Table 2.

Studies related to Statistical-based discharge time prediction

Study	Predicted parameter	Patient population	Methodology	Main factors	Dataset size
Hintz et al. [90]	Discharge time, LOS	Newborns patients	Linear and logistic regression	Clinical characteristics	(n=2,254)
Carter et al. [89]	LOS	Total knee replacement patients	Statistical tests	Age, sex, consultant	(n=2,130)
Aldebeyan et al. [62]	LOS	Lumbar spine fusion surgery patients	Multivariate logistic regression	Age, sex, comorbidities	(n=15092)
Shukla and Upadhyay [91]	LOS	General patients with insurance	Correlation and linear regression	Turn Around Time for insured patients	(n=443)
Zeppieri et al. [55]	LOS	Joint arthroplasty patients	RAPT, factorial analysis of variance	Social support, psychological distress	(n=231)
Lubelski et al. [64]	LOS	Spine surgery patients	Univariable and multivariable analyses	Demographic variables, insurance status, baseline comorbidities	(n=257)
Cohen et al. [58]	LOS	Joint arthroplasty patients	RAPT, Multiple logistic regression	RAPT scores, demographic, and medical factors	(n=1,264)
Alashqar et al. [92]	Discharge time	Benign minimally invasive hysterectomy patients	Multivariate logistic regression	Operative, and surgeon factors	(n=1,084)
LeBrun et al. [93]	LOS	Joint arthroplasty patients	RAPT scores, Multivariable analyses	BMI, Charlson comorbidity index, age	(n=18,000)