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Record W4399801613 · doi:10.1101/2024.06.18.24309137

Disruptions in Sleep Health and Independent Associations with Psychological Distress in Close Family Members of Cardiac Arrest Survivors: A Prospective Study

2024· preprint· en· W4399801613 on OpenAlexaff
Isabella M Tincher, Danielle A. Rojas, Sabine Abukhadra, Christine E. DeForge, Mina Yuan, S. Justin Thomas, Kristin Flanary, Daichi Shimbo, Nour Makarem, Bernard Chang, Sachin Agarwal

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsColumbia College
Fundersnot available
KeywordsPsychological distressPsychologyDistressSleep (system call)Clinical psychologyPsychiatryMedicineCardiovascular healthProspective cohort studyMental healthInternal medicineDisease

Abstract

fetched live from OpenAlex

Background: While recent guidelines have noted the deleterious effects of poor sleep on cardiovascular health, the upstream impact of cardiac arrest-induced psychological distress on sleep health metrics among families of cardiac arrest survivors remains unknown. Methods: Sleep health of close family members of consecutive cardiac arrest patients admitted at an academic center (8/16/2021 - 6/28/2023) was self-reported on the Pittsburgh Sleep Quality Index (PSQI) scale. The baseline PSQI administered during hospitalization was cued to sleep in the month before cardiac arrest. It was then repeated one month after cardiac arrest, along with the Patient Health Questionnaire-8 (PHQ-8) to assess depression severity. Multivariable linear regressions estimated the associations of one-month total PHQ-8 scores with changes in global PSQI scores between baseline and one month with higher scores indicating deteriorations. A prioritization exercise of potential interventions categorized into family's information and well-being needs to alleviate psychological distress was conducted at one month. Results: In our sample of 102 close family members (mean age 52±15 years, 70% female, 21% Black, 33% Hispanic), mean global PSQI scores showed a significant decline between baseline and one month after cardiac arrest (6.2±3.8 vs. 7.4±4.1; p<0.01). This deterioration was notable for sleep quality, duration, and daytime dysfunction components. Higher PHQ-8 scores were significantly associated with higher change in PSQI scores, after adjusting for family members' age, sex, race/ethnicity, and patient's discharge disposition [β=0.4 (95% C.I 0.24, 0.48); p<0.01]. Most (n=72, 76%) prioritized interventions supporting information over well-being needs to reduce psychological distress after cardiac arrest. Conclusions: There was a significant decline in sleep health among close family members of cardiac arrest survivors in the acute phase following the event. Psychological distress was associated with this sleep disruption. Further investigation into their temporal associations is needed to develop targeted interventions to support families during this period of uncertainty. WHAT IS KNOWN: Sleep health has been identified as a key element in maintaining cardiovascular health.Close family members of critically ill patients experience suboptimal sleep health and psychological distress may contribute to it. WHAT THE STUDY ADDS: It is breaking new ground in understanding the sleep health dynamics of close family members of cardiac arrest survivors, a critical but often overlooked group of caregivers.The study highlights significant associations between psychological distress and poor sleep that further deteriorates within the first month after a loved one's cardiac arrest.Families of cardiac arrest survivors expressed a high priority for information-based interventions to help alleviate psychological distress during the initial month following the cardiac event emphasizing the need for targeted, accessible, resources to address their psychological and potentially sleep-related challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.341
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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