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The number of discharge medications predicts thirty-day hospital readmission: a cohort study

Overview of attention for article published in BMC Health Services Research, July 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

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37 tweeters
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1 peer review site

Citations

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61 Dimensions

Readers on

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115 Mendeley
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Title
The number of discharge medications predicts thirty-day hospital readmission: a cohort study
Published in
BMC Health Services Research, July 2015
DOI 10.1186/s12913-015-0950-9
Pubmed ID
Authors

David Picker, Kevin Heard, Thomas C. Bailey, Nathan R. Martin, Gina N. LaRossa, Marin H. Kollef

Abstract

Hospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined. To determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications. The final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17-1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643-0.679]). The number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 114 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 17%
Student > Ph. D. Student 17 15%
Student > Bachelor 14 12%
Other 12 10%
Researcher 11 10%
Other 20 17%
Unknown 22 19%
Readers by discipline Count As %
Medicine and Dentistry 31 27%
Nursing and Health Professions 19 17%
Pharmacology, Toxicology and Pharmaceutical Science 12 10%
Engineering 5 4%
Social Sciences 4 3%
Other 19 17%
Unknown 25 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2019.
All research outputs
#912,483
of 17,342,898 outputs
Outputs from BMC Health Services Research
#287
of 5,875 outputs
Outputs of similar age
#15,288
of 240,615 outputs
Outputs of similar age from BMC Health Services Research
#1
of 1 outputs
Altmetric has tracked 17,342,898 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,875 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 240,615 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them