Personalised prediction of institutionalisation in Parkinson's: prognostic factor identification and model development and validation using IPD meta-analysis.
Personalised prediction of institutionalisation in Parkinson's: prognostic factor identification and model development and validation using IPD meta-analysis.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Aberdeen, GB · Author affiliation
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK. Electronic address: yan.li2@abdn.ac.uk.Location evidence
Newcastle upon Tyne, GB · Author affiliation
Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.Location evidence
Umeå, SE · Author affiliation
Department of Clinical Science, Neurosciences, Umeå University, Umeå, Sweden.Location evidence
Cambridge, GB · Author affiliation
John van Geest Centre for Brain Repai, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.Location evidence
Stavanger, NO · Author affiliation
The Norwegian Center for Movement Disorders, Stavanger University Hospital, Stavanger, Norway; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Norway.Location evidence
Bergen, NO · Author affiliation
Department of Clinical Medicine, University of Bergen, Bergen, Norway; Department of Neurology, Haukeland University Hospital, University of Bergen, Norway.Location evidence
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Original abstract
BACKGROUND: People with Parkinson's (PwP) who lose independence may need care in nursing homes or similar institutions if home care is insufficient. Institutionalisation has major social and financial implications. Better understanding of which PwP are most likely to be institutionalised would improve information provision, clinical risk stratification, and healthcare planning. OBJECTIVES: To identify risk factors for institutionalisation in PwP and develop models predicting individual institutionalisation risk. METHODS: We described institutionalisation in the Parkinson's Incidence Cohorts Collaboration, comprising 6 European incidence cohorts. We identified prognostic factors by two-stage individual-participant-data meta-analysis. Prognostic models predicting risk of institutionalisation within 7 years and 10 years were developed using the Royston-Parmar model. Heterogeneity in model performance was assessed using internal-external cross validation (IECV). RESULTS: In 1046 PwP, the cumulative incidence of institutionalisation by 10 years was 37.2%. The incidence rate ranged from 1.7 to 6.2 per 100 person-years. Older age, higher MDS-UPDRS part 3 and lower MMSE at baseline independently predicted higher institutionalisation risk. IECV showed good discrimination in the 10-year (C-statistics 0.73-0.81) and 7-year (0.71-0.84) models. However, calibration (agreement between predictions and observed outcomes) showed under- and over-prediction across studies. After updating model intercept and coefficients (recalibration), the calibration improved. CONCLUSION: 37% of PwP entered institutional care within ten years from diagnosis. Older age, higher MDS-UPDRS part 3 and lower MMSE predicted institutionalisation. The prognostic models discriminated well, but calibration varied between cohorts. We recommend further validation before applying the models in other settings.