Key result
Zero-inflated generalized Poisson modeling best predicts surgical length of stay across key clinical factors.
Why the study?
Assessing length of stay is important for organizing hospital services and effectively using limited health care resources. This study sought to identify predictors of length of stay among patients admitted to a general surgical unit.
What demographic and clinical factors predict length of stay in a general surgical unit, and which zero-inflated count regression model best fits the data?
Cross-Sectional (n=334)
No
What demographic and clinical factors predict length of stay in a general surgical unit, and which zero-inflated count regression model best fits the data?
Zero-inflated generalized Poisson regression effectively handles excess zeros and overdispersion when modeling hospital length of stay in general surgery, showing that cardiovascular comorbidities such as hypertension, heart disease, and hyperlipidemia significantly prolong hospitalization.
Answers are grounded in this paper and cite their sources.
May inform preoperative risk stratification in general surgery; leaves open whether targeting comorbidities improves outcomes.
Background: The effective use of limited health care resources is of prime importance. Assessing the length of stay (LOS) is especially important in organizing hospital services and health system. This study was conducted to identify predictors of LOS among patients who were admitted to a general surgical unit. Methods: In this cross-sectional study, the sample included all patients who were admitted to the general surgical unit of Shariati hospital in 2013 (n= 334). To determine the factors affecting LOS, Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated generalized Poisson (ZIGP) regression models were fitted using R software, and then the best model was selected. Results: Among all 334 patients, the mean (±SD) age of the patients was 45.2 (±16.47) years and 220 (65.9%) of them were male. The results revealed that based on ZIGP model, type of surgery (appendicitis, abdomen and its contents, hemorrhoids, lung, and skin), type of insurance, comorbid diseases (hypertension, heart disease, and hyperlipidemia), place of residence (local and non-local), age, and number of tests had significant effects on the LOS of GS patients. Conclusion: According to the Akaike information criterion (AIC) in each fitted model, it was found that ZIGP regression model is more appropriate than ZIP and ZINB regression models in assessing LOS in GS patients, especially due to the presence of excess zeros and overdispersion in count data.
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Hassankiadeh et al. (2017) conducted a cross-sectional in General surgical admission (n=334). Zero-inflated generalized Poisson (ZIGP) regression modeling of clinical and demographic predictors vs. Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models was evaluated on Length of stay (LOS) in days and model fit via Akaike Information Criterion (AIC). Zero-inflated generalized Poisson regression provided the best fit (AIC=1070) for modeling general surgical length of stay, identifying surgery type, comorbidities, age, and test count as key predictors.
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