Appl Clin Inform 2014; 05(03): 773-788
DOI: 10.4338/ACI-2014-04-RA-0040
Research Article
Schattauer GmbH

Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks

C.H. Olson
1   Biomedical Health Informatics, University of Minnesota, Minneapolis, Minnesota
,
M. Dierich
2   School of Nursing, University of Minnesota, Minneapolis, Minnesota
,
T. Adam
3   Pharmaceutical Care & Health Systems, University of Minnesota Minneapolis, Minnesota
,
B.L Westra
2   School of Nursing, University of Minnesota, Minneapolis, Minnesota
› Author Affiliations
Further Information

Correspondence to:

Catherine H Olson PhD
PhD Candidate, Biomedical Health Informatics
University of Minnesota
330 Diehl Hall
505 Essex Street SE
Minneapolis, MN 55455
Phone: 612–626–3348   

Publication History

received: 23 April 2014

accepted: 16 July 2014

Publication Date:
19 December 2017 (online)

 

Summary

Background: Unnecessary hospital readmissions are costly for the U.S. health care system. An automated algorithm was developed to target this problem and proven to predict elderly patients at greater risk of rehospitalization based on their medication regimens.

Objective: Improve the algorithm for predicting elderly patients’ risks for readmission by optimizing the sensitivity of its medication criteria.

Methods: Outcome and Assessment Information Set (OASIS) and medication data were reused from a study that defined and tested an algorithm for assessing rehospitalization risks of 911 patients from 15 Medicare-certified home health care agencies. Odds Ratio analyses, literature reviews and clinical judgments were used to adjust the scoring of patients’ High Risk Medication Regimens (HRMRs). Receiver Operating Characteristic (ROC) analysis evaluated whether these adjustments improved the predictive strength of the algorithm’s components.

Results: HRMR scores are composed of polypharmacy (number of drugs), potentially inappropriate medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, dose frequency, instructions or administration). Strongest ROC results for the HRMR components were Areas Under the Curve (AUC) of .68 for polypharmacy when excluding supplements; and .60 for PIM and .69 for MRCI using the original HRMR criteria. The “cut point” identifying MRCI scores as indicative of medication-related readmission risk was increased from 20 to 33.

Conclusion: The automated algorithm can predict elderly patients at risk of hospital readmissions and its underlying criteria is improved by a modification to its polypharmacy definition and MRCI cut point.

Citation: Olson CH, Dierich M, Adam T, Westra BL. Optimization of decision support tool using medication regimens to assess rehospitalization risks. Appl Clin Inf 2014; 5: 773–788

http://dx.doi.org/10.4338/ACI-2014-04-RA-0040


#

 


#

Conflicts of interest

The authors report no conflicts of interest in the production of this paper.

