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Importance of kidney function, number of chronic conditions and medications for hospitalisation in elderly in Blekinge County, Sweden: a case-control study

Por: Norstedt · I. · Thorell · K. · Halling · A.
Objectives

To study the association between risk for hospitalisation in an elderly population related to renal function, number of chronic diseases and number of prescribed drugs.

Design

A case–control study. Persons hospitalised were included and their controls were obtained from electronic hospital medical records. If data were lacking on creatinine levels, multiple imputation was used.

Setting

Blekinge County in southwestern Sweden.

Participants

Study of individuals aged 75 years or older in 2013. We identified a total of 2,941 patients with a first hospitalisation. Of these, 81 were excluded, 78 due to incomplete data and 3 because of lack of control persons. Controls were matched to the same sex and birth year, which resulted in 5720 persons.

Primary and secondary outcome measures

To analyse the OR for hospitalisation conditional logistic regression was used.

Results

A total of 695 persons lacked creatinine value. Using imputation values comparing persons with estimated glomerular filtration rate (eGFR) 2 with ≥30 univariate analyses showed an increased OR 2.35 (95% CI 1.83 to 3.03). Adjusted analyses demonstrated an OR of 1.90 (95% CI 1.46 to 2.47). Comparing eGFR2 against ≥45 univariate analyses showed OR 1.38 (95% CI 1.22 to 1.57). Adjusted analyses OR for the same group were 1.17 (95% CI 1.03 to 1.33). In both models, the OR for five or more chronic conditions and five or more medications showed a statistically increased risk for hospitalisation.

Conclusions

There is a need for systems using data collected in routine care to follow elderly patients to minimise avoidable hospitalisations that can cause adverse effects. Renal function, number of chronic conditions and medications are factors that are of significant importance. This study demonstrates the complexity of this patient group.

Comorbidities in heart failure patients that predict cardiovascular readmissions within 100 days—An observational study

by Mia Scholten, Jason Davidge, Björn Agvall, Anders Halling

Background

Heart failure (HF) commonly arises as a complication to cardiovascular diseases and is closely associated with various comorbidities. The impacts of these comorbidities in patients with HF are diverse. We aimed to analyze the increased risk for cardiovascular-related readmission within 100 days after discharge in patients with HF depending on their different comorbidities.

Methods

A population-based retrospective study was conducted in Region Halland with 5029 patients admitted to hospital with a diagnosis of HF during 2017–2019. The occurrence and number of comorbidities were recorded. Competing risk regression was employed to analyze the hazard ratio (HR) of 10 comorbidities for cardiovascular-related readmission within 100 days after discharge. A composite measure of the 10 common comorbidities was constructed with the comorbidities as dichotomous indicator variables and Rasch analysis. Receiver operating characteristic (ROC) and area under curve (AUC) after logistic regression were used to estimate how well the model explained the probability of death or readmission within 100 days after discharge according to their individual comorbidity level.

Results

HF patients with atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, peripheral artery disease or diabetes mellitus as comorbidities had an increased HR for readmission within 100 days after discharge. When these comorbidities were adjusted together, only atrial fibrillation, chronic kidney disease and chronic obstructive pulmonary disease had an increased HR for readmission. ROC analysis after the most complete models using logistic regression with the comorbidities as dichotomous indicator variables or Rasch analysis had a low AUC.

Conclusions

Atrial fibrillation, chronic kidney disease or chronic obstructive pulmonary disease were significantly associated with increased risk for readmission in HF patients, but ROC analysis showed a low AUC, which indicates that other factors are more important for predicting the increased risk of readmission.

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