Table 3. Open in new tab Performance...

Analysis

Sepsis Samples^a (n)

P Value

AUC (± 95% CI)

Sensitivity (± 95% CI)

Specificity (± 95% CI)

PPV

NPV

Applied Method

Escherichia coli^b

.0002

0.87 (0.74–1)

0.93 (0.66–1)

0.71 (0.42–0.92)

0.76

0.91

Gaussian Process

Escherichia coli t_-1

.0006

0.86 (0.71–1)

0.92 (0.64–1)

0.77 (0.46–0.95)

0.8

0.91

Support Vector Machine

Escherichia coli t_-2

<.0001

0.99 (0.95–1.0)

1 (0.66–1)

0.89 (0.52–1.0)

0.9

Gaussian Process

Escherichia coli t_-3

.0013

0.88 (0.72–1)

0.91 (0.59–1.0)

0.82 (0.48–0.98)

0.83

0.9

Random Forest

S. aureus^b

.0191

0.69 (0.52–0.85)

0.76 (0.53–0.92)

0.62 (0.38–0.82)

0.67

0.72

Gaussian Process

S. aureus t_-1

.0016

0.8 (0.64–0.96)

0.73 (0.45–0.92)

0.8 (0.52–0.96)

0.79

0.75

Support Vector Machine

S. aureus t_-2

.0406

0.7 (0.5–0.91)

0.85 (0.55–0.98)

0.62 (0.32–0.86)

0.69

0.8

Random Forest

S. aureus t_-3

.0002

0.85 (0.7–1)

0.88 (0.62–0.98)

0.81 (0.54–0.96)

0.82

0.87

Gaussian Process

S. epidermidis^b

.9823

0.63 (0.51–0.75)

0.74 (0.58–0.86)

0.55 (0.39–0.7)

0.62

0.68

Support Vector Machine

S. epidermidis t_-1

.0308

0.63 (0.5–0.76)

0.54 (0.37–0.71)

0.71 (0.54–0.85)

0.66

0.61

Gaussian Process

S. epidermidis t_-2

.0006

0.78 (0.64–0.92)

0.82 (0.60–0.95)

0.68 (0.45–0.86)

0.72

0.79

Sparse Logistic Regression

S. epidermidis t_-3

<.0001

0.90 (0.79–1.0)

0.84 (0.6–0.97)

0.89 (0.67–0.99)

0.89

0.85

Random Forest

Gram-negative bacteria^b

.0030

0.77 (0.63–0.9)

0.78 (0.58–0.91)

0.81 (0.62–0.94)

0.81

0.79

Support Vector Machine

Gram-positive bacteria^b

.0007

0.74 (0.61–0.88)

0.75 (0.55–0.89)

.75

Sparse Logistic Regression

CoNS^b

.1077

0.56 (0.47–0.65)

0.56 (0.44–0.68)

0.6 (0.48–0.72)

0.59

0.58

Random Forest

t_-1 to t_-3^b

127

.0437

0.56 (0.49–0.63)

0.69 (0.6–0.76)

0.44 (0.35–0.53)

0.55

0.58

Random Forest

t_-1

105

.0249

0.58 (0.5–0.66)

0.61 (0.51–0.7)

0.55 (0.45–0.65)

0.58

0.59

Random Forest

t_-2

.9898

0.61 (0.52–0.7)

0.91 (0.82–0.96)

0.29 (0.2–0.41)

0.56

0.77

Gaussian Process

t_-3

.9791

0.59 (0.51–0.68)

0.55 (0.43–0.66)

0.62 (0.5–0.72)

0.59

0.58

Random Forest

Mono-cultures^b

113

.0079

0.59 (0.52–0.67)

0.81 (0.72–0.87)

0.4 (0.31–0.49)

0.57

0.67

Random Forest

Table 3.

