Comparing regression modeling strategies for predicting hometime

Table 1 Baseline characteristics and outcomes of study cohort

Characteristic	Study Cohort (n = 75,475)
Female (%)	47.44
Median Age (Q1, Q3) – years	75 (64, 84)
Arrived by Ambulance (%)	71.19
Stroke Type (%)
Intra-cerebral Hemorrhage	12.87
Ischemic Stroke	87.12
Diabetes (%)	36.61
Atrial Fibrillation (%)	14.18
Hypertension (%)	82.76
Myocardial Infarction (%)	9.19
Neighbourhood Income Quintile (%)
Quintile 1 (lowest)	23.60
Quintile 2	21.99
Quintile 3	19.70
Quintile 4	17.75
Quintile 5 (highest)	16.96
Home Location (%)
Rural	12.40
Urban	87.60
Median Frailty Score^a (Q1, Q3)	4.2 (0.8, 9.1)
Median PaSSV Score^b (Q1, Q3)	7.7 (6.5, 8.7)
Received Thrombolysis (%)	13.36
Received Stroke Unit Care (%)	56.01
Median 90-day hometime (Q1, Q3)	59 (2, 83)
90-day location (%)
Acute Care	4.14
Rehabilitation	2.91
Long Term Care	6.91
Home	68.54
Death	17.49

Q1 First quartile, Q3 Third quartile, PaSSV Passive Surveillance Stroke seVerity indicator
^aA continuous score ranging from 0 – 99 where scores < 5 indicate low risk of frailty, scores from 5 – 15 indicate intermediate risk of frailty, and scores > 15 indicate high risk of frailty [24]
^bA continuous score where < 4 indicates severe stroke, 4 – 8 indicates moderate stroke severity, and > 8 indicates mild stroke severity [25]

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