Risk Prediction Score for Cancer Development in Patients With Acute Coronary Syndrome
| Autor: | Soichi Komaki, Hiroaki Kusaka, Soshiro Ogata, Masanobu Ishii, Yoshitaka Iwanaga, Takayuki Mori, Kyohei Marume, Reiko Toida, Kazumasa Kurogi, Michikazu Nakai, Ryota Kaichi, Sou Ikebe, Nobuyasu Yamamoto, Yoshihiro Miyamoto, Kenichi Tsujita, Hisao Ogawa |
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| Rok vydání: | 2021 |
| Předmět: |
Acute coronary syndrome
medicine.medical_specialty business.industry Cancer Red blood cell distribution width General Medicine medicine.disease Confidence interval Internal medicine medicine Clinical endpoint In patient Cancer development Cardiology and Cardiovascular Medicine business Risk assessment |
| Zdroj: | Circulation journal : official journal of the Japanese Circulation Society. |
| ISSN: | 1347-4820 |
| Popis: | Background Cancer is a known prognostic factor in patients with acute coronary syndrome (ACS), but few risk assessments of cancer development after ACS have been established.Methods and Results:Of the 573 consecutive ACS admissions between January 2015 and March 2018 in Nobeoka City, Japan, 552 were analyzed. Prevalent cancer was defined as a treatment history of cancer, and incident cancer as post-discharge cancer incidence. The primary endpoint was post-discharge cancer incidence, and the secondary endpoint was all-cause death during follow-up. All-cause death occurred in 9 (23.1%) patients with prevalent cancer, and in 17 (3.5%) without cancer. In the multivariable analysis, prevalent cancer was associated with all-cause death. To develop the prediction model for cancer incidence, 21 patients with incident cancer and 492 without cancer were analyzed. We compared the performance of D-dimer with that of the prediction model, which added age (≥65 years), smoking history, and high red blood cell distribution width to albumin ratio (RAR) to D-dimer. The areas under the receiver-operating characteristics curves of D-dimer and the prediction model were 0.619 (95% confidence interval: 0.512-0.725) and 0.774 (0.676-0.873), respectively. Decision curve analysis showed superior net benefits of the prediction model. Conclusions By adding elderly, smoking, and high RAR to D-dimer to the prediction model it became clinically useful for predicting cancer incidence after ACS. |
| Databáze: | OpenAIRE |
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