scholarly journals Effect of Comorbidity Assessed by the Charlson Comorbidity Index on the Length of Stay, Costs and Mortality among Older Adults Hospitalised for Acute Stroke

2018 ◽  
Vol 15 (11) ◽  
pp. 2532 ◽  
Author(s):  
Richard Ofori-Asenso ◽  
Ella Zomer ◽  
Ken Chin ◽  
Si Si ◽  
Peter Markey ◽  
...  
Keyword(s):  
Older Adults ◽  
Length Of Stay ◽  
Acute Stroke ◽  
Charlson Comorbidity Index ◽  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Tertiary Hospital ◽  
Hospital Death ◽  
Comorbidity Index

The burden of comorbidity among stroke patients is high. The aim of this study was to examine the effect of comorbidity on the length of stay (LOS), costs, and mortality among older adults hospitalised for acute stroke. Among 776 older adults (mean age 80.1 ± 8.3 years; 46.7% female) hospitalised for acute stroke during July 2013 to December 2015 at a tertiary hospital in Melbourne, Australia, we collected data on LOS, costs, and discharge outcomes. Comorbidity was assessed via the Charlson Comorbidity Index (CCI), where a CCI score of 0–1 was considered low and a CCI ≥ 2 was high. Negative binomial regression and quantile regression were applied to examine the association between CCI and LOS and cost, respectively. Survival was evaluated with the Kaplan–Meier and Cox regression analyses. The median LOS was 1.1 days longer for patients with high CCI than for those with low CCI. In-hospital mortality rate was 18.2% (22.1% for high CCI versus 11.8% for low CCI, p < 0.0001). After controlling for confounders, high CCI was associated with longer LOS (incidence rate ratio [IRR]; 1.35, p < 0.0001) and increased likelihood of in-hospital death (hazard ratio [HR]; 1.91, p = 0.003). The adjusted median, 25th, and 75th percentile costs were AUD$2483 (26.1%), AUD$1446 (28.1%), and AUD$3140 (27.9%) higher for patients with high CCI than for those with low CCI. Among older adults hospitalised for acute stroke, higher global comorbidity (CCI ≥ 2) was associated adverse clinical outcomes. Measures to better manage comorbidities should be considered as part of wider strategies towards mitigating the social and economic impacts of stroke.

Abstract MP53: 1,5-anhydroglucitol to Identify Older Adults with Diabetes at Risk of Hospitalization and All-cause Mortality

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Bethany Warren ◽  
Andreea Rawlings ◽  
A. Richey Sharrett ◽  
Josef Coresh ◽  
Anna Kottgen ◽  
...  
Keyword(s):  
Older Adults ◽  
At Risk ◽  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Glycemic Variability ◽  
Adverse Outcomes ◽  
Improve Risk Stratification ◽  
All Cause Mortality ◽  
Diagnosed Diabetes

Introduction: Older adults with diabetes have variable prognosis. There is critical need to improve risk stratification among this population to understand who is most likely to experience adverse outcomes. Low 1,5-anhydroglucitol (1,5-AG) is a biomarker of glycemic variability and has demonstrated value for identification of middle-aged adults with diabetes at risk for major clinical outcomes. Total hospitalizations are a useful summary measure of poor health outcomes. It is unknown whether 1,5-AG can identify older adults at risk for hospitalizations and all-cause mortality. Methods: We included 2,061 participants from the Atherosclerosis Risk in Communities (ARIC) Study with diagnosed diabetes who attended the 2011-2013 visit. We dichotomized 1,5-AG (≥6μg/mL; <6μg/mL) and followed participants until December 31, 2015. We examined the associations of 1,5-AG with total and diabetes-related hospitalizations using negative binomial regression and all-cause mortality using Cox regression. Results: Participants ranged in age from 67-90 years, 57% were female, 30% were black, and 17% had 1,5-AG <6μg/mL. Median HbA1c was 6.2% in those with 1,5-AG ≥6μg/mL and 7.8% in persons with 1,5-AG <6μg/mL. During a median of 3.6 years of follow-up, there were 2,813 hospitalizations (1,689 diabetes-related) and 247 deaths. Compared to 1,5-AG ≥6μg/mL, individuals with 1,5-AG <6μg/mL had a significantly higher risk of hospitalizations, diabetes-related hospitalizations, and death ( Table ). After adjustment for diabetes medication use or HbA1c, associations with hospitalizations were attenuated and non-significant, while the relationship with all-cause mortality remained. Conclusion: Among older adults with diagnosed diabetes, glycemic variability may be an important risk factor for major short-term complications.

scholarly journals The Prevalence of Multi-Drug Resistant Organisms and Their Outcomes in an ICU in Mauritius: An Observational Study

2020 ◽  
pp. 71-78
Author(s):  
Dooshanveer Chowbay Nuckchady ◽  
Samiihah Hafiz Boolaky
Keyword(s):  
Length Of Stay ◽  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Antibiotic Use ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Drug Resistant ◽  
Retrospective Case ◽  
Carbapenem Resistant

