Data mining for signals in spontaneous reporting databases: proceed with caution

PURPOSE: To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. METHODS/RESULTS: Commentary includes review of current data mining methodologies employed and their limitations, caveats to consider in the use of spontaneous reporting databases and caution against over-confidence in the results of data mining. CONCLUSIONS: Future research should focus on more clearly delineating the limitations of the various quantitative approaches as well as the incremental value that they bring to traditional methods of pharmacovigilance.     

Accuracy and correlates of maternal recall of birthweight and gestational age

Objective  To determine the accuracy of maternal recall of children birthweight (BW) and gestational age (GA), using the Danish Medical Birth Register (DBR) as reference and to examine the reliability of recalled BW and its potential correlates.
Design  Comparison of data from the DBR and the European Youth Heart Study (EYHS).
Setting  Schools in Odense, Denmark.
Population  A total of 1271 and 678 mothers of school children participated with information in the accuracy studies of BW and GA, respectively. The reliability sample of BW was composed of 359 women.
Method  The agreement between the two sources was evaluated by mean differences (MD), intraclass correlation coefficient (ICC) and Bland–Altman's plots. The misclassification of the various BW and GA categories were also estimated.
Main outcome measures  Differences between recalled and registered BW and GA.
Results  There was high agreement between recalled and registered BW (MD =−0.2 g; ICC = 0.94) and GA (MD = 0.3 weeks; ICC = 0.76). Only 1.6% of BW would have been misclassified into low, normal or high BW and 16.5% of GA would have been misclassified into preterm, term or post-term based on maternal recall. The logistic regression revealed that the most important variables in the discordance between recalled and registered BW were ethnicity and parity. Maternal recall of BW was highly reliable (MD =−5.5 g; ICC = 0.93), and reliability remained high across subgroups.
Conclusion  Maternal recall of BW and GA seems to be sufficiently accurate for clinical and epidemiological use.     

Midwifery data collection: options and opportunities

Most midwives are aware of the need to collect clinical practice data and of its usefulness in supporting the care they provide, which contributes to healthy outcomes for mothers and babies. For the individual midwife, there is more than one easily accessible, standardized data collection instrument from which to choose. However, despite these choices, in an American College of Nurse-Midwives (ACNM) Division of Research (DOR) survey on midwifery clinical data collection (N = 263), the majority of member respondents (n = 135; 51%) reported using a self-designed data collection tool, and more than one-third did not know of the ACNM Nurse-Midwifery Clinical Data Sets (NMCDS). On a larger scale, the midwifery profession is also in need of an organized and consistent approach to data collection for the purpose of capturing midwifery practice and outcomes in order to provide data to support legislation, practice, and policy changes. However, the profession currently lacks a single common midwifery practice database. In order to facilitate data aggregation that captures a larger view of midwifery practice at the local, regional, and national levels, it is imperative that all midwives collect relevant data that are uniform and standardized, and that the midwifery professional organizations move forward with the development of a common electronic database. This article describes currently available data collection tools as well as their best uses, applications to practice, and future directions.     

Meta-analysis using individual patient data from randomised trials to assess the effectiveness of laparoscopic uterosacral nerve ablation in the treatment of chronic pelvic pain: a proposed protocol

Background  Currently, there are a number of clinical trials, but no international collaboration for collating research on effectiveness of laparoscopic uterosacral nerve ablation (LUNA) for alleviating chronic pelvic pain.
Objective  Meta-analysis was used by collecting individual patient data (IPD) from the existing trials, to provide a comprehensive assessment of the effectiveness of LUNA that will be generalisable in various clinical contexts.
Methods  IPD will be sought and collected from all relevant (both already finished and continuing) randomised trials identified through previous systematic reviews. After obtaining raw data and cleaning the database, analysis will be of all patients ever randomised based on the intention-to-treat principle using endpoints measured at 12 months following randomisation.
Proposal  We will update searches, contact all authors, obtain data in whatever form it can be provided, build a single database, produce results for individual studies, have them verified by original authors, explore of any heterogeneity and reasons behind it and finally pool all raw data in to a meta-analysis using appropriate statistical methods. The project will test the effectiveness of LUNA for women with chronic pelvic pain. It will also motivate collaborating primary investigators to undertake new primary studies to corroborate or improve upon the conclusions derived from the retrospective analysis.     

面向临床应用的知识库平台设计

借鉴本体论及数理逻辑来表示知识模式,开发了知识通用逻辑引擎及专题知识引擎来处理应用逻辑,充分利用医院集成平台的优势,研发设计一种能够覆盖大部分临床应用需要的知识库平台体系结构,辅助医疗决策过程,并保障医疗安全.     

集成数据校正的乙烯装置进料数据采集

乙烯装置的模拟需要有准确的裂解炉进料信息,而通过对进料系统的模拟计算可以从较易测得的原料信息得到所需的裂解炉进料信息。通过对某石化企业乙烯装置进料系统进行模拟,根据体系特点集成数据校正以解决流量测量数据的不平衡和不完整的问题,利用数据的空间冗余提高了模拟计算所用数据的准确性和可靠性,并针对体系的特殊性提出了数据校正过程中出现几类负值的处理方法,最后模拟得到所需的裂解炉进料性质数据。     

在Excel中使用VBA编程处理RDS抽样调查数据

Excel是Office办公软件中的一员,其强大的数据处理功能使其在数据表处理方面占有非常重要的地位,特别是可以使用VBA进行编程来处理更加复杂的数据,针对RDS抽样调查数据如何处理进行了探索。