慢性阻塞性肺疾病患者30天再入院风险预测模型的研究进展

doi:10.3761/j.issn.2096-7446.2023.12.016

Abstract

Abstract: 30-day readmission refers to patients being readmitted to the hospital within 30 days after discharge. The high readmission rate among chronic obstructive pulmonary disease（COPD）patients poses a significant public health challenge. The risk prediction models can quickly screen out the high risk population of readmission and provide the basis for early prevention. This article reviews the basic characteristics,construction methods,and predictive performance of risk prediction models for COPD patients readmitted within 30 days,providing references for clinical healthcare professionals in preventing 30-day readmission among COPD patients. However,further improvements are needed in the selection of predictive factors and the evaluation and application of the models.

Key words: Pulmonary Disease, Chronic Obstructive, Patient Readmission, Forecasting Models, Statistical, Review Literature as Topic

LIU Xiaoling, ZHAO Hua, LU Wenyan, ZHANG Dan, ZHANG Xuemeng, HUANG Jing. Research progress on risk prediction models for 30-day readmission of patients with chronic obstructive pulmonary disease[J]. Chinese Journal of Emergency and Critical Care Nursing, 2023, 4(12): 1142-1146.

References

[1] World Health Organization. Chronic obstructive pulmonary disease(COPD)[EB/OL]. (2023-03-16)[2023-05-26]. https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd).
[2] 陈丽. 我国再入院率衡量住院医疗服务质量的适用性分析[J]. 中国医院管理,2016,36(1):48-50.
Chen L.Applicability analysis of employ readmission rate to evaluate the quality of inpatient healthcare in China[J]. Chin J Hosp Manag,2016,36(1):48-50.
[3] Kim TW,Choi ES,Kim WJ,et al.The association with COPD readmission rate and access to medical institutions in elderly patients[J]. Int J Chron Obstruct Pulmon Dis,2021,16:1599-1606.
[4] Jacobs DM,Noyes K,Zhao JW,et al.Early hospital readmissions after an acute exacerbation of chronic obstructive pulmonary disease in the nationwide readmissions database[J]. Ann Am Thorac Soc,2018,15(7):837-845.
[5] 杨艳霞,顾馨雨,龚浩,等. 慢性阻塞性肺疾病病人出院后30d内非计划再入院研究进展[J]. 护理研究,2023,37(8):1426-1430.
Yang YX,Gu XY,Gong H,et al.Research progress on unplanned readmission within 30 days after discharge in patients with chronic obstructive pulmonary disease[J]. Chin Nurs Res,2023,37(8):1426-1430.
[6] Shah T,Press VG,Huisingh-Scheetz M,et al.COPD readmissions:addressing COPD in the era of value-based health care[J]. Chest,2016,150(4):916-926.
[7] Moons KGM,Wolff RF,Riley RD,et al.PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med,2019,170(1):W1-W33.
[8] van Walraven C,Dhalla IA,Bell C,et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community[J]. J De L'association Med Can,2010,182(6):551-557.
[9] Donzé J,Aujesky D,Williams D,et al.Potentially avoidable 30-day hospital readmissions in medical patients:derivation and validation of a prediction model[J]. JAMA Intern Med,2013,173(8):632-638.
[10] Lau CS,Siracuse B,Chamberlain RS.Readmission After COPD Exacerbation Scale:determining 30-day readmission risk for COPD patients[J]. Int J Chronic Obstr Pulm Dis,2017,12:1891-1902.
[11] Hakim MA,Garden FL,Jennings MD,et al.Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease[J]. Clin Epidemiol,2018,10:51-59.
[12] Hong WL,Earnest A,Sultana P,et al.How accurate are vital signs in predicting clinical outcomes in critically ill emergency department patients[J]. Eur J Emerg Med,2013,20(1):27-32.