Aims: To assess the prevalence of multi-drug resistant organisms (MDRO) in an ICU of Mauritius and determine the relationship between antibiotic resistance and mortality as well as length of stay and duration of antibiotic use. Study Design: Retrospective case control study. Place and Duration of Study: This study examined the data of patients who were admitted from 2015 to 2016 at an ICU in Port Louis, Mauritius. Methodology: 128 patients on whom cultures were ordered were included. Adjustment was performed using multivariate Cox regression and negative binomial regression. Results: Out of 214 organisms that were isolated, 68% were an MDRO; 78% of Enterobacteriaceae were ESBL, 86% of Acinetobacter spp., 30% of Enterobacteriaceae and 80% of Pseudomonas spp. were carbapenem resistant while 53% of Staphylococcus aureus were MRSA. After adjustment, MDRO were linked to a non-statistically significant 13% increase in mortality (P = .056), a rise in hospital length of stay from 19 days to 29 days (P = .0013) and an escalation in duration of antibiotic use from 11 days to 24 days (P = 1.3E-10). Conclusion: Infections with MDRO are common in Mauritius and strategies should be put into place to reduce their prevalence.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

scholarly journals Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Lynn Robertson ◽  
Dolapo Ayansina ◽  
Marjorie Johnston ◽  
Angharad Marks ◽  
Corri Black
Keyword(s):  
At Risk ◽  
Length Of Stay ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Resource Planning ◽  
Kappa Statistics ◽  
Data Systems ◽  
Hospital Episode ◽  
Hospitalised Patients ◽  
Poor Outcomes

IntroductionMultimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients. AimTo evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland. MethodsLinked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate. ResultsIn 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli – agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)). ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose. Hightlights Operationalisation of two count-based multimorbidity measures using linked electronic hospitalepisode data was evaluated (Charlson and Tonelli). First study to compare the Tonelli measure with another measure for investigating multimor-bidity in hospitalised patients. Multimorbidity prevalence differed depending on measure used, but both multimorbidity mea-sures identified those at risk of poor outcomes. Operationalised multimorbidity tools have uses for future multimorbidity research and use insecondary care data systems. Multimorbidity measures are not interchangeable, and choice of measure should depend onpurpose.

scholarly journals Prestroke physical activity could influence acute stroke severity (part of PAPSIGOT)

Neurology ◽  
2018 ◽  
Vol 91 (16) ◽  
pp. e1461-e1467 ◽  
Author(s):  
Malin Reinholdsson ◽  
Annie Palstam ◽  
Katharina S. Sunnerhagen
Keyword(s):  
Physical Activity ◽  
Acute Stroke ◽  
Odds Ratio ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Activity Level ◽  
Stroke Severity ◽  
Cross Sectional ◽  
Severe Stroke ◽  
Younger Age

ObjectiveTo investigate the influence of prestroke physical activity (PA) on acute stroke severity.MethodsData from patients with first stroke were retrieved from registries with a cross-sectional design. The variables were PA, age, sex, smoking, diabetes, hypertension and statin treatment, stroke severity, myocardial infarction, new stroke during hospital stay, and duration of inpatient care at stroke unit. PA was assessed with Saltin-Grimby's 4-level Physical Activity Level Scale, and stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS). Logistic regression was used to predict stroke severity, and negative binomial regression was used to compare the level of PA and stroke severity.ResultsThe study included 925 patients with a mean age of 73.1 years, and 45.2% were women. Patients who reported light or moderate PA levels were more likely to present a mild stroke (NIHSS score 0 to 5) compared with physically inactive patients in a model that also included younger age as a predictor (odds ratio = 2.02 for PA and odds ratio = 0.97 for age). The explanatory value was limited at 6.8%. Prestroke PA was associated with less severe stroke, and both light PA such as walking at least 4 h/wk and moderate PA 2–3 h/wk appear to be beneficial. Physical inactivity was associated with increased stroke severity.ConclusionsThis study suggests that PA and younger age could result in a less severe stroke. Both light PA such as walking at least 4 h/wk and moderate PA 2–3 h/wk appear to be beneficial.

scholarly journals Niacin, lutein and zeaxanthin and physical activity have an impact on Charlson comorbidity index using zero-inflated negative binomial regression model: National Health and Nutrition Examination Survey 2013–2014

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Hantong Zhao ◽  
Changcong Wang ◽  
Yingan Pan ◽  
Yinpei Guo ◽  
Nan Yao ◽  
...  
Keyword(s):  
Physical Activity ◽  
National Health ◽  
Charlson Comorbidity Index ◽  
Negative Binomial ◽  
Nutrition Examination Survey ◽  
Recreational Activity ◽  
Work Activity ◽  
Health And Nutrition ◽  
Comorbidity Index ◽  
Nutrients Intake

