Prediction of emergence from prolonged disorders of consciousness from measures within the UK rehabilitation outcomes collaborative database: a multicentre analysis using machine learning

References

• Clinically assisted nutrition and hydration (CANH) and adults who lack the capacity to consent: Guidance for decision-making in England and Wales. London (UK): British Medical Association; 2018.
   Google Scholar
• Luaute J, Maucort-Boulch D, Tell L, et al. Long-term outcomes of chronic minimally conscious and vegetative states. Neurology. 2010;75(3):246–252.
   PubMed Web of Science ®Google Scholar
• Estraneo A, Moretta P, Loreto V, et al. Late recovery after traumatic, anoxic, or hemorrhagic long-lasting vegetative state. Neurology. 2010. 2010;75(3):239–245.
   PubMed Web of Science ®Google Scholar
• Whyte J, Nakase-Richardson R. Disorders of consciousness: outcomes, co-morbidities and care needs. Arch Phys Med Rehabil. 2013;94(10):1851–1854.
   PubMed Web of Science ®Google Scholar
• Estraneo A, Moretta P, Loreto V, et al. Predictors of recovery of responsiveness in prolonged anoxic vegetative state. Neurology. 2013;80(5):464–470.
   PubMed Web of Science ®Google Scholar
• Giacino JT, Kalmar K. The vegetative and minimally conscious states: a comparison of clinical features and functional outcome. J Head Trauma Rehab. 1997;12:36–51.
   Web of Science ®Google Scholar
• Ganesh S, Guernon A, Chalcraft L, et al. Medical comorbidities in disorders of consciousness patients and their association with functional outcomes. Arch Phys Med Rehab. 2013;94(10):1899–1907.
   PubMed Web of Science ®Google Scholar
• UKROC: UK Rehabilitation Outcomes Collaborative. London: UK Rehabilitation Outcomes Collaborative; 2010. Available from: https://www.kcl.ac.uk/nursing/departments/cicelysaunders/research/studies/uk-roc/index.aspx
   Google Scholar
• Hammond FM, Malec JF. The traumatic brain injury model systems: a longitudinal database, research, collaboration and knowledge translation. Eur J Phys Rehab Med. 2010;46:545–548.
   PubMed Web of Science ®Google Scholar
• Heinemann AW, Linacre JM, Wright BD, et al. Relationships between impairment and physical disability as measured by the functional independence measure. Arch Phys Med Rehabil. 1993;74(6):566–573.
   PubMed Web of Science ®Google Scholar
• Shavelle RM, Strauss DJ, Day SM, et al. Life expectancy. In: Zasler ND, Katz DI, Zafonte RD, editors. Brain injury medicine. New York (NY): Demos Medical Publishing; 2007. p. 247–261.
   Google Scholar
• Turner-Stokes L, Nyein K, Turner-Stokes T, et al. The UK FIM + FAM: development and evaluation. Clin Rehabil. 1999;13(4):277–287.
   PubMed Web of Science ®Google Scholar
• Witten IH, Frank E, Hall MA, et al. Data mining: practical machine learning tools and techniques. 4th ed. Cambridge (MA): Morgan Kaufmann; 2017.
   Google Scholar
• Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14:91–118.
   PubMed Web of Science ®Google Scholar
• Rajkumar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.
   PubMed Web of Science ®Google Scholar
• Chekroud AM, Zotti RJ, Shehzad Z, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243–250.
   PubMed Web of Science ®Google Scholar
• Verrusio W, Renzi A, Dellepiane U, et al. A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead. Eur Geriatr Med. 2018;9(5):651–657.
   PubMed Web of Science ®Google Scholar
• Xue Y, Liang H, Norbury J, et al. Predicting the risk of acute care readmissions among rehabilitation inpatients: a machine learning approach. J Biomed Inform. 2018;86:143–148.
   PubMed Web of Science ®Google Scholar
• Zhu M, Zhang Z, Hirdes JP, et al. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Med Inform Decis Mak. 2007;7(1):41.
   PubMedGoogle Scholar
• Honore H, Gade R, Nielsen JF, et al. Developing and validating an accelerometer-based algorithm with machine learning to classify physical activity after acquired brain injury. Brain Inj. 2021;35(4):460–467.
