Keywords
Drug Burden Index
Readmission
Length of Hospital Stay
How to Cite
Abstract
Background: The Drug Burden Index (DBI) is a pharmacotherapy risk assessment tool explored to evaluate its association with unplanned hospital-related outcomes.
Objective: To evaluate the DBI association with unplanned hospital readmissions, develop a prediction model for unplanned readmissions. To investigate DBI association with length of hospital stay (LOS).
Setting: Unplanned readmission data were collected for 1000 adult hospitalized patients at Antrim Area Hospital in Northern Ireland.
Method: The study was designed as a retrospective analysis. Logistic regression models were developed to determine the prediction power. Discriminative ability testing was carried out using the Receiver Operating Characteristic (ROC) curve. Youden's index formula was used to detect the cut-off points. Analysis of covariance (ANCOVA) was performed to determine whether LOS differed based on the DBI score. Finally, negative binomial regression was used to predict LOS based on DBI.
Results: Of the 1000 patient records, 43% were females, and a total of 885 (88.5%) were exposed to sedative and anticholinergic medications (DBI>0). Readmission rates at 7, 14, 30 and 90 days were 5.4%, 9.0%, 15.0% and 28.8% respectively. The odds ratio (OR) of readmission within seven days for patients with DBI>1 was 3.42 times higher than those who had their DBI=0 (OR= 3.42, 95% CI: 1.6–7.3; P= 0.001). The DBI category significantly predicts 7-day readmission (P=0.002), the area under the curve for the ROC curve was 0.65 (95% CI: 0.58 - 0.71; P<0.001). For 14-day readmissions, patients with a DBI >1, compared with DBI=0, had a reported higher Odds Ratio (OR = 2.19, 95% CI: 1.1– 4.4; P= 0.025). However, the DBI category prediction power for 14-day readmission was not significant (P=0.069). DBI failed to show an association with 30- and 90-day readmissions. The adjusted estimated marginal difference for LOS of patients with DBI>1 vs. DBI=0 was 2.7 (95%CI: 0.89 – 4.5; P=0.003).
Conclusion: DBI was a statistically significant tool to predict 7-days unplanned readmission. DBI was not a statistically significant predictor for readmission over longer periods. Higher DBI was associated with a longer LOS.
Impact on Practice Statements: Readmission within seven days of a patient's discharge can be predicted by the DBI, and a longer hospital stay was also associated with higher DBI. Accordingly, the hospital teams can consider reporting DBI scores and performing tailored discharge plans for patients who are at risk for seven days of unplanned readmission.
References
Ancelin ML, Artero S, Portet F, Dupuy A-M, Touchon J, Ritchie K. Non-degenerative mild cognitive impairment in elderly people and use of anticholinergic drugs: longitudinal cohort study. BMJ. 2006;332(7539):455–9. Available from: https://doi.org/10.1136/bmj.38740.439664.de
Carnahan RM, Lund BC, Perry PJ, Pollock BG, Culp KR. The Anticholinergic Drug Scale as a Measure of Drug-Related Anticholinergic Burden: Associations With Serum Anticholinergic Activity. J Clin Pharmacol. 2006;46(12):1481–6. 10. https://doi.org/1177/0091270006292126
Rudolph JL, Salow MJ, Angelini MC, McGlinchey RE. The anticholinergic risk scale and anticholinergic adverse effects in older persons. Arch Intern Med. 2008;168(5):508–13. Available from: https://doi.org/10.1001/archinternmed.2007.106
Ehrt U, Broich K, Larsen JP, Ballard C, Aarsland D. Use of drugs with anticholinergic effect and impact on cognition in Parkinson’s disease: a cohort study. J Neurol Neurosurg Psychiatry. 2010 Feb;81(2):160–5. Available from: https://doi.org/10.1136/jnnp.2009.186239
Sittironnarit G, Ames D, Bush AI, Faux N, Flicker L, Foster J, et al. Effects of anticholinergic drugs on cognitive function in older Australians: results from the AIBL study. Dement Geriatr Cogn Disord. 2011;31(3):173–8. Available from: https://doi.org/10.1159/000325171
Linjakumpu T, Hartikainen S, Klaukka T, Koponen H, Kivelä S-L, Isoaho R. A model to classify the sedative load of drugs. Int J Geriatr Psychiatry. 2003;18(6):542–4. https://doi.org/10.1002/gps.846
Linjakumpu TA, Hartikainen SA, Klaukka TJ, Koponen HJ, Hakko HH, Viilo KM, et al. Sedative Drug Use in the Home-Dwelling Elderly. Ann Pharmacother. 2004;38(12):2017–22. Available from: https://doi.org/10.1345/aph.1e067
