Predictive Factors of Unexpected Hospitalization within Six Months of Undergoing Percutaneous Coronary Intervention in Patients with Chronic Coronary Disease

Objective Recent guidelines recommend dual antiplatelet therapy (DAPT) for six months following percutaneous coronary intervention (PCI) in patients with chronic coronary disease, as unexpected hospitalization can trigger DAPT discontinuation. This study evaluated the predictive factors for unexpected hospitalization within six months after PCI in patients with chronic coronary disease. Methods This prospective multicenter study included 412 patients who underwent PCI for chronic coronary disease. Unexpected hospitalization was defined as a prolonged hospital stay, unscheduled readmission, and all-cause mortality. The predictive factors for unexpected hospitalization within six months post-PCI were evaluated using the Cox regression model. Results The rate of unexpected hospitalization 6 months after PCI was 10.8%±1.5%. Unexpected hospitalizations due to bleeding events accounted for 12.1% (n=5/41), whereas non-bleeding readmissions accounted for 87.9% (n=36/41). A multivariable analysis revealed that the number of Academic Research Consortium for High Bleeding Risk (ARC-HBR) major criteria met [adjusted hazard ratio (HR), 1.55; 95% confidence interval (CI), 1.05-2.29; p=0.026], body weight (adjusted HR, 2.44; 95% CI 1.33-4.49; p=0.004), and presence of diabetes mellitus (adjusted HR, 1.94; 95% CI 1.09-3.47; p=0.025) were independent risk factors for unexpected hospitalization. Among the major ARC-HBR criteria, oral anticoagulant use (adjusted HR, 2.39; 95% CI, 1.14-5.02, p=0.021) and active malignancy (adjusted HR, 3.85; 95% CI, 1.47-10.05; p=0.006) were significantly associated with unexpected hospitalization after adjusting for a low body weight and diabetes mellitus. Conclusions The majority of unexpected hospitalizations after PCI in patients with chronic coronary disease are attributed to non-bleeding causes. The assessment using major ARC-HBR criteria in these patients not only addresses bleeding risks but also underscores its predictive value in conjunction with a low body weight and diabetes mellitus for the prediction of unexpected hospitalization.

Authors

Institutions

Publication Details

Journal
Internal Medicine
Published
2024-11-07
DOI
https://doi.org/10.2169/internalmedicine.3929-24
Primary Topic
Antiplatelet Therapy and Cardiovascular Diseases
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive Factors of Unexpected Hospitalization within Six Months of Undergoing Percutaneous Coronary Intervention in Patients with Chronic Coronary Disease

Masahiro Kumada, Naoki Fukushima, Takashi Kitao, Tsutomu Nakagawa et al.
Internal Medicine
Antiplatelet Therapy and Cardiovascular Diseases
article

Predictive Factors of Unexpected Hospitalization within Six Months of Undergoing Percutaneous Coronary Intervention in Patients with Chronic Coronary Disease

Masahiro Kumada, Naoki Fukushima, Takashi Kitao, Tsutomu Nakagawa, Kensuke Yokoi, Kiyoshi Kume, Yasushi Sakata, Tatsuya Shiraki, Shungo Hikoso, Mitsuyoshi Takahara, Motoaki Kitagawa, Daisuke Nakamura, Isamu Mizote, Mayu Nishio, Tetsuo Furukawa, Keiji Hirooka, Kunihiko Nagai, Tohru Ohama
article en

Abstract

Objective Recent guidelines recommend dual antiplatelet therapy (DAPT) for six months following percutaneous coronary intervention (PCI) in patients with chronic coronary disease, as unexpected hospitalization can trigger DAPT discontinuation. This study evaluated the predictive factors for unexpected hospitalization within six months after PCI in patients with chronic coronary disease. Methods This prospective multicenter study included 412 patients who underwent PCI for chronic coronary disease. Unexpected hospitalization was defined as a prolonged hospital stay, unscheduled readmission, and all-cause mortality. The predictive factors for unexpected hospitalization within six months post-PCI were evaluated using the Cox regression model. Results The rate of unexpected hospitalization 6 months after PCI was 10.8%±1.5%. Unexpected hospitalizations due to bleeding events accounted for 12.1% (n=5/41), whereas non-bleeding readmissions accounted for 87.9% (n=36/41). A multivariable analysis revealed that the number of Academic Research Consortium for High Bleeding Risk (ARC-HBR) major criteria met [adjusted hazard ratio (HR), 1.55; 95% confidence interval (CI), 1.05-2.29; p=0.026], body weight (adjusted HR, 2.44; 95% CI 1.33-4.49; p=0.004), and presence of diabetes mellitus (adjusted HR, 1.94; 95% CI 1.09-3.47; p=0.025) were independent risk factors for unexpected hospitalization. Among the major ARC-HBR criteria, oral anticoagulant use (adjusted HR, 2.39; 95% CI, 1.14-5.02, p=0.021) and active malignancy (adjusted HR, 3.85; 95% CI, 1.47-10.05; p=0.006) were significantly associated with unexpected hospitalization after adjusting for a low body weight and diabetes mellitus. Conclusions The majority of unexpected hospitalizations after PCI in patients with chronic coronary disease are attributed to non-bleeding causes. The assessment using major ARC-HBR criteria in these patients not only addresses bleeding risks but also underscores its predictive value in conjunction with a low body weight and diabetes mellitus for the prediction of unexpected hospitalization.

Internal MedicineVol. 64(11)
Saga University (JP), Toyonaka Municipal Hospital (JP), Saiseikaisenri Hospital (JP), Ikeda Municipal Hospital (JP), Minoh City Hospital (JP), The University of Osaka (JP)
Good health and well-being
Openalex Percentile: Top 64%
Antiplatelet Therapy and Cardiovascular Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.