Open in new tab

Performance Characteristics for the Discrimination of Late-onset Sepsis and Matched Controls Using Fecal Volatile Organic Compounds

Analysis	Sepsis Samples^a (n)	P Value	AUC (± 95% CI)	Sensitivity (± 95% CI)	Specificity (± 95% CI)	PPV	NPV	Applied Method
Escherichia coli^b	14	.0002	0.87 (0.74–1)	0.93 (0.66–1)	0.71 (0.42–0.92)	0.76	0.91	Gaussian Process
Escherichia coli t_-1	13	.0006	0.86 (0.71–1)	0.92 (0.64–1)	0.77 (0.46–0.95)	0.8	0.91	Support Vector Machine
Escherichia coli t_-2	9	<.0001	0.99 (0.95–1.0)	1 (0.66–1)	0.89 (0.52–1.0)	0.9	1	Gaussian Process
Escherichia coli t_-3	11	.0013	0.88 (0.72–1)	0.91 (0.59–1.0)	0.82 (0.48–0.98)	0.83	0.9	Random Forest
S. aureus^b	21	.0191	0.69 (0.52–0.85)	0.76 (0.53–0.92)	0.62 (0.38–0.82)	0.67	0.72	Gaussian Process
S. aureus t_-1	15	.0016	0.8 (0.64–0.96)	0.73 (0.45–0.92)	0.8 (0.52–0.96)	0.79	0.75	Support Vector Machine
S. aureus t_-2	13	.0406	0.7 (0.5–0.91)	0.85 (0.55–0.98)	0.62 (0.32–0.86)	0.69	0.8	Random Forest
S. aureus t_-3	16	.0002	0.85 (0.7–1)	0.88 (0.62–0.98)	0.81 (0.54–0.96)	0.82	0.87	Gaussian Process
S. epidermidis^b	42	.9823	0.63 (0.51–0.75)	0.74 (0.58–0.86)	0.55 (0.39–0.7)	0.62	0.68	Support Vector Machine
S. epidermidis t_-1	35	.0308	0.63 (0.5–0.76)	0.54 (0.37–0.71)	0.71 (0.54–0.85)	0.66	0.61	Gaussian Process
S. epidermidis t_-2	22	.0006	0.78 (0.64–0.92)	0.82 (0.60–0.95)	0.68 (0.45–0.86)	0.72	0.79	Sparse Logistic Regression
S. epidermidis t_-3	19	<.0001	0.90 (0.79–1.0)	0.84 (0.6–0.97)	0.89 (0.67–0.99)	0.89	0.85	Random Forest
Gram-negative bacteria^b	27	.0030	0.77 (0.63–0.9)	0.78 (0.58–0.91)	0.81 (0.62–0.94)	0.81	0.79	Support Vector Machine
Gram-positive bacteria^b	28	.0007	0.74 (0.61–0.88)	0.75 (0.55–0.89)	0.75 (0.55–0.89)	.75	.75	Sparse Logistic Regression
CoNS^b	73	.1077	0.56 (0.47–0.65)	0.56 (0.44–0.68)	0.6 (0.48–0.72)	0.59	0.58	Random Forest
t_-1 to t_-3^b	127	.0437	0.56 (0.49–0.63)	0.69 (0.6–0.76)	0.44 (0.35–0.53)	0.55	0.58	Random Forest
t_-1	105	.0249	0.58 (0.5–0.66)	0.61 (0.51–0.7)	0.55 (0.45–0.65)	0.58	0.59	Random Forest
t_-2	78	.9898	0.61 (0.52–0.7)	0.91 (0.82–0.96)	0.29 (0.2–0.41)	0.56	0.77	Gaussian Process
t_-3	78	.9791	0.59 (0.51–0.68)	0.55 (0.43–0.66)	0.62 (0.5–0.72)	0.59	0.58	Random Forest
Mono-cultures^b	113	.0079	0.59 (0.52–0.67)	0.81 (0.72–0.87)	0.4 (0.31–0.49)	0.57	0.67	Random Forest

