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 | Â | 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 | Â | 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 |