Readmissions of medical patients: an external validation of two existing prediction scores.

BACKGROUND Hospital readmissions are increasingly used as a quality indicator with a belief that they are a marker of poor care and have led to financial penalties in UK and USA. Risk scoring systems, such as LACE and HOSPITAL, have been proposed as tools for identifying patients at high risk of readmission but have not been validated in international populations. AIM To perform an external independent validation of the HOSPITAL and LACE scores. DESIGN An unplanned secondary cohort study. METHODS Patients admitted to the medical admission unit at the Hospital of South West Jutland (10/2008-2/2009; 2/2010-5/2010) and the Odense University Hospital (6/2009-8/2011) were analysed. Validation of the scores using 30 day readmissions as the endpoint was performed. RESULTS A total of 19 277 patients fulfilled the inclusion criteria. Median age was 67 (range 18-107) years and 8977 (46.6%) were female. The LACE score had a discriminatory power of 0.648 with poor calibration and the HOSPITAL score had a discriminatory power of 0.661 with poor calibration. The HOSPITAL score was significantly better than the LACE score for identifying patients at risk of 30 day readmission (P < 0.001). The discriminatory power of both scores decreased with increasing age. CONCLUSION Readmissions are a complex phenomenon with not only medical conditions contributing but also system, cultural and environmental factors exerting a significant influence. It is possible that the heterogeneity of the population and health care systems may prohibit the creation of a simple prediction tool that can be used internationally.

[1]  T. Kent,et al.  Readmission after major pancreatic resection: a necessary evil? , 2011, Journal of the American College of Surgeons.

[2]  Zosia Kmietowicz.  Hospitals will be fined for emergency readmissions, says Lansley , 2010, BMJ : British Medical Journal.

[3]  Robert P Kocher,et al.  Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. , 2011, JAMA.

[4]  Elsebeth Lynge,et al.  The Danish National Patient Register , 2011, Scandinavian journal of public health.

[5]  Arpana R. Vidyarthi,et al.  Redefining readmission risk factors for general medicine patients. , 2011, Journal of hospital medicine.

[6]  P. Austin,et al.  Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community , 2010, Canadian Medical Association Journal.

[7]  T M Morgan,et al.  Functional outcomes of acute medical illness and hospitalization in older persons. , 1996, Archives of internal medicine.

[8]  M. Makary,et al.  Influence of patient, physician, and hospital factors on 30-day readmission following pancreatoduodenectomy in the United States. , 2013, JAMA surgery.

[9]  S. Conroy,et al.  Understanding readmissions: An in-depth review of 50 patients readmitted back to an acute hospital within 30 days , 2013 .

[10]  Chin-Shang Li,et al.  Hospital Readmissions: Necessary Evil or Preventable Target for Quality Improvement , 2014, Annals of surgery.

[11]  D. Wennberg,et al.  Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients , 2006, BMJ : British Medical Journal.

[12]  Amanda H. Salanitro,et al.  Risk prediction models for hospital readmission: a systematic review. , 2011, JAMA.

[13]  Sung-joon Min,et al.  Posthospital care transitions: patterns, complications, and risk identification. , 2004, Health services research.

[14]  W G Henderson,et al.  Predicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions. , 2000, Journal of clinical epidemiology.

[15]  T. Rea,et al.  Prediction of critical illness during out-of-hospital emergency care. , 2010, JAMA.

[16]  C. Pedersen,et al.  The Danish Civil Registration System , 2011, Scandinavian journal of public health.

[17]  M. Coory,et al.  Using routine inpatient data to identify patients at risk of hospital readmission , 2009, BMC health services research.

[18]  T A Brennan,et al.  Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan. , 1999, The American journal of medicine.

[19]  Mark V. Williams,et al.  Rehospitalizations among patients in the Medicare fee-for-service program. , 2009, The New England journal of medicine.

[20]  J. Schnipper,et al.  Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. , 2013, JAMA internal medicine.

[21]  PRISMA Analysis of 30 Day Readmissions to a Tertiary Cancer Hospital. , 2015, Acute medicine.

[22]  Reliability of readmission rates as a hospital quality measure in cardiac surgery. , 2014, The Annals of thoracic surgery.

[23]  Azeem Majeed,et al.  Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. , 2006, Journal of the Royal Society of Medicine.

[24]  E. Blackstone,et al.  Are all readmissions bad readmissions? , 2010, The New England journal of medicine.

[25]  V. K. Bhalla,et al.  Predicting readmissions: poor performance of the LACE index in an older UK population. , 2012, Age and ageing.