Association between body mass index, missing data, and mortality risk among critically ill patients: the role of missing-data imputation
DC Field | Value | Language |
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dc.contributor.author | Jee, Hee-Jung | - |
dc.contributor.author | Oh, Tak Kyu | - |
dc.contributor.author | Song, In-Ae | - |
dc.contributor.author | An, Hyonggin | - |
dc.date.accessioned | 2021-08-30T16:04:06Z | - |
dc.date.available | 2021-08-30T16:04:06Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 2224-5820 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/53671 | - |
dc.description.abstract | Background: We aimed to investigate the association between body mass index (BNII) at the time of intensive care unit (ICU) admission and 90-day mortality risk in complete-case datasets that ignore missing data as well as in datasets with multiple imputation for missing data. Methods: This retrospective study analysed the medical records of adult patients admitted to ICUs in a single tertiary academic hospital. For BM I analysis, data were classified into four groups: underweight (<20 kg/m), normal (20-24.9 kg/m(2)), overweight (25-29.9 kg/m(2)), and obese (030 kg/m(2)). Results: A total of 24,928 patients were examined. Among them, 5,916 (23.7%) patients had missing BNII data at ICU admission, and the missing mechanism was not missing completely at random. In the multivariable Cox regression analysis, the 90-clay mortality risk of underweight patients in the complete-case group increased by 1.49 times compared with that of normal BALI patients with a hazard ratio (HR) of 1.49 (95% confidence interval: 1.34-1.66; P<0.001), whereas the 90-day mortality risk of underweight patients in the multiple imputation group increased by 1.36 times compared with that of normal EMI patients (HR: 1.36, 95% confidence interval: 1.24-1.49; P<0.001). Conclusions: We showed that the occurrence of missing BM I data at ICU admission could affect the prediction of 90-day mortality in critically ill patients. Particularly, missing BMI data had the potential to slightly overestimate the 90-day mortality of underweight patients. Therefore, multiple imputation for missing BAII data can be an appropriate statistical option to reduce bias. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | AME PUBL CO | - |
dc.subject | MULTIPLE IMPUTATION | - |
dc.subject | HOSPITAL MORTALITY | - |
dc.subject | IMPACT | - |
dc.title | Association between body mass index, missing data, and mortality risk among critically ill patients: the role of missing-data imputation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | An, Hyonggin | - |
dc.identifier.doi | 10.21037/apm-20-203 | - |
dc.identifier.scopusid | 2-s2.0-85092009551 | - |
dc.identifier.wosid | 000576670500038 | - |
dc.identifier.bibliographicCitation | ANNALS OF PALLIATIVE MEDICINE, v.9, no.5, pp.2749 - + | - |
dc.relation.isPartOf | ANNALS OF PALLIATIVE MEDICINE | - |
dc.citation.title | ANNALS OF PALLIATIVE MEDICINE | - |
dc.citation.volume | 9 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 2749 | - |
dc.citation.endPage | + | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.subject.keywordPlus | MULTIPLE IMPUTATION | - |
dc.subject.keywordPlus | HOSPITAL MORTALITY | - |
dc.subject.keywordPlus | IMPACT | - |
dc.subject.keywordAuthor | Body mass index (BMI) | - |
dc.subject.keywordAuthor | critical care medicine | - |
dc.subject.keywordAuthor | epidemiology | - |
dc.subject.keywordAuthor | intensive care unit (ICU) | - |
dc.subject.keywordAuthor | mortality | - |
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