Lanska M J, Lanska D J, Baumann R J, Allen S L, Slone K G, Kryscio R J
Lexington-Fayette County Health Department, Kentucky 40508, USA.
Pediatr Neurol. 1996 Sep;15(2):120-3. doi: 10.1016/0887-8994(96)00155-5.
This population-based, retrospective cohort study of neonatal seizures included all 16,428 neonates born to residents of Fayette County, Kentucky, from 1985 to 1989. Eighty potential cases were ascertained by computer search of hospital-based medical record systems, birth certificate data files, and multiple-cause-of-death mortality data files. Medical records for potential cases were abstracted, and relevant portions were reviewed independently by three neurologists using prospectively determined criteria. Both unweighted and weighted kappa statistics were used to measure agreement between each pair of observers in the classification of potential cases as seizures, possible seizures, or not seizures, adjusting for the proportion of agreement expected by chance. Agreement in the classification of potential cases was excellent (kappa = 0.72-0.79, average = 0.76; weighted kappa = 0.85-0.88, average = 0.87). The kappa extension statistic of Kraemer was used to assess agreement in the classification of seizure types by a simplification of the classification scheme of Volpe. This documented excellent agreement between raters in the classification of seizure types (kappa e = 0.72). Experienced raters can reliably classify potential cases of neonatal seizures using seizure descriptions transcribed from medical records.
这项基于人群的新生儿癫痫回顾性队列研究纳入了1985年至1989年在肯塔基州费耶特县出生的所有16428名新生儿。通过对医院病历系统、出生证明数据文件和多死因死亡率数据文件进行计算机搜索,确定了80例可能的病例。对可能病例的病历进行了摘要,并由三位神经科医生根据预先确定的标准独立审查相关部分。使用未加权和加权kappa统计量来衡量每对观察者在将可能病例分类为癫痫、可能癫痫或非癫痫方面的一致性,并根据偶然预期的一致比例进行调整。在将可能病例分类方面的一致性非常好(kappa = 0.72 - 0.79,平均 = 0.76;加权kappa = 0.85 - 0.88,平均 = 0.87)。使用Kraemer的kappa扩展统计量,通过简化Volpe的分类方案来评估癫痫类型分类的一致性。这证明了评估者在癫痫类型分类方面具有高度一致性(kappa e = 0.72)。经验丰富的评估者可以使用从病历转录的癫痫描述可靠地对新生儿癫痫的可能病例进行分类。