Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Prevalence and Risk Factors of Chlamydia trachomatis Cervicitis in Pregnant Women at the Genital Tract Infection in Obstetrics Unit Care at Botucatu Medical School, São Paulo State University - UNESP, Brazil

Objective. To evaluate the prevalence of and risk factors for Chlamydia trachomatis cervicitis in pregnant women seen at the Genital Tract Infection in Obstetrics Unit Care in Botucatu Medical School, São Paulo State University - UNESP.Materials and Methods. Between June 2006 and February 2008, 101 pregnant women were included in this study. During the gynecologic examination, cervical secretions were collected using cytobrush Plus GT (CooperSurgical Inc) to assess C. trachomatis using polymerase chain reaction. Vaginal flora were examined by Gram stain, and socio-demographic data were extracted from medical records.Results. of the 101 patients, 26 (25.7%) were positive for C. trachomatis. The median age of the infected group was 24 years (range = 13-40 y), and 48.5% of them had abnormal vaginal flora. The presence of chlamydial infection was associated with smoking (odds ratio [OR] = 2.67, 95% confidence interval [CI] = 1.01-7.19), residing in a city with fewer than 100,000 inhabitants (OR = 2.86, 95% CI = 1.03-7.94), presence of condyloma acuminatum (p = .03), and presence of discreet inflammation on Pap smear (p = .02).Conclusions. The prevalence of C. trachomatis is high in pregnant women seen at the Genital Infection Unit Care, UNESP, and is related to many risk factors. Therefore, its screening is extremely important in reducing obstetrical and neonatal complications.

Interleukin 10 and tumor necrosis factor-alpha in pregnancy: aspects of interest in clinical obstetrics

The purpose of this study was to review the literature regarding the action of the cytokines interleukin 10 (IL-10) and tumor necrosis factor-alpha (TNF-α) in pregnancy and to emphasize the factors that are of interest to clinical obstetrics. The literature highlights several actions of IL-10 and TNF-α during pregnancy. The actions of these cytokines seem to be antagonistic and dependent on the balance between them, which is orchestrated by the specific immunosuppressive action of IL-10. TNF-α has a characteristic inflammatory action, and it is an additional diabetogenic factor in pregnancy. The loss of the control of the production of these cytokines, with increase of TNF-α, is related to the risk for developing obstetric complications, particularly recurrent fetal loss, gestational diabetes mellitus, hypertensive syndromes, and fetal growth restriction. However, study results are controversial and are not clearly defined. These issues are attributed to the heterogeneity of the studies, particularly regarding their sample sizes and sources, the evaluation methods, and the multiplicity of factors and conditions that influence cytokine production. These questions are fundamental and should be addressed in future investigations to obtain more consistent results that can be applied to obstetric practice.

Individual patient data meta-analysis of diagnostic and prognostic studies in obstetrics, gynaecology and reproductive medicine

BACKGROUND: In clinical practice a diagnosis is based on a combination of clinical history, physical examination and additional diagnostic tests. At present, studies on diagnostic research often report the accuracy of tests without taking into account the information already known from history and examination. Due to this lack of information, together with variations in design and quality of studies, conventional meta-analyses based on these studies will not show the accuracy of the tests in real practice. By using individual patient data (IPD) to perform meta-analyses, the accuracy of tests can be assessed in relation to other patient characteristics and allows the development or evaluation of diagnostic algorithms for individual patients. In this study we will examine these potential benefits in four clinical diagnostic problems in the field of gynaecology, obstetrics and reproductive medicine. METHODS/DESIGN: Based on earlier systematic reviews for each of the four clinical problems, studies are considered for inclusion. The first authors of the included studies will be invited to participate and share their original data. After assessment of validity and completeness the acquired datasets are merged. Based on these data, a series of analyses will be performed, including a systematic comparison of the results of the IPD meta-analysis with those of a conventional meta-analysis, development of multivariable models for clinical history alone and for the combination of history, physical examination and relevant diagnostic tests and development of clinical prediction rules for the individual patients. These will be made accessible for clinicians. DISCUSSION: The use of IPD meta-analysis will allow evaluating accuracy of diagnostic tests in relation to other relevant information. Ultimately, this could increase the efficiency of the diagnostic work-up, e.g. by reducing the need for invasive tests and/or improving the accuracy of the diagnostic workup. This study will assess whether these benefits of IPD meta-analysis over conventional meta-analysis can be exploited and will provide a framework for future IPD meta-analyses in diagnostic and prognostic research.

