Skip to main content

Table 3 Performance Statistics of Models

From: Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis

Model

Author (Year)

Duration

Cut-off values

Sen (%)

(95% CI)

Spe (%)

(95% CI)

PPV(%)

(95% CI)

NPV(%)

(95% CI)

C-index (95% CI)

Palliative Prognostic Index (PPI)

Cheng et al., 2012 [26]

3 weeksa

6

71

68

81

56

0.68

Maltoni et al., 2012 [28]

30 daysa

6

73.7 (68.4–79)

67.1 (61.7–72.6)

67.8 (62.4–73.2)

73.1 (67.7–78.5)

0.62 (0.60–0.65)

Hung, 2014 [30]

30 daysa

8

58.9

64.8

73.7

48.4

0.66 (0.63—0.69)

Kao et al., 2014 [31]

30 days

5

79.5

50.8

57.1

75

0.63 (0.61–0.65)

Kim et al., 2014 [32]

3 weeks

5

60

63.3

45.4

75.7

0.65 (0.61—0.70)

C. Palomar-Munoz et al., 2018 [40]

3 weeksa

6

79

51

66

66

N/A

Ermacora et al., 2018 [41]

30 days

N/A

N/A

0.72 (0.67–0.77)

Miyagi et al., 2020 [46]

3 weeks

N/A

N/A

0.76 (0.64–0.88)

Hiratsuka et al., 2022_c [51]

30 daysa

N/A

N/A

0.74 (0.72—0.76)

Combination of initial palliative prognostic Index (PPI) and week 1 PPI

Kao et al., 2014 [31]

30 days

4

66.9

77

70.6

73.8

0.71 (0.69—0.73)

Survival Prediction Score (SPS): 3-variable model

Chow et al., 2008 [21]

N/A

N/A

N/A

0.63

Number of risk factors (NRF): 3-variable model

Chow et al., 2008 [21]

N/A

N/A

N/A

0.63

A proposed prognostic 7-day survival formula

Chiang et al., 2009 [23]

1 week

0.2

71

75.7

26.8

90.1

N/A

Recursive partitioning: 2-variable model

Chow et al., 2009 [24]

N/A

N/A

N/A

0.61

Survival Prediction Score (SPS): 6-variable model

Chow et al., 2009 [22]

N/A

N/A

N/A

0.65

Number of risk factors (NRF): 6-variable model

Chow et al., 2009 [22]

N/A

N/A

N/A

0.65

Palliative Prognostic Score (PaP)

Scarpi et al., 2011 [25]

30 days

N/A

 

N/A

Maltoni et al., 2012 [28]

30 daysa

9

69.9 (64.4–75.4)

83.7 (79.3–88.2)

80.2 (75.0–85.3)

74.8 (70.0–79.5)

0.72 (0.70–0.73)

Kim et al., 2014 [32]

3 weels

10

72.9

74.2

59

84.3

0.81 (0.77—0.85)

[42]

30-days

N/A

N/A

0.87 (0.85—0.89)

Ermacora et al., 2018 [41]

30 days

N/A

N/A

0.82 (0.77–0.86)

Miyagi et al., 2020 [46]

3 weeks

N/A

N/A

0.86 (0.79–0.93)

Hiratsuka et al., 2022_a [49]

30 days

N/A

N/A

Japan = 0.75 (0.73–0.78),

Korea = 0.66 (0.6—0.72), Taiwan = 0.67 (0.61—0.74)

Hiratsuka et al., 2022_b [50]

30 daysa

N/A

91.1 (88.9–92.9)

40.2 (36.1–44.4)

68.8 (67.3–70.4)

75.6 (70.8–79.8)

Japan = 0.70 (0.68—0.73)

Korea = 0.71 (0.64—0.77)

Hiratsuka et al., 2022_c [51]

30 daysa

N/A

N/A

0.84 (0.82—0.86)

R. Mendis et al., 2015 [36]

30 days

N/A

N/A

0.71 (0.68–0.74)

Modified Palliative Prognostic Score—Delirium (D-PaP)

Hamano et al., 2018 [25]

30 days

N/A

N/A

N/A

Maltoni et al., 2012 [28]

3 weeks

9

72.9 (67.6–78.3)

80.2 (75.6–84.9)

77.6 (72.4–82.8)

75.9 (71.1–80.8)

76.7 (72.7—80.7)

Palliative Prognostic Score—Nomogram (PaP-Nomogram)

Scarpi et al., 2022 [54]

15-daysa

Various survival probability based on nomogram points

N/A

0.74 (0.72—0.75)

Cochin Risk Index Score (CRIS)

Durand et al., 2012 [27]

2 week

7

70

62

78

 

N/A

Palliative Performance Scale (PPS)

Maltoni et al., 2012 [28]

3 weeksa

60

N/A

0.63 (0.60–0.66)

Kim et al., 2014 [32]