  • References

  • 1 Hung WW, Ross JS, Boockvar KS, Siu AL. Recent trends in chronic disease, impairment and disability among older adults in the united states. BMC Geriatr 2011; 11: 47.
  • 2 Qato DM, Alexander GC, Conti RM, Johnson M, Schumm P, Lindau ST. Use of prescription and over-the-counter medications and dietary supplements among older adults in the united states. JAMA 2008; 300 (24) 2867-2878.
  • 3 Adults and Older Adult Adverse Drug Event. U.S. Centers for Disease Control and Prevention 10/02/2012 cited 04/22/2014
  • 4 Freund T, Campbell SM, Geissler S, Kunz CU, Mahler C, Peters-Klimm F, Szecsenyi J. Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions. Ann Fam Med 2013; 11 (04) 363-370.
  • 5 Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: Current strategies and future directions. Annu Rev Med 2013; 65: 471-485.
  • 6 Willson MN, Greer CL, Weeks DL. Medication regimen complexity and hospital readmission for an adverse drug event. Ann Pharmacother 2014; 48 (01) 26-32.
  • 7 Morandi A, Bellelli G, Vasilevskis EE, Turco R, Guerini F, Torpilliesi T, Speciale S, Emiliani V, Gentile S, Schnelle J, Trabucchi M. Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: The role of polypharmacy, functional status, and length of stay. J Am Med Dir Assoc 2013; 14 (10) 761-767.
  • 8 Sganga F, Landi F, Ruggiero C, Corsonello A, Vetrano DL, Lattanzio F, Cherubini A, Bernabei R, Onder G. Polypharmacy and health outcomes among older adults discharged from hospital: Results from the CRIME study. Geriatr Gerontol Int 2014 Jan 28. [Epub ahead of print].
  • 9 Sehgal V, Bajwa SJ, Sehgal R, Bajaj A, Khaira U, Kresse V. Polypharmacy and potentially inappropriate medication use as the precipitating factor in readmissions to the hospital. J Family Med Prim Care 2013; 2 (02) 194-199.
  • 10 Price SD, Holman CD, Sanfilippo FM, Emery JD. Association between potentially inappropriate medications from the beers criteria and the risk of unplanned hospitalization in elderly patients. Ann Pharmacother 2014; 48 (01) 6-16.
  • 11 Price SD, Holman CD, Sanfilippo FM, Emery JD. Impact of specific beers criteria medications on associations between drug exposure and unplanned hospitalisation in elderly patients taking high-risk drugs: A case-time-control study in western australia. Drugs Aging 2014; 31 (04) 311-325.
  • 12 Wimmer BC, Dent E, Bell JS, Wiese MD, Chapman I, Johnell K, Visvanathan R. Medication regimen complexity and unplanned hospital readmissions in older people. Ann Pharmacother 2014 May 27. [Epub ahead of print].
  • 13 Dierich M. High risk medication regimens and medication related predictors of hospital readmission in elderly home care patients. [Doctor of Philosophy]. Minneapolis, MN: University of Minnesota; 2010
  • 14 Abelson R. Hospitals question medicare rules on readmissions. New York Times; 2013. March 29, 2013
  • 15 Olson CH. Automation of a high risk medication regime algorithm in a home health care population. Journal of Biomedical Informatics 2014 Apr 13. [Epub ahead of print].
  • 16 Data Set.. Baltimore: Centers for Medicare and Medicaid Services [updated 2012 Aug 21; cited 2014 Aug3]. Available from: http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments OASIS/DataSet.html
  • 17 Fick D, Cooper J, Wade W, Waller J, Maclean JR, Beers M. Updating the beers criteria for potentially inappropriate medication use in older adults: Results of a US consensus panel of experts. Arch Intern Med 2003; 163 (22) 2716-2724.
  • 18 George J, Phun YT, Bailey MJ, Kong D, Stewart K. Development and validation of the medication regimen complexity index. Ann Pharmacother 2004; 38 (09) 1369-1376.
  • 19 Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994; 47 (11) 1245-1251.
  • 20 Harhay M, Lin E, Pai A, Harhay MO, Huverserian A, Mussell A, Abt P, Levine M, Bloom R, Shea JA, Troxel AB, Reese PP. Early rehospitalization after kidney transplantation: Assessing preventability and prognosis. Am J Transplant 2013; 13 (12) 3164-3172.
