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Procedure-based severity index for inpatients: development and validation using administrative database

Overview of attention for article published in BMC Health Services Research, July 2015
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Title
Procedure-based severity index for inpatients: development and validation using administrative database
Published in
BMC Health Services Research, July 2015
DOI 10.1186/s12913-015-0889-x
Pubmed ID
Authors

Hayato Yamana, Hiroki Matsui, Kiyohide Fushimi, Hideo Yasunaga

Abstract

Risk adjustment is important in studies using administrative databases. Although utilization of diagnostic and therapeutic procedures can represent patient severity, the usability of procedure records in risk adjustment is not well-documented. Therefore, we aimed to develop and validate a severity index calculable from procedure records. Using the Japanese nationwide Diagnosis Procedure Combination database of acute-care hospitals, we identified patients discharged between 1 April 2012 and 31 March 2013 with an admission-precipitating diagnosis of acute myocardial infarction, congestive heart failure, acute cerebrovascular disease, gastrointestinal hemorrhage, pneumonia, or septicemia. Subjects were randomly assigned to the derivation cohort or the validation cohort. In the derivation cohort, we used multivariable logistic regression analysis to identify procedures performed on admission day which were significantly associated with in-hospital death, and a point corresponding to regression coefficient was assigned to each procedure. An index was then calculated in the validation cohort as sum of points for performed procedures, and performance of mortality-predicting model using the index and other patient characteristics was evaluated. Of the 539 385 hospitalizations included, 270 054 and 269 331 were assigned to the derivation and validation cohorts, respectively. Nineteen significant procedures were identified from the derivation cohort with points ranging from -3 to 23, producing a severity index with possible range of -13 to 69. In the validation cohort, c-statistic of mortality-predicting model was 0.767 (95 % confidence interval: 0.764-0.770). The ω-statistic representing contribution of the index relative to other variables was 1.09 (95 % confidence interval: 1.03-1.17). Procedure-based severity index predicted mortality well, suggesting that procedure records in administrative database are useful for risk adjustment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 63 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 17%
Researcher 10 16%
Student > Ph. D. Student 10 16%
Other 7 11%
Student > Bachelor 3 5%
Other 12 19%
Unknown 11 17%
Readers by discipline Count As %
Medicine and Dentistry 28 44%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Computer Science 3 5%
Agricultural and Biological Sciences 3 5%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 8 13%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 July 2015.
All research outputs
#14,818,336
of 22,816,807 outputs
Outputs from BMC Health Services Research
#5,367
of 7,636 outputs
Outputs of similar age
#144,373
of 262,361 outputs
Outputs of similar age from BMC Health Services Research
#82
of 111 outputs
Altmetric has tracked 22,816,807 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,636 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 262,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.