Hospital and regional variations in intensive care unit admission for patients with invasive mechanical ventilation

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Abstract

Background

Patients who receive invasive mechanical ventilation (IMV) in the intensive care unit (ICU) have exhibited lower in-hospital mortality rates than those who are treated outside. However, the patient-, hospital-, and regional factors influencing the ICU admission of patients with IMV have not been quantitatively examined.

Methods

This retrospective cohort study used data from the nationwide Japanese inpatient administrative database and medical facility statistics. We included patients aged ≥ 15 years who underwent IMV between April 2018 and March 2019. The primary outcome was ICU admission on the day of IMV initiation. Multilevel logistic regression analyses incorporating patient-, hospital-, or regional-level variables were used to assess cluster effects by calculating the intraclass correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV).

Results

Among 83,346 eligible patients from 546 hospitals across 140 areas, 40.4% were treated in ICUs on their IMV start day. ICU admission rates varied widely between hospitals (median 0.7%, interquartile range 0–44.5%) and regions (median 28.7%, interquartile range 0.9–46.2%). Multilevel analyses revealed significant effects of hospital cluster (ICC 82.2% and MOR 41.4) and regional cluster (ICC 67.3% and MOR 12.0). Including patient-level variables did not change these ICCs and MORs, with a PCV of 2.3% and − 1.0%, respectively. Further adjustment for hospital- and regional-level variables decreased the ICC and MOR, with a PCV of 95.2% and 85.6%, respectively. Among the hospital- and regional-level variables, hospitals with ICU beds and regions with ICU beds had a statistically significant and strong association with ICU admission.

Conclusions

Our results revealed that primarily hospital and regional factors, rather than patient-related ones, opposed ICU admissions for patients with IMV. This has important implications for healthcare policymakers planning interventions for optimal ICU resource allocation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-024-00736-0.

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Most cited references 27

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A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena.

Juan Merlo, Basile Chaix, Henrik Ohlsson … (2006)

In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case in a didactic rather than a mathematical way. Data were used from the health survey conducted in 2000 in the county of Scania, Sweden, that comprised 10 723 persons aged 18-80 years living in 60 areas. Conducting multilevel logistic regression different techniques were applied to investigate whether the individual propensity to consult private physicians was statistically dependent on the area of residence (that is, intraclass correlation (ICC), median odds ratio (MOR)), the 80% interval odds ratio (IOR-80), and the sorting out index). The MOR provided more interpretable information than the ICC on the relevance of the residential area for understanding the individual propensity of consulting private physicians. The MOR showed that the unexplained heterogeneity between areas was of greater relevance than the individual variables considered in the analysis (age, sex, and education) for understanding the individual propensity of visiting private physicians. Residing in a high education area increased the probability of visiting a private physician. However, the IOR showed that the unexplained variability between areas did not allow to clearly distinguishing low from high propensity areas with the area educational level. The sorting out index was equal to 82%. Measures of variation in logistic regression should be promoted in social epidemiological and public health research as efficient means of quantifying the importance of the context of residence for understanding disparities in health and health related behaviour.

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Validity of diagnoses, procedures, and laboratory data in Japanese administrative data

Hayato Yamana, Mutsuko Moriwaki, Hiromasa Horiguchi … (2017)

Background Validation of recorded data is a prerequisite for studies that utilize administrative databases. The present study evaluated the validity of diagnoses and procedure records in the Japanese Diagnosis Procedure Combination (DPC) data, along with laboratory test results in the newly-introduced Standardized Structured Medical Record Information Exchange (SS-MIX) data. Methods Between November 2015 and February 2016, we conducted chart reviews of 315 patients hospitalized between April 2014 and March 2015 in four middle-sized acute-care hospitals in Shizuoka, Kochi, Fukuoka, and Saga Prefectures and used them as reference standards. The sensitivity and specificity of DPC data in identifying 16 diseases and 10 common procedures were identified. The accuracy of SS-MIX data for 13 laboratory test results was also examined. Results The specificity of diagnoses in the DPC data exceeded 96%, while the sensitivity was below 50% for seven diseases and variable across diseases. When limited to primary diagnoses, the sensitivity and specificity were 78.9% and 93.2%, respectively. The sensitivity of procedure records exceeded 90% for six procedures, and the specificity exceeded 90% for nine procedures. Agreement between the SS-MIX data and the chart reviews was above 95% for all 13 items. Conclusion The validity of diagnoses and procedure records in the DPC data and laboratory results in the SS-MIX data was high in general, supporting their use in future studies.

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Intermediate and advanced topics in multilevel logistic regression analysis

Peter C Austin, Juan Merlo (2017)

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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Author and article information

Contributors

Hiroyuki Ohbe:

ORCID: http://orcid.org/0000-0001-8544-2569

hohbey@gmail.com

Journal

Journal ID (nlm-ta): J Intensive Care

Journal ID (iso-abbrev): J Intensive Care

Title: Journal of Intensive Care

Publisher: BioMed Central (London )

ISSN (Electronic): 2052-0492

Publication date (Electronic): 5 June 2024

Publication date PMC-release: 5 June 2024

Publication date Collection: 2024

Volume: 12

Electronic Location Identifier: 21

Affiliations

[1 ]GRID grid.412757.2, ISNI 0000 0004 0641 778X, Department of Emergency and Critical Care Medicine, , Tohoku University Hospital, ; 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574 Japan

[2 ]Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, ( https://ror.org/057zh3y96) 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033 Japan

[3 ]Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, ( https://ror.org/03t78wx29) 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8551 Japan

[4 ]Data Science Center, Jichi Medical University, ( https://ror.org/010hz0g26) 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498 Japan

[5 ]GRID grid.519299.f, TXP Medical Co., Ltd., ; 41-1 H1O Kanda 706, Kanda Higashimatsushita-Cho, Chiyoda-Ku, Tokyo, 101-0042 Japan

[6 ]Department of Real-World Evidence, Graduate School of Medicine, The University of Tokyo, ( https://ror.org/057zh3y96) 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033 Japan

[7 ]Division of Emergency and Critical Care Medicine, Tohoku University Graduate School of Medicine, ( https://ror.org/01dq60k83) 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi 980-8575 Japan

[8 ]Department of Health Services Research, Graduate School of Medicine, The University of Tokyo, ( https://ror.org/057zh3y96) 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033 Japan

Author information

Hiroyuki Ohbe http://orcid.org/0000-0001-8544-2569

Article

Publisher ID: 736

DOI: 10.1186/s40560-024-00736-0

PMC ID: 11155017

PubMed ID: 38840225

SO-VID: c76a4acb-e11b-49af-8dd5-a808f8304bfc

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 March 2024

Date accepted : 29 May 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003478, Ministry of Health, Labour and Welfare;

Award ID: 21AA2007

Award ID: 22AA2003

Award Recipient : Hideo Yasunaga

Funded by: FundRef http://dx.doi.org/10.13039/501100009030, Strategic Promotion of Innovative R and D;

Award ID: JPJ012425

Award Recipient : Hiroyuki Ohbe

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Keywords: intensive care unit admission,invasive mechanical ventilation,health service research,critical care delivery,multilevel analysis

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Keywords: intensive care unit admission, invasive mechanical ventilation, health service research, critical care delivery, multilevel analysis

Hospital and regional variations in intensive care unit admission for patients with invasive mechanical ventilation

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Supplementary Information

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Most cited references 27

A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena.

Validity of diagnoses, procedures, and laboratory data in Japanese administrative data

Intermediate and advanced topics in multilevel logistic regression analysis

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