In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 11 ( 2021-11-23), p. e0259864-
Abstract:
Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort. Methods A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low , medium , and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression. Results The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575–0.635). When using stratified groups ( low , medium , high ), patients in the high -risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33–5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort. Conclusion This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high -risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0259864
DOI:
10.1371/journal.pone.0259864.g001
DOI:
10.1371/journal.pone.0259864.g002
DOI:
10.1371/journal.pone.0259864.g003
DOI:
10.1371/journal.pone.0259864.t001
DOI:
10.1371/journal.pone.0259864.t002
DOI:
10.1371/journal.pone.0259864.t003
DOI:
10.1371/journal.pone.0259864.t004
DOI:
10.1371/journal.pone.0259864.t005
DOI:
10.1371/journal.pone.0259864.s001
DOI:
10.1371/journal.pone.0259864.s002
DOI:
10.1371/journal.pone.0259864.s003
DOI:
10.1371/journal.pone.0259864.s004
DOI:
10.1371/journal.pone.0259864.s005
DOI:
10.1371/journal.pone.0259864.s006
DOI:
10.1371/journal.pone.0259864.s007
DOI:
10.1371/journal.pone.0259864.s008
DOI:
10.1371/journal.pone.0259864.s009
DOI:
10.1371/journal.pone.0259864.r001
DOI:
10.1371/journal.pone.0259864.r002
DOI:
10.1371/journal.pone.0259864.r003
DOI:
10.1371/journal.pone.0259864.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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