Sloane P, Ivey J, Roth M, Roederer M, Williams CS. Accounting for the sedative and analgesic effects of medication changes during patient participation in clinical research studies: Measurement development and application to a sample of institutionalized geriatric patients. Contemp Clin Trials. 2008;29(2):140–8. Available from: https://doi.org/10.1016/j.cct.2007.06.004
Boudreau RM, Hanlon JT, Roumani YF, Studenski SA, Ruby CM, Wright RM, et al. Central nervous system medication use and incident mobility limitation in community elders: the health, aging, and body composition study. Pharmacoepidemiol Drug Saf. 2009;18(10):916–22. https://doi.org/10.1002/pds.1797
Hanlon JT, Boudreau RM, Roumani YF, Newman AB, Ruby CM, Wright RM, et al. Number and Dosage of Central Nervous System Medications on Recurrent Falls in Community Elders: The Health, Aging and Body Composition Study. Journals Gerontol Ser A Biol Sci Med Sci. 2009;64A(4):492–8. https://doi.org/10.1093/gerona/gln043
Wright RM, Roumani YF, Boudreau R, Newman AB, Ruby CM, Studenski SA, et al. Effect of Central Nervous System Medication Use on Decline in Cognition in Community-Dwelling Older Adults: Findings from the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2009;57(2):243–50. https://doi.org/10.1111/j.1532-5415.2008.02127.x
Hilmer SN, Mager DE, Simonsick EM, Cao Y, Ling SM, Windham BG, et al. A drug burden index to define the functional burden of medications in older people. Arch Intern Med. 2007;167(8):781–7. https://doi.org/10.1001/archinte.167.8.781
Gnjidic D, Bell JS, Hilmer SN, Lönnroos E, Sulkava R, Hartikainen S. Drug Burden Index associated with function in community-dwelling older people in Finland: A cross-sectional study. Ann Med. 2012;44(5):458–67. https://doi.org/10.3109/07853890.2011.573499
Gnjidic D, Cumming RG, Le Couteur DG, Handelsman DJ, Naganathan V, Abernethy DR, et al. Drug Burden Index and physical function in older Australian men. Br J Clin Pharmacol. 2009 Jul;68(1):97–105. https://doi.org/10.1111/j.1365-2125.2009.03411.x
Gnjidic D, Le Couteur DG, Naganathan V, Cumming RG, Creasey H, Waite LM, et al. Effects of Drug Burden Index on Cognitive Function in Older Men. J Clin Psychopharmacol. 2012;32(2):273–7. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00004714-201204000-00018
Lowry E, Woodman RJ, Soiza RL, Hilmer SN, Mangoni AA. Drug Burden Index, Physical Function, and Adverse Outcomes in Older Hospitalized Patients. J Clin Pharmacol. 2012;52(10):1584–91. https://doi.org/10.1177/0091270011421489
Harrison SL, Kouladjian O’Donnell L, Bradley CE, Milte R, Dyer SM, Gnanamanickam ES, et al. Associations between the Drug Burden Index, Potentially Inappropriate Medications and Quality of Life in Residential Aged Care. Drugs Aging. 2018;35(1):83–91. https://doi.org/10.1007/s40266-017-0513-3
Wilson NM, Hilmer SN, March LM, Cameron ID, Lord SR, Seibel MJ, et al. Associations between drug burden index and physical function in older people in residential aged care facilities. Age Ageing. 2010;39(4):503–7. https://doi.org/10.1093/ageing/afq053
Gnjidic D, Hilmer SN, Blyth FM, Naganathan V, Cumming RG, Handelsman DJ, et al. High-Risk Prescribing and Incidence of Frailty Among Older Community-Dwelling Men. Clin Pharmacol Ther. 2012;91(3):521–8. https://doi.org/10.1038/clpt.2011.258
Floroff CK, Slattum PW, Harpe SE, Taylor P, Brophy GM. Potentially Inappropriate Medication Use is Associated with Clinical Outcomes in Critically Ill Elderly Patients with Neurological Injury. Neurocrit Care. 2014;21(3):526–33. https://doi.org/10.1007/s12028-014-9985-8
Best O, Gnjidic D, Hilmer SN, Naganathan V, McLachlan AJ. Investigating polypharmacy and drug burden index in hospitalised older people. Intern Med J. 2013;43(8):912–8. https://doi.org/10.1111/imj.12203
Nishtala PS, Hilmer SN, McLachlan AJ, Hannan PJ, Chen TF. Impact of Residential Medication Management Reviews on Drug Burden Index in Aged-Care Homes. Drugs Aging. 2009;26(8):677–86. https://doi.org/10.2165/11316440-000000000-00000
Kramlinger T, Wilson L. The Social Styles Handbook: Adapt Your Style to Win Trust (Wilson Learning Library). 2nd Editio. Nova Vista Publishing; 2011.
