BackgroundHospital inpatients often experience medical and psychiatric problems simultaneously. Although this implies a certain relationship between healthcare utilization and costs, this relationship has never been systematically reviewed.ObjectiveThe objective is to examine the extent to which medical-psychiatric comorbidities relate to health-economic outcomes in general and in different subgroups. If the relationship is significant, this would give additional reasons to facilitate the search for targeted and effective treatments for this complex population.MethodA systematic review in Embase, Medline, Psycinfo, Cochrane, Web of Science and Google Scholar was performed up to August 2016 and included cross-references from included studies. Only peer-reviewed empirical studies examining the impact of inpatient medical-psychiatric comorbidities on three health-economic outcomes (length of stay (LOS), medical costs and rehospitalizations) were included. Study design was not an exclusion criterion, there were no restrictions on publication dates and patients included had to be over 18 years. The examined populations consisted of inpatients with medical-psychiatric comorbidities and controls. The controls were inpatients without a comorbid medical or psychiatric disorder. Non-English studies were excluded.ResultsFrom electronic literature databases, 3165 extracted articles were scrutinized on the basis of title and abstract. This resulted in a full-text review of 86 articles: 52 unique studies were included. The review showed that the presence of medical-psychiatric comorbidity was related to increased LOS, higher medical costs and more rehospitalizations. The meta-analysis revealed that patients with comorbid depression had an increased mean LOS of 4.38 days compared to patients without comorbidity (95% CI: 3.07 to 5.68, I2 = 31%).ConclusionsMedical-psychiatric comorbidity is related to increased LOS, medical costs and rehospitalization; this is also shown for specific subgroups. This study had some limitations; namely, that the studies were very heterogenetic and, in some cases, of poor quality in terms of risk of bias. Nevertheless, the findings remain valid and justify the search for targeted and effective interventions for this complex population.

Major depressive disorder (MDD) is a highly prevalent condition with a lifetime prevalence of nearly 20% [1]. MDD is currently the second leading cause of disability worldwide, and the World Health Organization (WHO) has predicted that, by

Background Psychiatric and medical multimorbidity is associated with low quality of life, poor functioning and excess mortality. Differences in healthcare utilization between those receiving co-occurring medical and psychiatric healthcare (HUMPCs) and those only receiving medical (HUMCs) or only psychiatric healthcare (HUPCs) may indicate differences in care accessibility, help-seeking behavior and the risk patterns of medical illness. We aimed to assess the occurrence of psychiatric diagnostic groups in HUMPCs compared to HUPCs and to compare their medical and psychiatric claims expenditures. Methods Using Dutch claims data covering psychiatric and medical hospital care in 2010–2011, healthcare utilization differences between HUMPCs and HUPCs were expressed as differences and ratios, accounting for differences in age and sex between groups. Median claims expenditures were then compared between HUMPCs and HUPCs. Results HUMPCs had 40% higher median medical cost of claims compared to HUMCs and a 10% increased number of psychiatric claims compared to HUPCs. HUMPCs were more often diagnosed with: organic disorders; behavioral syndromes associated with physiological disturbances and physical factors; mood [affective] disorders; neurotic, stress related and somatoform disorders; and disorders of adult personality and behavior. By contrast, disorders of psychological development, schizophrenia, schizotypal and delusional disorders, behavioral and emotional disorders with usual onset occurring in childhood, and mental and behavioral disorders due to psychoactive substance abuse were less often diagnosed in this group. Conclusions Both medical and psychiatric disease become more costly where both are present. For HUMPCs the costs of both medical and psychiatric claims for almost all diagnostic groups were higher than for HUPCs and HUMCs.

Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (MRI) and functional MRI biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. Here, we used MRI data of 189 depressed patients from seven participating centers of the Global ECT-MRI Research Collaboration (GEMRIC) to develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting. We used multimodal data (i.e., clinical, structural MRI and resting-state functional MRI) and evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (minimal symptoms after treatment) using a support vector machine (SVM) classifier. Remission classification using a combination of gray matter volume with functional connectivity led to good performing models with 0.82-0.84 area under the curve (AUC) when trained and tested on samples coming from all centers, and remained acceptable when validated on other centers with 0.71-0.73 AUC. These results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

Background Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. Methods Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. Results Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82–0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70–0.73 AUC). Conclusions These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.