[Comparability of obstetric and perinatal data from England and Germany using standard-primip-groups - maternal and neonatal outcome]. / Vergleichbarkeit geburtshilflicher und perinataler Daten aus England und Deutschland durch Verwendung von Standard-Primip-Gruppen - Befinden von Mutter und Kind.

OBJECTIVE: Aim of the study was to assess whether different birth management of an english and german department can influence the maternal and neonatal outcome. MATERIAL AND METHODS: The database consisted of routinely published data from 1986-95 for two clinical units in Solihull (England) and Ibbenbueren (Germany) comprising 34 820 and 9 053 deliveries respectively. In order to standardise the obstetric risk profiles the heterogeneous primary groups were subdivided into "standard primip groups". A statistical comparison using the "binomial confidence interval method" was carried out. RESULTS: In the standardised comparison induction of labour, duration of labour 1-6 hours, vaginal delivery from cephalic presentation, elective caesarean section, both for cephalic and breech presentation, transfer to the childrens hospital were less frequent in Solihull. On the other hand, Solihull showed more frequent oxytocin administration, fetal blood analysis, epidural anaesthesia, episiotomies, duration of labour > 13 hours, forceps, ventouse and emergency caesarean section deliveries from cephalic presentation, vaginal deliveries or emergency caesarean sections from breech presentation, resuscitation of the newborn using mask and/or drugs, maternal blood loss > 1 000 ml as well as abnormalities of placental separation. Despite the different management of the departments being compared no significant differences in the incidence of perinatal hypoxia as determined by Apgar scores at 5 minutes nor in the fetal mortality rate between the units could be identified. CONCLUSION: Using standardised data the quality of obstetric and perinatal care in England and Germany can be reliably compared. Different birth management does not significantly influence the neonatal outcome.

Use of an artificial neural network (ANN) for classifying nursing care needed, using incomplete input data.

BACKGROUND: In German nursing insurance, the act of classifying the client into four categories of disability is based on legally defined distinct criteria. When classifying deceased persons it is often impossible to collect all the required information. PRIMARY OBJECTIVE: We aimed to determine the ability of an artificial neural network (ANN) to calculate the category of disability, to investigate the response of the ANN to input items of different nature, quantity and data quality, and to estimate the minimum number of training data required. RESEARCH DESIGN: The investigation was conducted as a retrospective observational study. METHODS AND PROCEDURES: The analysis was based on routine records of 14000 adult clients of the nursing insurance. Several ANNs were trained, varying nature, number and quality of the input items as well as the size of the training data set. Each ANN's classification competence was tested on independent validation data, judging the ANN's conformance to the result of the individual expert assessment, using kappa statistics. MAIN RESULTS: Fed with all 30 input items available, the net classified 80% of cases correctly (weighted kappa = 0.78). Using three input items, weighted kappa was 0.63. Severe misclassification (deviation by more than one category in either direction) ranged between 0.2% (all 30 input items) and 3.7% (3/30 items). The less complete the individual input items were, the less accurate was the net's estimate. A 20% rate of missing values was well tolerated. A training set comprising 500 cases was adequate. CONCLUSIONS: The input item set inherits redundancy. The ANN's ability to correctly respond to subsets of input items makes it a powerful tool in quality control. In the categorization of deceased persons when only an incomplete input item set is available, the ANN can achieve satisfactory results.