Does Sleep Play a Role in the Relationship Among Depression, Anxiety, and Mortality in Lung Transplanted Patients?
Bibliographic record
Abstract
To the Editor: We would like to congratulate Dr. Smith and colleagues for their elegant study, which included 132 lung transplant recipients who were followed for 6 months to assess psychosocial risk factors of mortality. The results showed that depression but not anxiety were associated with increased mortality following lung transplantation (1Smith PJ Blumenthal JA Trulock EP et al.Psychosocial predictors of mortality following lung transplantation.Am J Transplant. 2016; 16: 271-277Crossref PubMed Scopus (59) Google Scholar). Additionally, we would like to make some remarks about their study regarding an important confounding factor that was not assessed: sleep. Sleep is fundamental for the consolidation and maintenance of physiological and psychological processes. Individuals with psychiatric disorders, such as depression, often present several sleep disorders, mainly insomnia (2Kokras N Kouzoupis AV Paparrigopoulos T et al.Predicting insomnia in medical wards: The effect of anxiety, depression and admission diagnosis.Gen Hosp Psychiatry. 2011; 33: 78-81Crossref PubMed Scopus (15) Google Scholar). In turn, insomnia increases depression symptoms and poor mental health (3Suh S Yang HC Fairholme CP Kim H Manber R Shin C Who is at risk for having persistent insomnia symptoms? A longitudinal study in the general population in Korea.Sleep Med. 2014; 15: 180-186Crossref PubMed Scopus (24) Google Scholar). The association among these factors was also found in transplanted patients (4Fatigati A Alrawashdeh M DeVito Dabbs A Zaldonis J Bermudez C Predictors and outcomes of sleep quality the first year after lung transplantation.J Heart Lung Transplant. 2015; 34: S237-S238Abstract Full Text Full Text PDF Google Scholar). Considering that mood factors and poor sleep interact in multiple ways (5Ohayon MM Roth T Place of chronic insomnia in the course of depressive and anxiety disorders.J Psychiatr Res. 2003; 37: 9-15Crossref PubMed Scopus (661) Google Scholar), they could be involved in the decreased quality of life and premature mortality after lung transplantation. However, while there is an established association between sleep and mood in transplanted patients, causality is bidirectional. Thus, when dealing with transplanted recipients, immunosuppression condition and comorbidities may exacerbate depression symptoms, or vice-versa (6Patten SB Long-term medical conditions and major depression in a Canadian population study at waves 1 and 2.J Affect Disord. 2001; 63: 35-41Crossref PubMed Scopus (180) Google Scholar). Thus, we believe that the assessment of sleep and its features would better clarify the predictors of mortality in the posttransplant patients of the study from Smith et al, due to evidence of the relationship between poor sleep, mood disorders, and mortality. There are many tools of easy implementation in clinical framework, such as Pittsburgh Sleep Quality Index questionnaire and the Epworth Sleepiness Scale, which are validated and able to assess sleep entirely. This analysis would significantly contribute to the recognition of sleep as a screening tool for patients with emotional and psychological risk, who may be more prone to graft rejection and mortality, thus improving posttransplant recovery in a multidisciplinary approach for the patients. All the authors contributed to the conception and writing of the manuscript. Our studies are supported by a fellowship from the Associação Fundo de Incentivo à Pesquisa (AFIP) and the São Paulo Research Foundation (FAPESP, grant #2014/15259-2 to C.H.). S.T. and M.L.A. received CNPq fellowships. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".