Article Text

Original research
Development and validation of a score to assess complexity of general internal medicine patients at hospital discharge: a prospective cohort study
  1. Fabian D Liechti1,
  2. Thomas Beck1,
  3. Adrian Ruetsche2,
  4. Marie C Roumet3,
  5. Andreas Limacher3,
  6. Tobias Tritschler1,4,
  7. Jacques D Donzé1,5,6,7
  1. 1Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
  2. 2Department of Technology and Innovation, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
  3. 3CTU Bern, University of Bern, Bern, Switzerland
  4. 4Department of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
  5. 5Department of Medicine, Neuchâtel Hospital Network, Neuchâtel, Switzerland
  6. 6Division of General Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
  7. 7Division of General Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
  1. Correspondence to Dr Fabian D Liechti; fabian.liechti{at}insel.ch

Abstract

Objective We aimed to develop and validate a score to assess inpatient complexity and compare its performance with two currently used but not validated tools to estimate complexity (ie, Charlson Comorbidity Index (CCI), patient clinical complexity level (PCCL)).

Methods Consecutive patients discharged from the department of medicine of a tertiary care hospital were prospectively included into a derivation cohort from 1 October 2016 to 16 February 2017 (n=1407), and a temporal validation cohort from 17 February 2017 to 31 March 2017 (n=482). The physician in charge assessed complexity. Potential predictors comprised 52 parameters from the electronic health record such as health factors and hospital care usage. We fit a logistic regression model with backward selection to develop a prediction model and derive a score. We assessed and compared performance of model and score in internal and external validation using measures of discrimination and calibration.

Results Overall, 447 of 1407 patients (32%) in the derivation cohort, and 116 of 482 patients (24%) in the validation cohort were identified as complex. Eleven variables independently associated with complexity were included in the score. Using a cut-off of ≥24 score points to define high-risk patients, specificity was 81% and sensitivity 57% in the validation cohort. The score’s area under the receiver operating characteristic (AUROC) curve was 0.78 in both the derivation and validation cohort. In comparison, the CCI had an AUROC between 0.58 and 0.61, and the PCCL between 0.64 and 0.69, respectively.

Conclusions We derived and internally and externally validated a score that reflects patient complexity in the hospital setting, performed better than other tools and could help monitoring complex patients.

  • primary care
  • general medicine (see internal medicine)
  • quality in health care
  • social medicine
  • internal medicine

Data availability statement

Data are available on reasonable request from fabian.liechti@insel.ch (ORCID ID 0000-0003-1006-6903).

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Data availability statement

Data are available on reasonable request from fabian.liechti@insel.ch (ORCID ID 0000-0003-1006-6903).

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Footnotes

  • Contributors All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. FDL: Analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content. TB: Acquisition of data, analysis and interpretation of data, critical revision of the manuscript for important intellectual content. AR: Acquisition of data, analysis and interpretation of data, critical revision of the manuscript for important intellectual content. MCR: Statistical analysis, analysis and interpretation of data, critical revision of the manuscript for important intellectual content. AL: Analysis and interpretation of data, critical revision of the manuscript for important intellectual content. TT: Analysis and interpretation of data, critical revision of the manuscript for important intellectual content. JDD: Study concept and design, acquisition of data, analysis and interpretation of data, administrative, technical and material support, drafting of the manuscript, critical revision of the manuscript for important intellectual content.

  • Funding This work was supported by SGIM foundation. JDD is supported by Swiss National Science Foundation (grant number 170656). TT is supported by an Early Postdoc. Mobility Award from the Swiss National Science Foundation (SNSF P2ZHP3_177999) and a Fellowship Award from the CanVECTOR Network.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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