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.
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.
Authors: Alexandra Ortego; David F Gaieski; Barry D Fuchs; Tiffanie Jones; Scott D Halpern; Dylan S Small; S Cham Sante; Byron Drumheller; Jason D Christie; Mark E Mikkelsen Journal: Crit Care Med Date: 2015-04 Impact factor: 7.598
Authors: Tiffanie K Jones; Barry D Fuchs; Dylan S Small; Scott D Halpern; Asaf Hanish; Craig A Umscheid; Charles A Baillie; Meeta Prasad Kerlin; David F Gaieski; Mark E Mikkelsen Journal: Ann Am Thorac Soc Date: 2015-06
Authors: Jerry H Gurwitz; Terry S Field; Jessica Ogarek; Jennifer Tjia; Sarah L Cutrona; Leslie R Harrold; Shawn J Gagne; Peggy Preusse; Jennifer L Donovan; Abir O Kanaan; George Reed; Lawrence Garber Journal: J Am Geriatr Soc Date: 2014-04-29 Impact factor: 5.562
Authors: Jacques D Donzé; Mark V Williams; Edmondo J Robinson; Eyal Zimlichman; Drahomir Aujesky; Eduard E Vasilevskis; Sunil Kripalani; Joshua P Metlay; Tamara Wallington; Grant S Fletcher; Andrew D Auerbach; Jeffrey L Schnipper Journal: JAMA Intern Med Date: 2016-04 Impact factor: 21.873
Authors: Robert E Burke; Jeffrey L Schnipper; Mark V Williams; Edmondo J Robinson; Eduard E Vasilevskis; Sunil Kripalani; Joshua P Metlay; Grant S Fletcher; Andrew D Auerbach; Jacques D Donzé Journal: Med Care Date: 2017-03 Impact factor: 2.983