![]() ConclusãoĪ falha de crescimento é influenciada por fatores intrauterinos e posteriormente por diversas morbidades, tanto no período da internação como no pós-alta, tais variáveis estudadas deveriam ser priorizadas no seguimento.įailure to thrive during early childhood can have permanent harmful effects, especially in preterm infants (PI) 1 as growth, mainly in those born with very low birth weight (VLBW), is influenced by intrauterine and birth factors, as well as variables during hospitalization and post-hospital discharge, 2 causing future problems such as neurodevelopmental alterations 3,4 and metabolic syndrome. Doença Metabólica Óssea e Retinopatia da Prematuridade, durante o Período I e reinternações nos Períodos II e III de seguimento aumentam a chance de escore Z abaixo de −2 DP. Nascer Adequado para a Idade Gestacional aumenta a chance de apresentar escore Z do peso na alta hospitalar acima de −2 DP (OR = 10,217 IC95% 1117–93,436). A falha de crescimento (escore z abaixo de −2 DP) classificada como variável dependente do tipo dicotômica (0 – falha/1 – sucesso) e demais variáveis classificadas como variáveis explicativas para os períodos de internação e para cada um dos períodos de seguimento (I, II e III). As variáveis foram analisadas por regressão logística com programa XLStat 2014 (Microsoft®, WA, EUA). Incluídas aquelas que realizaram pelo uma consulta em cada um dos três períodos assim determinados: Período I – até 3 meses de Idade Corrigida (IC) Período II – entre 4 a 6 meses de IC e Período III – entre 7 a 12 meses de IC. ![]() MétodosĮstudo com crianças nascidas prematuras de muito baixo peso em acompanhamento de 2006 a 2013 em Ambulatório de Alto Risco de um Hospital Escola. Conclusionįailure to thrive is influenced by intrauterine factors and, subsequently, by several morbidities, both in the birth and hospitalization period, as well as in the post-discharge period and thus, such variables should be prioritized in the follow-up.ĭeterminar fatores de risco do período de internação neonatal e do seguimento ambulatorial associados à falha de crescimento no primeiro ano de vida de recém-nascidos de muito baixo peso. Metabolic bone disease and retinopathy of prematurity in Period I, as well as hospital readmissions in Periods II and III during follow-up increased the chance of Z-score < −2 SD. ResultsĬhildren born adequate for gestational age increased the chance of Z-score for weight at discharge > −2 SD (OR = 10.217 95% CI: 1.117–93.436). Failure to thrive ( Z-score below −2 SD) was classified as a dichotomous dependent variable (0 – failure/1 – success), while the other variables were classified as explanatory variables for the hospitalization periods and for each of the follow-up periods (I, II, and III). The variables were analyzed by logistic regression with XLSTAT 2014 software (Microsoft®, WA, USA). The study included newborns that attended at least one appointment in each of the three periods: Period I, up to 3 months of corrected age (CA) Period II, 4–6 months of CA and Period III, 7–12 months of CA. Study of preterm very low birth weight newborns followed from 2006 to 2013 in a public institutional hospital program. Repeat the test few more time by narrowing the range with smaller increment to get better weight and save variable by using options in weight estimation.To determine risk factors during neonatal hospital stay and follow-up associated with failure to thrive in the first year of life of very low birth weight newborns. The default power range is -2 to 2 by 0.5 in SPSS.> Click Ok > read the power for which log likelihood is maximize ![]() To compute weights in SPSS:Īnalyze > Regression > weight estimation > select dependent & independent variables (SPSS use these names for response and predictors) > select weight variable for which hetroscedasticity is detected. ![]() Now if the assumption of homoscedasticity is violated, then you can use regression with WLS weights. It is also better to plot Zresidual Vs all predictors. You can detect, if there is any pattern in these plots in SPSS using these steps:Īnalyze > Regression > linear > plots. In regression analysis, residuals should be independent from response variable, all of the predictors as well as the predicted value of response variable. ![]()
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