Highlighting the road towards new disease-specific comorbidity indices.
Ontology highlight
ABSTRACT:
SUBMITTER: Dell'Oglio P
PROVIDER: S-EPMC7354319 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Highlighting the road towards new disease-specific comorbidity indices.
Dell'Oglio Paolo P Mazzone Elio E Tian Zhe Z Karakiewicz Pierre I PI
Translational andrology and urology 20200601 3
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Project description:Alzheimer's disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.
| S-EPMC8509357 | biostudies-literature
Project description:ObjectiveTo determine comorbidity indices in people with HIV (PWH) and lifestyle-similar HIV-negative controls.DesignCross-sectional analysis of the Pharmacokinetic and clinical Observations in PeoPle over fiftY cohort study in the United Kingdom and Ireland.MethodsThe Elixhauser Comorbidity Index (ECI), Charlson Comorbidity Index and the Comorbidity Burden Index were compared between older PWH and HIV-negative controls using the Mann-Whitney U test; the magnitude of the difference between groups was quantified using the r effect size.ResultsThe 699 PWH and 304 HIV-negative controls were predominantly male (87.5% vs. 64.0%), white (86.3% vs. 90.0%) and had median ages of 57 and 58 years, respectively. Among PWH, the median (interquartile range) CD4 T-cell count was 624 (475, 811) cells/μl; 98.7% were on antiretroviral therapy. The median (interquartile range) ECI was 0 (0, 8) and 0 (-3, 1), Charlson Comorbidity Index was 2 (1, 5) and 1 (0, 1) and Comorbidity Burden Index 8.6 (2.2, 16.8) and 5.9 (0.6, 10.8), respectively. While all three indices were significantly higher in PWH than in controls (P < 0.001 for each), the magnitude of the differences between the two groups were small to medium, with effect sizes (95% confidence interval) of 0.21 (0.16, 0.27), 0.38 (0.32, 0.42) and 0.18 (0.11, 0.23), respectively.ConclusionThese three comorbidity indices are higher in PWH compared with HIV-negative controls, although the magnitude of differences between groups were small. Differences in the ECI, reportedly associated with poorer coronavirus disease 2019 outcomes, were driven by more individuals with HIV being within the higher end of the range.
| S-EPMC7493769 | biostudies-literature
Project description:BACKGROUND:Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients. OBJECTIVES:We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model. METHODS:We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time. RESULTS:In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses. CONCLUSIONS:Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.
| S-EPMC6794890 | biostudies-literature
Project description:Snakebite envenoming is a significant global health challenge, and for over a century, traditional plasma-derived antivenoms from hyperimmunized animals have been the primary treatment against this infliction. However, these antivenoms have several inherent limitations, including the risk of causing adverse reactions when administered to patients, batch-to-batch variation, and high production costs. To address these issues and improve treatment outcomes, the development of new types of antivenoms is crucial. During this development, key aspects such as improved clinical efficacy, enhanced safety profiles, and greater affordability should be in focus. To achieve these goals, modern biotechnological methods can be applied to the discovery and development of therapeutic agents that can neutralize medically important toxins from multiple snake species. This review highlights some of these agents, including monoclonal antibodies, nanobodies, and selected small molecules, that can achieve broad toxin neutralization, have favorable safety profiles, and can be produced on a large scale with standardized manufacturing processes. Considering the inherent strengths and limitations related to the pharmacokinetics of these different agents, a combination of them might be beneficial in the development of new types of antivenom products with improved therapeutic properties. While the implementation of new therapies requires time, it is foreseeable that the application of biotechnological advancements represents a promising trajectory toward the development of improved therapies for snakebite envenoming. As research and development continue to advance, these new products could emerge as the mainstay treatment in the future.
