IntroductionFollowing the release of DSM-5 in 2013, a newly introduced diagnostic category emerged in psychiatric classification—the disruptive mood dysregulation disorder (DMDD). DMDD is a ...depressive disorder that begins in childhood and is marked by a consistently irritable or angry mood, frequently accompanied by temper outbursts that are notably severe.ObjectivesIt is to study the prevalence and comorbidities of DMDD in Morocco, specifically at Arrazi University Psychiatric HospitalMethodsData were collected from youths aged 6 to 18 years who underwent a systematic assessment of symptoms. This assessment was extended to all patients consulting at Arrazi Hospital in Salé within the pediatric department over a period of four months (sample accrued from July 2023 to October 2023). The diagnosis of DMDD was established utilizing the diagnostic criteria outlined in DSM-5ResultsAbout 31% of the young participants met the operational criteria for DMDD. Those with DMDD exhibited increased comorbidity rates with attention-deficit/hyperactivity disorder (ADHD), another Depressive disorder and conduct disorder compared to those without DMDD. Additionally, they displayed elevated symptoms of aggressive behavior, rule-breaking, social issues, anxiety/depression, attention problems, and thought problems in comparison to all other participants without DMDD. It’s noteworthy that youth with DMDD presented with at least one psychiatric comorbidity.ConclusionsGiven that DMDD is accompanied by other comorbid psychiatric disorders, particularly depression and anxiety, and appears to impact familial and occupational status in adulthood, the early detection and subsequent effective treatment of DMDD symptoms are of utmost importance.Disclosure of InterestNone Declared
BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include ...comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate.
METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations.
RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding.
CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.
BACKGROUND:Recently, van Walraven developed a weighted summary score (VW) based on the 30 comorbidities from the Elixhauser comorbidity system. One of the 30 comorbidities, cardiac arrhythmia, is ...currently excluded as a comorbidity indicator in administrative datasets such as the Nationwide Inpatient Sample (NIS), prompting us to examine the validity of the VW score and its use in the NIS.
METHODS:Using data from the 2009 Maryland State Inpatient Database, we derived weighted summary scores to predict in-hospital mortality based on the full (30) and reduced (29) set of comorbidities and compared model performance of these and other comorbidity summaries in 2009 NIS data.
RESULTS:Weights of our derived scores were not sensitive to the exclusion of cardiac arrhythmia. When applied to NIS data, models containing derived summary scores performed nearly identically (c statistics for 30 and 29 variable-derived summary scores0.804 and 0.802, respectively) to the model using all 29 comorbidity indicators (c=0.809), and slightly better than the VW score (c=0.793). Each of these models performed substantially better than those based on a simple count of Elixhauser comorbidities (c=0.745) or a categorized count (0, 1, 2, or ≥3 comorbidities; c=0.737).
CONCLUSIONS:The VW score and our derived scores are valid in the NIS and are statistically superior to summaries using simple comorbidity counts. Researchers wishing to summarize the Elixhauser comorbidities with a single value should use the VW score or those derived in this study.
Background
Several contemporary risk stratification tools are now being used since the development of the Charlson Comorbidity Index (CCI) in 1987. The purpose of this systematic review and ...meta-analysis was to compare the utility of commonly used co-morbidity indices in predicting surgical outcomes.
Methods
A comprehensive review was performed to identify studies reporting an association between a pre-operative co-morbidity measurement and an outcome (30-day/in-hospital morbidity/mortality, 90-day morbidity/mortality, and severe complications). Meta-analysis was performed on the pooled data.
