%0 Journal Article %1 Donze2013 %A Donzé, Jacques %A Aujesky, Drahomir %A Williams, Deborah %A Schnipper, Jeffrey L %D 2013 %J JAMA internal medicine %K 80andover Adult Aged Algorithms Female Humans LogisticModels Male MiddleAged Models PatientDischarge PatientDischarge:statistics&numericaldata PatientReadmission PatientReadmission:statistics&numericaldata RetrospectiveStudies Theoretical %N 8 %P 632-8 %R 10.1001/jamainternmed.2013.3023 %T Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. %U http://www.ncbi.nlm.nih.gov/pubmed/23529115 %V 173 %X IMPORTANCE: Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit. OBJECTIVE: To derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge. DESIGN: Retrospective cohort study. SETTING: Academic medical center in Boston, Massachusetts. PARTICIPANTS: All patient discharges from any medical services between July 1, 2009, and June 30, 2010. MAIN OUTCOME MEASURES: Potentially avoidable 30-day readmissions to 3 hospitals of the Partners HealthCare network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort. RESULTS: Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30-day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: h emoglobin at discharge, discharge from an o ncology service, s odium level at discharge, p rocedure during the index admission, i ndex t ype of admission, number of a dmissions during the last 12 months, and l ength of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration. CONCLUSIONS AND RELEVANCE: This simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions. %@ 2168-6114; 2168-6106

@article{Donze2013, abstract = {IMPORTANCE: Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit. OBJECTIVE: To derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge. DESIGN: Retrospective cohort study. SETTING: Academic medical center in Boston, Massachusetts. PARTICIPANTS: All patient discharges from any medical services between July 1, 2009, and June 30, 2010. MAIN OUTCOME MEASURES: Potentially avoidable 30-day readmissions to 3 hospitals of the Partners HealthCare network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort. RESULTS: Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30-day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: h emoglobin at discharge, discharge from an o ncology service, s odium level at discharge, p rocedure during the index admission, i ndex t ype of admission, number of a dmissions during the last 12 months, and l ength of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration. CONCLUSIONS AND RELEVANCE: This simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Donzé, Jacques and Aujesky, Drahomir and Williams, Deborah and Schnipper, Jeffrey L}, biburl = {https://www.bibsonomy.org/bibtex/2378351d2d81b5310c817c870164f515c/jepcastel}, doi = {10.1001/jamainternmed.2013.3023}, interhash = {8b779fa7720c846255aab7b20199b48d}, intrahash = {378351d2d81b5310c817c870164f515c}, isbn = {2168-6114; 2168-6106}, issn = {2168-6114}, journal = {JAMA internal medicine}, keywords = {80andover Adult Aged Algorithms Female Humans LogisticModels Male MiddleAged Models PatientDischarge PatientDischarge:statistics&numericaldata PatientReadmission PatientReadmission:statistics&numericaldata RetrospectiveStudies Theoretical}, month = {4}, note = {7278<br/>JID: 101589534; aheadofprint; SO: JAMA Intern Med. 2013 Mar 25:1-7. doi: 10.1001/jamainternmed.2013.3023.;<br/>Models predictius; Proves diagnòstiques}, number = 8, pages = {632-8}, pmid = {23529115}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23529115}, volume = 173, year = 2013 }