1. Malik, AT, Quatman, CE, Phieffer, LS, Ly, TV, Khan, SN. Incidence, risk factors and clinical impact of postoperative delirium following open reduction and internal fixation (ORIF) for hip fractures: An analysis of 7859 patients from the ACS-NSQIP hip fracture procedure targeted database. Eur J Orthop Surg Traumatol. 2019;29(2):435-446. doi:10.1007/s00590-018-2308-6
Google Scholar | Crossref | Medline2. Brauer, C, Morrison, RS, Silberzweig, SB, Siu, AL. The cause of delirium in patients with hip fracture. Arch Intern Med. 2000;160(12):1856-1860. doi:10.1001/archinte.160.12.1856
Google Scholar | Crossref | Medline3. Holmes, JD, House, AO. Psychiatric illness in hip fracture. Age Ageing. 2000;29(6):537-546. doi:10.1093/ageing/29.6.537
Google Scholar | Crossref | Medline4. Kyziridis, TC . Post-operative delirium after hip fracture treatment - a review of the current literature. Psycho Soc Med. 2006;3:Doc01.
Google Scholar | Medline5. Edlund, A, Lundstrom, M, Brannstrom, B, Bucht, G, Gustafson, Y. Delirium before and after operation for femoral neck fracture. J Am Geriatr Soc. 2001;49(10):1335-1340. doi:10.1046/j.1532-5415.2001.49261.x
Google Scholar | Crossref | Medline | ISI6. Merchant, RA, Lui, KL, Ismail, NH, Wong, HP, Sitoh, YY. The relationship between postoperative complications and outcomes after hip fracture surgery. Ann Acad Med Singapore. 2005;34(2):163-168.
Google Scholar | Medline | ISI7. Leslie, DL, Marcantonio, ER, Zhang, Y, Leo-Summers, L, Inouye, SK. One-year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):27-32. doi:10.1001/archinternmed.2007.4
Google Scholar | Crossref | Medline8. Marcantonio, ER . In the clinic. Delirium. Ann Intern Med. 2011;154(11):6-16. doi:10.7326/0003-4819-154-11-201106070-01006
Google Scholar | Crossref9. Yang, Y, Zhao, X, Dong, T, Yang, Z, Zhang, Q, Zhang, Y. Risk factors for postoperative delirium following hip fracture repair in elderly patients: A systematic review and meta-analysis. Aging Clin Exp Res. 2017;29(2):115-126. doi:10.1007/s40520-016-0541-6
Google Scholar | Crossref | Medline10. Zaki, H-AE, Mousa, SM, El Said, SMS, Mortagy, AK. Morbidity and mortality following surgery for hip fractures in elderly patients. J Aging Res. 2019;2019:7084657. doi:10.1155/2019/7084657
Google Scholar | Crossref | Medline11. Inouye, S, Robinson, T, Blaum, C. Postoperative delirium in older adults: Best practice statement from the American Geriatrics Society. J Am Coll Surg. 2015;220(2):136-148.e1. doi:10.1016/j.jamcollsurg.2014.10.019
Google Scholar | Crossref | Medline | ISI12. Bohlken, J, Kostev, K. Prevalence and risk factors for delirium diagnosis in patients followed in general practices in Germany. Int psychogeriatrics. 2018;30(4):511-518. doi:10.1017/S1041610217002587
Google Scholar | Crossref | Medline13. Oberai, T, Laver, K, Crotty, M, Killington, M, Jaarsma, R. Effectiveness of multicomponent interventions on incidence of delirium in hospitalized older patients with hip fracture: A systematic review. Int Psychogeriatrics. 2018;30(4):481-492. doi:10.1017/S1041610217002782
