ArticleMateria socio-medica2017
Comparison of Statistical Models of Predict the Factors Affecting the Length of Stay (LOS) in the Intensive Care Unit (ICU) of a Teaching Hospital.
Article in Materia socio-medica, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Factors Affecting the Length of Stay in the Intensive Care Unit among Adults in Saudi Arabia: A Cross-Sectional Study.Journal of clinical medicine · 2023Article
- Perioperative Risk Stratification: A Need for an Improved Assessment in Surgery and Anesthesia-A Pilot Study.Medicina (Kaunas, Lithuania) · 2021Observational
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5 authors.
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Abstract
introductionModeling can be a useful tool to find out how the distributions of hospital length of stay (LOS) and the factors affecting the length of stay. The present study aims to determine factors affecting the length of stay and selecting suitable statistical models. MATERIAL AND
methodthis is a cross - sectional study of 565 patients who were treated in the intensive care unit of Imam Khomeini hospital in Ahwaz. Preliminary data were collected retrospectively through the medical records of all patients admitted on intensive care units of Ahwaz Imam Khomeini Hospital in 2015. Statistical analysis and multivariate regression models were done using of SPSS 21 and STATA 7 software.
resultsAverage length of stay in ICU was 8.16±0.75 days. The Mean and Median age of patients were 58.61±20 and 61 respectively, The Mean LOS for females (16.44±9.37 days) was more than the men (11.5±5.35 days) (p<0.01). The maximum and minimum lengths of stay belonged to patients with endocrine disorders (14.7±3.1 days) and patients with gastrointestinal disorders (5.53±1.1 days) respectively (p<0.01). The goodness of fit for Gamma model showed that this model was more suitable and powerful than Log-normal model to predict the factors affecting the patient's length of stay in intensive care units of hospital.
conclusionGamma regression model was more robust to predict factors regarding the hospital length of stay. According to Gamma model the key factor in predicting the length of stay in ICU was the type of disease diagnosis. The result of statistical modeling can help managers and policy makers to estimate hospital resources and allocate them for different hospital services.
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