Pharmacoepidemiology and drug safety · 2021
Confirming diagnoses of acute pancreatitis with commonly available electronic data.
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Nichols Gregory A, Déruaz-Luyet Anouk, Iliev Hristo, Brodovicz Kimberly G
Аннотация
BACKGROUND: Recorded diagnoses of acute pancreatitis (AP) are often inaccurate resulting in limited utility for case identification in large data sources, especially where electronic medical records (EMR) are not available. Our objectives were to validate diagnoses of AP and to identify an algorithm using additional data to enhance the identification of AP cases in different data sources.
METHODS: We randomly sampled 550 persons with an AP diagnosis from inpatient data or outpatient or emergency department diagnoses immediately preceding a hospitalization and 150 negative controls with a differential diagnosis (cholangitis or cholecystitis). We conducted an EMR review to confirm cases of AP and used logistic regression to develop EMR-based and claims-based algorithms to identify confirmed AP cases with variables typically available in electronic data sources. Algorithm performance was assessed using the C statistic, sensitivity, specificity, and positive and negative predictive value.
RESULTS: Of the 550 patients with an AP diagnosis, 467 (84.9%) were confirmed cases. An AP diagnosis alone had high sensitivity (98.9%), modest specificity (63.6%), and a C statistic of 0.813. An EMR-based model using an AP diagnosis, body mass index ≥30 kg/m2 , a serum lipase >3 times upper limit of normal and diabetes attained a C-statistic of 0.914. A claims-based model attained a C-statistic of 0.892 using an AP diagnosis and dichotomous variables for whether a serum lipase test and/or an abdominal ultrasound was performed.
CONCLUSIONS: Our simple algorithms increased the accuracy of identification of AP cases providing widespread applicability to epidemiological and drug safety studies.
METHODS: We randomly sampled 550 persons with an AP diagnosis from inpatient data or outpatient or emergency department diagnoses immediately preceding a hospitalization and 150 negative controls with a differential diagnosis (cholangitis or cholecystitis). We conducted an EMR review to confirm cases of AP and used logistic regression to develop EMR-based and claims-based algorithms to identify confirmed AP cases with variables typically available in electronic data sources. Algorithm performance was assessed using the C statistic, sensitivity, specificity, and positive and negative predictive value.
RESULTS: Of the 550 patients with an AP diagnosis, 467 (84.9%) were confirmed cases. An AP diagnosis alone had high sensitivity (98.9%), modest specificity (63.6%), and a C statistic of 0.813. An EMR-based model using an AP diagnosis, body mass index ≥30 kg/m2 , a serum lipase >3 times upper limit of normal and diabetes attained a C-statistic of 0.914. A claims-based model attained a C-statistic of 0.892 using an AP diagnosis and dichotomous variables for whether a serum lipase test and/or an abdominal ultrasound was performed.
CONCLUSIONS: Our simple algorithms increased the accuracy of identification of AP cases providing widespread applicability to epidemiological and drug safety studies.