Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Study Patients and Data Collection
2.2. Statistical Analysis and Machine Learning Methods
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Complication (n = 23) | No Complication (n = 164) | p-Value | |
---|---|---|---|---|
Sex | 1.000 | |||
Male | 18 (78.3) | 129 (78.7) | ||
Female | 5 (21.7) | 35 (21.3) | ||
Age | 0.420 | |||
<65 | 11 (47.8) | 64 (39) | ||
≥65 | 12 (52.2) | 100 (61) | ||
BMI | 0.921 | |||
<23 | 13 (61.9) | 93 (60.8) | ||
≥23 | 8 (38.1) | 60 (39.2) | ||
Smoking history | 0.025 | |||
Yes | 9 (39.1) | 29 (17.7) | ||
No | 14 (60.9) | 135 (82.3) | ||
Alcohol history | 0.115 | |||
Yes | 4 (17.4) | 12 (7.3) | ||
No | 19 (82.6) | 152 (92.7) | ||
Comorbidities | ||||
Hypertension | 0.013 | |||
Yes | 14 (60.9) | 56 (34.1) | ||
No | 9 (39.1) | 108 (65.9) | ||
Hyperlipidemia | 0.052 | |||
Yes | 5 (21.7) | 13 (7.9) | ||
No | 18 (78.3) | 151 (92.1) | ||
COPD | 0.477 | |||
Yes | 1 (4.3) | 18 (11) | ||
No | 22 (95.7) | 146 (89) | ||
Diabetes mellitus | 0.689 | |||
Yes | 5 (21.7) | 42 (25.6) | ||
No | 18 (78.3) | 122 (74.4) | ||
Gout | 1.000 | |||
Yes | 0 (0) | 4 (2.4) | ||
No | 23 (100) | 160 (97.6) | ||
BPH | 0.136 | |||
Yes | 0 (0) | 19 (11.6) | ||
No | 23 (100) | 145 (88.4) | ||
Parkinson’s disease | 1.000 | |||
Yes | 0 (0) | 1 (0.6) | ||
No | 23 (100) | 163 (99.4) | ||
Osteoporosis | 1.000 | |||
Yes | 0 (0) | 2 (1.2) | ||
No | 23 (100) | 162 (98.8) | ||
MI | 0.075 | |||
Yes | 2 (8.7) | 2 (1.2) | ||
No | 21 (91.3) | 162 (98.8) | ||
Heart disease | 0.044 | |||
Yes | 4 (17.4) | 8 (4.9) | ||
No | 19 (82.6) | 156 (95.1) | ||
Asthma | 1.000 | |||
Yes | 0 (0) | 3 (1.8) | ||
No | 23 (100) | 161 (98.2) | ||
Buger’s disease | 1.000 | |||
Yes | 0 (0) | 1 (0.6) | ||
No | 23 (100) | 163 (99.4) | ||
Angina | 0.123 | |||
Yes | 1 (4.3) | 0 (0) | ||
No | 22 (95.7) | 164 (100) | ||
Crohn’s disease | 1.000 | |||
Yes | 0 (0) | 1 (0.6) | ||
No | 23 (100) | 163 (99.4) | ||
HIV | 1.000 | |||
Yes | 0 (0) | 2 (1.2) | ||
No | 23 (100) | 162 (98.8) | ||
Hepatitis B | 1.000 | |||
Yes | 0 (0) | 3 (1.8) | ||
No | 23 (100) | 161 (98.2) | ||
Concomitant drug | ||||
Statins | 0.771 | |||
Yes | 3 (13) | 29 (17.7) | ||
No | 20 (87) | 135 (82.3) | ||
PPIs | 0.952 | |||
Yes | 8 (34.8) | 56 (34.1) | ||
No | 15 (65.2) | 108 (65.9) | ||
5-HT₃ Antagonists | 0.625 | |||
Yes | 3 (13) | 16 (9.8) | ||
No | 20 (87) | 148 (90.2) | ||
D2 antagonists | 0.231 | |||
Yes | 1 (4.3) | 1 (0.6) | ||
No | 22 (95.7) | 163 (99.4) | ||
Corticosteroids | 1.000 | |||
