Table 1.

Studies related to Statistical-based discharge destination prediction

Study	Predicted parameter	Patient population	Methodology	Main factors	Dataset size
Agarwal et al. [76]	Discharge destination	Stroke patients	Logistic regressions	Age, sex, and the presence of premorbid social support	(n=104)
Lutz [78]	Facility-based discharges	Stroke patients	Grounded dimensional analysis	Functional Independence Measure (FIM) score, age, sex	(n=90)
Pablo et al. [49]	Discharge destination	Elective total hip replacement patients	Multivariate regression	walking ability, age, obesity	(n=1,276)
De Guise et al. [66]	Discharge destination	Traumatic brain injury patients	Logistic regressions	Age, education, Glasgow Coma Scale score	(n=339)
Nguyen et al. [81]	Discharge destination	Stroke patients	Multivariate logistic regression	Immigrant status, marital status	(n=326)
Lim et al. [86]	Discharge destination	Traumatic elderly patients	Multivariable random effect mixed model	Sex, race, payment type	(n=47,234)
Brauer et al. [70]	Community-based discharges	Stroke patients	Logistic regression	Admission functional status, age	(n=566)
Van der Zwaluw et al. [77]	Discharge destination	Stroke patients	Logistic regression	Cognitive dysfunction, age, BI score	(n=287)
Kimmel et al. [48]	Facility-based discharges	Lower limb fracture patients	Multivariable logistic regression	Age, proximal fracture type, fund source for the admission	(n=1,429)
West et al. [138]	Community-based discharges	Stroke patients	Behavioural mapping, statistical tests, multivariable median regression	Age, stroke severity, premorbid function	(n=73)
Stineman et al. [79]	Community-based discharges	Stroke patients	Logistic regression	Previous living circumstances, comorbidities, hospital course	(n=6,515)
Sharareh et al. [52]	Discharge destination	Joint arthroplasty patients	Cross-sectional analysis of different factors	Living statuses	(n=50)
Ouellette et al. [73]	Community-based discharges	Stroke patients	Logistic regressions and chi-square analyses	Health factors at the time of admission	(n=407)
Schwarzkopf et al. [139]	Discharge destination	Total hip arthroplasty patients	Multinomial regression	Race, insurance, morbidity	(n=14,326)
Halawi et al. [53]	Facility-based discharges	Joint arthroplasty patients	Multivariable logistic regression	Caregiver support, and patient expectation of discharge destination, age	(n=372)
Hansen et al. [57]	Facility-based discharges	Joint arthroplasty patients	RAPT, Binary logistic regression	Age, sex, health condition	(n=3,213)
Roberts et al. [82]	Discharge destination	Stroke patients	Receiver operator characteristic curve analysis, Linear regression	Functional status	(n=481)
Gholson et al. [54]	Community-based discharges	Joint arthroplasty patients	Multivariate logistic regression	Age, preoperative functional status, elective surgery status	(n=108,396)
Aldebeyan et al. [62]	Facility-based discharges	Lumbar spine fusion surgery patients	Multivariate logistic regression	Age, sex, comorbidities	(n=15,092)
Zeppieri et al. [55]	Discharge destination	Joint arthroplasty patients	RAPT, factorial analysis of variance	Social support, psychological distress	(n=231)
Dibra et al. [51]	Discharge destination	Revision joint arthroplasty patients	RAPT, Univariable logistic regression	Patient-reported discharge expectation	(n=716)
Sattler et al. [56]	Discharge destination	Knee arthroplasty patients	Univariable and multivariable logistic regression	Psychological, functional, and socio-demographic factors	(n=100)
Lubelski et al. [64]	Facility-based discharges	Spine surgery patients	Univariable and multivariable	Demographic variables, insurance status, baseline comorbidities	(n=257)
Ayyala et al. [39]	Facility-based discharges	Abdominal wall reconstruction patients	Multivariate logistic regression	Sex, history of diabetes, history of hypertension	(n= 4,549)
Glauser et al. [50]	Facility-based discharges	Posterior lumbar fusion patients	RAPT, Logistic regression	RAPT score, LOS, age	(n=432)
Kim et al. [30]	Community-based discharges	Moderate stroke patients	Logistic regression, weighted scoring model	Demographic, clinical, and functional factors	(n=732)
Mehta et al. [59]	Discharge destination	Hip arthroplasty patients	Adjusted binary logistic regression	Community area deprivation index level	(n=84,931)
Gosling et al. [85]	Facility-based discharges	Cardiac surgery patients	Stepwise backward logistic regression, used 5-fold and leave-one-out cross-validation	Age, sex, long LOS prior to surgery	(n=3,760)
Cohen et al. [58]	Discharge destination	Joint arthroplasty patients	RAPT, Multiple logistic regression	RAPT scores, demographic, and medical factors	(n=1,264)
Pennicooke et al. [63]	Discharge destination	Lumbar spine surgery patients	Multivariable nonlinear logistic regression	Age	(n=61,315)
Ryder et al. [65]	Facility-based discharges	Hip fracture patients	Multinominal logistic regression	Age, impaired cognition, reduced walking ability	(n=29,881)
Oyesanya et al. [67]	Discharge destination	Traumatic brain injury patients	Logistic regression	Race and ethnicity	(n=99,614)
Oyesanya et al. [68]	Discharge destination	Traumatic brain injury patients	Logistic regression	Age, sex	(n=221,961)
Hadad et al. [61]	Discharge destination	Joint arthroplasty patients	Regression models	Demographics, health factors	(n=11,672)
Hirota et al. [88]	Discharge destination	Aspiration pneumonia patients	Multilevel logistic regression	Age, sex, health factors	(n=34,105)