Google Scholar | Crossref | Medline14. Power, C, Duffy, R, Bates, H, et al. The detection, diagnosis, and impact of cognitive impairment among inpatients aged 65 years and over in an Irish general hospital - a prospective observational study. Int psychogeriatrics. 2017;29(11):1879-1888. doi:10.1017/S1041610217001326
Google Scholar | Crossref | Medline15. Topol, EJ . High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
Google Scholar | Crossref | Medline16. Panch, T, Szolovits, P, Atun, R. Artificial intelligence, machine learning and health systems. J Glob Health. 2018;8(2):20303. doi:10.7189/jogh.08.020303
Google Scholar | Crossref | Medline17. Tran, B, Vu, G, Ha, G, et al. Global evolution of research in artificial intelligence in health and medicine: A Bibliometric Study. J Clin Med. 2019;8(3):360. doi:10.3390/jcm8030360
Google Scholar | Crossref18. Fontana, MA, Lyman, S, Sarker, GK, Padgett, DE, MacLean, CH. Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clin Orthop Relat Res. 2019;477(6):1267-1279. doi:10.1097/CORR.0000000000000687
Google Scholar | Crossref | Medline19. Ogink, PT, Karhade, AV, Thio, QCBS, et al. Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis. Eur Spine J. 2019;28(8):1775-1782. doi:10.1007/s00586-019-05936-z
Google Scholar | Crossref | Medline20. Karhade, AV, Thio, QCBS, Ogink, PT, et al. Development of machine learning algorithms for prediction of 30-day mortality after surgery for spinal metastasis. Clin Neurosurg. 2019;85(1):E83-E91. doi:10.1093/neuros/nyy469
Google Scholar | Crossref21. Shah, AA, Karhade, AV, Bono, CM, Harris, MB, Nelson, SB, Schwab, JH. Development of a machine learning algorithm for prediction of failure of nonoperative management in spinal epidural abscess. Spine J. 2019;19(10):1657-1665. doi:10.1016/j.spinee.2019.04.022
Google Scholar | Crossref | Medline22. Bongers, MER, Thio, QCBS, Karhade, AV, et al. Does the SORG algorithm predict 5-year survival in patients with chondrosarcoma? An external validation. Clin Orthop Relat Res. 2019;477(10):2296-2303. doi:10.1097/corr.0000000000000748
Google Scholar | Crossref | Medline23. Collins, GS, Reitsma, JB, Altman, DG, Moons, KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/bmj.g7594
Google Scholar | Crossref | Medline24. Ingraham, AM, Cohen, ME, Bilimoria, KY, et al. Association of surgical care improvement project infection-related process measure compliance with risk-adjusted outcomes: Implications for quality measurement. J Am Coll Surg. 2010;211(6):705-714. doi:10.1016/j.jamcollsurg.2010.09.006
Google Scholar | Crossref | Medline25. Berian, JR, Zhou, L, Russell, MM, et al. Postoperative Delirium as a target for surgical quality improvement. Ann Surg. 2018;268(1):93-99. https://journals.lww.com/annalsofsurgery/Fulltext/2018/07000/Postoperative_Delirium_as_a_Target_for_Surgical.16.aspx.