Two new flavonol glycosides and a metabolite profile of Bryophyllum pinnatum, a phytotherapeutic used in obstetrics and gynaecology

Bryophyllum pinnatum is a succulent perennial plant native to Madagascar which is used in anthroposophical medicine to treat psychiatric disorders and as a tocolytic agent to prevent premature labour. We performed a metabolite profiling study in order to obtain a comprehensive picture of the constituents in B. pinnatum leaves and to identify chromatographic markers for quality control and safety assessment of medicinal preparations. Preliminary HPLC-PDA-ESIMS analyses revealed that flavonoid glycosides were the main UV-absorbing constituents in the MeOH extract of B. pinnatum. Two phenolic glucosides, syringic acid β-D-glucopyranosyl ester (1) and 4'-O-β-D-glucopyranosyl-cis-p-coumaric acid (2), as well as nine flavonoids (3-11) including kaempferol, quercetin, myricetin, acacetin, and diosmetin glycosides were unambiguously identified by 1H and 2D NMR analysis after isolation from a MeOH extract. The flavonol glycosides quercetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside 7-O-β-D-glucopyranoside (3) and myricetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside (4) were new natural products. With the aid of HPLC-PDA-APCIMS and authentic references isolated from the related species B. daigremontianum, the presence of four bufadienolides, bersaldegenin-1-acetate (12), bryophyllin A (13), bersaldegenin-3-acetate (14), and bersaldegenin-1,3,5-orthoacetate (15) was detected in B. pinnatum.

The use of homeopathy in obstetrics in Switzerland

Objective In 1999, a first study conducted in the German speaking part of Switzerland revealed that homeopathy was used in 73% of the obstetrics. The aim of this follow-up study was to detect possible changes in the use of homeopathy since the first investigation, as well as to expand the survey to all regions of Switzerland in order to explore possible regional differences. Furthermore, the perceived positive and negative effects of homeopathy were of interest. Methods All maternity clinics (n = 119) and birthing centers (n = 25) in Switzerland were invited to participate in this online-survey (total N = 144). Results The data collection is still ongoing. Preliminary analyses show that of the 144 obstetrics contacted, 16 had shut down. 106 (82%) had returned the complete or partial questionnaire, whereof 86 (81%) were completed by maternity clinics and 20 (19%) by birthing centers. 70 (66%) questionnaires were returned from the German, 31 (29%) from the French and 5 (5%) from the Italian part of Switzerland. A total of 77 (73%) obstetrics indicated to apply homeopathy: 61 maternity clinics (71% of the responding ones) and 16 birth centers (80% of the responding ones). Clear regional differences in the use of homeopathy emerged: While 65 of the homeopathy-applying obstetrics were located in the German part of Switzerland (84% of the responding ones), only 11 (35% of the responding ones) and 1 (20% of the responding ones) were situated in the French and Italian regions respectively. More detailed results will be presented at the conference. Conclusion Preliminary results show that homeopathy is widely used in the obstetrics of Switzerland. Cultural differences appeared: Homeopathy is widely used in the German speaking part of Switzerland, while it is modestly employed in the French and Italian regions.

A CASE-CONTROL STUDY OF RISK FACTORS FOR ECTOPIC PREGNANCY (ROCHESTER, MINNESOTA, OBSTETRICS)

A population-based case-control study of risk factors for ectopic pregnancy has been conducted. The investigation includes 274 cases diagnosed in Rochester, Minnesota residents from 1935 through 1982, and 548 matched controls selected from live birth deliveries. Risk factor information documented prior to the last index menstrual period was obtained via medical record abstract for 22 potential risk factor variables.^ Univariate matched analyses revealed nine variables with significantly elevated odds ratios (ORs). Following conditional logistic regression for matched sets, four variables remained as significant risk factors for ectopic pregnancy. These risk factors with ORs and 95% confidence intervals (Cls) were: current intrauterine device use (OR = 13.7, Cl = 1.6 - 120.6), infertility (OR = 2.6, Cl = 1.6 - 4.2), pelvic inflammatory disease (OR = 3.3, Cl = 1.6 - 6.6), and tubal surgery (OR = 4.5, Cl = 1.5 - 13.9). After adjusting for these four major risk factors, the following variables did not have statistically significant ORs: abdominal/pelvic surgery (OR = 2.0), acute appendicitis (OR = 2.0), anovulation (OR = 1.2), clomiphene citrate use during the index conception (OR = 3.5), induced abortion (OR = 2.1), in utero exposure to diethylstilbestrol (OR = 1.6), myomas (OR = 0.7), ovarian cysts (OR = 1.0), and past intrauterine device use (OR = 1.2). ^