| S-EPMC10729942 | biostudies-literature
Project description:BackgroundSeveral different indices summarize patient comorbidity using health care data. An accurate index can be used to describe the risk profile of patients, and as an adjustment factor in analyses. How well these indices perform in persons with chronic kidney disease (CKD) is not well known.ObjectiveAssess the performance of 5 comorbidity indices at predicting mortality in 3 different patient groups with CKD: incident kidney transplant recipients, maintenance dialysis patients, and individuals with low estimated glomerular filtration rate (eGFR).DesignPopulation-based retrospective cohort study.SettingOntario, Canada, between 2004 and 2014.PatientsIndividuals at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an eGFR less than 45 mL/min per 1.73m2.MeasurementsFive comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins' Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Our primary outcome was 1-year all-cause mortality.MethodsComorbidity indices were estimated using information in the prior 2 years. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically.ResultsWe identified 4111 kidney transplant recipients, 23 897 individuals receiving maintenance dialysis, and 181 425 individuals with a low eGFR. Within 1 year, 108 (2.6%), 4179 (17.5%), and 17 898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate with median c-statistics less than 0.7 for all 5 comorbidity indices for all 3 groups. Calibration was also poor for all models.LimitationsThe study used administrative health care data so there is the potential for misclassification. Indices were modeled as continuous scores as opposed to indicators for individual conditions to limit overfitting.ConclusionsExisting comorbidity indices do not accurately predict 1-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.
| S-EPMC6195002 | biostudies-literature
Project description:BackgroundInterpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcomes.MethodsOnline surveys administered generic physical (PCS) and mental (MCS) QOL outcome measures, the Charlson Comorbidity Index (CCI), an expanded chronic condition checklist (CCC), and individualized QOL Disease-specific Impact Scale (QDIS) ratings in a developmental sample (N = 5490) of US adults. Controlling for sociodemographic variables, regression models compared 12- and 35-condition checklists, mortality vs. population QOL-weighting, and population vs. individualized QOL weighting methods. Analyses were cross-validated in an independent sample (N = 1220) representing the adult general population. Models compared estimates of variance explained (adjusted R2) and model fit (AIC) for generic PCS and MCS across aggregation methods at baseline and nine-month follow-up.ResultsIn comparison with sociodemographic-only regression models (MCS R2 = 0.08, PCS = 0.09) and Charlson CCI models (MCS R2 = 0.12, PCS = 0.16), increased variance was accounted for using the 35-item CCC (MCS R2 = 0.22, PCS = 0.31), population MCS/PCS QOL weighting (R2 = 0.31-0.38, respectively) and individualized QDIS weighting (R2 = 0.33 & 0.42). Model R2 and fit were replicated upon cross-validation.ConclusionsPhysical and mental outcomes were more accurately predicted using an expanded MCC checklist, population QOL rather than mortality CCI weighting, and individualized rather than population QOL weighting for each reported condition. The 3-min combination of CCC and QDIS ratings (QDIS-MCC) warrant further testing for purposes of predicting and interpreting QOL outcomes affected by MCC.
| S-EPMC9277868 | biostudies-literature
Project description:BackgroundPredicting risk of premature death is one of the most basic tasks in medicine and public health, but has proven to be difficult over the long term even with the best prognostic models. One popular strategy has been to improve prognostic models with candidate genes and other novel biomarkers. However, the gains in predictive power have been modest and the costs have been high, leading to a demand for cost-effective alternatives. We conducted a proof-of-principle investigation to examine whether simple, cheap, and noninvasive paper-and-pencil measures of social class and personality phenotype could improve the performance of one of the most widely used prediction models for all-cause mortality, the Charlson Comorbidity Index (CCI).MethodsWe used data from baseline and 25-year mortality follow-up of the UK Health and Lifestyle Study cohort. In a subset of the cohort, we first identified 5 psychosocial factors highly predictive of mortality: income, education, type A personality, communalism (preference for the company of others), and "lie" scale (a measure of denial, putatively associated with ill health). We then examined the predictive performance of the CCI with and without these measures in a validation subsample.ResultsAcross 5-, 10-, 15-, 20-, and 25-year time horizons, the psychosocially augmented CCI showed substantially better discrimination [area under the receiver-operating curves (95% confidence interval) from 0.83 (0.81-0.85) to 0.84 (0.83-0.86)] than the CCI [area under the receiver-operating curves from 0.74 (0.71-0.76) to 0.77 (0.76-0.79)]. These translated into net reclassification improvements from 27% (23%-31%) to 35% (32%-38%) of survivors and from 23% (17%-30%) to 34% (17%-30%) of decedents; and 23%-42% reductions in the Number Needed to Screen. Calibration improved at all time horizons except 25 years, where it was decreased.ConclusionWidespread attempts to improve prognostic models might consider not only novel biomarkers, but also psychosocial questionnaire measures.
