Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30).
BMJ Open
; 2(4)2012.
Article
em En
| MEDLINE
| ID: mdl-22885591
ABSTRACT
OBJECTIVES:
To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes.DESIGN:
Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping.SETTING:
HES data covering all NHS hospital admissions in England.PARTICIPANTS:
The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868). MAIN OUTCOMEMEASURES:
Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds.RESULTS:
The algorithm produces a 'risk score' ranging (0-1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70).CONCLUSIONS:
We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice.
Texto completo:
1
Bases de dados:
MEDLINE
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
BMJ Open
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos