Can the simple clinical score usefully predict the mortality risk and length of stay for a recently admitted patient?

Nguyen, Minh Thien; Woodman, Richard; Hakendorf, Paul; Thompson, Campbell; Faunt, Jeff

doi:10.1071/ah14123

Cited by 1 publication

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References 16 publications

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“…the SCS has been reported as inferior to assessments made by clinical physicians. 4 Another tool is the PRO-AGE scoring system, a predictive model for the hospitalisation and long-term stay of older patients admitted from emergency departments. 5 As an example, when used in scoring men who are ≥90 years old, had been hospitalised in the previous 6 months, had experienced weight loss of ≥5% in the previous year and who showed acute mental alteration, acute functional decline and fatigue, this system predicted hospitalisation of 7 days or more, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.79.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan

et al. 2022

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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 measuresPatients’ 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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Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan

et al. 2022

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Can the simple clinical score usefully predict the mortality risk and length of stay for a recently admitted patient?

Abstract: There was no useful prospective role for the SCS in predicting LOS and mortality of in-patients newly admitted to a General Medicine service.

Cited by 1 publication

References 16 publications

Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan

Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan

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