  • 21 Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 2013; 4 (02) 627-635.
  • 22 Tape TG. The Area Under an ROC Curve. Omaha: University of Nebraska Medical Center; [cited 2014 Aug 30] Available from: http://gim.unmc.edu/dxtests/roc3.htm
  • 23 Heng EL, Bolger AP, Kempny A, Davlouros P, Davidson S, Gatzoulis MA, Babu-Narayan SV. 46 serum BNP and clinical outcomes prediction in tetralogy of fallot: A prospective analysis. Heart 2014; 100 (Suppl. 03) A25-A26.
  • 24 Hiersch L, Yogev Y, Domniz N, Meizner I, Bardin R, Melamed N. The role of cervical length in women with threatened preterm labor –is it a valid predictor at any gestational age?. Am J Obstet Gynecol. 2014 Jun 4 [Epub ahead of print].
  • 25 Malik N, Banning A, Gershlick A. 69 development and validation of a stent thrombosis risk scoring system. Heart 2014; 100 (Suppl. 03) A39-A40.
  • 26 Akyuz A, Alpsoy S, Akkoyun DC, Degirmenci H, Guler N. Heart rate recovery may predict the presence of coronary artery disease. Anadolu Kardiyol Derg 2014; 14 (04) 351-356.
  • 27 Cheung MR. Optimization of predictors of ewing sarcoma cause-specific survival: A population study. Asian Pac J Cancer Prev 2014; 15 (10) 4143-4145.
  • 28 Terao M, Koga K, Fujimoto A, Wada-Hiraike O, Osuga Y, Yano T, Kozuma S. Factors that predict poor clinical course among patients hospitalized with pelvic inflammatory disease. J Obstet Gynaecol Res 2013; 40 (02) 495-500.
  • 29 Cardoso LG, Chiavone PA. The APACHE II measured on patients’ discharge from the intensive care unit in the prediction of mortality. Rev Lat Am Enfermagem 2013; 21 (03) 811-819.
  • 30 Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45 1–2 23-41.
  • 31 Florkowski CM. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: Communicating the performance of diagnostic tests. Clin Biochem Rev 2008; 29 (Suppl. 01) S83-S87.
  • 32 Beloosesky Y, Nenaydenko O, Gross Nevo RF, Adunsky A, Weiss A. Rates, variability, and associated factors of polypharmacy in nursing home patients. Clin Interv Aging 2013; 8: 1585-1590.
  • 33 Abdulraheem I. Polypharmacy: A risk factor for geriatric syndrome, morbidity & mortality. Journal of Aging Science 2013; 1: (e103)
  • 34 American Geriatrics Society 2012 Beers Criteria Update Expert Panel.. American geriatrics society updated beers criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2012; 60 (04) 616-631.
  • 35 Mursu J, Robien K, Harnack LJ, Park K, Jacobs Jr. DR. Dietary supplements and mortality rate in older women: The iowa women’s health study. Arch Intern Med 2011; 171 (18) 1625-1633.
  • 36 Kaufman DW, Kelly JP, Rosenberg L, Anderson TE, Mitchell AA. Recent patterns of medication use in the ambulatory adult population of the united states: The slone survey. JAMA 2002; 287 (03) 337-344.
  • 37 Yuan L, Kaplowitz N. Mechanisms of drug-induced liver injury. Clin Liver Dis 2013; 17 (Suppl. 04) 507-518 vii.
  • 38 Leise MD, Poterucha JJ, Talwalkar JA. Drug-induced liver injury. Mayo Clin Proc 2014; 89 (01) 95-106.
  • 39 Vieira de Lima TJ, Garbin CA, Garbin AJ, Sumida DH, Saliba O. Potentially inappropriate medications used by the elderly: Prevalence and risk factors in brazilian care homes. BMC Geriatr 2013; 13: 52.
  • 40 Weng MC, Tsai CF, Sheu KL, Lee YT, Lee HC, Tzeng SL, Ueng KC, Chen CC, Chen SC. The impact of number of drugs prescribed on the risk of potentially inappropriate medication among outpatient older adults with chronic diseases. QJM 2013; 106 (11) 1009-1015.
  • 41 Borenstein J, Aronow HU, Bolton LB, Choi J, Bresee C, Braunstein GD. Early recognition of risk factors for adverse outcomes during hospitalization among medicare patients: A prospective cohort study. BMC Geriatr 2013; 13: 72.
  • 42 Hajjar ER, Cafiero AC, Hanlon JT. Polypharmacy in elderly patients. Am J Geriatr Pharmacother 2007; 5 (04) 345-351.
  • 43 Baldoni AD, Ayres LR, Martinez EZ, Dewulf ND, Dos Santos V, Pereira LR. Factors associated with potentially inappropriate medications use by the elderly according to beers criteria 2003 and 2012. Int J Clin Pharm 2013; 36 (02) 316-324.

