PAPP-A y β-hCG libre como predictores de preeclampsia y bajo peso al nacer en una población latina

Introducción: La Preeclampsia ocurre entre el 2-7% de los embarazos. Previos estudios han sugerido la asociación entre los niveles alterados de PAPP-A y la β-hCG libre con el desarrollo de Preeclampsia (PE) y/o Bajo Peso al Nacer (BPN). Metodología: El diseño del estudio es de Prueba Diagnóstica con enfoque de casos y controles. Las mediciones séricas de PAPP-A y la β-hCG libre, fueron realizadas entre la semana 11-13.6 días durante 2 años. Resultados: La cohorte incluyó 399 pacientes, la incidencia de PE fue de 2,26% y de BPN fue de 14.54%. El punto de corte del percentil 10 fue MoM PAPP-A: 0,368293 y MoM β-hCG libre: 0,412268; la especificidad en PE leve fue de 90,5 y para BPN de 90. Los MoM de la β-hCG libre, la edad y el peso materno se comportan como factores de riesgo, mientras que mayores valores de MoM de la PAPP-A y mayor número de partos factores de protección. Para el BPEG severo la edad materna y la paridad se comportan como factores de riesgo, mientras que un aumento promedio de los valores de los MoM de la PAPP-A y la β-hCG libre, como factores de protección en el desarrollo de BPEG Severo. Conclusiones: Existe una relación significativa entre los valores alterados de PAPP-A y de β-hCG libre, valorados a la semana 11 a 13 con la incidencia de Preeclampsia y de Bajo Peso al nacer en fetos cromosómicamente normales, mostrando unos niveles significativamente más bajos a medida que aumentaba la severidad de la enfermedad.

A prediction model for establishing the disability degree in adults with spinal cord injury: results based on WHO-DAS II perspective

Objective: To establish a prediction model of the degree of disability in adults with Spinal CordInjury (SCI ) based on the use of the WHO-DAS II . Methods: The disability degree was correlatedwith three variable groups: clinical, sociodemographic and those related with rehabilitation services.A model of multiple linear regression was built to predict disability. 45 people with sci exhibitingdiverse etiology, neurological level and completeness participated. Patients were older than 18 andthey had more than a six-month post-injury. The WHO-DAS II and the ASIA impairment scale(AIS ) were used. Results: Variables that evidenced a significant relationship with disability were thefollowing: occupational situation, type of affiliation to the public health care system, injury evolutiontime, neurological level, partial preservation zone, ais motor and sensory scores and number ofclinical complications during the last year. Complications significantly associated to disability werejoint pain, urinary infections, intestinal problems and autonomic disreflexia. None of the variablesrelated to rehabilitation services showed significant association with disability. The disability degreeexhibited significant differences in favor of the groups that received the following services: assistivedevices supply and vocational, job or educational counseling. Conclusions: The best predictiondisability model in adults with sci with more than six months post-injury was built with variablesof injury evolution time, AIS sensory score and injury-related unemployment.

Clinical prediction of occupational and non-specific low back pain

Non-specific Occupational Low Back Pain (NOLBP) is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 metthe purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise, clinical prediction rules are tools that aim to facilitate decision making about the evaluation, diagnosis, prognosis and intervention for low back pain, which should incorporate risk factors of physical, psychological and social.