Blalock SJ, Renfro CP, Robinson JM, Farley JF, Busby-Whitehead J, Ferreri SP. Using the Drug Burden Index to identify older adults at highest risk for medication-related falls. BMC Geriatr. 2020;20(1):208. https://doi.org/10.1186/s12877-020-01598-5
Ie K, Chou E, Boyce RD, Albert SM. Fall Risk-Increasing Drugs, Polypharmacy, and Falls Among Low-Income Community-Dwelling Older Adults. Sands LP, editor. Innov Aging. 2021;5(1). Available from: https://academic.oup.com/innovateage/article/doi/10.1093/geroni/igab001/6071372
Lönnroos E, Gnjidic D, Hilmer SN, Bell JS, Kautiainen H, Sulkava R, et al. Drug Burden Index and Hospitalisationamong Community-Dwelling Older People. Drugs Aging. 2012;29(5):395–404. https://doi.org/10.2165/11631420-000000000-00000
Gnjidic D, Hilmer SN, Hartikainen S, Tolppanen A-M, Taipale H, Koponen M, et al. Impact of High Risk Drug Use on Hospitalisationand Mortality in Older People with and without Alzheimer’s Disease: A National Population Cohort Study. Sleegers K, editor. PLoS One. 2014;9(1):e83224. https://doi.org/10.1371/journal.pone.0083224
Dispennette R, Elliott D, Nguyen L, Richmond R. Drug Burden Index Score and Anticholinergic Risk Scale as Predictors of Readmission to the Hospital. Consult Pharm. 2014;29(3):158–68. https://doi.org/10.4140/tcp.n.2014.158
Hsieh FY. Sample size tables for logistic regression. Stat Med. 1989;8(7):795–802. https://doi.org/10.1002/sim.4780080704
Væth M, Skovlund E. A simple approach to power and sample size calculations in logistic regression and Cox regression models. Stat Med. 2004;23(11):1781–92. https://doi.org/10.1002/sim.1753
Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014;67(3):451–70. https://doi.org/10.1111/bmsp.12028
Ma Y, Hart JD, Janicki R, Carroll RJ. Local and omnibus goodness-of-fit tests in classical measurement error models. J R Stat Soc Ser B (Statistical Methodol. 2011;73(1):81–98. https://doi.org/10.1111/j.1467-9868.2010.00751.x
Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 22nd ed. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2013. 1–11 p. (Wiley Series in Probability and Statistics). https://doi.org/10.1002/9781118548387
Efron B, Tibshirani R. Improvements on Cross-Validation: The 632+ Bootstrap Method. J Am Stat Assoc. 1997;92(438):548–60. https://doi.org/10.1080/01621459.1997.10474007
Altman DG, Vergouwe Y, Royston P, Moons KGM. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338(may28 1):b605–b605. https://doi.org/10.1136/bmj.b605
Royston P, Moons KGM, Altman DG, Vergouwe Y. Prognosis and prognostic research: Developing a prognostic model. BMJ. 2009;338(mar31 1):b604–b604. https://doi.org/10.1136/bmj.b604
Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and Optimal Cut-Point Estimated from Observations Affected by a Lower Limit of Detection. Biometrical J. 2008;50(3):419–30. https://doi.org/10.1002/bimj.200710415
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5. https://doi.org/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3
Hilbe JM. Negative Binomial Regression. 2nd editio. Cambridge University Press; 2011. 180–85 p.