Results
A total of 111 included studies were included with a total cohort size 25,011,834 patients. The studies reporting the 5-item Modified Frailty Index (mFI-5) demonstrated a statistical association with an increase in the odds of in-hospital/30-day mortality (OR:1.97,95%CI: 1.55–2.49,
p
< 0.01). The pooled CCI results demonstrated an increase in the odds for in-hospital/30-day mortality (OR:1.44,95%CI: 1.27–1.64,
p
< 0.01). Pooled results for co-morbidity indices utilizing a scale-based continuous predictor were significantly associated with an increase in the odds of in-hospital/30-day morbidity (OR:1.32, 95% CI: 1.20–1.46,
p
< 0.01). On pooled analysis, the categorical results showed a higher odd for in-hospital/30-day morbidity (OR:1.74,95% CI: 1.50–2.02,
p
< 0.01). The mFI-5 was significantly associated with severe complications (Clavien-Dindo ≥ III) (OR:3.31,95% CI:1.13–9.67,
p
< 0.04). Pooled results for CCI showed a positive trend toward severe complications but were not significant.
Conclusion
The contemporary frailty-based index, mFI-5, outperformed the CCI in predicting short-term mortality and severe complications post-surgically. Risk stratification instruments that include a measure of frailty may be more predictive of surgical outcomes compared to traditional indices like the CCI.
OBJECTIVE:We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health ...outcomesin-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness.
DATA SOURCES:We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012.
METHODS:The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics.
RESULTS:Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776–0.778) for the mortality index and 0.634 (95% confidence interval, 0.633–0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly.
CONCLUSIONS:These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.
•84.4% Of depressed patients had at least one comorbidity, where the most prevalent was hypertension, then anxiety and cerebral infarction.•The number of connections involving organic mental ...disorders, anxiety or somatoform disorders in depressed patients was 8-fold increases than in controls.•Cardiovascular diseases showed high centrality in the comorbidity network of depression.•The older the depressed patients, the more severe of the central diseases.•Intricate but distinct communities appeared within the comorbidity network of depression.
Depression is a psychiatric disorder with a high comorbidity burden; however, previous comorbidity studies predominately focused on a few common diseases and relied on self-reported data. We aimed to investigate the comorbid status of depression concerning the entire spectrum of chronic diseases using network analysis.
Totally, 22,872 depressed inpatients and one-to-one matched controls were enrolled in the retrospective study. Hospital discharge records were aggregated to measure the comorbidities, where those with a prevalence ≥ 1% were selected for further analysis. Based on the co-occurrence frequency, sex- and age-specific comorbidity networks in depressed patients were constructed and the results were compared with the controls. Louvain algorithm was used to detect the highly interlinked communities.
Depressed patients had 4 comorbidities on average, and 84.4% had at least one comorbidity. The comorbidity network in depression cases was more complex than controls (connections of 839 vs. 369). Intricate but distinct communities appeared within the comorbidity network in depressed patients, where the largest community included cerebrovascular diseases, chronic ischaemia heart disease, atherosclerosis and osteoporosis. Sex-specific central diseases existed, and cardiovascular diseases were the major central diseases to both gender. The older the depressed patients, the more severe the central diseases in the comorbidity network.
The causality of the observed interactions could not be determined.
The application of network analysis on longitudinal healthcare datasets to assess comorbidity patterns can supplement the traditional clinical study approaches. The findings would improve our understanding of depression-related comorbidities and enhance the integrated management of depression.
•COVID -19 cases are now confirmed in multiple countries.•Assessed the prevalence of comorbidities in infected patients.•Comorbidities are risk factors for severe compared with non-severe ...patients.•Help the health sector guide vulnerable populations and assess the risk of deterioration.
An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China; the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries.
The aim of this meta-analysis was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients.
A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through February 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models.
Seven studies were included in the meta-analysis, including 1 576 infected patients. The results showed the most prevalent clinical symptom was fever (91.3%, 95% CI: 86–97%), followed by cough (67.7%, 95% CI: 59–76%), fatigue (51.0%, 95% CI: 34–68%) and dyspnea (30.4%, 95% CI: 21–40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0–27.2%) and diabetes (9.7%, 95% CI: 7.2–12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8–13.8%) and respiratory system disease (1.5%, 95% CI: 0.9–2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46–3.83), 2.46 (95% CI: 1.76–3.44) and 3.42 (95% CI: 1.88–6.22) respectively.
We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.
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