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Rao C, Zhong Q, Wu R, Li Z, Duan Y, Zhou Y, Wang C, Chen X, Wang R, He K. Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study. BMC Cardiovasc Disord 2024; 24:113. [PMID: 38365597 PMCID: PMC10874050 DOI: 10.1186/s12872-024-03770-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/05/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear. METHODS We retrospectively analysed data from 1918 patients who underwent PCI. Patients were categorized into four groups based on body mass index (BMI, normal weight: BMI < 25 kg/m2; overweight and obese: BMI ≥ 25 kg/m2) and DM status (presence or absence). The primary endpoint was the occurrence of major adverse cardiac and cerebrovascular events (MACCE; defined as all-cause death, myocardial infarction, stroke, and unplanned repeat revascularization). RESULTS During a median follow-up of 7.0 years, no significant differences in MACCE, myocardial infarction, or stroke were observed among the four groups. Overweight and obese individuals exhibited lower all-cause mortality rates compared with normal-weight patients (without DM: hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.37 to 0.78; with DM: HR: 0.57, 95% CI: 0.38 to 0.86). In non-diabetic patients, the overweight and obese group demonstrated a higher risk of unplanned repeat revascularization than the normal-weight group (HR:1.23, 95% CI:1.03 to 1.46). After multivariable adjustment, overweight and obesity were not significantly associated with MACCE, all-cause death, myocardial infarction, stroke, or unplanned repeat revascularization in patients with and without diabetes undergoing PCI. CONCLUSION Overweight and obesity did not demonstrate a significant protective effect on long-term outcomes in patients with and without diabetes undergoing PCI.
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Affiliation(s)
- Chongyou Rao
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
- Graduate School of Chinese, PLA General Hospital, Beijing, 100853, China
| | - Qin Zhong
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
- Graduate School of Chinese, PLA General Hospital, Beijing, 100853, China
| | - Rilige Wu
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - Zongren Li
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - Yongjie Duan
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - You Zhou
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Chi Wang
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - Xu Chen
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - Ruiqing Wang
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China
| | - Kunlun He
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese, PLA General Hospital, 28 Fuxing RD, Beijing, 100853, China.
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Shalit N, Fire M, Ben-Elia E. A supervised machine learning model for imputing missing boarding stops in smart card data. Public Transp 2022; 15:287-319. [PMID: 38625321 PMCID: PMC9734418 DOI: 10.1007/s12469-022-00309-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 04/17/2024]
Abstract
Public transport has become an essential part of urban existence with increased population densities and environmental awareness. Large quantities of data are currently generated, allowing for more robust methods to understand travel behavior by harvesting smart card usage. However, public transport datasets suffer from data integrity problems; boarding stop information may be missing due to imperfect acquirement processes or inadequate reporting. This study introduces a supervised machine learning method to impute missing boarding stops based on ordinal classification using GTFS timetable, smart card, and geospatial datasets. A new metric, Pareto Accuracy, is suggested to evaluate algorithms where classes have an ordinal nature. The results are based on a case study in the city of Beer Sheva, Israel, consisting of one month of smart card data. We show that our proposed method is robust to irregular travelers and significantly outperforms well-known imputation methods without the need to mine any additional datasets. The data validation from another Israeli city using transfer learning shows the presented model is general and context-free. The implications for transportation planning and travel behavior research are further discussed.
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Affiliation(s)
- Nadav Shalit
- Data4Good Lab, Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael Fire
- Data4Good Lab, Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Eran Ben-Elia
- GAMESLab, Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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