3 weeks a

30

65

69.8

52.3

79.7

0.729 (0.68—0.77)

Hiratsuka et al., 2022_c [51]

30 days a

Not specified

N/A

0.73 (0.70—0.75)

Prognostic Scale for terminal hospitalized chinese cancer patients (8-variable)

Huang et al., 2014 [29]

30 days

4

70

77

78

73

N/A

A graphic tool to estimate individualized survival curves (5-variable)

Chiang et al., 2015 [35]

Analysis by survival curve only

N/A

N/A

0.69

PRONOPALL score (4-variables)

Bourgeois etal., 2017 [38]

2 monthsa

N/A

89.4

60.9

41.2

76.9

0.81 (0.75—0.87)

Objective Prognostic Score (OPS)

Yoon et al., 2014 [33]

3 week

3

83.6

56.8

77.8

65.6

0.74

Yoon et al., 2017 [38]

3-week

3

73.6

66.2

79.8

58

0.74 (0.68—0.81)

Ermacora et al., 2018 [41]

30 days

N/A

N/A

0.70 (0.64–0.75)

Hiratsuka et al., 2022_b [50]

30 daysa

3

43.6 (40.1–47.1)

87.8 (84.7–90.4)

83.8 (80.3–86.7)

51.7 (50.0–53.5

Japan: 0.70 (0.68—0.73)

Korea: 0.71 (0.64—0.77)

Imminent Mortality Predictor for Advanced Cancer (IMPAC)

Adelson et al., 2018 [39]

90-daysa

50%

40

N/A

60

N/A

0.72

Objective Prognostic Index for advanced cancer (OPI-AC) (7-days)

Hamano et al., 2018 [42]

7-days a

N/A

N/A

0.77 (0.66—0.87)

Prognosis in Palliative Care study (PiPS-B14/56)

Hamano et al., 2018 [42]

14-days a

N/A

N/A

0.86 (0.84—0.89)

Six adaptable prognosis prediction (SAP) model

Hamano et al., 2018[42]

30-days a

N/A

N/A

0.74 (0.65—0.83)

Nomogram based parameters to predict 90-days survival

Zhao et al., 2019 [43]

90 days

N/A

N/A

0.75 (0.70—0.80)

Artifical Neural network for 30-days survival prediction

Arkin et al., 2020 [44]

30-days

N/A

38

100

N/A

N/A

0.86

Logistic regression for 30-days survival

Arkin et al., 2020 [44]

30-days

N/A

48

84

N/A

N/A

0.76

Prognostic model for advanced cancer (PRO-MAC)

Hum et al., 2020 [45]

30-days a

4

66.9

68.1

57.1

76.5

0.73 (0.69–0.75)

Supportive and Palliative Care indicator tools

Chan et al., 2022 [48]

6 months

N/A

83.5

61

66.4

80

N/A

Rothman Index

Chan et al., 2022 [48]

6 months

60

69.7

11.9

42.2

29.8

N/A

Patient-Generated Subjective Global Assessment Short form (PG-SGA SF)

Cunha et al., 2022

90 days

15

60.2

70.1

N/A

N/A

0.75 (0.67—0.80)

Modified Barretos Prognostic Nomogram (BPN)—with laboratory values

Preto et al., 2022 [53]

30-daysa

N/A

N/A

0.78 (0.74—0.81)

Modified Barretos Prognostic Nomogram (BPN)—without laboratory values

Preto et al., 2022 [53]

30-daysa

N/A

N/A

0.74 (0.71—0.77)

Machine learning (Gradient-boosted trees binary classifier)

Zachariah et al., 2022 [55]

90-days

N/A

29.5

N/A

60

N/A

0.81 (0.83—0.91)

Objective Palliative Prognostic Score

Chen et al., 2015 [34]

1 week

3 out of 6 variables reached

68.8

86

55.9

91.4

0.82 (0.75—0.89)

Clinical Model

Owusuaa et al., 2022 [52]

1-year

40%

80

69

65

83

0.76 (0.73–0.78)

Extended Model

Owusuaa et al., 2022 [52]

1-year

40%

76

72

66

81

0.78 (0.76–0.80)

Data mining techniques (random forest algorithms, support-vector machine algorithms, back-propagation neural network algorithms)

Yang et al., 2021 [47]b

Classification into < 30 days, 30–90 and > 90 days

N/A

N/A

N/A

  1. Fields marked as N/A indicate that the information was not reported in the original study.
  2. aAuthors also evaluated model performance at prediction intervals other than those listed in this table
  3. bThe author reported model accuracy rather than classification statistics and/or C-statistics:
  4. random forest algorithm: 81.94% (SD: +- 6.12%), back-propagation neural network: 72.90% (SD: +- 8.08%)
  5. Sen sensitivity, Spe specificity, PPV positive predictive value, NPV negative predictive value