Correspondence to:

Catherine H Olson PhD
PhD Candidate, Biomedical Health Informatics
University of Minnesota
330 Diehl Hall
505 Essex Street SE
Minneapolis, MN 55455
Phone: 612–626–3348   

  • References

  • 1 Hung WW, Ross JS, Boockvar KS, Siu AL. Recent trends in chronic disease, impairment and disability among older adults in the united states. BMC Geriatr 2011; 11: 47.
  • 2 Qato DM, Alexander GC, Conti RM, Johnson M, Schumm P, Lindau ST. Use of prescription and over-the-counter medications and dietary supplements among older adults in the united states. JAMA 2008; 300 (24) 2867-2878.
  • 3 Adults and Older Adult Adverse Drug Event. U.S. Centers for Disease Control and Prevention 10/02/2012 cited 04/22/2014
  • 4 Freund T, Campbell SM, Geissler S, Kunz CU, Mahler C, Peters-Klimm F, Szecsenyi J. Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions. Ann Fam Med 2013; 11 (04) 363-370.
  • 5 Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: Current strategies and future directions. Annu Rev Med 2013; 65: 471-485.
  • 6 Willson MN, Greer CL, Weeks DL. Medication regimen complexity and hospital readmission for an adverse drug event. Ann Pharmacother 2014; 48 (01) 26-32.
  • 7 Morandi A, Bellelli G, Vasilevskis EE, Turco R, Guerini F, Torpilliesi T, Speciale S, Emiliani V, Gentile S, Schnelle J, Trabucchi M. Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: The role of polypharmacy, functional status, and length of stay. J Am Med Dir Assoc 2013; 14 (10) 761-767.
  • 8 Sganga F, Landi F, Ruggiero C, Corsonello A, Vetrano DL, Lattanzio F, Cherubini A, Bernabei R, Onder G. Polypharmacy and health outcomes among older adults discharged from hospital: Results from the CRIME study. Geriatr Gerontol Int 2014 Jan 28. [Epub ahead of print].
  • 9 Sehgal V, Bajwa SJ, Sehgal R, Bajaj A, Khaira U, Kresse V. Polypharmacy and potentially inappropriate medication use as the precipitating factor in readmissions to the hospital. J Family Med Prim Care 2013; 2 (02) 194-199.
  • 10 Price SD, Holman CD, Sanfilippo FM, Emery JD. Association between potentially inappropriate medications from the beers criteria and the risk of unplanned hospitalization in elderly patients. Ann Pharmacother 2014; 48 (01) 6-16.
  • 11 Price SD, Holman CD, Sanfilippo FM, Emery JD. Impact of specific beers criteria medications on associations between drug exposure and unplanned hospitalisation in elderly patients taking high-risk drugs: A case-time-control study in western australia. Drugs Aging 2014; 31 (04) 311-325.
  • 12 Wimmer BC, Dent E, Bell JS, Wiese MD, Chapman I, Johnell K, Visvanathan R. Medication regimen complexity and unplanned hospital readmissions in older people. Ann Pharmacother 2014 May 27. [Epub ahead of print].
  • 13 Dierich M. High risk medication regimens and medication related predictors of hospital readmission in elderly home care patients. [Doctor of Philosophy]. Minneapolis, MN: University of Minnesota; 2010
  • 14 Abelson R. Hospitals question medicare rules on readmissions. New York Times; 2013. March 29, 2013
  • 15 Olson CH. Automation of a high risk medication regime algorithm in a home health care population. Journal of Biomedical Informatics 2014 Apr 13. [Epub ahead of print].
  • 16 Data Set.. Baltimore: Centers for Medicare and Medicaid Services [updated 2012 Aug 21; cited 2014 Aug3]. Available from: http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments OASIS/DataSet.html
  • 17 Fick D, Cooper J, Wade W, Waller J, Maclean JR, Beers M. Updating the beers criteria for potentially inappropriate medication use in older adults: Results of a US consensus panel of experts. Arch Intern Med 2003; 163 (22) 2716-2724.
  • 18 George J, Phun YT, Bailey MJ, Kong D, Stewart K. Development and validation of the medication regimen complexity index. Ann Pharmacother 2004; 38 (09) 1369-1376.
  • 19 Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994; 47 (11) 1245-1251.
  • 20 Harhay M, Lin E, Pai A, Harhay MO, Huverserian A, Mussell A, Abt P, Levine M, Bloom R, Shea JA, Troxel AB, Reese PP. Early rehospitalization after kidney transplantation: Assessing preventability and prognosis. Am J Transplant 2013; 13 (12) 3164-3172.
