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首页> 外文期刊>JMIR Medical Informatics >Transforming a Patient Registry Into a Customized Data Set for the Advanced Statistical Analysis of Health Risk Factors and for Medication-Related Hospitalization Research: Retrospective Hospital Patient Registry Study
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Transforming a Patient Registry Into a Customized Data Set for the Advanced Statistical Analysis of Health Risk Factors and for Medication-Related Hospitalization Research: Retrospective Hospital Patient Registry Study

机译：将患者登记处转换为定制数据集，以进行健康风险因素的高级统计分析和与药物相关的住院研究：回顾性医院患者注册表研究

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Background Hospital patient registries provide substantial longitudinal data sets describing the clinical and medical health statuses of inpatients and their pharmacological prescriptions. Despite the multiple advantages of routinely collecting multidimensional longitudinal data, those data sets are rarely suitable for advanced statistical analysis and they require customization and synthesis. Objective The aim of this study was to describe the methods used to transform and synthesize a raw, multidimensional, hospital patient registry data set into an exploitable database for the further investigation of risk profiles and predictive and survival health outcomes among polymorbid, polymedicated, older inpatients in relation to their medicine prescriptions at hospital discharge. Methods A raw, multidimensional data set from a public hospital was extracted from the hospital registry in a CSV (.csv) file and imported into the R statistical package for cleaning, customization, and synthesis. Patients fulfilling the criteria for inclusion were home-dwelling, polymedicated, older adults with multiple chronic conditions aged ≥65 who became hospitalized. The patient data set covered 140 variables from 20,422 hospitalizations of polymedicated, home-dwelling older adults from 2015 to 2018. Each variable, according to type, was explored and computed to describe distributions, missing values, and associations. Different clustering methods, expert opinion, recoding, and missing-value techniques were used to customize and synthesize these multidimensional data sets. Results Sociodemographic data showed no missing values. Average age, hospital length of stay, and frequency of hospitalization were computed. Discharge details were recoded and summarized. Clinical data were cleaned up and best practices for managing missing values were applied. Seven clusters of medical diagnoses, surgical interventions, somatic, cognitive, and medicines data were extracted using empirical and statistical best practices, with each presenting the health status of the patients included in it as accurately as possible. Medical, comorbidity, and drug data were recoded and summarized. Conclusions A cleaner, better-structured data set was obtained, combining empirical and best-practice statistical approaches. The overall strategy delivered an exploitable, population-based database suitable for an advanced analysis of the descriptive, predictive, and survival statistics relating to polymedicated, home-dwelling older adults admitted as inpatients. More research is needed to develop best practices for customizing and synthesizing large, multidimensional, population-based registries.
机译：背景技术医院患者注册表提供了描述住院患者及其药理学处方的临床和医疗健康状况的大量纵向数据集。尽管常规收集多维纵向数据的多个优点，但这些数据集很少适用于高级统计分析，并且它们需要定制和合成。目的该研究的目的是描述用于转换和综合原始，多维，医院患者注册表数据的方法，进入可利用的数据库，以进一步调查多种多样性，多种多化的住院患者的风险谱和预测和生存健康结果关于他们的医学处方在医院出院的处方。方法从公共医院中，从CSV（.csv）文件中的医院注册表中提取了原始的多维数据，并进入R统计包以进行清洁，定制和合成。符合含有标准的患者是家居住宅，聚体化，老年人，慢性病症≥65岁均为住院治疗。从2015年到2018年，患者数据集涵盖了140个聚体，家庭住宅住院住院中的140个变量，从2015年到2018年。根据类型的，每个变量都被探索和计算，以描述分布，缺失值和关联。使用不同的聚类方法，专家观点，重新编码和缺失值，用于自定义和综合这些多维数据集。结果社会渗目数据显示没有缺失值。计算平均年龄，医院住院时间和住院频率。卸货详情被重新编制和总结。清理临床资料，并应用了管理缺失值的最佳实践。利用经验和统计最佳实践提取七种医学诊断，手术干预，体细胞，体细胞，认知和药物数据，每次呈现尽可能准确地呈现在其中的患者的健康状况。经过重新编码和总结医疗，合并症和药物数据。结论获得了更清洁，更好的结构数据集，结合了实证和最佳实践统计方法。整体策略提供了一种可利用的基于人口的数据库，适用于与金属化，家庭住宅成年人有关的描述性，预测和生存统计数据的高级分析，该数据库被视为住院患者。需要更多的研究来开发定制和综合大型多维人口的基于人口的注册管理机构的最佳实践。

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