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Nomogram for Persistent Post-Stroke Depression and Decision Curve Analysis.

Yan Lan¹, Chensheng Pan¹, Xiuli Qiu¹

¹Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Clinical Interventions in Aging

|April 12, 2022

Summary

Persistent post-stroke depression (PSD) affects over half of patients within a year. Key predictors include admission scores on the Hamilton Depression Scale-17, direct bilirubin, free thyroxine (FT4), and activated partial thromboplastin time (APTT).

Keywords:

decision curve analysis nomogram persistent post-stroke depression

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Area of Science:

Neurology
Psychiatry
Clinical Medicine

Background:

Persistent post-stroke depression (PSD) is linked to poor patient outcomes.
Identifying predictors of PSD is crucial for timely intervention and improved prognosis.

Purpose of the Study:

To investigate predictors of persistent PSD in acute ischemic stroke patients.
To develop and validate a predictive nomogram for persistent PSD using decision curve analysis (DCA).

Main Methods:

A multicenter prospective study involving 875 acute ischemic stroke patients with 1-year follow-up.
Data collected included sociodemographics, vascular risk factors, clinical information, serum biochemical indicators, and cytokines.
Persistent depression was defined as depression present at all follow-up points since diagnosis.

Main Results:

The cumulative incidence of PSD within 1 year was 58.6% (289 patients).
Predictors of persistent PSD included admission Hamilton Depression Scale-17 (HAMD-17) score, serum direct bilirubin, free serum thyroxine (FT4) level, and activated partial thromboplastin time (APTT).
The developed nomogram showed good predictive ability (C-index: 0.655 ± 0.039) and favorable clinical utility via DCA.

Conclusions:

Persistent depression post-stroke is associated with specific clinical and biochemical markers.
The developed nomogram offers a clinically useful tool for predicting persistent PSD.
Early identification of these predictors can aid in managing depression and improving stroke recovery.