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Record W4221044201 · doi:10.1016/j.chest.2022.02.051

Psychiatric Outcomes in ICU Patients With Family Visitation

2022· article· en· W4221044201 on OpenAlexafffund
Stephana J. Moss, Brianna K. Rosgen, Filipe R. Lucini, Karla D. Krewulak, Andrea Soo, Christopher J. Doig, Scott B. Patten, Henry T. Stelfox, Kirsten M. Fiest

Bibliographic record

VenueCHEST Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsCalgary Laboratory ServicesUniversity of CalgaryHotchkiss Brain InstituteAlberta Health Services
FundersCanadian Institutes of Health ResearchUniversity of Calgary
KeywordsMedicineIncidence (geometry)AnxietyRetrospective cohort studyLogistic regressionPopulationPsychiatryCohortPediatricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Lack of family visitation in the ICU can have long-term consequences on patients in the ICU after discharge. The effect of family visitation on the incidence of patient psychiatric disorders is unknown. RESEARCH QUESTION: What is the association between family visitation in the ICU and incidence of psychiatric outcomes in patients in the ICU 1 year after hospital discharge? STUDY DESIGN AND METHODS: This study assessed a population-based retrospective cohort of adult patients admitted to the ICU from January 1, 2014, through May 30, 2017, surviving to hospital discharge with ICU length of stay of ≥ 3 days. To be eligible, patients needed to have minimum of 5 years of administrative data before ICU admission and a minimum of 1 year of follow-up data after hospital discharge. An internally validated algorithm that interpreted natural language in health records determined patients with or without in-person family (ie, relatives, friends) visitation during ICU stay. The primary outcome was risk of an incidence of psychiatric disorder (composite outcome), including anxiety, depressive, trauma- and stressor-related, psychotic, and substance use disorders, identified using coding algorithms for administrative databases. Propensity scores were used in inverse probability weighted logistic regression models, and average treatment effects were converted to risk ratios (RRs) with 95% CIs. Secondary outcomes were incidences of diagnoses by type of psychiatric disorder. RESULTS: We included 14,344 patients with (96% [n = 13,771]) and without (4.0% [n = 573]) in-person family visitation who survived hospital discharge. More than one-third of patients received a diagnosis of any psychiatric disorder within 1 year after discharge (34.9%; 95% CI, 34.1%-35.6%). Patients most often received diagnoses of anxiety disorders (17.5%; 95% CI, 16.9%-18.1%) and depressive disorders (17.2%; 95% CI, 16.6%-17.9%). After inverse probability weighting of 13,731 patients, in-person family visitation was associated with a lower risk of received a diagnosis of any incident psychiatric disorder within 1 year after discharge (RR, 0.79; 95% CI, 0.68-0.92). INTERPRETATION: ICU family visitation is associated with a decreased risk of psychiatric disorders in critically ill patients up to 1 year after hospital discharge.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.349
Teacher spread0.305 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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