Analysis	Sepsis Samples^a (n)	P Value	AUC (± 95% CI)	Sensitivity (± 95% CI)	Specificity (± 95% CI)	PPV	NPV	Applied Method
Escherichia coli^b	14	.0002	0.87 (0.74–1)	0.93 (0.66–1)	0.71 (0.42–0.92)	0.76	0.91	Gaussian Process
Escherichia coli t_-1	13	.0006	0.86 (0.71–1)	0.92 (0.64–1)	0.77 (0.46–0.95)	0.8	0.91	Support Vector Machine
Escherichia coli t_-2	9	<.0001	0.99 (0.95–1.0)	1 (0.66–1)	0.89 (0.52–1.0)	0.9	1	Gaussian Process
Escherichia coli t_-3	11	.0013	0.88 (0.72–1)	0.91 (0.59–1.0)	0.82 (0.48–0.98)	0.83	0.9	Random Forest
S. aureus^b	21	.0191	0.69 (0.52–0.85)	0.76 (0.53–0.92)	0.62 (0.38–0.82)	0.67	0.72	Gaussian Process
S. aureus t_-1	15	.0016	0.8 (0.64–0.96)	0.73 (0.45–0.92)	0.8 (0.52–0.96)	0.79	0.75	Support Vector Machine
S. aureus t_-2	13	.0406	0.7 (0.5–0.91)	0.85 (0.55–0.98)	0.62 (0.32–0.86)	0.69	0.8	Random Forest
S. aureus t_-3	16	.0002	0.85 (0.7–1)	0.88 (0.62–0.98)	0.81 (0.54–0.96)	0.82	0.87	Gaussian Process
S. epidermidis^b	42	.9823	0.63 (0.51–0.75)	0.74 (0.58–0.86)	0.55 (0.39–0.7)	0.62	0.68	Support Vector Machine
S. epidermidis t_-1	35	.0308	0.63 (0.5–0.76)	0.54 (0.37–0.71)	0.71 (0.54–0.85)	0.66	0.61	Gaussian Process
S. epidermidis t_-2	22	.0006	0.78 (0.64–0.92)	0.82 (0.60–0.95)	0.68 (0.45–0.86)	0.72	0.79	Sparse Logistic Regression
S. epidermidis t_-3	19	<.0001	0.90 (0.79–1.0)	0.84 (0.6–0.97)	0.89 (0.67–0.99)	0.89	0.85	Random Forest
Gram-negative bacteria^b	27	.0030	0.77 (0.63–0.9)	0.78 (0.58–0.91)	0.81 (0.62–0.94)	0.81	0.79	Support Vector Machine
Gram-positive bacteria^b	28	.0007	0.74 (0.61–0.88)	0.75 (0.55–0.89)	0.75 (0.55–0.89)	.75	.75	Sparse Logistic Regression
CoNS^b	73	.1077	0.56 (0.47–0.65)	0.56 (0.44–0.68)	0.6 (0.48–0.72)	0.59	0.58	Random Forest
t_-1 to t_-3^b	127	.0437	0.56 (0.49–0.63)	0.69 (0.6–0.76)	0.44 (0.35–0.53)	0.55	0.58	Random Forest
t_-1	105	.0249	0.58 (0.5–0.66)	0.61 (0.51–0.7)	0.55 (0.45–0.65)	0.58	0.59	Random Forest
t_-2	78	.9898	0.61 (0.52–0.7)	0.91 (0.82–0.96)	0.29 (0.2–0.41)	0.56	0.77	Gaussian Process
t_-3	78	.9791	0.59 (0.51–0.68)	0.55 (0.43–0.66)	0.62 (0.5–0.72)	0.59	0.58	Random Forest
Mono-cultures^b	113	.0079	0.59 (0.52–0.67)	0.81 (0.72–0.87)	0.4 (0.31–0.49)	0.57	0.67	Random Forest

Corresponding Area Under the Curves, Sensitivity, Specificity, Positive and Negative Predictive Values are Displayed

Abbreviations: AUC ± 95% CI, area under the curve with 95% confidence interval; CoNS, coagulase negative Staphylococcus; NPV, negative predictive value; PPV, positive predictive value; S, Staphylococcus.

^aCorresponding number of fecal samples from controls were analyzed.

^bfor this analysis only the last fecal sample produced prior to late-onset sepsis was used.

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