Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach
Background Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a machine learning (ML) model to predict cardiac eve...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-10-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/9/10/e002545.full |
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| author | Tomas G Neilan Judith Mueller Samuel Peter Heilbroner Reed Few Jitesh Chalwa Francois Charest Somasekhar Suryadevara Christine Kratt Andres Gomez-Caminero Brian Dreyfus |
| author_facet | Tomas G Neilan Judith Mueller Samuel Peter Heilbroner Reed Few Jitesh Chalwa Francois Charest Somasekhar Suryadevara Christine Kratt Andres Gomez-Caminero Brian Dreyfus |
| author_sort | Tomas G Neilan |
| collection | DOAJ |
| description | Background Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a machine learning (ML) model to predict cardiac events in this at-risk population.Methods We leveraged the CancerLinQ database curated by the American Society of Clinical Oncology and applied an XGBoosted decision tree to predict cardiac events in patients taking programmed death receptor-1 (PD-1) or programmed death ligand-1 (PD-L1) therapy. All curated data from patients with non-small cell lung cancer, melanoma, and renal cell carcinoma, and who were prescribed PD-1/PD-L1 therapy between 2013 and 2019, were used for training, feature interpretation, and model performance evaluation. A total of 356 potential risk factors were included in the model, including elements of patient medical history, social history, vital signs, common laboratory tests, oncological history, medication history and PD-1/PD-L1-specific factors like PD-L1 tumor expression.Results Our study population consisted of 4960 patients treated with PD-1/PD-L1 therapy, of whom 418 had a cardiac event. The following were key predictors of cardiac events: increased age, corticosteroids, laboratory abnormalities and medications suggestive of a history of heart disease, the extremes of weight, a lower baseline or on-treatment percentage of lymphocytes, and a higher percentage of neutrophils. The final model predicted cardiac events with an area under the curve–receiver operating characteristic of 0.65 (95% CI 0.58 to 0.75). Using our model, we divided patients into low-risk and high-risk subgroups. At 100 days, the cumulative incidence of cardiac events was 3.3% in the low-risk group and 6.1% in the high-risk group (p<0.001).Conclusions ML can be used to predict cardiac events in patients taking PD-1/PD-L1 therapy. Cardiac risk was driven by immunological factors (eg, percentage of lymphocytes), oncological factors (eg, low weight), and a cardiac history. |
| format | Article |
| id | doaj-art-df40c0f34d4746c3a6cd70940f120f07 |
| institution | DOAJ |
| issn | 2051-1426 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | BMJ Publishing Group |
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| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-df40c0f34d4746c3a6cd70940f120f072025-08-20T03:17:52ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262021-10-0191010.1136/jitc-2021-002545Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approachTomas G Neilan0Judith Mueller1Samuel Peter Heilbroner2Reed Few3Jitesh Chalwa4Francois Charest5Somasekhar Suryadevara6Christine Kratt7Andres Gomez-Caminero8Brian Dreyfus9Cardio-Oncology Program, Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USAData Science, ConcertAI, New York, New York, USAData Science, ConcertAI, New York, New York, USAData Science, ConcertAI, New York, New York, USAData Science, ConcertAI, New York, New York, USAData Science, ConcertAI, New York, New York, USAData Science, ConcertAI, New York, New York, USABristol Myers Squibb, New York, New York, USABristol Myers Squibb, New York, New York, USABristol Myers Squibb, New York, New York, USABackground Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a machine learning (ML) model to predict cardiac events in this at-risk population.Methods We leveraged the CancerLinQ database curated by the American Society of Clinical Oncology and applied an XGBoosted decision tree to predict cardiac events in patients taking programmed death receptor-1 (PD-1) or programmed death ligand-1 (PD-L1) therapy. All curated data from patients with non-small cell lung cancer, melanoma, and renal cell carcinoma, and who were prescribed PD-1/PD-L1 therapy between 2013 and 2019, were used for training, feature interpretation, and model performance evaluation. A total of 356 potential risk factors were included in the model, including elements of patient medical history, social history, vital signs, common laboratory tests, oncological history, medication history and PD-1/PD-L1-specific factors like PD-L1 tumor expression.Results Our study population consisted of 4960 patients treated with PD-1/PD-L1 therapy, of whom 418 had a cardiac event. The following were key predictors of cardiac events: increased age, corticosteroids, laboratory abnormalities and medications suggestive of a history of heart disease, the extremes of weight, a lower baseline or on-treatment percentage of lymphocytes, and a higher percentage of neutrophils. The final model predicted cardiac events with an area under the curve–receiver operating characteristic of 0.65 (95% CI 0.58 to 0.75). Using our model, we divided patients into low-risk and high-risk subgroups. At 100 days, the cumulative incidence of cardiac events was 3.3% in the low-risk group and 6.1% in the high-risk group (p<0.001).Conclusions ML can be used to predict cardiac events in patients taking PD-1/PD-L1 therapy. Cardiac risk was driven by immunological factors (eg, percentage of lymphocytes), oncological factors (eg, low weight), and a cardiac history.https://jitc.bmj.com/content/9/10/e002545.full |
| spellingShingle | Tomas G Neilan Judith Mueller Samuel Peter Heilbroner Reed Few Jitesh Chalwa Francois Charest Somasekhar Suryadevara Christine Kratt Andres Gomez-Caminero Brian Dreyfus Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach Journal for ImmunoTherapy of Cancer |
| title | Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach |
| title_full | Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach |
| title_fullStr | Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach |
| title_full_unstemmed | Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach |
| title_short | Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach |
| title_sort | predicting cardiac adverse events in patients receiving immune checkpoint inhibitors a machine learning approach |
| url | https://jitc.bmj.com/content/9/10/e002545.full |
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