4.3 Article

Towards individualized monitoring of cognition in multiple sclerosis in the digital era: A one-year cohort study

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ELSEVIER SCI LTD
DOI: 10.1016/j.msard.2022.103692

关键词

Multiple sclerosis; Cognition; Outpatient monitoring; Smartphone; Digital technology; Patient-specific modeling

资金

  1. PPP Allowance by Health-Holland, Top Sector Life Sciences Health [LSHM16060-SGF]
  2. Stichting MS Research [16-946 MS]
  3. Dutch Research Council (NWO)
  4. Nationaal MS fonds

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The use of a smartphone-adapted Symbol Digit Modalities Test (sSDMT) allows for individualized monitoring of cognitive impairment in multiple sclerosis (MS) patients. By utilizing a local linear trend model, the detection of reliable changes in sSDMT scores is improved. This fine-grained monitoring approach can complement current clinical assessment and enhance clinical care in MS.
Background: Cognitive impairment is frequent in multiple sclerosis (MS), but reliable, sensitive and individualized monitoring in clinical practice is still limited. Smartphone-adapted tests may enhance the assessment of function as tests can be performed more frequently and within the daily living environment. The objectives were to prove reproducibility of a smartphone-based Symbol Digit Modalities Test (sSDMT), its responsiveness to relevant change in clinical cognitive outcomes, and develop an individual-based monitoring method for cognition. Methods: In a one-year cohort study with 102 patients with MS, weekly sSDMTs were performed and analyzed on reproducibility parameters: the standard error of measurement (SEM) and smallest detectable change (SDC). Responsiveness of the sSDMT to relevant change in the 3-monthly clinically assessed SDMT (i.e. 4-point change) was quantified with the area under the receiver operating characteristic curve (AUC). Curve fitting of the weekly sSDMT scores of individual patients was performed with a local linear trend model to estimate and visualize the de-noised cognitive state and 95% confidence interval (CI). The optimal assessment frequency was determined by analyzing the CI bandwidth as a function of sSDMT assessment frequency. Results: Weekly sSDMT showed improved reproducibility estimates (SEM=2.94, SDC=8.15) compared to the clinical SDMT. AUC-values did not exceed 0.70 in classifying relevant change in cSDMT. However, utilizing weekly sSDMT measurements, estimated state curves and the 95% CI were plotted showing detailed changes within individuals over time. With a test frequency of once per 12 days, 4-point changes in sSDMT can be detected. Conclusion: A local linear trend model applied on sSDMT scores of individual patients increases the signal-to-noise ratio substantially, which improves the detection of statistically reliable changes. Therefore, this fine-grained individual-based monitoring approach can be used to complement current clinical assessment to enhance clinical care in MS.

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