RESUMEN
PURPOSE: Treatment outcomes of conservative and surgical treatment of Legg-Calvé-Perthes disease (LCPD) have been shown to be conditioned by a number of factors that may vary across different populations. This retrospective study aimed to evaluate factors affecting radiographically assessed treatment outcomes in patients treated surgically or conservatively for LCPD at Faculty Hospital Motol, Prague, Czech Republic, between the years 2006 and 2019. METHODS: Data of forty-seven children comprising 52 hips were analysed. Treatment outcomes were evaluated according to Stulberg classification. Predictors included the initial stage of fragmentation of the hip joint according to Herring classification, type of treatment (conservative or surgical), age at the time of diagnosis and sex. RESULTS: Older age and severity of LCPD according to Herring classification but not the type of treatment were the strongest factors determining treatment outcomes. Treatment outcomes were comparable in patients treated conservatively or surgically both across the whole cohort of patients and a group of young children < six years of age. CONCLUSIONS: Results strengthen the roles of severity of the LCPD at onset of treatment and age of the patient in predicting treatment outcomes in patients with LCPD. Conservative and surgical treatments appear to yield similar treatment outcomes irrespective of age of patients.
Asunto(s)
Enfermedad de Legg-Calve-Perthes , Humanos , Niño , Preescolar , Enfermedad de Legg-Calve-Perthes/diagnóstico por imagen , Enfermedad de Legg-Calve-Perthes/cirugía , Estudios Retrospectivos , Osteotomía/métodos , Articulación de la Cadera , Resultado del TratamientoRESUMEN
Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire week. In this paper the actigraphy data is described by a weighted mixture of two distributions where the weight evolves along the day according to the patient circadian cycle. Thus, one of the distributions is mainly associated with the wakefulness state while the other is associated with the sleep state. Actigraphy data, acquired from 20 healthy patients and manually segmented by trained technicians, is used to characterize the acceleration magnitude during sleep and wakefulness states. Several mixture combinations are tested and statistically validated with conformity measures. It is shown that both distributions can co-exist at a certain time with varying importance along the circadian cycle.
Asunto(s)
Actigrafía/instrumentación , Sueño , Vigilia , Actigrafía/métodos , Ritmo Circadiano , Redes de Comunicación de Computadores , Diseño de Equipo , Humanos , Modelos Estadísticos , Monitoreo Fisiológico/instrumentación , Monitoreo Fisiológico/métodos , Telemedicina/instrumentación , Telemedicina/métodos , Factores de TiempoRESUMEN
The diagnosis of Sleep disorders, highly prevalent in the western countries, typically involves sophisticated procedures and equipments that are intrusive to the patient. Wrist actigraphy, on the contrary, is a non-invasive and low cost solution to gather data which can provide valuable information in the diagnosis of these disorders. The acquired data may be used to infer the Sleep/Wakefulness (SW) state of the patient during the circadian cycle and detect abnormal behavioral patterns associated with these disorders. In this paper a classifier based on Autoregressive (AR) model coefficients, among other features, is proposed to estimate the SW state. The real data, acquired from 23 healthy subjects during fourteen days each, was segmented by expert medical personal with the help of complementary information such as light intensity and Sleep e-Diary information. Monte Carlo tests with a Leave-One-Out Cross Validation (LOOCV) strategy were used to assess the performance of the classifier which achieves an accuracy of 96%.