   PubMed Web of Science ®Google Scholar
• Abujaber A, Fadlalla A, Gammoh D, et al. Machine learning model to predict ventilator associated pneumonia in patients with traumatic brain injury: the C.5 decision tree approach. Brain Inj. 2021;35(9):1095–1102.
   PubMed Web of Science ®Google Scholar
• Shtar G, Rokach L, Shapira B, et al. Using machine learning to predict rehabilitation outcomes in postacute hip fracture patients. Arch Phys Med Rehabil. 2021;102(3):386–394.
   PubMed Web of Science ®Google Scholar
• NHS standard contract for specialist rehabilitation for patients with highly complex needs (all ages): D02. London: NHS England; 2013 [cited 2014]. Available from: http://www.england.nhs.uk/wp-content/uploads/2014/04/d02-rehab-pat-high-needs-0414.pdf
   Google Scholar
• Cost-efficient service provision in neurorehabilitation: defining needs, costs and outcomes for people with long term neurological conditions (RP-PG-0407-10185). Lead applicant: Professor Lynne Turner-Stokes. London (UK): National Institute for Health Research Programme Grant for Applied Research, Northwick Park Hospital; 2008.
   Google Scholar
• National definition set for specialised services no 7: "complex specialised rehabilitation for brain injury and complex disability (adult). 3rd ed. London (UK): Department of Health; 2009.
   Google Scholar
• Turner-Stokes L, Thu A, Williams H, et al. The Neurological Impairment Scale: reliability and validity as a predictor of functional outcome in neurorehabilitation. Disabil Rehabil. 2014;36(1):23–31.
   PubMed Web of Science ®Google Scholar
• Siegert RJ, Medvedev O, Turner-Stokes L. Dimensionality and scaling properties of the patient categorisation tool in patients with complex rehabilitation needs following acquired brain injury [multicenter study]. J Rehabil Med. 2018;50(5):435–443.
   PubMed Web of Science ®Google Scholar
• The Patient Categorisation Tool (PCAT). UK Rehabilitation Outcomes Collaborative. London (UK): King’s College London; 2012 [cited 2017 May 18]. Available from: http://www.kcl.ac.uk/lsm/research/divisions/cicelysaunders/research/studies/uk-roc/tools.aspx
   Google Scholar
• Turner-Stokes L, Williams H, Siegert RJ. The Rehabilitation Complexity Scale version 2: a clinimetric evaluation in patients with severe complex neurodisability. J Neurol Neurosurg Psychiatry. 2010;81(2):146–153.
   PubMed Web of Science ®Google Scholar
• Turner-Stokes L, Scott H, Williams H, et al. The Rehabilitation Complexity Scale – Extended: detection of patients with highly complex needs. Disabil Rehabil. 2012;34(9):715–720.
   PubMed Web of Science ®Google Scholar
• Turner-Stokes L, Tonge P, Nyein K, et al. The Northwick Park Dependency Score (NPDS): a measure of nursing dependency in rehabilitation. Clin Rehabil. 1998;12(4):304–318.
   PubMed Web of Science ®Google Scholar
• Siegert RJ, Turner-Stokes L. Psychometric evaluation of the Northwick Park dependency Scale. J Rehabil Med. 2010;42(10):936–943.
   PubMed Web of Science ®Google Scholar
• Turner-Stokes L, Siegert RJ. A comprehensive psychometric evaluation of the UK FIM + FAM [research support, Non-U.S]. Disabil Rehabil. 2013;35(22):1885–1895.
   PubMed Web of Science ®Google Scholar
• Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85(12):2020–2029.
   PubMed Web of Science ®Google Scholar
• Shiel A, Horn SA, Wilson BA, et al. The Wessex Head Injury Matrix (WHIM) main scale: a preliminary report on a scale to assess and monitor patient recovery after severe head injury. Clin Rehabil. 2000;14(4):408–416.
   PubMed Web of Science ®Google Scholar
• Turner-Stokes L, Rose H, Knight A, et al. Prolonged disorders of consciousness: identification using the UK FIM + FAM and cohort analysis of outcomes from a UK national clinical database. Disabil Rehabil. 2022;15:1–10.
   Google Scholar