Abstract

BackgroundInformation to the patient about the long-term prognosis of symptom burden and functioning is an integrated part of clinical practice, but relies mostly on the clinician’s personal experience. Relevant prognostic models based on patient-reported outcome (PRO) data with repeated measurements are rarely available.ObjectiveThe aim was to describe a generic method for individual long-term prognosis of symptom burden and functioning that implied few statistical presumptions, to evaluate an implementation for prognosis of depressive symptoms in stroke patients and to provide open access to a Web-based prototype of this implementation for individual use.MethodsThe method used to describe individual prognosis of a PRO outcome was based on the selection of a specific subcohort of patients who have the same score as the patient in question at the same time (eg, after diagnosis or treatment start), plus or minus one unit of minimal clinically important difference. This subcohort’s experienced courses were then used to provide quantitative measures of prognosis over time. A cohort of 1404 stroke patients provided data for a simulation study and a prototype for individual use. Members of the cohort answered questionnaires every 6 months for 3.5 years. Depressive symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS) and a single item from the SF-12 (MH4) health survey. Four approaches were compared in a simulation study in which the prognosis for each member of the cohort was individually assessed.ResultsThe mean standard deviations were 40% to 70% higher in simulated scores. Mean errors were close to zero, and mean absolute errors were between 0.46 and 0.66 SD in the four approaches. An approach in which missing HADS scores were estimated from the single-item SF-12 MH4 performed marginally better than methods restricted to questionnaires with a genuine HADS score, which indicates that data collected with shorter questionnaires (eg, in clinical practice) may be used together with longer versions with the full scale, given that the design includes at least two simultaneous measurements of the full scale and the surrogate measure.ConclusionsThis is the first description and implementation of a nonparametric method for individual PRO-based prognosis. Given that relevant PRO data have been collected longitudinally, the method may be applied to other patient groups and to any outcome related to symptom burden and functioning. This initial implementation has been deliberately made simple, and further elaborations as well as the usability and clinical validity of the method will be scrutinized in clinical practice. An implementation of the prototype is available online at www.prognosis.dk.

Highlights

  • Prognosis may be defined as foreseeing, predicting, or estimating future outcome based on the patient’s clinical profile [1]

  • Given that relevant patient-reported outcome (PRO) data have been collected longitudinally, the method may be applied to other patient groups and to any outcome related to symptom burden and functioning

  • This initial implementation has been deliberately made simple, and further elaborations as well as the usability and clinical validity of the method will be scrutinized in clinical practice

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Summary

Introduction

Prognosis may be defined as foreseeing, predicting, or estimating future outcome based on the patient’s clinical profile [1]. A prognostic model may be developed to predict the short-term outcome after intracerebral hemorrhage. The prediction of outcome in patients with intracerebral hemorrhage can be used in the emergency department for decision support to differentiate between patients who might benefit from intensive care and those who have such poor prognosis that they will not benefit from intensive care [5]. Another important use of prognostic knowledge is to inform the stroke patient and relatives [5]. Relevant prognostic models based on patient-reported outcome (PRO) data with repeated measurements are rarely available

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