Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review*. Issue 2 (February 2017)
- Record Type:
- Journal Article
- Title:
- Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review*. Issue 2 (February 2017)
- Main Title:
- Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review*
- Authors:
- Verburg, Ilona Willempje Maria
Atashi, Alireza
Eslami, Saeid
Holman, Rebecca
Abu-Hanna, Ameen
de Jonge, Everet
Peek, Niels
de Keizer, Nicolette Fransisca - Abstract:
- Abstract : Objective: We systematically reviewed models to predict adult ICU length of stay. Data Sources: We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models. Study Selection: We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models. Data Extraction: Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R 2 across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. Data Synthesis: The number of admissions ranged from 253 to 178, 503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R 2 was 0.05–0.28 across patients and 0.01–0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22. Conclusion: NoAbstract : Objective: We systematically reviewed models to predict adult ICU length of stay. Data Sources: We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models. Study Selection: We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models. Data Extraction: Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R 2 across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. Data Synthesis: The number of admissions ranged from 253 to 178, 503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R 2 was 0.05–0.28 across patients and 0.01–0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22. Conclusion: No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation. Abstract : Supplemental Digital Content is available in the text. … (more)
- Is Part Of:
- Critical care medicine. Volume 45:Issue 2(2017)
- Journal:
- Critical care medicine
- Issue:
- Volume 45:Issue 2(2017)
- Issue Display:
- Volume 45, Issue 2 (2017)
- Year:
- 2017
- Volume:
- 45
- Issue:
- 2
- Issue Sort Value:
- 2017-0045-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-02
- Subjects:
- benchmarking -- intensive care units -- length of stay -- prediction -- review
Critical care medicine -- Periodicals
Soins intensifs -- Périodiques
616.028 - Journal URLs:
- http://journals.lww.com/ccmjournal/Pages/default.aspx ↗
http://journals.lww.com ↗ - DOI:
- 10.1097/CCM.0000000000002054 ↗
- Languages:
- English
- ISSNs:
- 0090-3493
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3487.451000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 5068.xml