Bosboom PR, Alfonso H, Almeida OP, Beer C. Use of Potentially Harmful Medications and Health-Related Quality of Life among People with Dementia Living in Residential Aged Care Facilities. Dement Geriatr Cogn Dis Extra. 2012;2(1):361–71. https://doi.org/10.1159/000342172
Hilmer SN, Mager DE, Simonsick EM, Ling SM, Windham BG, Harris TB, et al. Drug Burden Index Score and Functional Decline in Older People. Am J Med. 2009;122(12):1142-1149.e2. https://doi.org/10.1016/j.amjmed.2009.02.021
Wilson NM, Hilmer SN, March LM, Cameron ID, Lord SR, Seibel MJ, et al. Associations Between Drug Burden Index and Falls in Older People in Residential Aged Care. J Am Geriatr Soc. 2011;59(5):875–80. https://doi.org/10.1111/j.1532-5415.2011.03386.x
Wilson NM, Hilmer SN, March LM, Chen JS, Gnjidic D, Mason RS, et al. Associations between Drug Burden Index and Mortality in Older People in Residential Aged Care Facilities. Drugs Aging. 2012;29(2):157–65. https://doi.org/10.2165/11598570-000000000-00000
Gnjidic D, Couteur DG Le, Abernethy DR, Hilmer SN. A Pilot Randomized Clinical Trial Utilizing the Drug Burden Index to Reduce Exposure to Anticholinergic and Sedative Medications in Older People. Ann Pharmacother. 2010;44(11):1725–32. https://doi.org/10.1345/aph.1p310
Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol. 1995;48(12):1495–501. https://doi.org/10.1345/aph.1p310
Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 199548(12):1503–10. https://doi.org/10.1016/0895-4356(95)00510-2
Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. https://doi.org/10.1016/0895-4356(95)00048-8
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. https://doi.org/10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4
Vittinghoff E, McCulloch CE. Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression. Am J Epidemiol. 2007;165(6):710–8. https://doi.org/10.1093/aje/kwk052
Mangoni AA, van Munster BC, Woodman RJ, de Rooij SE. Measures of Anticholinergic Drug Exposure, Serum Anticholinergic Activity, and All-cause Postdischarge Mortality in Older Hospitalized Patients with Hip Fractures. Am J Geriatr Psychiatry. 2013;21(8):785–93. https://doi.org/10.1016/j.jagp.2013.01.012
Lieberman JA. Managing anticholinergic side effects. Prim Care Companion J Clin Psychiatry. 2004;6(Suppl 2):20–3. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16001097
Muench J, Hamer AM. Adverse effects of antipsychotic medications. Am Fam Physician. 2010;81(5):617–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20187598
Bewick V, Cheek L, Ball J. Statistics review 14: Logistic regression. Crit Care. 2005;9(1):112–8. https://doi.org/10.1186/cc3045
Thomas JW. Does risk-adjusted readmission rate provide valid information on hospital quality? Inquiry. 1996;33(3):258–70. Available from: http://www.ncbi.nlm.nih.gov/pubmed/8883460
Philbin EF DT. Prediction of hospital readmission for heart failure: development of a simple risk score based on administrative data. J Am Coll Cardiol. 1999;33(6):1560–1566.
Hasan O, Meltzer DO, Shaykevich SA, Bell CM, Kaboli PJ, Auerbach AD, et al. Hospital Readmission in General Medicine Patients: A Prediction Model. J Gen Intern Med. 2010;25(3):211–9. https://doi.org/10.1007/s11606-009-1196-1
Van Walraven C, Dhalla I, Bell C. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–557.
Arnold ME, Buys L, Fullas F. Impact of pharmacist intervention in conjunction with outpatient physician follow-up visits after hospital discharge on readmission rate. Am J Health Syst Pharm. 2015;72(11 Suppl 1):S36-42. https://doi.org/10.2146/sp150011
Horne R. Compliance, adherence, and concordance: implications for asthma treatment. Chest. 2006;130(1 Suppl):65S-72S. https://doi.org/10.1378/chest.130.1_suppl.65s
Low LL, Liu N, Wang S, Thumboo J, Ong MEH, Lee KH. Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of HospitalisationSeverity. Steyerberg EW, editor. PLoS One. 2016;11(12):e0167413. https://doi.org/10.1371/journal.pone.0167413
Vigod SN, Kurdyak PA, Seitz D, Herrmann N, Fung K, Lin E, 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. https://doi.org/10.1016/j.jpsychires.2014.12.003
Abate SM, Mantefardo B, Basu B. Postoperative mortality among surgical patients with COVID-19: a systematic review and meta-analysis. Patient Saf Surg. 2020;14(1). https://doi.org/10.1186/s13037-020-00262-6
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2022 Odeh M et al