Google Scholar | Crossref | Medline26. Mitasova, A, Kostalova, M, Bednarik, J, et al. Poststroke delirium incidence and outcomes: Validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Crit Care Med. 2012;40(2):484-490. doi:10.1097/CCM.0b013e318232da12
Google Scholar | Crossref | Medline27. Hestermann, U, Backenstrass, M, Gekle, I, et al. Validation of a German version of the Confusion Assessment Method for delirium detection in a sample of acute geriatric patients with a high prevalence of dementia. Psychopathology. 2009;42(4):270-276. doi:10.1159/000224151
Google Scholar | Crossref | Medline28. McNicoll, L, Pisani, MA, Ely, EW, Gifford, D, Inouye, SK. Detection of delirium in the intensive care unit: Comparison of confusion assessment method for the intensive care unit with confusion assessment method ratings. J Am Geriatr Soc. 2005;53(3):495-500. doi:10.1111/j.1532-5415.2005.53171.x
Google Scholar | Crossref | Medline | ISI29. Card, E, Pandharipande, P, Tomes, C, et al. Emergence from general anaesthesia and evolution of delirium signs in the post-anaesthesia care unit. BJA Br J Anaesth. 2014;115(3):411-417. doi:10.1093/bja/aeu442
Google Scholar | Crossref30. Stekhoven, DJ, Buhlmann, P. MissForest--non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28(1):112-118. doi:10.1093/bioinformatics/btr597
Google Scholar | Crossref | Medline | ISI31. Ogink, PT, Karhade, AV, Thio, QCBS, et al. Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods. Eur spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc. 2019;28(6):1433-1440. doi:10.1007/s00586-019-05928-z
Google Scholar | Crossref32. Steyerberg, EW, Vickers, AJ, Cook, NR, et al. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi:10.1097/EDE.0b013e3181c30fb2
Google Scholar | Crossref | Medline | ISI33. Cox, DR . Two further applications of a model for binary regression. Biometrika. 1958;45(3/4):562-565. doi:10.2307/2333203
Google Scholar | Crossref34. Steyerberg, EW, Vergouwe, Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925-1931. doi:10.1093/eurheartj/ehu207
Google Scholar | Crossref | Medline | ISI35. Van Calster, B, Vickers, AJ. Calibration of risk prediction models: Impact on decision-analytic performance. Med Decis Making. 2015;35(2):162-169. doi:10.1177/0272989X14547233
Google Scholar | SAGE Journals | ISI36. Vickers, AJ, Elkin, EB. Decision curve analysis: A novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-574. doi:10.1177/0272989X06295361
Google Scholar | SAGE Journals | ISI37. Smith, TO, Cooper, A, Peryer, G, Griffiths, R, Fox, C, Cross, J. Factors predicting incidence of post-operative delirium in older people following hip fracture surgery: A systematic review and meta-analysis. Int J Geriatr Psychiatry. 2017;32(4):386-396. doi:10.1002/gps.4655
Google Scholar | Crossref | Medline38. Lundstrom, M, Olofsson, B, Stenvall, M, et al. Postoperative delirium in old patients with femoral neck fracture: A randomized intervention study. Aging Clin Exp Res. 2007;19(3):178-186. doi:10.1007/bf03324687
Google Scholar | Crossref | Medline | ISI39. Arshi, A, Lai, WC, Chen, JB, Bukata, SV, Stavrakis, AI, Zeegen, EN. Predictors and Sequelae of Postoperative Delirium in Geriatric Hip Fracture Patients. Geriatr Orthop Surg Rehabil. 2018;9:2151459318814823. doi:10.1177/2151459318814823
Google Scholar | SAGE Journals | ISI40. Mossello, E, Rivasi, G, Tortù, V, et al. Renal function and delirium in older fracture patients: Different information from different formulas? Eur J Intern Med. 2019;71:70-75. doi:10.1016/j.ejim.2019.10.022
Google Scholar | Crossref | Medline41. Lindroth, H, Bratzke, L, Purvis, S, et al. Systematic review of prediction models for delirium in the older adult inpatient. BMJ Open. 2018;8(4):e019223. doi:10.1136/bmjopen-2017-019223
Google Scholar | Crossref | Medline42. Marcantonio, ER, Flacker, JM, Wright, RJ, Resnick, NM. Reducing Delirium after hip fracture: A randomized trial. J Am Geriatr Soc. 2001;49(5):516-522. doi:10.1046/j.1532-5415.2001.49108.x
Google Scholar | Crossref | Medline | ISI43. Susano, MJ, Scheetz, SD, Grasfield, RH, et al. Retrospective analysis of perioperative variables associated with postoperative delirium and other adverse outcomes in older patients after spine surgery. J Neurosurg Anesthesiol. 2019;31(4):385-391. https://journals.lww.com/jnsa/Fulltext/2019/10000/Retrospective_Analysis_of_Perioperative_Variables.7.aspx.
Google Scholar | Crossref | Medline