Utility of models to predict 28-day or 30-day unplanned hospital readmissions: An updated systematic review

BMJ Open

Volumn 6, Issue 6, 2016, Pages

  Zhou, Huaqiong ^a,b   Della, Phillip R ^b   Roberts, Pamela ^b   Goh, Louise ^b   Dhaliwal, Satvinder S ^b  

^a PRINCESS MARGARET HOSPITAL FOR CHILDREN   (Australia)

^b CURTIN UNIVERSITY   (Australia)

Author keywords

EPIDEMIOLOGY; PUBLIC HEALTH

Indexed keywords

ANTIBIOTIC AGENT;

CARDIOVASCULAR DISEASE; HEART FAILURE; HOME CARE; HOSPITAL READMISSION; HUMAN; LABORATORY TEST; LENGTH OF STAY; META ANALYSIS; MULTICENTER STUDY (TOPIC); PNEUMONIA; RANDOMIZED CONTROLLED TRIAL (TOPIC); REVIEW; SYSTEMATIC REVIEW; SYSTEMATIC REVIEW (TOPIC); TELEMONITORING; UNPLANNED HOSPITAL READMISSION; COMORBIDITY; PROGNOSIS; RISK ASSESSMENT; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TIME FACTOR;

COMORBIDITY; HUMANS; LENGTH OF STAY; MODELS, STATISTICAL; PATIENT READMISSION; PROGNOSIS; RISK ASSESSMENT; TIME FACTORS;

EID: 84977156485     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2016-011060     Document Type: Review

References (106)

