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Record W4283753816 · doi:10.30978/unj2022-1-24

Assessment and prediction of affective disorders in patients after cerebral stroke using modern measurement scales

2022· article· en· W4283753816 on OpenAlexaboutno aff
Yuriy Flomin, S. O. Malyarov, V. G. Guryanov, L. I. Sokolova

Bibliographic record

VenueUkrainian Neurological Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentConfidence intervalStroke (engine)Modified Rankin ScaleLogistic regressionHospital Anxiety and Depression ScaleOdds ratioDepression (economics)AnxietyIntracerebral hemorrhageInternal medicinePhysical therapyIschemic strokeCognitive impairmentPsychiatryDiseaseSubarachnoid hemorrhageIschemia

Abstract

fetched live from OpenAlex

Objective — to analyze the results of scale‑based assessments of post‑stroke depression (PSD) and post‑stroke anxiety disorders (PSAD) in different phases of cerebral stroke (CS) as well as to determine independent predictors of PSD at discharge from the Stroke Center (StC), and to evaluate the characteristics of the respective predictive models. Methods and subjects. Two hundred patients, including 92 (46.0 %) women and 108 (54.0 %) men with the median age of 65.6 years (IQR 58.2 — 75.1) were enrolled. The health status of all patients was assessed after hospitalization using the National Institutes of Health Stroke Scale (NIHSS), Barthel Index, Modified Rankin Scale, Mini‑Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). 172 (86.0 %) patients were diagnosed with ischemic stroke (IS), 28 (14.0 %) — intracerebral hemorrhage. Among patients with IS, 58 (33.7 %) had an atherothrombotic subtype, 85 (49.4 %) had a cardioembolic subtype, 16 (9.3 %) had a lacunar subtype, 13 (7.6 %) had another or unknown subtype. The PSD and PSAD were assessed using the Hospital Anxiety and Depression Scale (HADS) and the Patient Health Questionnaire (PHQ‑9) before discharge from the Stroke Center. The impact of factors was assessed by odds ratio (OR) and its 95 % confidence interval (95 % CI). The method of constructing and analyzing logistic regression models was used to determine independent predictors of PSD at discharge. Results. The baseline NIHSS score ranged from 1 to 29. The mRS scores upon admission were from 1 to 5, and the BI scores from 0 to 100. Forty‑one (20.5 %) patients were admitted in the hyperacute period, 55 (27.5 %) in the acute period, 68 (34.0 %) in the early subacute period, 13 (6.5 %) in the late subacute period, and 23 (11.5 %) in the chronic phase of stroke. The HADS‑D score ranged from 0 to 18, and the HADS‑A score from 0 to 15. PHQ‑9 scores ranged from 0 to 21. Based on the HADS score, 19 (9.5 %) of the patients had clinically significant PSD and 16 (8.0 %) of the participants had clinically significant PSAD. According to the total HADS score, 22 (11.0 %) of the patients had clinically significant affective disorders. With PHQ‑9 showed that clinically significant PSD was detected in 45 (22.5 %) patients. The HADS and PHQ‑9 scores had a strong positive significant correlation, but neither of them correlated with the age or sex of the patients, the subtype or severity of CS. However, univariate analysis showed that the risk of clinically significant PSD at discharge (according to HADS‑D) was significantly directly related to age and atrial fibrillation in addition to inverse relationship with the BI, MMSE and MoCA scores, LA subtype of IS and ICH. The risk of moderate to severe PSD (according to PHQ‑9) had a statistically significant direct corelation with the initial NIHSS score, as well as an inverse corelation with the baseline BI, MMSE, and MoCA scores. In multivariate analysis, 4 features were independently associated with PSD (HADS‑D > 10) at discharge: initial MMSE score (OR 0.93; 95 % CI 0.88 — 0.98, on average, for each additional point, p = 0.006), arterial hypertension (OR 8.5; 95 % CI 0.9 — 76.3; p = 0.057) or obesity (OR 0.23; 95 % CI 0.05 — 1.14; p = 0.072) as well as hospitalization after 30 days from CS onset. The predictive model based on these 4 variables had excellent sensitivity (94.7 %) and satisfactory specificity (73.3 %) and could assess the risk of developing PSD with good accuracy (AUC = 0.847). Furthermore, three factors were independent predictors of moderate or severe PSD (PHQ‑9 > 9) at discharge: age (OR 1.04; 95 % CI 1.00 — 1.08, on average, for each additional year, p = 0.028), the baseline MoCA score (OR 0.94; 95 % CI 0.91 — 0.98, on average, with an increase in the score for each additional point, p = 0.005) and UN subtype of IS. The prognostic model based on the latter 3 variables had satisfactory sensitivity (65.1 %) and specificity (75.5 %), but good accuracy of PSD prediction (AUC = 0.735). Conclusions. The HADS and PHQ‑9 scores in CS patients varied widely, and indicated high prevalence of clinically significant PSD and PSAD. HADS and PHQ‑9 scores correlated with each other, but not with age, sex, subtype, or severity of stroke. Elderly patients with significant cognitive impairment on admission were at a higher risk of affective disorders. The prognostic models allow accurate PSD prediction, which can contribute to the timely detection and initiation of PSD treatment in patients at risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.255
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2022
Admission routes1
Has abstractyes

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