Methods Inf Med 2022; 61(01/02): 055-060
DOI: 10.1055/s-0042-1742671
Short Paper

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

Alexander Gerharz*
1   Department of Statistics, Technical University of Dortmund, Dortmund, Germany
,
Carmen Ruff*
2   Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany
,
Lucas Wirbka
2   Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany
,
Felicitas Stoll
2   Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany
,
Walter E. Haefeli
2   Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany
,
Andreas Groll
1   Department of Statistics, Technical University of Dortmund, Dortmund, Germany
,
Andreas D. Meid
2   Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany
› Author Affiliations
Funding This work was supported by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGB V - Fünftes Buch Sozialgesetzbuch), grant number: 01VSF18019. The funding body did not play any role in the design of the study, collection, analyses, and interpretation of data or the manuscript. A.D.M. is funded by the Physician–Scientist Programme of the Medical Faculty of Heidelberg University.

Abstract

Background Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

Objectives To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

Methods In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined (“stacked”) to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

Results While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66–0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64–0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63–0.66]), HF (c-statistics: 0.61 [95% CI: 0.60–0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56–0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47–0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63–0.64].

Conclusion PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

* Both authors contributed equally.


Ethical Approval

In Germany, claims data analyses do not require ethics committee approval by law. All data were completely anonymized for the investigators. The data that support the findings of this study are available from the health insurance company AOK Baden-Württemberg (third-party data). Restrictions apply to the availability of these data which were used under license for this study. Data are only available with the permission of AOK Baden-Württemberg.


Authors' Contributions

All authors were involved in the conception and design of the study, interpretation of data, and critical revision of the manuscript. A.G., C.R., A.G., and A.D.M. were additionally involved in the acquisition of data, statistical analysis, and drafting of the manuscript; W.E.H., A.G., and A.D.M. supervised the project.


Supplementary Material



Publication History

Received: 27 September 2021

Accepted: 28 December 2021

Article published online:
10 February 2022

© 2022. Thieme. All rights reserved.

Thieme Medical Publishers
333 Seventh Avenue, New York, NY 10001, USA.

