Evidence map›Paper›PMID 29659630›Full record

ArticlePloS one2018

Body Mass Index of 92,027 patients acutely admitted to general hospitals in Denmark: Associated clinical characteristics and 30-day mortality.

Sigrid Bjerge Gribsholt, Lars Pedersen, Bjørn Richelsen, Olaf Dekkers, Reimar Wernich Thomsen

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Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Epidemiology and outcomes of obese critically ill patients in Australia and New Zealand.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2020
    Observational
  3. Observational
  4. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sigrid Bjerge GribsholtDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-8681-6822
Lars PedersenDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
Bjørn RichelsenDepartment of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark.
Olaf DekkersDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
Reimar Wernich ThomsenDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData are sparse on the range of BMI among patients acutely admitted to general hospitals. We investigated BMI values and associated patient characteristics, reasons for hospital admission, and mortality in Denmark.

methodsWe identified all persons with an acute inpatient admission 2011-2014 in Central Denmark Region and assessed BMI measurements recorded in the Clinical Information System. We used cross-sectional and cohort analyses to examine the BMI distribution and its association with demographic characteristics, comorbidities, medication use, tobacco smoking, reasons for admission, and 30-day mortality.

resultsAmong 92,027 acutely admitted patients (median age 62 years, 49% female) with a BMI measurement, 4% had a BMI (kg/m2) <18.5, 42% a BMI between 18.5 and 25, 34% a BMI between 25 and 30, and 20% a BMI ≥30. Compared with normal-weight patients, 30-day mortality was high among patients with BMI <18.5 (7.5% vs. 2.8%, age- and smoking-adjusted odds ratio (aOR) 2.4; 95% confidence interval (CI): 2.0-2.9, whereas patients with overweight (aOR 0.7; 95% CI: 0.6-0.8) and obesity class I (aOR 0.8; 95% CI: 0.6-0.9)). Compared with the total population, patients with BMI <18.5 were older (68 years median); more were female (73%); more had comorbidities (Charlson Comorbidity Index score >0 in 42% vs. 33% overall), more were current smokers (45% vs. 27% overall), and acute admissions due to respiratory diseases or femoral fractures were frequent. In contrast, patients with BMI ≥30 were relatively young (59 years median), fewer smoked (24%): type 2 diabetes, sleep disorders, cholelithiasis, and heart failure were frequent diagnoses. Prevalence of therapies for metabolic syndrome, pain, and psychiatric disorders increased with higher BMI, while patients with BMI <18.5 frequently used asthma medications, glucocorticoids, and antibiotics.

conclusionIn patients acutely admitted to general hospitals, reasons for hospital admission and associated clinical characteristics differ substantially according to BMI range. BMI <18.5 is a clinical predictor of high short-term mortality.

Indexed as

Body Mass IndexHospitals, GeneralPatient AdmissionPublic Health SurveillanceAcute DiseaseAdolescentAdultAgedAged, 80 and overComorbidityDenmarkFemaleHospital MortalityHumansMaleMiddle Aged

Identifiers

PMID29659630
PMCPMC5901987

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.