可用來預測極早產嬰兒住院天數的臨床因素


  【24drs.com】December 17, 2009 — 根據線上發表於12月14日小兒科(Pediatrics)期刊的回溯分析結果,有一些可用來預測極早產嬰兒住院天數的臨床因素。
  
  第一作者、Lucile Packard兒童醫院史丹佛大學醫學院的Susan R. Hintz醫師在新聞稿中表示,希望聽眾記住的是,我們無法從住院第一天很準確地預測出院時間;住院期間更重要的事情是,注意極早產嬰兒出院時的影響。
  
  研究目標是發展、確認與比較多種預測妊娠年齡27週以下嬰兒出院時間的模式,可能的預測因子包括時間相關變項以及5個關鍵風險因素,研究樣本包括出院時存活的2,254名估計妊娠年齡27週以下嬰兒,2002年7月至2005年12月間,出生於國立Eunice Kennedy Shriver兒童健康與人類發展研究中心新生兒研究網絡。
  
  出院時使用月經過後年紀,研究者將出院時間建立為連續變項,以及作為分類變項(提早或延遲出院)。他們接著發展3個線性和邏輯回歸模式,納入時間相關變項:只有出生前後因素、出生前後加早期新生兒因素、出生前後加早期新生兒因素與後期因素。他們也使用累計出現的5個關鍵因素作為預測因子,分析提早和延遲出院的模式。此外,他們使用決定係數(R 2),以比較線性模式之預測能力,根據接受者操作特徵曲線的曲線下面積(AUC)比較邏輯模式的預測能力。
  
  雖然出院時的月經過後年紀的預測力不佳,包含後期臨床特徵的模式可以比較準確預測提早或延遲出院。生產前後模式中,完整模式的AUC是0.76- 0.83相較於0.56- 0.69。簡化的關鍵風險因素模式顯示,提早或延遲出院的預測可能性和觀察到的比率相似,而且其AUC (0.75 - 0.77)與納入完整因素的模式相似。預測比一般出院延遲的這5個關鍵因素或整組因素為:出生體重小於750克、住院時需要手術、敗血症或胃腸道感染、慢性肺部問題、視網膜發育嚴重問題。
  
  Hintz醫師表示,令人鼓舞的是,這相當有效率,5因素模式和其他我們用來預測嬰兒提早或延遲出院之更複雜的統計模式一樣好。
  
  這項分析的限制包括,無法一般化到所有機構,每個地點的照護方式有差異。
  
  研究作者寫道,如果只有考量出生前後的因素,對於提早或延遲出院的預測力不佳,但是納入稍後發生的疾病之後,則可明顯改善。使用少數關鍵風險因素的預測模式,效果和完整模式一樣好,可以是臨床可運用的策略。
  
  國家健康研究中心與國立Eunice Kennedy Shriver兒童健康與人類發展研究中心支持本研究。研究作者皆宣告沒有相關財務關係。
  
  Pediatrics. 線上發表於2009年12月14日。

Clinical Factors May Predict Length of Hospital Stay for Extremely Preterm Infants

By Laurie Barclay, MD
Medscape Medical News

December 17, 2009 — Clinical factors may predict length of hospital stay for extremely preterm infants, according to the results of a retrospective analysis reported online in the December 14 issue of Pediatrics.

"The take-home message is that we can't predict time to discharge very accurately from day one of hospitalization," lead author Susan R. Hintz, MD, MS, from Lucile Packard Children's Hospital and Stanford University School of Medicine in Stanford, California, said in a news release. "Really important things happen during the course of hospitalization that affect when an extremely premature baby will be discharged."

The goal of this study was to develop, validate, and compare the ability of several models to predict the time to hospital discharge for infants younger than 27 weeks' estimated gestational age. Time-dependent covariates as well as 5 key risk factors were considered as potential predictors. The study sample consisted of 2254 infants younger than 27 weeks' estimated gestational age who were born at a Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network site between July 2002 and December 2005 and who survived to discharge.

Using postmenstrual age at discharge, the investigators modelled time to discharge as a continuous variable and as a categoric variable (early vs late discharge). They then developed 3 linear and logistic regression models with time-dependent covariate inclusion: perinatal factors only, perinatal plus early-neonatal factors, and perinatal plus early-neonatal plus later factors. They also assessed models for early and late discharge using the cumulative presence of 5 key risk factors as predictors, and they compared predictive capabilities using the coefficient of determination (R 2) for the linear models and the area under the curve (AUC) of the receiver operating characteristic curve for the logistic models.

Although prediction of postmenstrual age at discharge was poor, models including later clinical characteristics more accurately predicted early or late discharge. The AUC for full models was 0.76 to 0.83 vs 0.56 to 0.69 for perinatal factor models. Simplified key-risk-factors models showed that predicted probabilities for early and late discharge compared favorably with the observed rates and that the AUC (0.75 - 0.77) was similar to those of the models that included the full factor set. The 5 key factors or groups of factors predicting later-than-usual hospital discharge were birth weight less than 750 g, the need for surgery during hospitalization, sepsis or gastrointestinal tract infections, chronic lung problems, and severe problems with retinal development.

"It was encouraging that this very streamlined, five-factor model was as good as the much more complicated statistical model that we used to predict if a baby would be discharged early or late," Dr. Hintz said.

Limitations of this analysis include lack of generalizability to all institutions and differences in care approach between sites.

"Prediction of early or late discharge is poor if only perinatal factors are considered, but it improves substantially with knowledge of later-occurring morbidities," the study authors write. "Predictive models that use a few key risk factors are comparable to the full models and may offer a clinically applicable strategy."

The National Institutes of Health and the Eunice Kennedy Shriver National Institute of Child Health and Human Development supported this study. The study authors have disclosed no relevant financial relationships.

Pediatrics. Published online December 14, 2009.

    
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