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.

Details

Serval ID
serval:BIB_23E7F1CBEE88
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
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.
Journal
JMIR medical informatics
Author(s)
Taushanov Z., Verloo H., Wernli B., Di Giovanni S., von Gunten A., Pereira F.
ISSN
2291-9694 (Print)
Publication state
Published
Issued date
11/05/2021
Peer-reviewed
Oui
Volume
9
Number
5
Pages
e24205
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
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.
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.
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.
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.
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.
RR2-10.1136/bmjopen-2019-030030.
Keywords
cluster analysis, hierarchical 2-step clustering, hospital, multidimensional, population based, raw data, registry, retrospective
Pubmed
Open Access
Yes
Create date
19/05/2021 15:27
Last modification date
28/05/2021 6:36
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