• Journal of Korean Academy of Nursing
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• v.13 no.1
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• pp.7-21
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• 1983

For the flexible and rational distribution of limited existing health resources based on measurements of individual risk, the socalled Risk Approach is being proposed by the World Health Organization as a managerial tool in maternal and child health care program. This approach, in principle, puts us under the necessity of developing a technique by which we will be able to measure the degree of risk or to discriminate the future outcomes of pregnancy on the basis of prior information obtainable at prenatal care delivery settings. Numerous recent studies have focussed on the identification of relevant risk factors as the Prior infer mation and on defining the adverse outcomes of pregnancy to be dicriminated, and also have tried on how to develope scoring system of risk factors for the quantitative assessment of the factors as the determinant of pregnancy outcomes. Once the scoring system is established the technique of classifying the patients into with normal and with adverse outcomes will be easily de veloped. The scoring system should be developed to meet the following four basic requirements. 1) Easy to construct 2) Easy to use 3) To be theoretically sound 4) To be valid In searching for a feasible methodology which will meet these requirements, the author has attempted to apply the“Likelihood Method”, one of the well known principles in statistical analysis, to develop such scoring system according to the process as follows. Step 1. Classify the patients into four groups: Group $A_1$: With adverse outcomes on fetal (neonatal) side only. Group $A_2$: With adverse outcomes on maternal side only. Group $A_3$: With adverse outcome on both maternal and fetal (neonatal) sides. Group B: With normal outcomes. Step 2. Construct the marginal tabulation on the distribution of risk factors for each group. Step 3. For the calculation of risk score, take logarithmic transformation of relative proport-ions of the distribution and round them off to integers. Step 4. Test the validity of the score chart. h total of 2, 282 maternity records registered during the period of January 1, 1982-December 31, 1982 at Ewha Womans University Hospital were used for this study and the“Questionnaire for Maternity Record for Prenatal and Intrapartum High Risk Screening”developed by the Korean Institute for Population and Health was used to rearrange the information on the records into an easy analytic form. The findings of the study are summarized as follows. 1) The risk score chart constructed on the basis of“Likelihood Method”ispresented in Table 4 in the main text. 2) From the analysis of the risk score chart it was observed that a total of 24 risk factors could be identified as having significant predicting power for the discrimination of pregnancy outcomes into four groups as defined above. They are: (1) age (2) marital status (3) age at first pregnancy (4) medical insurance (5) number of pregnancies (6) history of Cesarean sections (7). number of living child (8) history of premature infants (9) history of over weighted new born (10) history of congenital anomalies (11) history of multiple pregnancies (12) history of abnormal presentation (13) history of obstetric abnormalities (14) past illness (15) hemoglobin level (16) blood pressure (17) heart status (18) general appearance (19) edema status (20) result of abdominal examination (21) cervix status (22) pelvis status (23) chief complaints (24) Reasons for examination 3) The validity of the score chart turned out to be as follows: a) Sensitivity: Group $A_1$: 0.75 Group $A_2$: 0.78 Group $A_3$: 0.92 All combined : 0.85 b) Specificity : 0.68 4) The diagnosabilities of the“score chart”for a set of hypothetical prevalence of adverse outcomes were calculated as follows (the sensitivity“for all combined”was used). Hypothetidal Prevalence : 5％ 10％ 20％ 30％ 40％ 50％ 60％ Diagnosability : 12% 23% 40% 53% 64% 75% 80%.

• Neonatal Medicine
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• v.18 no.2
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• pp.221-227
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• 2011

Purpose: The objectives were to identify the characteristics of neonates with hydrops fetalis, and to identify the risk factors associated with mortality. Methods: A retrospective review of AMC (Asan Medical Center) dataset was performed from January 1990 to June 2009. The characteristics of 71 patients with hydrops fetalis were investigated and they were divided into two groups: the survived group and the expired group. Various perinatal and neonatal factors in two groups were compared to find out risk factors associated with mortality based on univariate analysis, followed by multiple regression analyses (SPSS version 18.0). Results: Of those 71 neonates (average gestational age: 33 weeks, birth weight: 2.6 kg), 38 survived, 33 died, resulting in overall mortality rate of 46.5%. The most common etiology was idiopathic followed by chylothorax, cardiac anomalies, twin-to-twin transfusion, meconium peritonitis, cardiac arrythmias, and congenital infections. Factors that were associated independently with mortality in logistic regression analyses were low 5-minutes Apgar score, hyaline membrane disease and delayed in achieving 50th percentile ideal body weight for appropriate gestational age by 10 days. Conclusion: In this study, 5-minutes Apgar score, hyaline membrane disease and delayed in achieving 50th percentile ideal body weight for appropriate gestational age by 10 days were significant risk factors associated with mortality in hydrops fetalis. Therefore, the risk of death among neonates with hydrops fetalis depends on the illness immediately after birth and severity of hydrops fetalis. Informations from this study may prove useful in prediction of prognosis to neonates with hydrops fetalis.