CLC Number:
- R473.54
| [1] Ponikowski P, Voors AA, Anker SD, et al.2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure[J]. Rev Esp Cardiol (Engl Ed), 2016, 69(12): 1167.DOI: 10.1016/j.rec.2016.11.005. [2] Antonione R, Sinagra G, Moroni M, et al.Palliative care in the cardiac setting: a consensus document of the Italian Society of Cardiology/Italian Society of Palliative Care (SIC/SICP)[J]. G Ital Cardiol (2006), 2019, 20(1):46-61.DOI:10.1714/3079.30720. [3] 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2022概要[J].中国循环杂志,2022, 37(6):553-578.DOI: 10.3969/j.issn.1000-3614.2023.06.001. [4] Tsao CW, Aday AW, Almarzooq ZI, et al.Heart disease and stroke statistics—2022 update: a report from the American Heart Association[J]. Circulation, 2022, 145(8):e153-e639.DOI: 10.1161/CIR.0000000000001052. [5] Bambhroliya AB, Donnelly JP, Thomas EJ, et al.Estimates and temporal trend for US nationwide 30-day hospital readmission among patients with ischemic and hemorrhagic stroke[J]. JAMA Netw Open, 2018, 1(4): e181190-e181190.DOI: 10.1001/jamanetworkopen.2018.1190. [6] Kwok CS, Shah B, Al-Suwaidi J, et al.Timing and causes of unplanned readmissions after percutaneous coronary intervention: insights from the nationwide readmission database[J]. JACC Cardiovasc Interv, 2019, 12(8):734-748.DOI:10.1016/j.jcin.2019.02.007. [7] Reddy YNV, Borlaug BA.Readmissions in heart failure: it's more than just the medicine[J]. Mayo Clin Proc, 2019,94(10):1919-1921.DOI: 10.1016/j.mayocp.2019.08.015. [8] 国家卫生健康委员会. 心血管系统疾病相关专业医疗质量控制指标(2021年版)[J].中国循环杂志,2021,36(8):733-742.DOI: 10.3969/j.issn.1000-3614.2021.08.002. [9] Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J]. PLoS Med,2014,11(10):e1001744.DOI:10.1371/journal.pmed.1001744. [10] Wolff RF, Moons KGM, Riley RD, et al.PROBAST: a tool to assess the risk of bias and applicability of prediction model studies[J]. Ann Intern Med, 2019, 170(1): 51-58.DOI: 10.7326/M18-1376. [11] 陈香萍,张奕,庄一渝,等. PROBAST:诊断或预后多因素预测模型研究偏倚风险的评估工具[J].中国循证医学杂志,2020,20(6):737-744.DOI:10.7507/1672-2531.201910087. [12] Fleming LM, Gavin M, Piatkowski G, et al.Derivation and validation of a 30-day heart failure readmission model[J]. Am J Cardiol, 2014, 114(9): 1379-1382.DOI: 10.1016/j.amjcard.2014.07.071. [13] Frizzell JD, Liang L, Schulte PJ, et al.Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches[J]. JAMA Cardiol, 2017, 2(2):204-209.DOI: 10.1001/jamacardio.2016.3956. [14] Mahajan SM, Burman P, Newton A, et al.A validated risk model for 30-day readmission for heart failure[J].Stud Health Technol Inform, 2017(245):506-510.DOI:10.3233/978-1-61499-830-3-506. [15] Allam A, Nagy M, Thoma G, et al.Neural networks versus Logistic regression for 30 days all-cause readmission prediction[J]. Sci Rep, 2019, 9(1):9277.DOI:10.1038/s41598-019-45685-z. [16] Delgado JF, Ferrero Gregori A, Fernández LM, et al.Patient-associated predictors of 15-and 30-day readmission after hospitalization for acute heart failure[J]. Curr Heart Fail Rep,2019(16):304-314. DOI:10.1007/s11897-019-00442-1. [17] Liu X, Chen Y, Bae J, et al.Predicting heart failure readmission from clinical notes using deep learning[C].2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019:2642-2648.DOI:10.1109/BIBM47256.2019.8983095. [18] Sohrabi B, Vanani IR, Gooyavar A, et al.Predicting the readmission of heart failure patients through data analytics[J]. J Inf Knowl Manag,2019,18(1):1950012.DOI:10.1142/S0219649219500126. [19] Sharma V, Kulkarni V, McAlister F, et al. Predicting 30-day readmissions in patients with heart failure using administrative data: a machine learning approach[J]. J Card Fail, 2022, 28(5):710-722.DOI: 10.1016/j.cardfail.2021.12.004. [20] 李代毅. 