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Published on: January 16, 2019
Predicting intensive care unit length of stay: comparing physician assessments with software predictions in a
Thiago Tavares Dos Santos1, Luciana Seidel de Crignis Resende1, Leandro Utino Taniguchi2,3
1Intensive Care Unit, Hospital Beneficência Portuguesa Mirante, São Paulo, SP, Brazil.
Objective:
To compare the predictive abilities of medical professionals and the Epimed Monitor Performance© (EMP) software for intensive care unit length of stay and to assess EMP software effectiveness in identifying patients at risk of prolonged stays.
Methods:
This prospective multicenter observational study was conducted in three Brazilian intensive care units, enrolling adult patients admitted between August and December 2019. Data were collected using the Epimed Monitor System©. Both physicians and the EMP system predicted intensive care unit length of stay.
Results:
A total of 555 patients were included. The median age was 63.3 years (IQR=48.3-74.3), and 58.5% were males. Comorbidities were present in 72%, with 37.2% having cancer. The median intensive care unit length of stay was 3 days (IQR=2-6). Observed length of stay showed a correlation of 0.34 (p<0.001) with physician prediction and 0.36 (p<0.001) with EMP. Categorizing the length of stay improved the predictive performance by 60% for both methods. EMP demonstrated good accuracy in identifying patients at risk of prolonged stay (AUC: 0.76; 95%CI=0.70-0.81).
Conclusion:
Absolute prediction of intensive care unit length of stay remains challenging, but period categorization is a viable alternative. EMP aids in identifying patients at risk of prolonged stays and thus can complement clinical judgment. Our findings highlight the value of integrating predictive tools with medical expertise to enhance intensive care unit planning, decision making, and resource allocation.
