Literature DB >> 28416652

Simplification of the HOSPITAL score for predicting 30-day readmissions.

Carole E Aubert1, Jeffrey L Schnipper2,3, Mark V Williams4, Edmondo J Robinson5, Eyal Zimlichman6, Eduard E Vasilevskis7,8,9, Sunil Kripalani7,8, Joshua P Metlay10, Tamara Wallington11, Grant S Fletcher12, Andrew D Auerbach13, Drahomir Aujesky1, Jacques D Donzé1,2,3.   

Abstract

OBJECTIVE: The HOSPITAL score has been widely validated and accurately identifies high-risk patients who may mostly benefit from transition care interventions. Although this score is easy to use, it has the potential to be simplified without impacting its performance. We aimed to validate a simplified version of the HOSPITAL score for predicting patients likely to be readmitted. DESIGN AND
SETTING: Retrospective study in 9 large hospitals across 4 countries, from January through December 2011. PARTICIPANTS: We included all consecutively discharged medical patients. We excluded patients who died before discharge or were transferred to another acute care facility. MEASUREMENTS: The primary outcome was any 30-day potentially avoidable readmission. We simplified the score as follows: (1) 'discharge from an oncology division' was replaced by 'cancer diagnosis or discharge from an oncology division'; (2) 'any procedure' was left out; (3) patients were categorised into two risk groups (unlikely and likely to be readmitted). The performance of the simplified HOSPITAL score was evaluated according to its overall accuracy, its discriminatory power and its calibration.
RESULTS: Thirty-day potentially avoidable readmission rate was 9.7% (n=11 307/117 065 patients discharged). Median of the simplified HOSPITAL score was 3 points (IQR 2-5). Overall accuracy was very good with a Brier score of 0.08 and discriminatory power remained good with a C-statistic of 0.69 (95% CI 0.68 to 0.69). The calibration was excellent when comparing the expected with the observed risk in the two risk categories.
CONCLUSIONS: The simplified HOSPITAL score has good performance for predicting 30-day readmission. Prognostic accuracy was similar to the original version, while its use is even easier. This simplified score may provide a good alternative to the original score depending on the setting. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Entities:  

Keywords:  Adverse events, epidemiology and detection; Hospital medicine; Risk management; Transitions in care

Mesh:

Substances:

Year:  2017        PMID: 28416652     DOI: 10.1136/bmjqs-2016-006239

Source DB:  PubMed          Journal:  BMJ Qual Saf        ISSN: 2044-5415            Impact factor:   7.035


  4 in total

1.  What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort.

Authors:  Tristan Struja; Ciril Baechli; Daniel Koch; Sebastian Haubitz; Andreas Eckart; Alexander Kutz; Martha Kaeslin; Beat Mueller; Philipp Schuetz
Journal:  J Gen Intern Med       Date:  2020-01-21       Impact factor: 5.128

2.  Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death.

Authors:  Yongkang Zhang; Yiye Zhang; Evan Sholle; Sajjad Abedian; Marianne Sharko; Meghan Reading Turchioe; Yiyuan Wu; Jessica S Ancker
Journal:  PLoS One       Date:  2020-06-25       Impact factor: 3.240

3.  Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan.

Authors:  Mei-Chin Su; Yi-Jen Wang; Tzeng-Ji Chen; Shiao-Hui Chiu; Hsiao-Ting Chang; Mei-Shu Huang; Li-Hui Hu; Chu-Chuan Li; Su-Ju Yang; Jau-Ching Wu; Yu-Chun Chen
Journal:  Int J Environ Res Public Health       Date:  2020-02-02       Impact factor: 3.390

4.  Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence.

Authors:  C Beau Hilton; Alex Milinovich; Christina Felix; Nirav Vakharia; Timothy Crone; Chris Donovan; Andrew Proctor; Aziz Nazha
Journal:  NPJ Digit Med       Date:  2020-04-03
  4 in total

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