| S-EPMC4658312 | biostudies-literature
Project description:BackgroundWe aimed to clarify the association between new indices in a locomotive syndrome risk test and decline in mobility.MethodsIn the third survey of the Research on Osteoarthritis/osteoporosis Against Disability (ROAD) study, data on the indices were obtained from 1575 subjects (513 men, 1062 women) of the 1721 participants in mountainous and coastal areas. As outcome measures for decline in mobility, we used the five-times-sit-to-stand test (FTSST) and walking speed with cutoff values of 12 s and 0.8 m/s, respectively.ResultsWe first estimated the prevalence of the indices in locomotive syndrome risk test stage 1, including two-step test score <1.3, difficulty with one-leg standing from a 40-cm-high seat in the stand-up test, and 25-question GLFS score ≥7, which were found to be 57.4, 40.6, and 22.6 %, respectively. Next, we investigated the prevalence of the indices in locomotive syndrome risk test stage 2, including two-step test score <1.1, difficulty with standing from a 20-cm-high seat using both legs in the stand-up test, and 25-question GLFS score ≥16, which were found to be 21.1, 7.9, and 10.6 %, respectively. Logistic regression analysis using slow FTSST time or slow walking speed as the objective factor, and presence or absence of indices as the independent factor, after adjusting for confounders, showed all three indices in both stages 1 and 2 were significantly and independently associated with immobility. Finally, we clarified the risk of immobility according to an increasing number of indices in both stages 1 and 2 and found that the odds ratio for both slow FTSST time and slow walking speed increased exponentially.ConclusionWe found that the three indices independently predicted immobility and that accumulation of indices increased the risk of immobility exponentially.
| S-EPMC4575347 | biostudies-literature
Project description:In low- and middle-income countries (LMICs), tetanus continues to be a major public health concern. Although vaccination campaigns have been effective in lowering the incidence of tetanus worldwide, some areas continue to experience a considerable number of cases and fatalities. Adult tetanus is frequently underreported because there is insufficient systematic surveillance and reporting. A high proportion of tetanus patients die because of a lack of adequate critical care services, particularly ventilator support, with limited access to existing facilities due to high costs. Hence, the case fatality rate of adult tetanus remains high. Women and children are protected because of regular and booster immunization strategies implemented around the world. However, men are disproportionately affected by tetanus. Booster dosage based on the World Health Organization (WHO)-recommended schedule should be given to eligible children and adolescent boys. In addition, tetanus vaccination needs to be promoted among adults in vulnerable jobs. Functional strategies could help pave the way toward the protection of all against tetanus.
| S-EPMC10513208 | biostudies-literature
Project description:Many factors may influence the risk of being infected by SARS-CoV-2, the coronavirus responsible for coronavirus disease 2019 (COVID-19). Exposure to the virus cannot explain the variety of an individual's responses to the virus and the high differences of effect that the virus may cause to some. While a person's preexisting condition and their immune defenses have been confirmed to play a major role in the disease progression, there is still much to learn about hosts' genetic makeup towards COVID-19 susceptibility and risk. The host genetic makeup may have direct influence on the grade of predisposition and outcomes of COVID-19. In this study, we aimed to investigate the presence of relevant genetic single nucleotide polymorphisms (SNPs), the peripheral blood level of IL6, vitamin D and arterial blood gas (ABG) markers (pH, oxygen-SpO2 and carbon dioxide-SpCO2) on two groups, COVID-19 (n = 41, study), and the healthy (n = 43, control). We analyzed cytokine and interleukin genes in charge of both pro-inflammatory and immune-modulating responses and those genes that are considered involved in the COVID-19 progression and complications. Thus, we selected major genes, such as IL1β, IL1RN (IL-1 β and α receptor) IL6, IL6R (IL-6 receptor), IL10, IFNγ (interferon gamma), TNFα (tumor necrosis factor alpha), ACE2 (angiotensin converting enzyme), SERPINA3 (Alpha-1-Antiproteinase, Antitrypsin member of Serpin 3 family), VDR (vitamin D receptor Tak1, Bsm1 and Fok1), and CRP (c-reactive protein). Though more research is needed, these findings may give a better representation of virus pleiotropic activity and its relation to the immune system.
| S-EPMC9689844 | biostudies-literature