Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality
Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital...
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MDPI AG
2025-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/4/336 |
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| author | Vahe S. Panossian Yu Ma Bolin Song Jefferson A. Proaño-Zamudio Veerle P. C. van Zon Ikemsinachi C. Nzenwa Azadeh Tabari George C. Velmahos Haytham M. A. Kaafarani Dimitris Bertsimas Dania Daye |
| author_facet | Vahe S. Panossian Yu Ma Bolin Song Jefferson A. Proaño-Zamudio Veerle P. C. van Zon Ikemsinachi C. Nzenwa Azadeh Tabari George C. Velmahos Haytham M. A. Kaafarani Dimitris Bertsimas Dania Daye |
| author_sort | Vahe S. Panossian |
| collection | DOAJ |
| description | Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital mortality. Methods: OPTs were trained on patients with blunt splenic injuries in the ACS-TQIP 2013–2019 to prescribe one of the three interventions: splenectomy, angioembolization (AE), or observation. Prescriptive trees were derived in two separate patient cohorts: those who presented with a systolic blood pressure (SBP) < 70 mmHg and those with an SBP ≥ 70 mmHg. Splenic injury severity was graded using the American Association of Surgical Trauma (AAST) grading scale. Counterfactual estimation was used to predict the effects of interventions on overall in-hospital mortality. Results: Among 54,345 patients, 3.1% underwent splenic AE, 13.1% splenectomy, and 83.8% were managed with observation. In patients with SBP < 70 mmHg, AE was recommended for shock index (SI) < 1.5 or without transfusion, while splenectomy was indicated for SI ≥ 1.5 with transfusion. For patients with SBP ≥ 70 mmHg, AE was recommended for AAST grades 4–5, or grades 1–3 with SI ≥ 1.2; observation was recommended for grades 1–3 with SI < 1.2. Predicted mortality using OPT-prescribed treatments was 18.4% for SBP < 70 mmHg and 4.97% for SBP ≥ 70 mmHg, compared to observed rates of 36.46% and 7.60%, respectively. Conclusions: Interpretable AI models may serve as a decision aid to improve mortality in patients presenting with a blunt splenic injury. Our data-driven prescriptive OPT models may aid in prescribing the appropriate management in this patient cohort based on their characteristics. |
| format | Article |
| id | doaj-art-b10ddec65adf4745a1e5a04f401d3c75 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Bioengineering |
| spelling | doaj-art-b10ddec65adf4745a1e5a04f401d3c752025-08-20T02:28:27ZengMDPI AGBioengineering2306-53542025-03-0112433610.3390/bioengineering12040336Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient MortalityVahe S. Panossian0Yu Ma1Bolin Song2Jefferson A. Proaño-Zamudio3Veerle P. C. van Zon4Ikemsinachi C. Nzenwa5Azadeh Tabari6George C. Velmahos7Haytham M. A. Kaafarani8Dimitris Bertsimas9Dania Daye10Division of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USAOperations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USAOperations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USADivision of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USADivision of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USADivision of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USADepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USADivision of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USADivision of Trauma, Emergency Surgery, Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USAOperations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USABackground: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital mortality. Methods: OPTs were trained on patients with blunt splenic injuries in the ACS-TQIP 2013–2019 to prescribe one of the three interventions: splenectomy, angioembolization (AE), or observation. Prescriptive trees were derived in two separate patient cohorts: those who presented with a systolic blood pressure (SBP) < 70 mmHg and those with an SBP ≥ 70 mmHg. Splenic injury severity was graded using the American Association of Surgical Trauma (AAST) grading scale. Counterfactual estimation was used to predict the effects of interventions on overall in-hospital mortality. Results: Among 54,345 patients, 3.1% underwent splenic AE, 13.1% splenectomy, and 83.8% were managed with observation. In patients with SBP < 70 mmHg, AE was recommended for shock index (SI) < 1.5 or without transfusion, while splenectomy was indicated for SI ≥ 1.5 with transfusion. For patients with SBP ≥ 70 mmHg, AE was recommended for AAST grades 4–5, or grades 1–3 with SI ≥ 1.2; observation was recommended for grades 1–3 with SI < 1.2. Predicted mortality using OPT-prescribed treatments was 18.4% for SBP < 70 mmHg and 4.97% for SBP ≥ 70 mmHg, compared to observed rates of 36.46% and 7.60%, respectively. Conclusions: Interpretable AI models may serve as a decision aid to improve mortality in patients presenting with a blunt splenic injury. Our data-driven prescriptive OPT models may aid in prescribing the appropriate management in this patient cohort based on their characteristics.https://www.mdpi.com/2306-5354/12/4/336traumaartificial intelligencespleendata-driven decision makingpersonalized medicine |
| spellingShingle | Vahe S. Panossian Yu Ma Bolin Song Jefferson A. Proaño-Zamudio Veerle P. C. van Zon Ikemsinachi C. Nzenwa Azadeh Tabari George C. Velmahos Haytham M. A. Kaafarani Dimitris Bertsimas Dania Daye Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality Bioengineering trauma artificial intelligence spleen data-driven decision making personalized medicine |
| title | Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality |
| title_full | Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality |
| title_fullStr | Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality |
| title_full_unstemmed | Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality |
| title_short | Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality |
| title_sort | using interpretable artificial intelligence algorithms in the management of blunt splenic trauma applications of optimal policy trees as a treatment prescription aid to improve patient mortality |
| topic | trauma artificial intelligence spleen data-driven decision making personalized medicine |
| url | https://www.mdpi.com/2306-5354/12/4/336 |
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