ArticleBMJ open2022
Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case-control study in Japan.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Association of Timely Outpatient Follow-Up and Readmission Risk in a Mobile Integrated Health Program.Population health management · 2024Observational
- Frequency of Difficult Patient Encounters in a Japanese University Hospital and Community Hospitals: A Cross-sectional Study.Internal medicine (Tokyo, Japan) · 2023Article
- Evaluation of Complexity Measurement Tools for Correlations with Health-Related Outcomes, Health Care Costs and Impacts on Healthcare Providers: A Scoping Review.International journal of environmental research and public health · 2022Article
- Translation and Cross-Cultural Adaptation of the Japanese Version of the INTERMED Self-Assessment Questionnaire (IMSA) for Patient-Case Complexity Assessment.International journal of general medicine · 2022Article
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15 authors.
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Abstract
objectiveTo clarify the factors associated with prolonged hospital stays, focusing on the COMplexity PRediction Instrument (COMPRI) score's accuracy in predicting the length of stay of newly hospitalised patients in general internal medicine wards.
designA case-control study.
settingThree general internal medicine wards in Chiba Prefecture, Japan.
participantsThirty-four newly hospitalised patients were recruited between November 2017 and December 2019, with a final analytic sample of 33 patients. We included hospitals in different cities with general medicine outpatient and ward facilities, who agreed to participate. We excluded any patients who were re-hospitalised within 2 weeks of a prior discharge. PRIMARY AND SECONDARY OUTCOME MEASURES: Patients' COMPRI scores and their consequent lengths of hospital stay.
resultsThe 17 patients (52%) allocated to the long-term hospitalisation group (those hospitalised ≥14 days) had a significantly higher average age, COMPRI score and percentage of participants with comorbid chronic illnesses than the short-term hospitalisation group (<14 days). A logistic regression model (model A, comprising only the COMPRI score as the explanatory variable) and a multiple logistic regression model (model B, comprising variables other than the COMPRI score as explanatory variables) were created as prediction models for the long-term hospitalisation group. When age ≥75 years, a COMPRI score ≥6 and a physician with 10 years' experience were set as explanatory variables, model A showed better predictive accuracy compared with model B (fivefold cross-validation, area under curve of 0.87 vs 0.78). The OR of a patient with a COMPRI score of ≥6 joining the long-term hospitalisation group was 4.25 (95% CI=1.43 to 12.63).
conclusionsClinicians can use the COMPRI score when screening for complexity assessment to identify hospitalised patients at high risk of prolonged hospitalisation. Providing such patients with multifaceted and intensive care may shorten hospital stays.
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