BACKGROUND: Niemann-Pick disease Type C (NP-C) is difficult to diagnose due to heterogeneous and nonspecific clinical presentation. The NP-C Suspicion Index (SI) was developed to identify patients with a high likelihood of NP-C; however, it was less reliable in patients aged <4 years. METHODS: An early-onset NP-C SI was constructed following retrospective chart review of symptom presentation in 200 patients from nine centres comprised of 106 NP-C cases, 31 non-cases and 63 controls. Statistical analyses defined strength of association between symptoms and a diagnosis of NP-C and assigned risk prediction scores to each symptom. RESULTS: Visceral symptoms were amongst the strongest predictors. Except for gelastic cataplexy and vertical supranuclear gaze palsy, central nervous system symptoms were not discriminatory in this population. Performance of the early-onset NP-C SI was superior versus the original NP-C SI in patients aged ≤4 years. CONCLUSIONS: The early-onset NP-C SI can help physicians, especially those with limited knowledge of NP-C, to identify patients aged ≤4 years who warrant further investigation for NP-C.

• Klíčová slova
• Diagnosis, Diagnostics, Early-onset, Infant, NP-C, Niemann-Pick disease Type C, Paediatric, Screening, Suspicion Index,
• MeSH
• hodnocení rizik MeSH
• kojenec MeSH
• lidé MeSH
• logistické modely MeSH
• metody pro podporu rozhodování * MeSH
• Niemannova-Pickova nemoc typu C diagnóza   MeSH
• novorozenec MeSH
• předškolní dítě MeSH
• retrospektivní studie MeSH
• senzitivita a specificita MeSH
• studie případů a kontrol MeSH
• ukazatele zdravotního stavu * MeSH
• věkové faktory MeSH
• Check Tag
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• novorozenec MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Niemann-Pick disease Type C (NP-C) is a lysosomal lipid storage disorder with varying symptomatology depending on the age of onset. The diagnosis of NP-C is challenging due to heterogeneous nonspecific clinical presentation of the disease. NP-C Suspicion Index (SI) was developed to aid screening and identification of patients with suspicion of NP-C for further clinical evaluation. Here we assess the performance of five NP-C SI models to identify patients with NP-C compared with clinical practice to determine the best SI model for identification of each clinical form of NP-C by age. METHODS: This was a post hoc analysis of a retrospective chart review of patient data collected from five expert NP-C centers. The study assessed the proportion of patients with NP-C who could have been identified using the Original SI, Refined SI, 2/7 SI, 2/3 SI, and Early-Onset SI and evaluated the performance of each SI against clinical practice. A score above a threshold of 70 points for the Original SI, 40 points for the Refined SI, 6 points for the Early-Onset SI, and 2 points for the 2/7 and 2/3 SIs represented identification of NP-C. RESULTS: The study included 63 patients, and of these, 23.8% had a family history of NP-C. Of the available SI tools, the Refined SI performed well in identifying patients with NP-C across all age groups (77.8% infantile, 100% juvenile and 100% adult groups), and earlier identification than clinical diagnosis would have been possible in 50.0% of infantile, 72.7% of juvenile and 87.0% of adult patients. Patients who were not detected by the Refined SI prior to clinical diagnosis mainly presented with delayed developmental milestones, visceral manifestations, neurologic hypotonia, clumsiness, ataxia, vertical supranuclear gaze palsy, parent or siblings with NP-C, dysarthria/dysphagia and psychotic symptoms. CONCLUSION: This study demonstrated the applicability of various SI models for screening and identification of patients with NP-C for further clinical evaluation. Although NP-C is rare and the patient population is limited, this study was conducted in a real-world setting and confirms SI models as useful screening tools that facilitate identification of patients with NP-C earlier in their disease course.

• Klíčová slova
• Clinical diagnosis, Hepatosplenomegaly, NP-C, NP-C Suspicion Index, NP-C disability scales, Neonatal jaundice, Neurologic findings, Niemann-Pick disease Type C, Patient detection, Screening,
• MeSH
• hodnocení rizik MeSH
• lidé MeSH
• Niemannova-Pickova nemoc typu C diagnóza   MeSH
• novorozenecká žloutenka diagnóza   MeSH
• psychotické poruchy diagnóza   MeSH
• retrospektivní studie MeSH
• věkové faktory MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Niemann-Pick disease type C (NP-C) is a rare and progressive autosomal recessive disease leading to disabling neurological manifestation and premature death. The disease is prone to underdiagnosis because of its highly heterogeneous presentation. NP-C is characterized by visceral, neurological, and psychiatric manifestation, and its clinical picture varies according to age at onset. Although cataplexy is one of its characteristic symptoms, particularly in the late infantile and juvenile form, sleep disturbances are described only exceptionally. A combination of splenomegaly, vertical supranuclear gaze palsy, and cataplexy creates a most useful suspicion index tool for the disease. In adolescent and adult patients, when intellectual deterioration progresses and emotional reactions become flat, cataplexy usually disappears. Pathological findings in the brainstem in NP-C mouse model are compatible with the patients' symptoms including cataplexy. The authors observed cataplexy in 5 (3 with late infantile and 2 with juvenile form) out of 22 NP-C cases followed up in the past 20 years.

• MeSH
• kataplexie diagnóza   patologie   patofyziologie   terapie   MeSH
• lidé MeSH
• Niemannova-Pickova nemoc typu C diagnóza   patologie   patofyziologie   terapie   MeSH
• poruchy spánku a bdění diagnóza   patologie   patofyziologie   terapie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH