Lopez Derrick, Lu Juan, Sanfilippo Frank M, Katzenellenbogen Judith M, Briffa Tom, Nedkoff Lee
Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Crawley, Western Australia, Australia.
Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia.
Clin Epidemiol. 2024 Dec 27;16:921-928. doi: 10.2147/CLEP.S497760. eCollection 2024.
Measures of disease burden using hospital administrative data are susceptible to over-inflation if the patient is transferred during their episode of care. We aimed to identify and compare measures of coronary heart disease (CHD) and myocardial infarction (MI) episodes using six algorithms that account for transfers.
We used person-linked hospitalisations for CHD and MI for 2000-2016 in Western Australia based on the interval between discharge and subsequent admission (date, datetime algorithms), pathway (admission source, discharge destination) and any combination to generate machine learning models (random forest [RF], gradient boosting machine [GBM]). The date and datetime algorithms used deidentified patient identifiers to identify records belonging to the same individual. We calculated counts, age-standardised rates (ASR) and age-adjusted trends for CHD and MI for each algorithm.
Counts of CHD increased from 11,733 in 2000 to 13,274 in 2016, while MI increased from 2605 to 4480 using the date algorithm. Correspondingly ASR for CHD decreased from 2086.2 to 1463.1 while MI increased from 468.2 to 498.1 per 100,000 person-years. ASR for CHD and MI for datetime algorithm were consistently 1-2% higher than the date algorithm. Differences in ASR of CHD and MI counts increased over time with the admission source, RF and GBM algorithms relative to the date algorithm. Age-adjusted trends in CHD and MI episode rates using RF and GBM differed significantly from all other algorithms. Only 86.7% and 87.6% of MI episodes identified by the date algorithm were identified by the admission source and discharge destination algorithms, respectively.
The date and datetime algorithms produced the most valid measures of CHD and MI episodes. Findings underscore the importance of identifying admission and discharge dates/times belonging to the same individual in enumerating these episodes.
如果患者在其治疗期间被转院，使用医院管理数据衡量疾病负担容易出现过度夸大的情况。我们旨在识别并比较六种考虑了转院情况的算法对冠心病（CHD）和心肌梗死（MI）发作的衡量结果。
我们利用西澳大利亚州2000年至2016年期间冠心病和心肌梗死患者的关联住院数据，依据出院与随后入院之间的间隔（日期、日期时间算法）、就医途径（入院来源、出院去向）以及任何组合方式来生成机器学习模型（随机森林[RF]、梯度提升机[GBM]）。日期和日期时间算法使用去标识化的患者标识符来识别属于同一患者的记录。我们计算了每种算法下冠心病和心肌梗死的病例数、年龄标准化率（ASR）以及年龄调整趋势。
使用日期算法时，冠心病病例数从2000年的11733例增加到2016年的13274例，而心肌梗死病例数从2605例增加到4480例。相应地，冠心病的年龄标准化率从每10万人年2086.2降至1463.1，而心肌梗死的年龄标准化率从468.2升至498.1。日期时间算法下冠心病和心肌梗死的年龄标准化率始终比日期算法高1%-2%。相对于日期算法，冠心病和心肌梗死病例数的年龄标准化率差异随时间推移在入院来源、随机森林和梯度提升机算法中有所增加。使用随机森林和梯度提升机算法得出的冠心病和心肌梗死发作率的年龄调整趋势与所有其他算法显著不同。日期算法识别出的心肌梗死发作病例中，分别只有86.7%和87.6%能被入院来源和出院去向算法识别出来。
日期和日期时间算法得出的冠心病和心肌梗死发作衡量结果最为有效。研究结果强调了在统计这些发作情况时识别属于同一患者的入院和出院日期/时间的重要性。
