Prediction of hospitalization and treatment trend of heart failure in a tertiary hospital based on time series model
Liu Min1，Guo Xianxi2,3， Wu Yue２,3
1 Department of Pharmacy, the Third Clinical Medical College of China Three Gorges University, Sinopharm Gezhouba Central Hospital, Yichang 443002, China;
2 Pharmacy Department, Renmin Hospital of Wuhan University, WuHan 430060, China; 3 School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
Abstract：Objective To establish a prediction model for the number of hospitalized patients with heart failure and average hopitalization days by using Winters exponential smoothing method, and to explore its application value and provide scientific basis for hospital management.Methods The number of hospitalized patients with heart failure and their average hopitalization days were collected from the electronic medical record system of a tertiary hospital, and the Winston exponential smoothing model was constructed by means of model diagnosis and parameter optimization to predict and analyze the trend of admission and treatment of heart failure in this hospital. The predicted results were evaluated.Results The data of patients with heart failure from January 2007 to December 2016 in this hospital were set as training samples for simulation construction and parameter optimization, and the data from January to December 2017 were used as test samples for prediction and verification. The results showed that the mean absolute percentage error (MAPE) of the prediction model was 7.055%, the mean stationary R2 was 0.738, and the MAPE of the prediction model was 4.323%, the mean stationary R2 was 0.698. The actual number of inpatients and the average length of stay were within 95% confidence interval of the predicted value, indicating that the Winters exponential smoothing model could be used to predict the number of inpatients and average length of stay of heart failure.Conclusion The Winters exponential smoothing model can predict the seasonal trend of the number and average length of stay of patients with heart failure, and provide a scientific basis for the rational allocation of medical resources of hospitals.
刘敏，郭咸希，吴玥. 基于时间序列模型的某三甲医院心力衰竭入院人数及治疗趋势预测[J]. 中国医院统计, 2022, 29(3): 161-168.
Liu Min，Guo Xianxi， Wu Yue. Prediction of hospitalization and treatment trend of heart failure in a tertiary hospital based on time series model. journal1, 2022, 29(3): 161-168.
［1］国家卫生计生委合理用药专家委员会，中国药师协会．心力衰竭合理用药指南:第2版［J］．中国医学前沿杂志(电子版)，2019，11(7)：1-78．DOI:10.12037/YXQY.2019.07-01.
［2］HAO G, WANG X, CHEN Z, et al. Prevalence of heart failure and left ventricular dysfunction in China: The China Hypertension Survey, 2012-2015［J］. Eur J Heart Fail, 2019, 21(11):1329-1337. DOI:10.1002/ejhf.1629.
［3］黄峻. 心力衰竭2019：进展和思考［J］. 中华心力衰竭和心肌病杂志,2020,4(1):52-59. DOI:10.3760/cma.j.cn101460-20200221-00016.
［4］丁勇，吴静，武丹，等．ARIMA乘积季节模型预测我国戊肝的发病趋势［J］．南京医科大学学报(自然科学版)，2020，40(11)：1725-1729．DOI:10.7655/NYDXBNS20201128.
［5］王晓丽，施天行，杨思睿，等．温特斯乘法指数平滑法在高血压就诊人次预测中的应用［J］．中国卫生信息管理杂志，2016，13(5)：524-526．DOI:10.3969/j.issn.1672-5166.2016.05.017.
［6］童强，张克功，杜吉梁．指数平滑预测法及其在经济预测中的应用［J］．经济研究导刊，2013(4)：11-12．
［7］常军，李祯，李素萍．温特斯法在夏季温度预测中的应用［J］．气象科技，2005，33(S1)：105-107．DOI:10.19517/j.1671-6345.2005.s1.024.
［8］孙乔，袁政安，陶芳芳，等．温特斯乘法模型在呼吸道症候群监测中的应用［J］．中华疾病控制杂志，2011，15(10)：905-908．
［9］张晓庆．关于线性季节加法趋势预测模型的探讨［J］．统计与决策，2005(17)：22-23．DOI:10.3969/j.issn.1002-6487.2005.17.010.
［10］BOULAY F, BERTHIER F, SISTERON O, et al. Seasonal variation in chronic heart failure hospitalizations and mortality in France［J］. Circulation, 1999, 100(3):280-286. DOI:10.1161/01.cir.100.3.280.
［11］STEWART S, MCINTYRE K, CAPEWELL S, et al. Heart failure in a cold climate. Seasonal variation in heart failurerelated morbidity and mortality［J］. J Am Coll Cardiol, 2002, 39(5):760-766. DOI:10.1016/s0735-1097(02)01685-6.
［12］MART-NEZ-SELL-S M, GARC-A ROBLES J A, PRIETO L, et al. Annual rates of admission and seasonal variations in hospitalizations for heart failure［J］. Eur J Heart Fail, 2002, 4(6):779-786. DOI:10.1016/s1388-9842(02)00116-2.
［13］FELDMAN D E, PLATT R, D-RY V, et al. Seasonal congestive heart failure mortality and hospitalisation trends, Quebec 1990-1998［J］. J Epidemiol Community Health, 2004, 58(2):129-130. DOI:10.1136/jech.58.2.129.
［14］蒋桂花，杜金玲，张珉珉，等．急性心血管疾病的发生与气温变化的关系［J］．山东医药，2015，55(41)：63-64．DOI:10.3969/j.issn.1002-266X.2015.41.026.
［15］贾子舟，张钰嘉，荣书玲，等．LSTM神经网络模型在冠心病月度入院人数预测中的研究［J］．中西医结合心脑血管病杂志，2021，19(18)：3145-3148．DOI:10.12102/j.issn.1672-1349.2021.18.016.
［16］马亮亮，田富鹏．不同时间序列分析方法在高血压发病率预测中的比较［J］．中国老年学杂志，2010，30(13)：1777-1780．DOI:10.3969/j.issn.1005-9202.2010.13.001.
［17］OGAWA M, TANAKA F, ONODA T, et al. A community based epidemiological and clinical study of hospitalization of patients with congestive heart failure in northern iwate, Japan［J］. Circ J, 2007, 71(4):455-459. DOI:10.1253/circj.71.455.
［18］DAZ A, FERRANTE D, BADRA R, et al. Seasonal variation and trends in heart failure morbidity and mortality in a south American community hospital［J］. Congest Heart Fail, 2007, 13(5):263-266. DOI:10.1111/j.1527-5299.2007.07124.x.
［19］FARES A. Winter cardiovascular diseases phenomenon［J］. N Am J Med Sci, 2013, 5(4):266-279. DOI:10.4103/1947-2714.110430.
［20］BARNETT A G, DE LOOPER M, FRASER J F. The seasonality in heart failure deaths and total cardiovascular deaths［J］. Aust N Z J Public Health, 2008, 32(5):408-413. DOI:10.1111/j.1753-6405.2008.00270.x.
［21］QIU H, YU I T S, TSE L A, et al. Is greater temperature change within a day associated with increased emergency hospital admissions for heart failure? ［J］. Circ Heart Fail, 2013, 6(5):930-935. DOI:10.1161/CIRCHEARTFAILURE.113.000360.