Yes | 1 (4.3) | 7 (4.3) | ||
No | 22 (95.7) | 157 (95.7) | ||
Antihistamines | 0.204 | |||
Yes | 3 (13) | 10 (6.1) | ||
No | 20 (87) | 154 (93.9) | ||
Diuretics | 1.000 | |||
Yes | 1 (4.3) | 12 (7.3) | ||
No | 22 (95.7) | 152 (92.7) | ||
β-blockers | 0.061 | |||
Yes | 3 (13) | 5 (3) | ||
No | 20 (87) | 159 (97) | ||
P2Y12 inhibitors | 0.032 | |||
Yes | 4 (17.4) | 7 (4.3) | ||
No | 19 (82.6) | 157 (95.7) | ||
5HT₄ agonists | 0.327 | |||
No | 22 (95.7) | 162 (98.8) | ||
Yes | 1 (4.3) | 2 (1.2) | ||
Antiepileptics | 1.000 | |||
Yes | 0 (0) | 1 (0.6) | ||
No | 23 (100) | 163 (99.4) | ||
Antibiotics | 0.744 | |||
Yes | 2 (8.7) | 21 (12.8) | ||
No | 21 (91.3) | 143 (87.2) | ||
Alpha-blockers | 0.476 | |||
Yes | 1 (4.3) | 19 (11.6) | ||
No | 22 (95.7) | 145 (88.4) | ||
5α-Reductase inhibitors | 1.000 | |||
Yes | 1 (4.3) | 12 (7.3) | ||
No | 22 (95.7) | 152 (92.7) | ||
NSAIDs | 0.261 | |||
Yes | 2 (8.7) | 32 (19.5) | ||
No | 21 (91.3) | 132 (80.5) | ||
Metformin | 0.185 | |||
Yes | 5 (21.7) | 19 (11.6) | ||
No | 18 (78.3) | 145 (88.4) | ||
Antipsychotics | 0.327 | |||
Yes | 1 (4.3) | 2 (1.2) | ||
No | 22 (95.7) | 162 (98.8) | ||
Anticoagulants | 0.350 | |||
Yes | 5 (21.7) | 23 (14) | ||
No | 18 (78.3) | 141 (86) | ||
ACE inhibitors/ARBs | 0.684 | |||
Yes | 2 (8.7) | 12 (7.3) | ||
No | 21 (91.3) | 152 (92.7) | ||
Zolpidem | 0.327 | |||
Yes | 1 (4.3) | 2 (1.2) | ||
No | 22 (95.7) | 162 (98.8) | ||
TCAs | 1.000 | |||
Yes | 0 (0) | 1 (0.6) | ||
No | 23 (100) | 163 (99.4) | ||
Opioids | 0.038 | |||
Yes | 12 (52.2) | 120 (73.2) | ||
No | 11 (47.8) | 44 (26.8) | ||
Aspirin | 1.000 | |||
Yes | 0 (0) | 5 (3) | ||
No | 23 (100) | 159 (97) | ||
Dopamine | 0.327 | |||
Yes | 1 (4.3) | 2 (1.2) | ||
No | 22 (95.7) | 162 (98.8) | ||
Benzodiazepines | 0.738 | |||
Yes | 3 (13) | 19 (11.6) | ||
No | 20 (87) | 145 (88.4) | ||
Antivirals | 1.000 | |||
Yes | 0 (0) | 3 (1.8) | ||
No | 23 (100) | 161 (98.2) | ||
SSRIs, SNRIs | 0.600 | |||
Yes | 0 (0) | 7 (4.3) | ||
No | 23 (100) | 157 (95.7) | ||
Cancer stage | 0.428 | |||
1 | 0 (0.0) | 1 (0.6) | ||
2 | 0 (0.0) | 3 (1.8) | ||
3 | 3 (13.0) | 9 (5.5) | ||
4 | 20 (87.0) | 150 (92.0) | ||
Diagnosis | 0.223 | |||
Bladder cancer | 0 (0) | 15 (9.1) | ||
Colon cancer | 0 (0) | 3 (1.8) | ||
Gastric cancer | 0 (0) | 8 (4.9) | ||
Hepatocellular cancer | 1 (4.3) | 12 (7.3) | ||
Lung cancer | 19 (82.6) | 75 (45.7) | ||
Pancreatic cancer | 0 (0) | 2 (1.2) | ||
Rectal cancer | 0 (0) | 3 (1.8) | ||
Renal cancer | 0 (0) | 3 (1.8) | ||
Stomach cancer | 0 (0) | 3 (1.8) | ||