Abstract Background Combined with the increasing life expectancy, chronic medical conditions have gradually become the dominant cause of death and disability, and multimorbidity became an increasingly serious public health challenge. However, most existing studies have focused on the coexistence of specific diseases or relatively few diseases. Given one person may have multiple diseases at the same time, we applied Charlson Comorbidity Index (CCI) to systematically evaluate one’s 10-year mortality. In this study, we explored the effects of nutrients and physical activity on CCI using National Health and Nutrition Examination Survey (NHANES) 2013–2014 data. Methods The study sample consists of one continuous cycle (2013–2014) of NHANES, and 4386 subjects were included in the study. Nutrients intake was measured by dietary recall, and physical activity was evaluated by the Global Physical Activity Questionnaire respectively. Besides, CCI was the sum of the scores assigned for each medical condition. We utilized zero-inflated negative binomial (ZINB) model to investigate the effects in nutrients intake and physical activity on CCI by adjusting for seven sociodemographic characteristics, smoking and drinking. Results Among the 4386 participants, 2018 (68.7%) are Non-Hispanic White, over half participants (78.6%) drink. In count part (CCI ≥ 0), holding other variables constant, the expected change in CCI for a one-unit increase in niacin is 1.621(RR = 1.621, p = 0.016), in lutein + zeaxanthin is 0.974 (RR = 0.974, p = 0.031), and in sedentary time is 1.035 (RR = 1.035, p = 0.005). Moreover, those who do not have vigorous work activity would be more likely to have higher CCI than those who have (RR = 1.275, P = 0.045). In logit part (CCI = 0), the log odds of having CCI equals zero would increase by 0.541 and 0.708 for every additional vigorous recreational activity (OR = 0.541, p = 0.004) and moderate recreational activity (OR = 0.708, p = 0.017) respectively. Conclusions Lutein and zeaxanthin intake, vigorous work activity, vigorous recreational activity and moderate recreational activity may be good for one’s health. Rather, increasing niacin intake and sedentary activity may be likely to raise 10-year mortality. Our findings may be significant for preventing diseases and improving health, furthermore, reducing people’s financial burden on healthcare.

086 The rapid efficacy of natalizumab vs fingolimod in patients with active relapsing-remitting multiple sclerosis (RRMS): results from reveal, a randomised, head-to-head study

2018 ◽  
Vol 89 (6) ◽  
pp. A35.1-A35
Author(s):  
Helmut Butzkueven ◽  
Douglas Jeffery ◽  
Douglas L Arnold ◽  
Massimo Filippi ◽  
Jeroen JG Geurts ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Patient Characteristics ◽  
Estimating Equation ◽  
Study Patient ◽  
Cumulative Probability ◽  
Open Label ◽  
Randomised Study ◽  
Kaplan Meier

IntroductionREVEAL was designed as a 1 year, multicentre, randomised, rater- and sponsor-blinded, prospective study comparing natalizumab and fingolimod in patients with active RRMS. Although the study closed early (for non-safety/non-efficacy reasons), data permitted comparison of effects occurring shortly after treatment initiation. This analysis compares onset of efficacy with natalizumab and fingolimod in REVEAL.MethodsPatients were randomised to open-label intravenous natalizumab 300 mg every 4 weeks (n=54) or oral fingolimod 0.5 mg once daily (n=54). Magnetic resonance imaging was scheduled every 4 weeks for the first 24 weeks and at weeks 36 and 52. Analyses included Kaplan-Meier and Cox regression, negative binomial regression (annualised relapse rate [ARR] and number of T1 gadolinium-enhancing [Gd+] lesions) and a negative binomial generalised estimating equation (cumulative Gd +lesions over time).ResultsAs expected for a randomised study, patient characteristics and follow-up time (median 39 weeks) were generally similar between groups. Natalizumab patients were less likely than fingolimod patients to develop new Gd +lesions (for ≥1 lesion, cumulative probability 40.68% vs 57.99%; hazard ratio [HR]=1.678 [95% CI: 0.865 to 3.255]; p=0.1258; for ≥2 lesions, cumulative probability 11.54% vs 48.48%; HR=4.053 [95% CI: 1.474 to 11.144]; p=0.007). Natalizumab patients consistently had 63%–72% fewer Gd +lesions than fingolimod patients, with between-group differences apparent within 4 weeks and reaching significance by 12 weeks (p=0.030). ARR was 83% lower with natalizumab than with fingolimod (0.05 vs 0.29; p=0.023), and cumulative probability of relapse was 1.85% with natalizumab vs 22.28% with fingolimod (HR=12.184 [95% CI: 1.552 to 95.634]; p=0.017). Adverse events were consistent with known safety profiles.ConclusionThese results suggest that natalizumab reduces disease activity more rapidly and to a greater extent than fingolimod in patients with active RRMS. Given the early study closure, available data did not permit primary endpoint evaluation, and interpretation of these results requires caution.Study SupportBiogen.

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