A Clustering-based Approach to Determine a Standardized Statistic for Daily Activities of Elderly Living Alone

Gerka, Alexander; Lins, Christian; Pfingsthorn, Max; Eichelberg, Marco; Müller, Sebastian; Stolle, Christian; Hein, Andreas

doi:10.5220/0007369302640271

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“…Three of six preliminarily computed hierarchical clusters were included in the modelling analysis as confounding variables: the mobility cluster, the dependency on the activities of daily living cluster and the mental state cluster 46. These three clusters were selected because of their significant contributions to hospital readmissions 48–50. The data set of medical information was composed of patients’ principal medical diagnosis and four secondary medical diagnoses, based on ICD-10.…”

Section: Methodsmentioning

confidence: 99%

“…46 These three clusters were selected because of their significant contributions to hospital readmissions. [48][49][50] The data set of medical information was composed of patients' principal medical diagnosis and four secondary medical diagnoses, based on ICD-10. Finally, the year of hospitalisation was introduced as a control variable, based on the fact that earlier admission to hospital during this period led to a higher probability of unplanned readmissions during the entire period covered.…”

Section: Health Variablesmentioning

confidence: 99%

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Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

et al. 2021

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ObjectivesThe present study analysed 4 years of a hospital register (2015–2018) to determine the risk of 30-day hospital readmission associated with the medical conditions and drug regimens of polymedicated, older inpatients discharged home.DesignRegistry-based cohort study.SettingValais Hospital—a public general hospital centre in the French-speaking part of Switzerland.ParticipantsWe explored the electronic records of 20 422 inpatient stays by polymedicated, home-dwelling older adults held in the hospital’s patient register. We identified 13 802 hospital readmissions involving 8878 separate patients over 64 years old.Outcome measuresSociodemographic characteristics, medical conditions and drug regimen data associated with risk of readmission within 30 days of discharge.ResultsThe overall 30-day hospital readmission rate was 7.8%. Adjusted multivariate analyses revealed increased risk of hospital readmission for patients with longer hospital length of stay (OR=1.014 per additional day; 95% CI 1.006 to 1.021), impaired mobility (OR=1.218; 95% CI 1.039 to 1.427), multimorbidity (OR=1.419 per additional International Classification of Diseases, 10th Revision condition; 95% CI 1.282 to 1.572), tumorous disease (OR=2.538; 95% CI 2.089 to 3.082), polypharmacy (OR=1.043 per additional drug prescribed; 95% CI 1.028 to 1.058), and certain specific drugs, including antiemetics and antinauseants (OR=3.216 per additional drug unit taken; 95% CI 1.842 to 5.617), antihypertensives (OR=1.771; 95% CI 1.287 to 2.438), drugs for functional gastrointestinal disorders (OR=1.424; 95% CI 1.166 to 1.739), systemic hormonal preparations (OR=1.207; 95% CI 1.052 to 1.385) and vitamins (OR=1.201; 95% CI 1.049 to 1.374), as well as concurrent use of beta-blocking agents and drugs for acid-related disorders (OR=1.367; 95% CI 1.046 to 1.788).ConclusionsThirty-day hospital readmission risk was associated with longer hospital length of stay, health disorders, polypharmacy and drug regimens. The drug regimen patterns increasing the risk of hospital readmission were very heterogeneous. Further research is needed to explore hospital readmissions caused solely by specific drugs and drug–drug interactions.

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Section: Methodsmentioning

confidence: 99%