Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan
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 inte...
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| Published in | BMJ open Vol. 12; no. 4; p. e051891 |
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| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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England
British Medical Journal Publishing Group
21.04.2022
BMJ Publishing Group LTD BMJ Publishing Group |
| Series | Original research |
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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 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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| AbstractList | 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. To 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.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.A case-control study.DESIGNA case-control study.Three general internal medicine wards in Chiba Prefecture, Japan.SETTINGThree general internal medicine wards in Chiba Prefecture, Japan.Thirty-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.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.Patients' COMPRI scores and their consequent lengths of hospital stay.PRIMARY AND SECONDARY OUTCOME MEASURESPatients' COMPRI scores and their consequent lengths of hospital stay.The 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).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).Clinicians 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.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. To 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. A case-control study. Three general internal medicine wards in Chiba Prefecture, Japan. Thirty-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. Patients' COMPRI scores and their consequent lengths of hospital stay. The 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). Clinicians 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. Objective To 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.Design A case–control study.Setting Three general internal medicine wards in Chiba Prefecture, Japan.Participants Thirty-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.Results The 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).Conclusions Clinicians 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. |
| Author | Noda, Kazutaka Ishizuka, Kosuke Uehara, Takanori Ikusaka, Masatomi Ban, Toshiaki Hirose, Yuta Shikino, Kiyoshi Ohira, Yoshiyuki Miyahara, Shigeyoshi Yamauchi, Yosuke Kishi, Yasuhiro Yanagita, Yasutaka Tsukamoto, Tomoko Yokokawa, Daiki Hayashi, Yasushi |
| AuthorAffiliation | 5 Department of General Medicine , International University of Health and Welfare, School of Medicine , Narita , Japan 2 Department of Psychiatry , Nippon Medical School Musashikosugi Hospital , Kawasaki , Japan 1 Department of General Medicine , Chiba University Hospital , Chiba , Japan 6 Department of General Medicine , Eastern Chiba Medical Center , Togane , Japan 4 Department of Internal Medicine , Funabashi Futawa Byoin , Funabashi , Japan 3 Department of Internal Medicine , Isumi Medical Center , Isumi , Japan |
| AuthorAffiliation_xml | – name: 3 Department of Internal Medicine , Isumi Medical Center , Isumi , Japan – name: 2 Department of Psychiatry , Nippon Medical School Musashikosugi Hospital , Kawasaki , Japan – name: 6 Department of General Medicine , Eastern Chiba Medical Center , Togane , Japan – name: 1 Department of General Medicine , Chiba University Hospital , Chiba , Japan – name: 4 Department of Internal Medicine , Funabashi Futawa Byoin , Funabashi , Japan – name: 5 Department of General Medicine , International University of Health and Welfare, School of Medicine , Narita , Japan |
| Author_xml | – sequence: 1 givenname: Daiki orcidid: 0000-0003-0944-8664 surname: Yokokawa fullname: Yokokawa, Daiki email: dyokokawa6@gmail.com organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 2 givenname: Kiyoshi orcidid: 0000-0002-3721-3443 surname: Shikino fullname: Shikino, Kiyoshi organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 3 givenname: Yasuhiro surname: Kishi fullname: Kishi, Yasuhiro organization: Department of Psychiatry, Nippon Medical School Musashikosugi Hospital, Kawasaki, Japan – sequence: 4 givenname: Toshiaki surname: Ban fullname: Ban, Toshiaki organization: Department of Internal Medicine, Isumi Medical Center, Isumi, Japan – sequence: 5 givenname: Shigeyoshi surname: Miyahara fullname: Miyahara, Shigeyoshi organization: Department of Internal Medicine, Funabashi Futawa Byoin, Funabashi, Japan – sequence: 6 givenname: Yoshiyuki surname: Ohira fullname: Ohira, Yoshiyuki organization: Department of General Medicine, International University of Health and Welfare, School of Medicine, Narita, Japan – sequence: 7 givenname: Yasutaka surname: Yanagita fullname: Yanagita, Yasutaka organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 8 givenname: Yosuke surname: Yamauchi fullname: Yamauchi, Yosuke organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 9 givenname: Yasushi surname: Hayashi fullname: Hayashi, Yasushi organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 10 givenname: Kosuke surname: Ishizuka fullname: Ishizuka, Kosuke organization: Department of General Medicine, Eastern Chiba Medical Center, Togane, Japan – sequence: 11 givenname: Yuta surname: Hirose fullname: Hirose, Yuta organization: Department of Internal Medicine, Funabashi Futawa Byoin, Funabashi, Japan – sequence: 12 givenname: Tomoko surname: Tsukamoto fullname: Tsukamoto, Tomoko organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 13 givenname: Kazutaka surname: Noda fullname: Noda, Kazutaka organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 14 givenname: Takanori surname: Uehara fullname: Uehara, Takanori organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan – sequence: 15 givenname: Masatomi surname: Ikusaka fullname: Ikusaka, Masatomi organization: Department of General Medicine, Chiba University Hospital, Chiba, Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35450890$$D View this record in MEDLINE/PubMed |
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| Snippet | ObjectiveTo clarify the factors associated with prolonged hospital stays, focusing on the COMplexity PRediction Instrument (COMPRI) score’s accuracy in... To clarify the factors associated with prolonged hospital stays, focusing on the COMplexity PRediction Instrument (COMPRI) score's accuracy in predicting the... Objective To clarify the factors associated with prolonged hospital stays, focusing on the COMplexity PRediction Instrument (COMPRI) score’s accuracy in... |
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| SubjectTerms | Age Aged Case-Control Studies Emergency medical care general medicine (see internal medicine) General practice / Family practice Hospitalization Hospitals Humans Illnesses Internal medicine Japan Length of Stay Medical history Medicine Older people Patient Discharge Patients Physicians primary care |
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| Title | Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case–control study in Japan |
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