Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

Velikova, Galina; Potter, Caroline; Batchelder, Laurie; A’Court, Christine; Baker, Matthew; Bostock, Jennifer; Coulter, Angela; Fitzpatrick, Ray; Forder, Julien E.; Fox, D; Geneen, Louise J; Gibbons, Elizabeth; Jenkinson, Crispin; Jones, Karen; Kelly, Laura; Peters, Michele; Mulhern, Brendan; Labeit, Alexander; Rowen, Donna; Meadows, Keith; Elliott, Jackie; Knowles, Emma; Keetharuth, Anju Devianee; Connell, Janice; Carlton, Jill; Buck, Elizabeth Taylor; Ricketts, Thomas C.; Barkham, Michael; Goswami, Pushpendra; Salek, Sam; Ionova, Tatyana; Olíva, Esther Natalie; Fielding, Adele K.; Καρακάντζα, Μαρίνα; Al‐Ismail, Saad; Collins, Graham P.; McConnell, Stewart; Langton, Catherine; Jennings, Daniel M; Else, Roger; Kell, Jonathan; Ward, Helen; Day, Sophie E.; Lumley, Elizabeth; Phillips, Patrick; Duncan, Rosie; Buckley‐Woods, Helen; Aber, Ahmed; Jones, G.; Michaels, Jonathan; Porter, Ian; Gangannagaripalli, Jaheeda; Davey, Antoinette; Ricci‐Cabello, Ignacio; Haywood, Kirstie L.; Hansen, Stine Thestrup; Valderas, José M; Roberts, Deb; Gumber, Anil; Podmore, Bélène; Hutchings, Andrew; Meulen, Jan van der; Aggarwal, Ajay; Konan, Sujith; Price, Andrew; Jackson, William M.; Bottomley, Nick; Philiips, Michael; Knightley-Day, Toby; Beard, David; Greenhalgh, Joanne; Gooding, Kate; Valderas, Chema; Wright, Judy; Dalkin, Sonia; Meads, David; Black, Nick; Fawkes, Carol; Froud, Robert; Carnes, Dawn; Cook, Jonathan; Dakin, Helen; Smith, James; Kang, Sujin; Griffiths, Catrin; Guest, Ella; Harcourt, Diana; Murphy, Mairead; Hollinghurst, Sandra; Salisbury, Chris; Gao, Anqi; Lemańska, Agnieszka; Chen, Tao; Dearnaley, David P.; Jena, R.; Sydes, Matthew R.; Faithfull, Sara; Ades, A E; Kounali, Daphne; Lu, Guobing; Rombach, Ines; Gray, Alastair; Rivero‐Arias, Oliver; Holch, Patricia; Holmes, Marie; Rodgers, Zoe; Dickinson, Sarah; Clayton, Beverly; Davidson, Susan E; Routledge, Jacqui; Glennon, J P; Henry, A.; Franks, K.; Maguire, Roma; McCann, Lisa; Young, Teresa; Armes, Jo; Harris, Jenny; Miaskowski, Christine; Kotronoulas, Grigorios; Miller, Morven; Ream, Emma; Patiraki, Elizabeth; Geiger, A.; Berg, Geir Vegard; Flowerday, Adrian; Donnan, Peter T.; McCrone, Paul; Apostolidis, K.; Fox, Patricia; Furlong, Eileen; Kearney, Nora; Gibbons, Chris; Fischer, Felix; Coste, Joël; Martinez, Jose Valderas; Rose, Matthias; Leplège, Alain; Shingler, S.L.; Aldhouse, Natalie; Al-Zubeidi, Tamara; Trigg, Andrew; Kitchen, Helen; Green, Colin; Coast, Joanna; Smith, Sarah C.; Hendriks, Jolijn; Shah, Koonal; Ramos-Goñi, Juan-Manuel; Kreimeier, Simone; Herdman, Mike; Devlin, Nancy; Finch, Aureliano Paolo; Brazier, John; Mukuria, Clara; Zamora, Bernarda; Parkin, David; Feng, Yong; Bateman, Andrew; Patton, Thomas; Gutacker, Nils

doi:10.1186/s12955-017-0757-y

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 129 patients with different HM's were recruited from six secondary care hospitals across England and Wales (Goswami et al, 2016). The content analysis of the transcribed interviews was carried out using the NVivo 11, qualitative data analysis software, to identify the themes and sub-themes reported important by the HM patients (Goswami et al, 2017;Velikova et al, 2017). The analysis was validated independently by another researcher.…”

Section: Methodsmentioning

confidence: 99%

Development of a Novel Hematological Malignancy Specific Patient-Reported Outcome Measure (HM-PRO): Content Validity

Goswami

Oliva

Ionova³

et al. 2020

Front. Pharmacol.