1. Bosco JA III, Karkenny AJ, Hutzler LH, et al. Cost burden of 30-day readmissions following Medicare total hip and knee arthroplasty. J Arthroplasty 2014;29:903-5.
2. Joynt KE, Jha AK. Thirty-day readmissions-truth and consequences. N Engl J Med 2012;366:1366-9.
3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009;360:1418-28.
4. Centers for Medicare & Medicaid Services. Table 5.1b. Discharges, Total Days of Care, Total Charges, and Program Payments for Medicare Beneficiaries Discharged from Short-Stay Hospitals, by Type of Entitlement: Calendar Years 1972-2006. 2007: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareMedicaidStatSupp/2007.html
5. Health & Social Care Information Centre. Hospital Episode Statistics, Admitted Patient Care-England, 2010-11 [NS]. 2011: http://www.hscic.gov.uk/pubs/hesadmitted1011
6. Stefan MS, Pekow PS, Nsa W, et al. Hospital performance measures and 30-day readmission rates. J Gen Intern Med 2013;28:377-85.
7. Wish JB. The role of 30-day readmission as a measure of quality. Clin J Am Soc Nephrol 2014;9:440-2.
8. U.S. Department of Health & Human Services. Readmissions to hospital: percentage of unplanned readmissions within 28 days of separation, during the 6-month time period. 2012. http://www.qualitymeasures.ahrq.gov.
9. Australian Institute of Health and Welfare. National healthcare agreement: PI23-Unplanned hospital readmission rates. 2013. http://meteor.aihw.gov.au/content/index.phtml/itemId/497129
10. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306:1688-98.
11. Billings J, Blunt I, Steventon A, et al. Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ Open 2012;2:pii: e001667.
12. Choudhry SA, Li J, Davis D, et al. A public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model. Online J Public Health Inform 2013;5:219.
13. Cotter PE, Bhalla VK, Wallis SJ, et al. Predicting readmissions: poor performance of the LACE index in an older UK population. Age Ageing 2012;41:784-9.
14. Donzé J, Aujesky D, Williams D, et al. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013;173:632-8.
15. Hebert C, Shivade C, Foraker R, et al. Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study. BMC Med Inform Decis Mak 2014;14:65.
16. Escobar GJ, Ragins A, Scheirer P, et al. Nonelective rehospitalizations and postdischarge mortality: predictive models suitable for use in real time. Med Care 2015;53:916-23.
17. Yu S, Farooq F, van Esbroeck A, et al. Predicting readmission risk with institution-specific prediction models. Artif Intell Med 2015;65:89-96.
18. Baillie CA, VanZandbergen C, Tait G, et al. The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission. J Hosp Med 2013;8:689-95.
19. Gildersleeve R, Cooper P. Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Appl Clin Inform 2013;4:153-69.
20. Kruse RL, Hays HD, Madsen RW, et al. Risk factors for all-cause hospital readmission within 30 days of hospital discharge. J Clin Outcomes Manage 2013;20:203-14.
21. Richmond DM. Socioeconomic predictors of 30-day hospital readmission of elderly patients with initial discharge destination of home health care. University of Alabama at Birmingham, 2013.
22. Shulan M, Gao K, Moore CD. Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci 2013;16:167-75.
23. van Walraven C, Wong J, Forster AJ, et al. Predicting post-discharge death or readmission: deterioration of model performance in population having multiple admissions per patient. J Eval Clin Pract 2013;19:1012-18.
24. Khan A, Malone ML, Pagel P, et al. An electronic medical record-derived real-time assessment scale for hospital readmission in the elderly. WMJ 2012;111:119-23.
25. Lee EW. Selecting the best prediction model for readmission. J Prev Med Public Health 2012;45:259-66.
26. van Walraven C, Wong J, Forster AJ. Derivation and validation of a diagnostic score based on case-mix groups to predict 30-day death or urgent readmission. Open Med 2012;6:e90-100.
27. van Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med 2012;6: e80-90.
28. Iannuzzi JC, Chandra A, Kelly KN, et al. Risk score for unplanned vascular readmissions. J Vasc Surg 2014;59:1340-47.e1.
29. Keyhani S, Myers LJ, Cheng E, et al. Effect of clinical and social risk factors on hospital profiling for stroke readmission: a cohort study. Ann Intern Med 2014;161:775-84.
30. Rana S, Tran T, Wei L, et al. Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust Health Rev 2014;38:377-82.
31. Shahian DM, He X, O'Brien S, et al. Development of a clinical registry-based 30-day readmission measure for coronary artery bypass grafting surgery. Circulation 2014;130:399-409.
32. Shams I, Ajorlou S, Yang K. A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Manag Sci 2015;18:19-34.
33. Sharif R, Parekh TM, Pierson KS, et al. Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014;11:685-94.
34. Lucas DJ, Haider A, Haut E, et al. Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP. Ann Surg 2013;258:430-9.
35. Wallmann R, Llorca J, Gomez-Acebo I, et al. Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int J Cardiol 2013;164:193-200.
36. Wasfy JH, Rosenfield K, Zelevinsky K, et al. A Prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention. Circ Cardiovasc Qual Outcomes 2013;6:429-35.
37. Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes 2011;4:243-52.
38. Betihavas V, Frost SA, Newton PJ, et al. An absolute risk prediction model to determine unplanned cardiovascular readmissions for adults with chronic heart failure. Heart Lung Circ 2015;24:1068-73.
