Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners

Uncini, Antonino; Aretusi, Graziano; Manganelli, Fiore; Sekiguchi, Yukari; Laurent, Magy; Tozza, Stefano; Tsuneyama, Atsuko; Lefour, Sophie; Kuwabara, Satoshi; Santoro, Lucio; Ippoliti, Luigi

doi:10.1007/s10072-020-04499-y

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Thus, in order not to miss early diagnosis of CIDP, several diagnostic criteria have been proposed and the most widely accepted criteria are those recommended by EFNS/PNS which appear to have the best combination of sensitivity (73%) and specificity (90%) for the diagnosis of CIDP [5][6][7]. Importantly, our 16% rate of patients not fulfilling the EFNS/PNS criteria is fully in line with sensitivity of applied criteria [6,14]. Anyway, these patients have been considered affected by CIDP because of their clinical features together with supportive EFNS/PNS criteria.…”

Section: Discussionmentioning

confidence: 69%

The neurophysiological lesson from the Italian CIDP database

et al. 2021

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Introduction Electrophysiological diagnosis of chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) may be challenging. Thus, with the aim ofproviding some practical advice in electrophysiological approach to a patient with suspected CIDP, we analyzed electrophysiological data from 499 patients enrolled inthe Italian CIDP Database. Methods We calculated the rate of each demyelinating feature, the rate of demyelinating features per nerve, the diagnostic rate for upper andlower limb nerves, and, using a ROC curve analysis, the diagnostic accuracy of each couple of nerves and each demyelinating feature, for every CIDP subtype.Moreover, we compared the electrophysiological data of definite and probable CIDP patients with those of possible and not-fulfilling CIDP patients, and by a logisticregression analysis, we estimated the odds ratio (OR) to make an electrophysiological diagnosis of definite or probable CIDP. Results The ulnar nerve had the highestrate of demyelinating features and, when tested bilaterally, had the highest diagnostic accuracy except for DADS in which peroneal nerves were the most informative.In possible and not-fulfilling CIDP patients, a lower number of nerves and proximal temporal dispersion (TD) measurements had been performed compared to definiteand probable CIDP patients. Importantly, OR for each tested motor nerve and each TD measurement was 1.59 and 1.33, respectively. Conclusion Our findingsdemonstrated that the diagnosis of CIDP may be missed due to inadequate or incomplete electrophysiological examination or interpretation. At the same time, thesedata taken together could be useful to draw a thoughtful electrophysiological approach to patients suspected of CIDP.

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Section: Discussionmentioning

confidence: 69%

The neurophysiological lesson from the Italian CIDP database

et al. 2021

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“…In addition, it should be considered that even if CTS is considered a main feature of ATTRv [37], presenting many years before other more severe features, CTS is also highly frequent in other etiologies [38,39]. Ataxia in ATTRv is an expression of a prominent sensory fiber involvement, which characterizes the early phase of ATTRv [40], and often precedes the more disabling motor damage [14,41]. Gastrointestinal involvement is frequent in ATTRv [42], and diarrhea, constipation, or weight loss may be present since the onset of the disease, even anticipating neurological symptoms [43].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

et al. 2023

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Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings. Results: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test. Conclusions: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings.

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“…These data support the use of HGS measures, suggesting that they might be more sensitive than neurophysiology alone in the assessment of strength in the upper limbs. Indeed, despite being a very sensitive and reliable tool in the clinical onset, conventional neurophysiology might be less reliable in the follow-up, when motor nerves become not elicitable or too altered to allow an indirect evaluation of residual strength ( 22 , 23 ). From this perspective, HGS may offer the possibility of qualifying residual strength in the hands and appreciate even little changes that might escape with conventional neurophysiology.…”

Section: Discussionmentioning

confidence: 99%

Motor Conduction Studies and Handgrip in Hereditary TTR Amyloidosis: Simple Tools to Evaluate the Upper Limbs

et al. 2022

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PurposeHereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is caused by mutations in the TTR gene, leading to misfolded monomers that aggregate generating amyloid fibrils. The clinical phenotype is heterogeneous, and characterized by a multisystemic disease affecting the sensorimotor and autonomic functions along with other organs.Materials and MethodsAll the patients were assessed by complete neurological assessment, neurophysiological evaluation, of the median nerve, and handgrip analysis. The data are presented as means and standard deviations. Parametric and non-parametric assessments have been performed to identify differences between groups. Pearson's correlation has been carried out when appropriate.ResultsTwenty patients with ATTRv (66.1 ± 8.4 years; eight females) and 30 controls (61.1 ± 11.6 years; 16 females) were enrolled. Handgrip strength was reduced in patients with ATTR in both right and left hands compared to the controls. Significant differences were found between patients and controls in the right (handgrip right, HGSR TTR 21.1 ± 13 kg vs. HGSR Control 29.4 ± 11.3 kg, p = 0.017) and left (handgrip left, HGSL TTR 22.2 ± 10.7 kg. vs. HGSL Control 31 ± 11.3 kg, p = 0.007). NIS and CMAP amplitude of the median nerve were related to HGS measures for both hands in patients with ATTRv.ConclusionsThe progression of bilateral carpal tunnel syndrome is related to neurophysiological data in the median nerve in ATTRv. Also, handgrip measures might represent an important tool for the assessment of disease progression in ATTRv. We propose using a combination of CMAP amplitude and HGS for the assessment of hand motor strength in ATTRv.

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Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners

Cited by 6 publications

References 27 publications

The neurophysiological lesson from the Italian CIDP database

The neurophysiological lesson from the Italian CIDP database

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

Motor Conduction Studies and Handgrip in Hereditary TTR Amyloidosis: Simple Tools to Evaluate the Upper Limbs

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