Other | 2 (8.7) | 38 (23.2) | ||
ECOGPS | 0.464 | |||
0 | 0 (0) | 1 (0.6) | ||
1 | 21 (91.3) | 129 (79.6) | ||
2 | 2 (8.7) | 18 (11.1) | ||
3 | 0 (0) | 14 (8.6) |
Characteristics | Crude OR (95% CI) | p-Value | Adjusted OR (95% CI) | p-Value |
---|---|---|---|---|
Sex | 1.024 (0.355–2.952) | 0.965 | ||
Age < 65 | 0.698 (0.291–1.677) | 0.422 | ||
BMI | 0.954 (0.373–2.438) | 0.921 | ||
Heart disease | 4.105 (1.129–14.932) | 0.032 | ||
P2Y12 inhibitors | 4.722 (1.265–17.631) | 0.021 | ||
Smoking history | 2.993 (1.183–7.574) | 0.021 | 3.748 (1.338–10.496) | 0.012 |
Hypertension | 3.000 (1.223–7.360) | 0.016 | 4.093 (1.478–11.332) | 0.007 |
Opioids | 0.400 (0.165–0.972) | 0.043 | 0.248 (0.090–0.683) | 0.007 |
Machine Learning Model | AUROC (95% CI) | AUPRC (95% CI) |
---|---|---|
Logistic regression | 0.71 (0.587–0.827) | 0.47 (0.312–0.622) |
Elastic net | 0.71 (0.588–0.829) | 0.47 (0.314–0.625) |
Random Forest | 0.77 (0.648–0.883) | 0.51 (0.357–0.666) |
SVM (Linear) | 0.57 (0.394–0.752) | 0.36 (0.216–0.497) |
SVM (Radial) | 0.69 (0.539–0.838) | 0.45 (0.310–0.600) |
Method | Hyperparameter | |
---|---|---|
Model Specification and Search Grids | Selected Values | |
Elastic net | λ: 100 equally spaced values in logarithmic scale between 10−4 and 0 | λ: 0.01261857 |
α: 0, 0.2, 0.4, 0.6, 0.8, 1 | α: 0.6 | |
Random forests | mtry: 1, 2, 3, 4, 5, 6, 7 | mtry: 1 |
SVM with linear kernel | C: 0, 0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 5 | C: 1 |
SVM with radial kernel | Sigma: 2−15, 2−13, 2−11, 2−9, 2−7, 2−5, 2−3, 2−1, 2, 23 | Sigma: 0.125 |
C: 2−5, 2−3, 2−1, 2, 23, 25, 27, 29, 211, 213, 215 | C: 128 |
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Kim, W.; Cho, Y.-A.; Kim, D.-C.; Jo, A.-R.; Min, K.-H.; Lee, K.-E. Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models. Cancers 2021, 13, 5465. https://doi.org/10.3390/cancers13215465
Kim W, Cho Y-A, Kim D-C, Jo A-R, Min K-H, Lee K-E. Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models. Cancers. 2021; 13(21):5465. https://doi.org/10.3390/cancers13215465
Chicago/Turabian StyleKim, Woorim, Young-Ah Cho, Dong-Chul Kim, A-Ra Jo, Kyung-Hyun Min, and Kyung-Eun Lee. 2021. "Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models" Cancers 13, no. 21: 5465. https://doi.org/10.3390/cancers13215465
APA StyleKim, W., Cho, Y.-A., Kim, D.-C., Jo, A.-R., Min, K.-H., & Lee, K.-E. (2021). Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models. Cancers, 13(21), 5465. https://doi.org/10.3390/cancers13215465