[13] Bashir B,Schneider D,Naglak MC,et al.Evaluation of prediction strategy and care coordination for COPD readmissions[J]. Hosp Pract(1995),2016,44(3):123-128.
[14] Burke RE,Schnipper JL,Williams MV,et al.The HOSPITAL score predicts potentially preventable 30-day readmissions in conditions targeted by the hospital readmissions reduction program[J]. Med Care,2017,55(3):285-290.
[15] Goto T,Yoshida K,Faridi MK,et al.Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation[J]. BMC Pulm Med,2020,20(1):107.
[16] 张瑞,吴珍珍,常艳,等. 老年慢性阻塞性肺疾病患者30 d内急性加重再入院风险预测模型的构建与验证[J]. 中国呼吸与危重监护杂志,2021,20(7):457-464.
Zhang R,Wu ZZ,Chang Y,et al.Construction and verification of risk prediction model for readmission of elderly patients with chronic obstructive pulmonary disease with acute exac-erbation within 30 days[J]. Chin J Respir Crit Care Med,2021,20(7):457-464.
[17] 张桂梅,陈蜀,宋云华,等. AECOPD患者再入院危险因素分析及预测模型的构建[J]. 昆明医科大学学报,2022,43(8):184-190.
Zhang GM,Chen S,Song YH,et al.Risk factors of readmission in patients with acute exacerbation of chronic obstruc-tive pulmonary disease and establishment of risk prediction model[J]. J Kunming Med Univ,2022,43(8):184-190.
[18] 王海英,陈莉,王允,等. 环境因素对慢性阻塞性肺疾病患者的预后影响[J]. 公共卫生与预防医学,2023,34(3):83-87.
Wang HY,Chen L,Wang Y,et al.Initial analysis of the environmental factors on the prognosis of patients with chronic obstructive pulmonary disease[J]. J Public Health Prev Med,2023,34(3):83-87.
[19] Tong HZ.A note on support vector machines with polynomial kernels[J]. Neural Comput,2016,28(1):71-88.
[20] Zhang R,Lu HY,Chang Y,et al.Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model[J]. BMC Pulm Med,2022,22(1):292.
[21] Li M,Cheng K,Ku K,et al.Modelling 30-day hospital readmission after discharge for COPD patients based on electronic health records[J]. NPJ Prim Care Respir Med,2023,33(1):16.
[22] Chen CX,Geng LW,Zhou S.Retraction Note:design and implementation of bank CRM system based on decision tree algorithm[J]. Neural Comput & Applic,2023,35(6):4803.
[23] Wang Q,Pei G,Chen L,et al.Factors affecting the length of stay and hospital readmission rates after an acute exacerbation of chronic obstructive pulmonary disease:a systematic review and meta-analysis[J]. Ann Transl Med,2022,10(4):175.
[24] Goto T,Jo T,Matsui H,et al.Machine learning-based predic-tion models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease[J]. COPD,2019,16(5/6):338-343.
[25] Sheller MJ,Edwards B,Reina GA,et al.Federated learning in medicine:facilitating multi-institutional collaborations without sharing patient data[J]. Sci Rep,2020,10:12598.
[26] Alvarez-Romero C,Martinez-Garcia A,Ternero Vega J,et al.Predicting 30-day readmission risk for patients with chronic obstructive pulmonary disease through a federated machine learning architecture on findable,accessible,interoperable,and reusable(FAIR）data:development and validation study[J]. JMIR Med Inform,2022,10(6):e35307.
[27] Moons KGM,Wolff RF,Riley RD,et al.PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med,2019,170(1):W1-W33.
[28] Peng JN,Yu Q,Fan SL,et al.High blood eosinophil and YKL-40 levels,as well as low CXCL9 levels,are associated with increased readmission in patients with acute exacerbation of chronic obstructive pulmonary disease[J]. Int J Chron Obstruct Pulmon Dis,2021,16:795-806.

Research progress on risk prediction models for 30-day readmission of patients with chronic obstructive pulmonary disease

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