Observational studyEinstein (Sao Paulo, Brazil)2025
Predicting intensive care unit length of stay: comparing physician assessments with software predictions in a multicenter study.
Observational study in Einstein (Sao Paulo, Brazil), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsThis 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.
resultsA 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).
conclusionAbsolute 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.