Assessment of delayed diagnosis and treatment in multiple sclerosis patients during 1990–2016

Ghiasian, Masoud; Faryadras, Mohammad; Mansour, Maryam; Khanlarzadeh, Elham; Mazaheri, Shahir

doi:10.1007/s13760-020-01528-7

Cited by 18 publications

(24 citation statements)

References 22 publications

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“…These findings regarding the difference between gender were in line with the previous reports 6 , 14 , 16 , 19 , 26 . In contrast to the study from Iran, patient delay in seeking medical advice was noted in men compared to women 18 . This was explained by the men’s delayed help-seeking behavior, thus potentially hindering diagnostic processes 27 .…”

Section: Discussioncontrasting

confidence: 88%

“…In this study, the median delay between the onset of symptoms and MS diagnosis was three months. This was a month longer than the median delay of two months reported in a study from Iran by Ghiasian et al 18 . In Portuguese, the median time between the first presentation and diagnosis was 9 months 19 .…”

Section: Discussioncontrasting

confidence: 52%

“…In Colombia, no link was found between age at onset and delayed diagnosis 16 . These differences between studies could be also attributed to differences in MS prevalence, differences in MS risk factors leading to the variability of the age at onset in different countries, or even different paths of access to the diagnostic facilities 18 .…”

Section: Discussionmentioning

confidence: 99%

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Multiple sclerosis diagnostic delay and its associated factors in Upper Egyptian patients

Khedr

Malky

Hussein

et al. 2023

Sci Rep

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The earlier the diagnosis of multiple sclerosis (MS), the sooner disease-modifying treatments can be initiated. However, significant delays still occur in developing countries. We aimed to identify factors leading to delayed diagnosis of MS in Upper Egypt. One hundred forty-two patients with remitting relapsing MS (RRMS) were recruited from 3 MS units in Upper Egypt. Detailed demographic and clinical data were collected. Neurological examination and assessment of the Disability Status Scale (EDSS) were performed. The mean age was 33.52 ± 8.96 years with 72.5% of patients were females. The mean time from symptom onset to diagnosis was 18.63 ± 27.87 months and the median was 3 months. Seventy-two patients (50.7%) achieved diagnosis within three months after the first presenting symptom (early diagnosis), while seventy patients (49.3%) had more than three months delay in diagnosis (delayed diagnosis). Patients with a delayed diagnosis frequently presented in the period before 2019 and had a significantly higher rate of initial non-motor presentation, initial non-neurological consultations, prior misdiagnoses, and a higher relapse rate. Another possible factor was delayed MRI acquisition following the initial presentation in sixty-six (46.5%) patients. Multivariable logistic regression analysis demonstrated that earlier presentation, initial non-neurological consultation, and prior misdiagnosis were independent predictors of diagnostic delay. Despite advances in MS management in Egypt, initial non-neurological consultation and previous misdiagnoses are significant factors responsible for delayed diagnosis in Upper Egypt.

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Section: Discussioncontrasting

confidence: 88%

Section: Discussioncontrasting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

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Multiple sclerosis diagnostic delay and its associated factors in Upper Egyptian patients

Khedr

Malky

Hussein

et al. 2023

Sci Rep

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“…The time between disease onset and first visit was longer in PP/PR and SP phenotypes, and this is a diagnostic challenge for these patients, who are also older and more likely to be female. Greater attention is needed in intercepting these late-onset MS cases considering also socio-cultural and environmental factors in long MS [ 18 , 19 ].…”

Section: Discussionmentioning

confidence: 99%

Do patients’ and referral centers’ characteristics influence multiple sclerosis phenotypes? Results from the Italian multiple sclerosis and related disorders register

et al. 2022

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Background Multiple sclerosis (MS) is characterized by phenotypical heterogeneity, partly resulting from demographic and environmental risk factors. Socio-economic factors and the characteristics of local MS facilities might also play a part. Methods This study included patients with a confirmed MS diagnosis enrolled in the Italian MS and Related Disorders Register in 2000–2021. Patients at first visit were classified as having a clinically isolated syndrome (CIS), relapsing–remitting (RR), primary progressive (PP), progressive-relapsing (PR), or secondary progressive MS (SP). Demographic and clinical characteristics were analyzed, with centers’ characteristics, geographic macro-areas, and Deprivation Index. We computed the odds ratios (OR) for CIS, PP/PR, and SP phenotypes, compared to the RR, using multivariate, multinomial, mixed effects logistic regression models. Results In all 35,243 patients from 106 centers were included. The OR of presenting more advanced MS phenotypes than the RR phenotype at first visit significantly diminished in relation to calendar period. Females were at a significantly lower risk of a PP/PR or SP phenotype. Older age was associated with CIS, PP/PR, and SP. The risk of a longer interval between disease onset and first visit was lower for the CIS phenotype, but higher for PP/PR and SP. The probability of SP at first visit was greater in the South of Italy. Discussion Differences in the phenotype of MS patients first seen in Italian centers can be only partly explained by differences in the centers’ characteristics. The demographic and socio-economic characteristics of MS patients seem to be the main determinants of the phenotypes at first referral.

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“…While predicting disability progression in MS is beneficial in both a clinical and at-home context, earlier diagnosis and treatment of MS is considered the best path to fighting it (Miller, 2004). Multiple studies have shown a delay between symptom development, to first medical visit and then finally to diagnosis, with an average of 1-2 years between symptom onset and diagnosis (Ghiasian et al, 2021;Fernández et al, 2010). We believe that the usage of smartphones can enable large-scale diagnosis of MS in a more timely manner.…”

Section: Smartphone-based Diagnosis Of Msmentioning

confidence: 92%

Disability prediction in multiple sclerosis using performance outcome measures and demographic data

Roy¹,

Mincu²,

Proleev³

et al. 2022

Preprint

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Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, * These authors contributed equally † Data used in the preparation of this article were obtained from the Multiple Sclerosis Outcome Assessments Consortium (MSOAC). As such, the investigators within MSOAC contributed to the design and implementation of the MSOAC Placebo database and/or provided placebo data, but did not participate in the analysis of the data or the writing of this report.

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Assessment of delayed diagnosis and treatment in multiple sclerosis patients during 1990–2016

Cited by 18 publications

References 22 publications

Multiple sclerosis diagnostic delay and its associated factors in Upper Egyptian patients

Multiple sclerosis diagnostic delay and its associated factors in Upper Egyptian patients

Do patients’ and referral centers’ characteristics influence multiple sclerosis phenotypes? Results from the Italian multiple sclerosis and related disorders register

Disability prediction in multiple sclerosis using performance outcome measures and demographic data

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