心力衰竭30天再入院风险预测模型的构建及药物治疗管理平台的初步探索[D].重庆:重庆医科大学,2022.DOI: 10.27674/d.cnki.gcyku.2022.001476. [21] Patel N, Chakraborty S, Bandyopadhyay D, et al.Association between depression and readmission of heart failure: a national representative database study[J]. Prog Cardiovasc Dis, 2020, 63(5):585-590.DOI:10.1016/j.pcad.2020.03.014. [22] 彭杰文, 陶明, 徐元锂, 等. 慢性心力衰竭患者再入院风险因素的Meta分析[J].中华现代护理杂志, 2021,27(7):857-864.DOI:10.3760/cma.j.cn115682-20200728-04617. [23] Lee HB, Bienvenu OJ, Cho SJ, et al.Personality disorders and traits as predictors of incident cardiovascular disease: findings from the 23-year follow-up of the Baltimore ECA study[J]. Psychosomatics, 2010, 51(4):289-296.DOI:10.1176/appi.psy.51.4.289. [24] Aronow WS, Shamliyan TA.Dietary sodium interventions to prevent hospitalization and readmission in adults with congestive heart failure[J]. Am J Med, 2018, 131(4):365-370.DOI: 10.1016/j.amjmed.2017.12.014. [25] Lee KS, Lennie TA, Heo S, et al.Prognostic importance of sleep quality in patients with heart failure[J]. Am J Crit Care, 2016, 25(6):516-525.DOI:10.4037/ajcc2016219. [26] Han Q, Ren J, Tian J, et al.A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure[J]. Health Qual Life Outcomes, 2020, 18(1):1-8.DOI:10.1186/s12955-020-01534-6. [27] Kitamura M, Izawa KP, Taniue H, et al.Relationship between activities of daily living and readmission within 90 days in hospitalized elderly patients with heart failure[J]. Biomed Res Int, 2017(2017):7420738. DOI:10.1155/2017/7420738. [28] 陈华,孙兴兰,肖丹,等.心力衰竭患者易损期容量管理的最佳证据总结[J].护理学报,2022, 29(21):38-42.DOI:10.16460/j.issn1008-9969.2022.21.038. [29] Shin S, Austin PC, Ross HJ, et al.Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality[J]. ESC Heart Fail, 2021, 8(1): 106-115.DOI:10.1002/ehf2.13073. [30] Mahajan S, Burman P, Hogarth M.Analyzing 30-day readmission rate for heart failure using different predictive models[J]. Stud Health Technol Inform, 2016(225):143-147.DOI:10.3233/978-1-61499-658-3-143. [31] Awan SE, Bennamoun M, Sohel F, et al.Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics[J]. ESC Heart Fail, 2019, 6(2):428-435.DOI:10.1002/ehf2.12419. [32] Chen S, Hu W, Yang Y, et al.Predicting six-month re-admission risk in heart failure patients using multiple machine learning methods: a study based on the Chinese heart failure population database[J]. J Clin Med, 2023, 12(3): 870.DOI:10.3390/jcm12030870. [33] Kang Y, Topaz M, Dunbar SB, et al.The utility of nursing notes among medicare patients with heart failure to predict 30-day rehospitalization:a pilot study[J].J Cardiovasc Nurs, 2022, 37(6):E181-E186.DOI:10.1097/JCN.0000000000000871. [34] Jiang W, Siddiqui S, Barnes S, et al.Readmission risk trajectories for patients with heart failure using a dynamic prediction approach: retrospective study[J]. JMIR Med Inform, 2019, 7(4).DOI:10.2196/14756. |
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