Self Cite

View full text Add to dashboard Cite

Background: The quality of life of patients at all stages of hematological malignancy is greatly affected by the disease and its treatment. There is a wide range of healthrelated quality of life (HRQoL) issues important to these patients. Any new instrument developed to measure HRQoL of such patients should be content valid, i.e., the items should be comprehensively relevant to the patients and their health condition. The aim of the present study was to examine content validity of a hematological malignancy specific patient reported outcome measure (HM-PRO) developed for use in routine clinical practice. Methods: Following literature review and semi-structured interviews, the generated themes and sub-themes were discussed to develop the prototype version of the HM-PRO. A 4-step approach was used for content validation: initial testing and cognitive interviewing; item rating; content validity panel meeting; final field testing and cognitive interviewing. Additional questions related to patients' perception of recall period and preferred sentence structure (i.e., question or statement) of the items were also asked during cognitive interviews. Results: The content analysis of 129 transcribed semi-structured interviews resulted in the prototype version of the instrument consisting of 58 items grouped into two parts: Part A (impact/HRQoL-34 items) and Part B (signs and symptoms-24 items). The initial testing showed intra-class correlation coefficient (ICC) of >0.8 for both Part A and Part B. Item rating for language clarity, completeness, relevance, and response scale by experts and patients showed content validity index for scales average >0.8 for both Part A and Part B, except 0.64 for relevance for Part A by the patient panel. The final testing of the revised version of the instrument showed the Cronbach's alpha value of

show abstract

Section: Methodsmentioning

confidence: 99%

Development of a Novel Hematological Malignancy Specific Patient-Reported Outcome Measure (HM-PRO): Content Validity

Goswami

Oliva

Ionova³

et al. 2020

Front. Pharmacol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The CAT version of the questionnaire used 43% and 75% fewer items than the WHOQOL-BREF and WHOQOL-100 questionnaires respectively, at comparable levels of reliability [5]. The WHOQOL CAT, when administered through Concerto, takes a mean of 121 seconds to complete, approximately 10 minutes faster than the WHOQOL-100 [29].…”

Section: Quality Of Life Assessmentmentioning

confidence: 99%

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning (Preprint)

Harrison¹,

Loe²,

Lis³

et al. 2020

Preprint

View full text Add to dashboard Cite

UNSTRUCTURED Patient-reported assessments are transforming many facets of healthcare, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden, improve accuracy and provide individualize, actionable feedback. The Concerto platform is a highly adaptable, secure and easy-to-use console for developing and administering advanced patient-reported assessments that can harness the power of CAT and machine learning. In this paper, we introduce readers to contemporary assessment techniques and the Concerto platform. We review advances in the field of patient-reported assessment that have been driven by the Concerto platform and explain how to create an advanced, adaptive assessment, for free, with no prior experience of CAT or programming.

show abstract

“…Between 70–95% of PD patients are likely to experience some degree of CI as PD advances, with PDD frequently developing 10–20 years post diagnosis [ 8 , 9 ], with potential for severe impacts on overall quality of life, familial relationships and societal functioning [ 10 ]. Treatment and disease management create severe burdens at medical [ 11 ], economic [ 12 , 13 ] and personal levels, with identification of a direct, specific cause for CI development remaining a working, disputed research area [ 14 , 15 ]. Presentation of CI is complex and diverse, occurring across a number of cognitive domains [ 16 ], including visuospatial [ 17 ], working memory [ 18 ] and psycho-motor speed [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

View full text Add to dashboard Cite

Background Parkinson’s Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. Methods To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson’s Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. Results Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson’s Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson’s Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. Conclusions Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

show abstract

Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

Cited by 7 publications

References 0 publications

Development of a Novel Hematological Malignancy Specific Patient-Reported Outcome Measure (HM-PRO): Content Validity

Development of a Novel Hematological Malignancy Specific Patient-Reported Outcome Measure (HM-PRO): Content Validity

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning (Preprint)

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Contact Info

Product

Resources

About