Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation

References

• Centers for Disease Control and Prevention (CDC). FastStats: Chronic Obstructive Pulmonary Disease (COPD) Includes: Chronic Bronchitis and Emphysema; 2022. Available from: https://www.cdc.gov/nchs/fastats/copd.htm. Accessed February 24, 2022.
   Google Scholar
• Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418–1428. doi:10.1056/NEJMsa0803563
   PubMed Web of Science ®Google Scholar
• Halpern MT, Stanford RH, Borker R. The burden of COPD in the U.S.A.: results from the confronting COPD survey. Respir Med. 2003;97:S81–S89. doi:10.1016/S0954-6111(03)80028-8
   PubMed Web of Science ®Google Scholar
• Roberts C, Lowe D, Bucknall C, Ryland I, Kelly Y, Pearson M. Clinical audit indicators of outcome following admission to hospital with acute exacerbation of chronic obstructive pulmonary disease. Thorax. 2002;57(2):137–141. doi:10.1136/thorax.57.2.137
   PubMed Web of Science ®Google Scholar
• CMS. Hospital Readmissions Reduction Program (HRRP). Available from: https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program. Accessed February 24, 2022.
   Google Scholar
• Shah T, Churpek MM, Coca Perraillon M, Konetzka RT. Understanding why patients with COPD get readmitted. Chest. 2015;147(5):1219–1226. doi:10.1378/chest.14-2181
   PubMed Web of Science ®Google Scholar
• Allaudeen N, Schnipper JL, Orav EJ, Wachter RM, Vidyarthi AR. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011;26(7):771–776. doi:10.1007/s11606-011-1663-3
   PubMed Web of Science ®Google Scholar
• Press VG. Is it time to move on from identifying risk factors for 30-day chronic obstructive pulmonary disease readmission? A call for risk prediction tools. Ann Am Thorac Soc. 2018;15(7):801–803. doi:10.1513/AnnalsATS.201804-246ED
   PubMed Web of Science ®Google Scholar
• Press VG, Myers LC, Feemster LC. Preventing COPD readmissions under the hospital readmissions reduction program: how far have we come? Chest. 2021;159(3):996–1006. doi:10.1016/j.chest.2020.10.008
   PubMed Web of Science ®Google Scholar
• Press VG, Au DH, Bourbeau J, et al. Reducing chronic obstructive pulmonary disease hospital readmissions. An official American thoracic society workshop report. Ann Am Thorac Soc. 2019;16(2):161–170. doi:10.1513/AnnalsATS.201811-755WS
   PubMed Web of Science ®Google Scholar
• Donzé JD, Williams MV, Robinson EJ, et al. International Validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016;176(4):496–502. doi:10.1001/jamainternmed.2015.8462
   PubMed Web of Science ®Google Scholar
• Echevarria C, Steer J, Heslop-Marshall K, et al. The PEARL score predicts 90-day readmission or death after hospitalisation for acute exacerbation of COPD. Thorax. 2017;72(8):686–693. doi:10.1136/thoraxjnl-2016-209298
   PubMed Web of Science ®Google Scholar
• Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 2016;44(2):368–374. doi:10.1097/CCM.0000000000001571
   PubMed Web of Science ®Google Scholar
• Churpek MM, Yuen TC, Winslow C, et al. Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients. Am J Respir Crit Care Med. 2014;190(6):649–655. doi:10.1164/rccm.201406-1022OC
   PubMed Web of Science ®Google Scholar
• Churpek MM, Yuen TC, Park SY, Gibbons R, Edelson DP. Using electronic health record data to develop and validate a prediction model for adverse outcomes on the wards. Crit Care Med. 2014;42(4):841–848. doi:10.1097/CCM.0000000000000038
   PubMed Web of Science ®Google Scholar
• Stein BD, Bautista A, Schumock GT, et al. The validity of international classification of diseases, ninth revision, clinical modification diagnosis codes for identifying patients hospitalized for COPD exacerbations. Chest. 2012;141(1):87–93. doi:10.1378/chest.11-0024
   PubMed Web of Science ®Google Scholar
• Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Ann Intern Med. 2015;162(1):55–63. doi:10.7326/M14-0697
   PubMed Web of Science ®Google Scholar
• Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638. doi:10.1001/jamainternmed.2013.3023
   PubMed Web of Science ®Google Scholar
• Rinne ST, Graves MC, Bastian LA, et al. Association between length of stay and readmission for COPD. Am J Manag Care. 2017;23(8):e253–e258.
   PubMed Web of Science ®Google Scholar
• Garcia-Aymerich J. Risk factors of readmission to hospital for a COPD exacerbation: a prospective study. Thorax. 2003;58(2):100–105. doi:10.1136/thorax.58.2.100
   PubMed Web of Science ®Google Scholar
• Moore BJ, White S, Washington R, Coenen N, Elixhauser A. Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ elixhauser comorbidity index. Med Care. 2017;55(7):698–705. doi:10.1097/MLR.0000000000000735
   PubMed Web of Science ®Google Scholar
• Hackmann G, Chen M, Chipara O, et al. Toward a two-tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
   PubMedGoogle Scholar
• van den Boogaard M, Pickkers P, Slooter AJC, et al. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. BMJ. 2012;344:e420. doi:10.1136/bmj.e420
   PubMed Web of Science ®Google Scholar
• Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically III hospitalized adults. Chest. 1991;100(6):1619–1636. doi:10.1378/chest.100.6.1619
   PubMed Web of Science ®Google Scholar
• Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic Minority Over-sampling Technique. J Artif Intell Res. 2002;16:321–357. doi:10.1613/jair.953
   Web of Science ®Google Scholar
• Qi Y. Random Forest for Bioinformatics. In: Zhang C, Ma Y, editors. Ensemble Machine Learning: Methods and Applications. US: Springer; 2012:307–323. doi:10.1007/978-1-4419-9326-7_11
   Google Scholar
• Fernandez-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15(1):3133–3181.
   Google Scholar
• Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340–1347. doi:10.1093/bioinformatics/btq134
   PubMed Web of Science ®Google Scholar
• DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845. doi:10.2307/2531595
   PubMed Web of Science ®Google Scholar
• Min X, Yu B, Wang F. Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: a case study on COPD. Sci Rep. 2019;9:2362. doi:10.1038/s41598-019-39071-y
   PubMed Web of Science ®Google Scholar