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

Palamuthusingam, D, Ratnayake, G, Kuenstner, K, Hawley, CM, Pascoe, EM, Jose, MD ORCID: 0000-0002-9589-0071, Johnson, DW and Fahim, M 2020 , 'Identifying new-onset conditions and pre-existing conditions using lookback periods in Australian health administrative datasets' , International Journal for Quality in Health Care , pp. 1-9 , doi: 10.1093/intqhc/mzaa154.

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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 aimof this study was to determine if an algorithm to lookback across the previous admissions couldmake this distinction.Methods: All patients requiring kidney replacement therapy (KRT) identified in the Australia andNew Zealand Dialysis and Transplant Registry in New South Wales, South Australia and Tasmaniabetween 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 priorto the index admission were investigated. Conditions identified in an index admission but not in thelookback periods were classified as a new-onset condition. Conditions identified in both the indexadmission and the lookback period were deemed to be pre-existing. The degrees of agreement weredetermined using the kappa statistic. Conditions examined for new onset were myocardial infarction, pulmonary embolism and pneumonia. Conditions examined for prior existence were diabetesmellitus, hypertension and kidney failure. Secondary analyses evaluated whether the conditionsidentified 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 singleadmission 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 forall conditions (k = 0.94–1.00). This was consistent across the different time eras. All lookback periodsidentified additional new-onset conditions that were not classified by COF: Lookback Period 1 pickedup 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-existingconditions (64–80%). The level of agreement was moderate to strong and was similar across thetime 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 >1admission (87–95%).Conclusion: The described algorithm using a lookback period is a pragmatic, reliable and robustmeans of identifying the new-onset and pre-existing patient conditions, thereby enriching theexisting datasets predating the availability of the COF. The findings also highlight the value ofconcatenating a series of hospital patient admissions to more comprehensively adjudicate thepre-existing conditions, rather than assessing the index admission alone.

Item Type: Article
Authors/Creators:Palamuthusingam, D and Ratnayake, G and Kuenstner, K and Hawley, CM and Pascoe, EM and Jose, MD and Johnson, DW and Fahim, M
Keywords: International Classification of Disease, hospital complications, comorbidity, admissions, administrative datasets
Journal or Publication Title: International Journal for Quality in Health Care
Publisher: Oxford Univ Press
ISSN: 1353-4505
DOI / ID Number: 10.1093/intqhc/mzaa154
Copyright Information:

© The Author(s) 2020. Published by Oxford University Press on behalf of International Society for Quality in Health Care.

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