Open forum infectious diseases · 2026
Identification of Invasive Aspergillosis in Electronic Health Records.
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Rayens Emily, Skela Jessica, Ackerson Bradley K, Pomichowski Magdalena E, Price Lance B, Tartof Sara Y
Аннотация
BACKGROUND: Consensus definitions for clinical identification of invasive aspergillosis (IA) are lacking, likely owing to difficulties in diagnosis. While the European Organisation for Research and Treatment of Cancer-Mycoses Study Group (EORTC/MSG) has provided benchmark guidelines to define IA, the criteria are highly restrictive. In this study, we constructed alternative definitions of IA using structured data from electronic health records (EHRs).
METHODS: From October 1, 2015, to March 31, 2022, we identified patients with IA from EHRs at Kaiser Permanente Southern California using definitions comprised of combinations of diagnosis codes, antifungal prescriptions, and/or positive mycological findings. Here, we report patient characteristics, including demographic information, underlying comorbidities, and immunosuppression, diagnostic measures, and morbidity and mortality after diagnosis for the population comprising each IA definition.
RESULTS: Between 86 and 4580 individuals were identified as possible IA cases across 7 definitions. All IA definitions that except for that only required positive mycology had similar patient characteristics and outcomes to those meeting 2008 EORTC/MSG criteria. The largest of these cohorts was comprised of 1067 patients, compared with the 86 that met EORTC/MSG criteria. Across IA definitions, all-cause mortality ranged from 11.7% to 33.1% within 6 months following diagnosis. The number of IA cases began trending upward between 2019 and 2020 and continued steadily through the coronavirus disease 2019 pandemic.
CONCLUSIONS: IA definitions based on structured data can increase efficiency and identify clinically significant IA cases that might otherwise go unrepresented. Variability in the number of patients identified between definitions further highlights critical deficits in current IA diagnostics.
METHODS: From October 1, 2015, to March 31, 2022, we identified patients with IA from EHRs at Kaiser Permanente Southern California using definitions comprised of combinations of diagnosis codes, antifungal prescriptions, and/or positive mycological findings. Here, we report patient characteristics, including demographic information, underlying comorbidities, and immunosuppression, diagnostic measures, and morbidity and mortality after diagnosis for the population comprising each IA definition.
RESULTS: Between 86 and 4580 individuals were identified as possible IA cases across 7 definitions. All IA definitions that except for that only required positive mycology had similar patient characteristics and outcomes to those meeting 2008 EORTC/MSG criteria. The largest of these cohorts was comprised of 1067 patients, compared with the 86 that met EORTC/MSG criteria. Across IA definitions, all-cause mortality ranged from 11.7% to 33.1% within 6 months following diagnosis. The number of IA cases began trending upward between 2019 and 2020 and continued steadily through the coronavirus disease 2019 pandemic.
CONCLUSIONS: IA definitions based on structured data can increase efficiency and identify clinically significant IA cases that might otherwise go unrepresented. Variability in the number of patients identified between definitions further highlights critical deficits in current IA diagnostics.