39. Di Tano G, De Maria R, Gonzini L, et al. The 30-day metric in acute heart failure revisited: data from IN-HF Outcome, an Italian nationwide cardiology registry. Eur J Heart Fail 2015;17:1032-41.
40. Huynh QL, Saito M, Blizzard CL, et al. Roles of nonclinical and clinical data in prediction of 30-day rehospitalization or death among heart failure patients. J Card Fail 2015;21:374-81.
41. Raposeiras-Roubin S, Abu-Assi E, Cambeiro-Gonzalez C, et al. Mortality and cardiovascular morbidity within 30 days of discharge following acute coronary syndrome in a contemporary European cohort of patients: how can early risk prediction be improved? The six-month GRACE risk score. Revista Portuguesa de Cardiologia 2015;34:383-91.
42. Sudhakar S, Zhang W, Kuo YF, et al. Validation of the readmission risk score in heart failure patients at a tertiary hospital. J Card Fail 2015;21:885-91.
43. Fleming LM, Gavin M, Piatkowski G, et al. Derivation and validation of a 30-day heart failure readmission model. Am J Cardiol 2014;114:1379-82.
44. Wang H, Robinson RD, Johnson C, et al. Using the LACE index to predict hospital readmissions in congestive heart failure patients. BMC Cardiovasc Disord 2014;14:97.
45. Eapen ZJ, Liang L, Fonarow GC, et al. Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients. JACC Heart Fail 2013;1:245-51.
46. Zai AH, Ronquillo JG, Nieves R, et al. Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program. Int J Telemed Appl 2013;2013:305819.
47. Au AG, McAlister FA, Bakal JA, et al. Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J 2012;164:365-72.
48. Watson AJ, O'Rourke J, Jethwani K, et al. Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure. Psychosomatics 2011;52:319-27.
49. Mather JF, Fortunato GJ, Ash JL, et al. Prediction of pneumonia 30-day readmissions: a single-center attempt to increase model performance. Respir Care 2014;59:199-208.
50. Lindenauer PK, Normand SL, Drye EE, et al. Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. J Hosp Med 2011;6:142-50.
51. Shadmi E, Flaks-Manov N, Hoshen M, et al. Predicting 30-day readmissions with preadmission electronic health record data. Med Care 2015;53:283-9.
52. Tsui E, Au SY, Wong CP, et al. Development of an automated model to predict the risk of elderly emergency medical admissions within a month following an index hospital visit: a Hong Kong experience. Health Inform J 2015;21:46-56.
53. Donze J, Lipsitz S, Schnipper JL. Risk factors for potentially avoidable readmissions due to end-of-life care issues. J Hosp Med 2014;9:310-14.
54. He D, Mathews SC, Kalloo AN, et al. Mining high-dimensional administrative claims data to predict early hospital readmissions. J Am Med Inform Assoc 2014;21:272-9.
55. Taha M, Pal A, Mahnken JD, et al. Derivation and validation of a formula to estimate risk for 30-day readmission in medical patients. Int J Qual Health Care 2014;26:271-7.
56. Tan SY, Low LL, Yang Y, et al. Applicability of a previously validated readmission predictive index in medical patients in Singapore: a retrospective study. BMC Health Serv Res 2013;13:366.
57. Zapatero A, Barba R, Marco J, et al. Predictive model of readmission to internal medicine wards. Eur J Intern Med 2012;23:451-6.
58. Gruneir A, Dhalla IA, van Walraven C, et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med 2011;5:e104-e11.
59. Singal AG, Rahimi RS, Clark C, et al. An automated model using electronic medical record data identifies patients with cirrhosis at high risk for readmission. Clin Gastroenterol Hepatol 2013;11:1335-41.e1.
60. Volk ML, Tocco RS, Bazick J, et al. Hospital re-admissions among patients with decompensated cirrhosis. Am J Gastroenterol 2012;107:247-52.
61. Perkins RM, Rahman A, Bucaloiu ID, et al. Readmission after hospitalization for heart failure among patients with chronic kidney disease: a prediction model. Clin Nephrol 2013;80:433-40.
62. Nijhawan AE, Clark C, Kaplan R, et al. An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients. J Acquir Immune Defic Syndr 2012;61:349-58.
63. Whitlock TL, Tignor A, Webster EM, et al. A scoring system to predict readmission of patients with acute pancreatitis to the hospital within thirty days of discharge. Clin Gastroenterol Hepatol 2011;9:175-80; quiz e18.
64. Taber DJ, Palanisamy AP, Srinivas TR, et al. Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation. Transplantation 2015;99:324-30.
65. Lawson EH, Hall BL, Louie R, et al. Identification of modifiable factors for reducing readmission after colectomy: a national analysis. Surgery 2014;155:754-66.
66. Iannuzzi JC, Fleming FJ, Kelly KN, et al. Risk scoring can predict readmission after endocrine surgery. Surgery 2014;156:1432-38; discussion 1438-40.
67. Mesko NW, Bachmann KR, Kovacevic D, et al. Thirty-day readmission following total hip and knee arthroplasty: a preliminary single institution predictive model. J Arthroplasty 2014;29:1532-8.
68. Moore L, Stelfox HT, Turgeon AF, et al. Derivation and validation of a quality indicator for 30-day unplanned hospital readmission to evaluate trauma care. J Trauma Acute Care Surg 2014;76:1310-16.
69. Graboyes EM, Liou TN, Kallogjeri D, et al. Risk factors for unplanned hospital readmission in otolaryngology patients. Otolaryngol Head Neck Surg 2013;149:562-71.
70. Vigod SN, Kurdyak PA, Seitz D, et al. READMIT: a clinical risk index to predict 30-day readmission after discharge from acute psychiatric units. J Psychiatr Res 2015;61:205-13.
