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Table 1 Summary statistics and comparison between readmitted and non-readmitted patients: Demographics and patient-reported variables

From: Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

Feature

Non-readmitted cases (n = 3434)

Readmitted cases (n = 251)

P-valued

Demographics

 Agea, mean (SD)

69.539 (8.8)

69.956 (8.9)

0.472

 Sexa, % female

63.8%

61.0%

0.397

 BMIa, mean (SD)

33.1 (6.4)

34.3 (7.8)

0.017

 Smokinga

269 (7.8%)

21 (8.4%)

0.856

Low SES

 Pensioner card

1702 (49.6%)b

152 (60.6%)

0.001

 SEIFA score

  

0.045

  1

374 (10.9%)a

19 (7.6%)

 

  2

228 (6.6%)a

18 (7.2%)

 

  3

273 (8.0%)a

11 (4.4%)

 

  4

289 (8.4%)a

17 (6.8%)

 

  5

404 (11.8%)a

26 (10.4%)

 

  6

264 (7.7%)a

19 (7.6%)

 

  7

589 (17.2%)a

45 (17.9%)

 

  8

371 (10.8%)a

30 (12.0%)

 

  9

450 (13.1%)a

41 (16.3%)

 

  10

192 (5.6%)a

25 (10.0%)

 

Poor access to post-op care: lives far from hospital, lack of access to allied health support, lack of access to telehealth supporta

  

0.049

 Major cities in Australia

2645 (77.0%)

208 (82.9%)

 

 Inner regional Australia

649 (18.9%)

38 (15.1%)

 

 Outer regional or remote Australia

131 (3.8%)

4 (1.6%)

 

 Missing

9 (0.3%)

1 (0.4%)

 

Patient-related biopsychosocial: lower education level, poor health literacy, non-English speaking

  

0.567

 Interpreter required

567 (16.5%)a

37 (14.7%)

 

 Missing

31 (0.9%)

5 (2.0%)

 

Patient-reported variables

 Preoperative patient-reported level of functiona, mean (SD)

  Mental function

44.4 (15.1)

42.7 (16.3)

0.133

  Physical function

24.7 (7.8)

23.9 (7.5)

0.157

 Preoperative patient-reported pain levelsc

  

0.330

  One

22 (0.6%)

1 (0.4%)

 

  Two

125 (3.6%)

6 (2.4%)

 

  Three

450 (13.1%)

26 (10.4%)

 

  Four

1375 (40.0%)

106 (42.2%)

 

  Five

960 (28.0%)

84 (33.5%)

 

  Missing

502 (14.6%)

28 (11.2%)

 
  1. Categorical variables were compared using the chi-squared test, or Fisher’s exact test in cases of counts below 10
  2. SD Standard deviation, BMI Body mass index, SEIFA Socioeconomic Indexes for Areas [28], SES socioeconomic status
  3. aVariable derived from SMART registry
  4. bVariable derived from the administrative database
  5. c “How much did pain interfere with your normal work?”: One = Not at all; Two = A little bit; Three = Moderately; Four = Quite a bit; Five = Extremely
  6. dContinuous variables were compared using Student’s t-test