Identifying new-onset conditions and pre-existing conditions using lookback periods in Australian health administrative datasets

Background The condition onset flag (COF) variable was introduced into the hospitalization coding practice in 2008 to help distinguish between the new and pre-existing conditions. However, Australian datasets collected prior to 2008 lack the COF, potentially leading to data waste. The aim of this study was to determine if an algorithm to lookback across the previous admissions could make this distinction. Methods All patients requiring kidney replacement therapy (KRT) identified in the Australia and New Zealand Dialysis and Transplant Registry in New South Wales, South Australia and Tasmania between July 2008 and December 2015 were linked with hospital admission datasets using probabilistic linkage. Three different lookback periods entailing either one, two or three admissions prior to the index admission were investigated. Conditions identified in an index admission but not in the lookback periods were classified as a new-onset condition. Conditions identified in both the index admission and the lookback period were deemed to be pre-existing. The degrees of agreement were determined using the kappa statistic. Conditions examined for new onset were myocardial infarction, pulmonary embolism and pneumonia. Conditions examined for prior existence were diabetes mellitus, hypertension and kidney failure. Secondary analyses evaluated whether the conditions identified as pre-existing using COF were captured consistently in the subsequent admissions. Results 11 140 patients on KRT with 69 403 admissions were analysed. Lookback over a single admission interval (Period 1) provided the highest rates of true positives with COF for all three new-onset conditions, ranging from 89% to 100%. The levels of agreement were almost perfect for all conditions (k = 0.94–1.00). This was consistent across the different time eras. All lookback periods identified additional new-onset conditions that were not classified by COF: Lookback Period 1 picked up a further 474 myocardial infarction, 84 pulmonary embolism and 1092 pneumonia episodes. Lookback Period 1 had the highest percentage of true positives when identifying the pre-existing conditions (64–80%). The level of agreement was moderate to strong and was similar across the time eras. Secondary analysis showed that not all pre-existing conditions identified using COF carried forward to the subsequent admission (61–82%) but increased when looking forward across >1 admission (87–95%). Conclusion The described algorithm using a lookback period is a pragmatic, reliable and robust means of identifying the new-onset and pre-existing patient conditions, thereby enriching the existing datasets predating the availability of the COF. The findings also highlight the value of concatenating a series of hospital patient admissions to more comprehensively adjudicate the pre-existing conditions, rather than assessing the index admission alone.