivors and significantly impacts functional recovery. This study aimed to develop and validate a prediction model for PSD using polysomnography (PSG) parameters combined with clinical characteristics.

Methods:

We conducted a prospective cohort study enrolling 437 acute ischemic stroke patients who underwent PSG assessment within 7 days of stroke onset between January 2022 and December 2024. The primary outcome was PSD at 3-month follow-up, defined as Patient Health Questionnaire-9 (PHQ-9) score ≥10. Multivariate logistic regression identified independent predictors, and machine learning algorithms were compared for model performance.

Results:

Among 390 patients completing follow-up, 139 (35.6%) developed PSD. Independent predictors included sleep latency (OR = 2.14, 95% CI: 1.68–2.73), arousal index (OR = 2.13, 95% CI: 1.65–2.94), sleep efficiency (OR = 0.74, 95% CI: 0.55–0.96), heart rate variability RMSSD (OR = 0.77, 95% CI: 0.61–0.96), NIHSS score (OR = 1.35, 95% CI: 1.04–1.76), and prior stroke history (OR = 1.35, 95% CI: 1.09–1.71). The gradient boosting model achieved the highest discriminative performance (AUC = 0.763; bootstrap-validated AUC = 0.738, 95% CI: 0.682–0.794). Risk stratification demonstrated a five-fold gradient in PSD rates across probability categories (14.3 to 70.3%).

Conclusion:

PSG parameters, particularly sleep efficiency, sleep latency, and arousal index, are significant independent predictors of PSD. Integrating objective sleep assessment into early stroke management may facilitate identification of high-risk patients for targeted preventive interventions.