Skip to main content

Table 1 Latent profile analysis for choosing the best number of profiles

From: Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis

      

Number of patients in each latent profile

 

Number of classes

AIC

CAIC

BIC

SABIC

Entropy

1

2

3

4

5

6

7

P value

2

2,254,774.736

2,255,429.502

2,255,353.502

2,255,111.98

0.975

12,865 (86)

2128 (14)

     

0.001

3

2,244,946.905

2,245,825.67

2,245,723.67

2,245,399.522

0.955

11,525 (77)

1580 (11)

1888 (13)

    

0.001

4

2,235,891.639

2,236,994.403

2,236,866.403

2,236,459.629

0.953

10,340 (69)

1305 (9)

1635 (11)

1713 (11)

   

0.001

5

2,232,244.359

2,233,571.121

2,233,417.121

2,232,927.722

0.887

8010 (53)

1188 (8)

2561 (17)

1552 (10)

1682 (11)

  

0.063

6

2,228,493.639

2,230,044.4

2,229,864.4

2,229,292.374

0.886

7586 (51)

1200 (8)

2367 (16)

1476 (10)

1599 (11)

765 (5)

 

0.075

7

2,223,495.867

2,225,270.627

2,225,064.627

2,224,409.976

0.887

7108 (47)

1080 (7)

2272 (15)

1293 (9)

1051 (7)

1505 (10)

684 (5)

0.081

  1. P value was reported for the bootstrap likelihood ratio test comparing the current model (k class) to the model with k-1 class.
  2. Abbreviations: AIC Akaike information criterion, BIC Bayesian information criteria, CAIC consistent Akaike information criterion, SABIC sample size adjusted Bayesian information criteria
Back to article page

Critical Care

ISSN: 1364-8535