 
  • References

  • 1 Wauters M, Elseviers M, Vaes B. et al. Too many, too few, or too unsafe? Impact of inappropriate prescribing on mortality, and hospitalization in a cohort of community-dwelling oldest old. Br J Clin Pharmacol 2016; 82 (05) 1382-1392
  • 2 Meid AD, Groll A, Heider D. et al. Prediction of drug-related risks using clinical context information in longitudinal claims data. Value Health 2018; 21 (12) 1390-1398
  • 3 Krumholz HM. Post-hospital syndrome–an acquired, transient condition of generalized risk. N Engl J Med 2013; 368 (02) 100-102
  • 4 Ruff C, Gerharz A, Groll A. et al. Disease-dependent variations in the timing and causes of readmissions in Germany: a claims data analysis for six different conditions. PLoS One 2021; 16 (04) e0250298
  • 5 Meid AD, Groll A, Schieborr U, Walker J, Haefeli WE. How can we define and analyse drug exposure more precisely to improve the prediction of hospitalizations in longitudinal (claims) data?. Eur J Clin Pharmacol 2017; 73 (03) 373-380
  • 6 Hill-Taylor B, Sketris I, Hayden J, Byrne S, O'Sullivan D, Christie R. Application of the STOPP/START criteria: a systematic review of the prevalence of potentially inappropriate prescribing in older adults, and evidence of clinical, humanistic and economic impact. J Clin Pharm Ther 2013; 38 (05) 360-372
  • 7 Angel Y, Zeltser D, Berliner S. et al. Hospitalization as an opportunity to correct errors in anticoagulant treatment in patients with atrial fibrillation. Br J Clin Pharmacol 2019; 85 (12) 2838-2847
  • 8 Jack BW, Chetty VK, Anthony D. et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009; 150 (03) 178-187
  • 9 Low LL, Tan SY, Ng MJ. et al. Applying the integrated practice unit concept to a modified virtual ward model of care for patients at highest risk of readmission: a randomized controlled trial. PLoS One 2017; 12 (01) e0168757
  • 10 Counter D, Millar JWT, McLay JS. Hospital readmissions, mortality and potentially inappropriate prescribing: a retrospective study of older adults discharged from hospital. Br J Clin Pharmacol 2018; 84 (08) 1757-1763
  • 11 Varga S, Alcusky M, Keith SW. et al. Hospitalization rates during potentially inappropriate medication use in a large population-based cohort of older adults. Br J Clin Pharmacol 2017; 83 (11) 2572-2580
  • 12 Brunetti E, Aurucci ML, Boietti E. et al. Clinical implications of potentially inappropriate prescribing according to STOPP/START version 2 criteria in older polymorbid patients discharged from geriatric and internal medicine wards: A prospective observational multicenter study. J Am Med Dir Assoc 2019; 20 (11) 1476.e1-1476.e10
  • 13 Fabbietti P, Di Stefano G, Moresi R. et al. Impact of potentially inappropriate medications and polypharmacy on 3-month readmission among older patients discharged from acute care hospital: a prospective study. Aging Clin Exp Res 2018; 30 (08) 977-984
  • 14 Fabbietti P, Ruggiero C, Sganga F. et al. Effects of hyperpolypharmacy and potentially inappropriate medications (PIMs) on functional decline in older patients discharged from acute care hospitals. Arch Gerontol Geriatr 2018; 77: 158-162
  • 15 Brown JD, Hutchison LC, Li C, Painter JT, Martin BC. Predictive validity of the Beers and Screening Tool of Older Persons' Potentially Inappropriate Prescriptions (STOPP) criteria to detect adverse drug events, hospitalizations, and emergency department visits in the United States. J Am Geriatr Soc 2016; 64 (01) 22-30
  • 16 Huibers CJA, Sallevelt BTGM, de Groot DA. et al. Conversion of STOPP/START version 2 into coded algorithms for software implementation: a multidisciplinary consensus procedure. Int J Med Inform 2019; 125: 110-117
  • 17 O'Mahony D, Gudmundsson A, Soiza RL. et al. Prevention of adverse drug reactions in hospitalized older patients with multi-morbidity and polypharmacy: the SENATOR* randomized controlled clinical trial. Age Ageing 2020; 49 (04) 605-614
  • 18 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27
  • 19 Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox's proportional hazards model via coordinate descent. J Stat Softw 2011; 39 (05) 1-13
  • 20 Youden WJ. Index for rating diagnostic tests. Cancer 1950; 3 (01) 32-35
  • 21 Pencina MJ, D'Agostino Sr. RB, D'Agostino Jr. RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008; 27 (02) 157-172 , discussion 207–212
  • 22 Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med 2006; 166 (17) 1822-1828
  • 23 Stauffer BD, Fullerton C, Fleming N. et al. Effectiveness and cost of a transitional care program for heart failure: a prospective study with concurrent controls. Arch Intern Med 2011; 171 (14) 1238-1243
  • 24 Hansen LO, Greenwald JL, Budnitz T. et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med 2013; 8 (08) 421-427
  • 25 Hyttinen V, Jyrkkä J, Valtonen H. A systematic review of the impact of potentially inappropriate medication on health care utilization and costs among older adults. Med Care 2016; 54 (10) 950-964
  • 26 De Vincentis A, Gallo P, Finamore P. et al. Potentially inappropriate medications, drug-drug interactions, and anticholinergic burden in elderly hospitalized patients: does an association exist with post-discharge health outcomes?. Drugs Aging 2020; 37 (08) 585-593
  • 27 Gillespie U, Alassaad A, Hammarlund-Udenaes M. et al. Effects of pharmacists' interventions on appropriateness of prescribing and evaluation of the instruments' (MAI, STOPP and STARTs') ability to predict hospitalization–analyses from a randomized controlled trial. PLoS One 2013; 8 (05) e62401
  • 28 Ena J, Gómez-Huelgas R, Gracia-Tello BC. et al; Grupo de Diabetes, Obesidad y Nutrición de la Sociedad Española de Medicina Interna. Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments [in Spanish]. Rev Clin Esp (Barc) 2018; 218 (06) 271-278
  • 29 Formiga F, Masip J, Chivite D, Corbella X. Applicability of the heart failure readmission risk score: a first European study. Int J Cardiol 2017; 236: 304-309
  • 30 Sawhney S, Marks A, Fluck N, McLernon DJ, Prescott GJ, Black C. Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions. BMC Nephrol 2017; 18 (01) 9
  • 31 Artetxe A, Beristain A, Graña M. Predictive models for hospital readmission risk: a systematic review of methods. Comput Methods Programs Biomed 2018; 164: 49-64
  • 32 Tulloch AD, David AS, Thornicroft G. Exploring the predictors of early readmission to psychiatric hospital. Epidemiol Psychiatr Sci 2016; 25 (02) 181-193
  • 33 Glans M, Kragh Ekstam A, Jakobsson U, Bondesson Å, Midlöv P. Risk factors for hospital readmission in older adults within 30 days of discharge - a comparative retrospective study. BMC Geriatr 2020; 20 (01) 467
  • 34 Glans M, Kragh Ekstam A, Jakobsson U, Bondesson Å, Midlöv P. Medication-related hospital readmissions within 30 days of discharge-A retrospective study of risk factors in older adults. PLoS One 2021; 16 (06) e0253024
  • 35 Linkens AEMJH, Milosevic V, van der Kuy PHM, Damen-Hendriks VH, Mestres Gonzalvo C, Hurkens KPGM. Medication-related hospital admissions and readmissions in older patients: an overview of literature. Int J Clin Pharm 2020; 42 (05) 1243-1251
  • 36 Uitvlugt EB, Janssen MJA, Siegert CEH. et al. Medication-related hospital readmissions within 30 days of discharge: prevalence, preventability, type of medication errors and risk factors. Front Pharmacol 2021; 12: 567424