  • 21 Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 2013; 4 (02) 627-635.
  • 22 Tape TG. The Area Under an ROC Curve. Omaha: University of Nebraska Medical Center; [cited 2014 Aug 30] Available from: http://gim.unmc.edu/dxtests/roc3.htm
  • 23 Heng EL, Bolger AP, Kempny A, Davlouros P, Davidson S, Gatzoulis MA, Babu-Narayan SV. 46 serum BNP and clinical outcomes prediction in tetralogy of fallot: A prospective analysis. Heart 2014; 100 (Suppl. 03) A25-A26.
  • 24 Hiersch L, Yogev Y, Domniz N, Meizner I, Bardin R, Melamed N. The role of cervical length in women with threatened preterm labor –is it a valid predictor at any gestational age?. Am J Obstet Gynecol. 2014 Jun 4 [Epub ahead of print].
  • 25 Malik N, Banning A, Gershlick A. 69 development and validation of a stent thrombosis risk scoring system. Heart 2014; 100 (Suppl. 03) A39-A40.
  • 26 Akyuz A, Alpsoy S, Akkoyun DC, Degirmenci H, Guler N. Heart rate recovery may predict the presence of coronary artery disease. Anadolu Kardiyol Derg 2014; 14 (04) 351-356.
  • 27 Cheung MR. Optimization of predictors of ewing sarcoma cause-specific survival: A population study. Asian Pac J Cancer Prev 2014; 15 (10) 4143-4145.
  • 28 Terao M, Koga K, Fujimoto A, Wada-Hiraike O, Osuga Y, Yano T, Kozuma S. Factors that predict poor clinical course among patients hospitalized with pelvic inflammatory disease. J Obstet Gynaecol Res 2013; 40 (02) 495-500.
  • 29 Cardoso LG, Chiavone PA. The APACHE II measured on patients’ discharge from the intensive care unit in the prediction of mortality. Rev Lat Am Enfermagem 2013; 21 (03) 811-819.
  • 30 Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45 1–2 23-41.
  • 31 Florkowski CM. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: Communicating the performance of diagnostic tests. Clin Biochem Rev 2008; 29 (Suppl. 01) S83-S87.
  • 32 Beloosesky Y, Nenaydenko O, Gross Nevo RF, Adunsky A, Weiss A. Rates, variability, and associated factors of polypharmacy in nursing home patients. Clin Interv Aging 2013; 8: 1585-1590.
  • 33 Abdulraheem I. Polypharmacy: A risk factor for geriatric syndrome, morbidity & mortality. Journal of Aging Science 2013; 1: (e103)
  • 34 American Geriatrics Society 2012 Beers Criteria Update Expert Panel.. American geriatrics society updated beers criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2012; 60 (04) 616-631.
  • 35 Mursu J, Robien K, Harnack LJ, Park K, Jacobs Jr. DR. Dietary supplements and mortality rate in older women: The iowa women’s health study. Arch Intern Med 2011; 171 (18) 1625-1633.
  • 36 Kaufman DW, Kelly JP, Rosenberg L, Anderson TE, Mitchell AA. Recent patterns of medication use in the ambulatory adult population of the united states: The slone survey. JAMA 2002; 287 (03) 337-344.
  • 37 Yuan L, Kaplowitz N. Mechanisms of drug-induced liver injury. Clin Liver Dis 2013; 17 (Suppl. 04) 507-518 vii.
  • 38 Leise MD, Poterucha JJ, Talwalkar JA. Drug-induced liver injury. Mayo Clin Proc 2014; 89 (01) 95-106.
  • 39 Vieira de Lima TJ, Garbin CA, Garbin AJ, Sumida DH, Saliba O. Potentially inappropriate medications used by the elderly: Prevalence and risk factors in brazilian care homes. BMC Geriatr 2013; 13: 52.
  • 40 Weng MC, Tsai CF, Sheu KL, Lee YT, Lee HC, Tzeng SL, Ueng KC, Chen CC, Chen SC. The impact of number of drugs prescribed on the risk of potentially inappropriate medication among outpatient older adults with chronic diseases. QJM 2013; 106 (11) 1009-1015.
  • 41 Borenstein J, Aronow HU, Bolton LB, Choi J, Bresee C, Braunstein GD. Early recognition of risk factors for adverse outcomes during hospitalization among medicare patients: A prospective cohort study. BMC Geriatr 2013; 13: 72.
  • 42 Hajjar ER, Cafiero AC, Hanlon JT. Polypharmacy in elderly patients. Am J Geriatr Pharmacother 2007; 5 (04) 345-351.
  • 43 Baldoni AD, Ayres LR, Martinez EZ, Dewulf ND, Dos Santos V, Pereira LR. Factors associated with potentially inappropriate medications use by the elderly according to beers criteria 2003 and 2012. Int J Clin Pharm 2013; 36 (02) 316-324.