71. Hosmer DW, Lemeshow S. Applied logistic regression. John Wiley and Sons: New York, 2000.
72. Hayden JA, Cote P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med 2006;144:427-37.
73. Brown SE, Ratcliffe SJ, Kahn JM, et al. The epidemiology of intensive care unit readmissions in the United States. Am J Respir Crit Care Med 2012;185:955-64.
74. Abu-Awwad R, Buran G. Predictors of early readmission to the intensive care unit. Chest 2012;142:280A.
75. Weidmann ZM, Breidthardt T, Twerenbold R, et al. Prediction of mortality using quantification of renal function in acute heart failure. Int J Cardiol 2015;201:650-7.
76. Yu X, Sun Y, Zhao Y, et al. Prognostic value of plasma galectin-3 levels in patients with coronary heart disease and chronic heart failure. Int Heart J 2015;56:314-18.
77. Alassaad A, Melhus H, Hammarlund-Udenaes M, et al. A tool for prediction of risk of rehospitalisation and mortality in the hospitalised elderly: secondary analysis of clinical trial data. BMJ Open 2015;5:e007259.
78. Ambler GK, Brooks DE, Al Zuhir N, et al. Effect of frailty on shortand mid-term outcomes in vascular surgical patients. Br J Surg 2015;102:638-45.
79. Bohlen Delgado AP, Marmelo B, Abreu L, et al. A score of risk of events in elderly patients with acute myocardial infarction without ST elevation. Eur J Heart Fail 2015;17:345.
80. Brudvik KW, Mise Y, Conrad C, et al. Definition of readmission in 3,041 patients undergoing hepatectomy. J Am Coll Surg 2015;221:38-46.
81. Cui Y, Metge C, Ye X, et al. Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada. J Health Serv Res Policy 2015;20:83-91.
82. Fry DE, Pine M, Locke D, et al. Composite measurement of outcomes in Medicare inpatient laparoscopic cholecystectomy. J Am Coll Surg 2015;221:102-9.
83. Sciatti E, Vizzardi E, Bonadei I, et al. Prognostic value of RV isovolumic acceleration and tissue strain in moderate HFrEF. Eur J Clin Invest 2015;45:1052-9.
84. Shirakabe A, Hata N, Kobayashi N, et al. Serum heart-type fatty acid-binding protein level can be used to detect acute kidney injury on admission and predict an adverse outcome in patients with acute heart failure. Circ J 2015;79:119-28.
85. Sinning JM, Wollert KC, Sedaghat A, et al. Risk scores and biomarkers for the prediction of 1-year outcome after transcatheter aortic valve replacement. Am Heart J 2015;170:821-9.
86. Sur MD, Namm JP, Hemmerich JA, et al. Radiographic sarcopenia and self-reported exhaustion independently predict NSQIP serious complications after pancreaticoduodenectomy in older adults. Ann Surg Oncol 2015;22:3897-904.
87. Takahashi T, Kumamaru M, Jenkins S, et al. In-patient step count predicts re-hospitalization after cardiac surgery. J Cardiol 2015;66:286-91.
88. Ben-Chetrit E, Chen-Shuali C, Zimran E, et al. A simplified scoring tool for prediction of readmission in elderly patients hospitalized in internal medicine departments. Isr Med Assoc J 2012;14:752-6.
89. Hasin T, Marmor Y, Kremers W, et al. Readmissions after implantation of axial flow left ventricular assist device. J Am Coll Cardiol 2013;61:153-63.
90. Lemke KW, Weiner JP, Clark JM. Development and validation of a model for predicting inpatient hospitalization. Med Care 2012;50:131-9.
91. Allison GM, Muldoon EG, Kent DM, et al. Prediction model for 30-day hospital readmissions among patients discharged receiving outpatient parenteral antibiotic therapy. Clin Infect Dis 2014;58:812-19.
92. Allaudeen N, Schnipper J, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011;26:771-6.
93. Van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010;182:551-7.
94. Halfon P, Eggli Y, Prêtre-Rohrbach I, et al. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care 2006;44:972-81.
95. Halfon P, Eggli, Y, van Melle G, et al. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002;55:573-87.
96. Hechenbleikner EM, Makary MA, Samarov DV, et al. Hospital readmission by method of data collection. J Am Coll Surg 2013;216:1150-8.
97. Van Walraven C, Bennett C, Jennings A, et al. Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ 2011;183:e391-402.
98. van Walraven C, Jennings A, Forster AJ. A meta-analysis of hospital 30-day avoidable readmission rates. J Eval Clin Pract 2012;18:1211-18.
99. Aase K, Laugaland KA, Dyrstad DN, et al. Quality and safety in transitional care of the elderly: the study protocol of a case study research design (phase 1). BMJ Open 2013;3:pii: e003506.
100. Garvey KC, Wolpert HA, Rhodes ET, et al. Health care transition in patients with Type 1 diabetes. Diabetes Care 2012;35:1716-22.
101. Della P, Michael R. Evaluating global trends in clinical communication. ACORN 2012;25:19-20.
102. Rothwell M, Jukka C, Lum E, et al. Retrospective analysis of emergency readmissions to rural and regional hospitals. J Pharm Pract Res 2011;41:413-20.
103. Russell L, Doggett J, Dawda P, et al. Patient safety-handover of care between primary and acute care. 2011. http://leadclinicians. health.gov.au/internet/lcg/publishing.nsf/Content/0BC80B14F241017BCA257A590021F2E8/$File/Transitions%20of %20Care%20report%20FINAL%207May13.pdf
104. Feudtner C, Levin JE, Srivastava R, et al. How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study. Pediatrics 2009;123:286-93.
105. Tahiri Y, Fischer JP, Wink JD, et al. Analysis of risk factors associated with 30-day readmissions following pediatric plastic surgery: a review of 5376 procedures. Plast Reconstr Surg 2015;135:521-9.
106. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology 2010;21:128-38.