Skip to main content

Table 4 Performance of the ICD-9-CM-based and alternative models for identifying CABG SSIs (training data: medical center A)

From: Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?

 

Sensitivity

Specificity

PPV

NPV

Accuracy

ICD-9-CM-based model

37.50% (9/24)

96.27% (956/993)

19.57% (9/46)

98.46% (956/971)

94.89% (965/1017)

Model 1

4.17% (1/24)

99.90% (992/993)

50.00% (1/2)

97.73% (992/1015)

97.64% (993/1017)

Model 2

54.17% (13/24)

96.78% (961/993)

28.89% (13/45)

98.87% (961/972)

95.77% (974/1017)

Model 3

100.00% (24/24)

3.22% (32/993)

2.44% (24/985)

100.00% (32/32)

5.51% (56/1017)

Model 4

100.00% (24/24)

94.56 (939/993)

30.77% (24/78)

100.00% (939/939)

94.69% (963/1017)

Model 5

87.50% (21/24)

99.40% (987/993)

77.78% (21/27)

99.70% (987/990)

99.12% (1008/1017)

  1. % (numerator/denominator).
  2. ICD-9-CM: The International Classification of Diseases, Ninth Revision, Clinical Modification; PPV: positive predictive value; NPV: negative predictive value. Model 1: the classification algorithms (strict); Model 2: the classification algorithms (moderate); Model 3: the classification algorithms (loose); Model 4: the multivariable regression model: Model 5: data mining-decision tree model.
Back to article page

BMC Medical Informatics and Decision Making

ISSN: 1472-6947

Contact us