Treatment for depression comorbid with dementia

Baruch, Nina; Burgess, Jennifer; Pillai, Manjunadh; Allan, Charlotte L.

doi:10.1136/ebmental-2019-300113

Cited by 22 publications

(11 citation statements)

References 45 publications

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Section: Discussionmentioning

confidence: 99%

“…The implication of this work is that the important predictive effect of mental health on longitudinal cognition should be noted, 28 and its comorbid relationship with other conditions Figure 1 Receiver operating characteristic (ROC) for recurrent neural network models I-IV. Original research: Digital mental health such as CVD likewise to be considered in primary care and other clinical settings.…”

Section: Discussionmentioning

confidence: 99%

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Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

Gheorghe

Gallacher

et al. 2020

Evid Based Mental Health

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BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

Gheorghe

Gallacher

et al. 2020

Evid Based Mental Health

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“…The implication of this work is that the important predictive effect of mental health on longitudinal cognition should be noted, 28 and its comorbid relationship with other conditions such as cardiovascular disease likewise to be considered in primary care and other clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Psychiatric comorbid disorders of cognition: A machine learning approach using 1,175 UK Biobank participants

Gheorghe

Gallacher

et al. 2019

Preprint

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Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as chronic and, although they may be pathologically related, they may also act independently 1. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are important longitudinal predictors of cognitive change. Methods UK Biobank participants used at three time points (n= 502,664): baseline, 1st follow-up (n= 20,257) and 1st imaging study (n=40,199). Participants with no missing data were 1,175 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. Conclusions Outcomes suggest psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. Clinical implications Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

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“…27 As regards patients depressed with dementia, few trial are available, but appear that ECT may be useful, also because the use of ECT allow to avoid the use of drugs and it is very fast in the results. 53 Obviously, several recommendations can be made about the administration of ECT to older patients, because age is also a risk factor, rates of cardiovascular complications among patients older than 80 years of age are higher than those among patients who are 65 to 80 years of age (36% vs 12%).…”

Section: Electroconvulsive Therapy In the Eldermentioning

confidence: 99%

Electroconvulsive therapy: old acquaintance for the youngster or new opportunity for the elder? Review of the literature and applications in the geriatric population

Palleschi

Trubian

Palleschi³

2019

Geriatr Care

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Electroconvulsive therapy (ECT) is among the most controversial and mythicized therapies in the psychiatric and medical clinical context. Historically, this practice was used in some contexts as a torture or a coercive method and the lay public as well as the collective imaginary has always considered and represented this treatment as inhuman. Prejudices and limited knowledge of this therapeutic tool have contributed to consider this procedure as a violent act and an assault to human dignity, progressively reducing its employment in time. Despite these prejudices, in the international literature several studies have demonstrated a considerable evidence in support of the use of ECT for some psychiatric diseases, considering it among the safest and most effective treatments. Especially in case of life-risk diseases, such as major depression, ECT proved to be necessary in dealing with pharmaco-resistance and bringing to remission psychotic depression with high risk of suicide. Furthermore, to date ECT results to be the treatment of choice in depressed patients that cannot be treated with pharmacotherapy, e.g. in pregnant women, in which the use of antidepressant drugs exposes the fetus to a teratogenic risk, or in elder patients, in which multiple-drug treatments and interactions have to be carefully considered. The purpose of this paper is to provide a review of the literature regarding ECT and concise guidelines for this treatment and its clinical outcomes, with special reference to geriatric population.

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Treatment for depression comorbid with dementia

Cited by 22 publications

References 45 publications

Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

Psychiatric comorbid disorders of cognition: A machine learning approach using 1,175 UK Biobank participants

Electroconvulsive therapy: old acquaintance for the youngster or new opportunity for the elder? Review of the literature and applications in the geriatric population

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