Utility of Electronic Medical Record for Recruitment in Clinical Research: from Rare to Common Disease

Abstract

Background Recruitment for clinical trials is a major challenge. Movement disorders, which do not have associated diagnostic laboratory tests, may be especially prone to inaccuracy in coding. Our objective was to evaluate the accuracy of diagnostic codes such as cervical dystonia (CD) and PD in an electronic medical record. Methods Retrospective chart review was performed to confirm the ICD-9 diagnoses of PD, CD and diabetes mellitus type 2 (DM-2), using published clinical diagnostic criteria (PD, CD) and hemoglobin A1c ≥ 6.5 (DM-2). Results 421 charts (n=129, n=142, n=150 for PD, CD and DM-2, respectively) were reviewed. The accuracy rate was different between all diseases examined with an overall p<0.001. In post hoc pairwise comparisons, the accuracy of DM-2 diagnosis by ICD-9 (96.6%) was greater than CD (88.0%) and both greater than PD (55.0%) (p≤0.003). Conclusions Using an electronic medical record based screening of clinically diagnosed diseases such as CD may be more accurate than previously thought and may identify potential clinical trial participants even without confirmatory lab tests available.

Identifying Parkinson’s disease and parkinsonism cases using routinely-collected healthcare data: a systematic review

“…We obtained key additional information from the authors of two studies[20,24]. Of the 18 included articles, 13 reported PPV[11,13–24], four reported sensitivity[25–28] and one reported both[12]. Four articles contained more than one study[11–13,17].…”

“…Three were from the UK[20,21,28], six from mainland Europe[12,13,18,25–27], eight from the USA[14–17,19,22–24], and one from Canada[11]. There were 12 PPV estimates and two sensitivity estimates from hospital data[11–19], two PPV and 10 sensitivity estimates from mortality data[12,25–28], two PPV estimates from primary care data[20], four PPV estimates from prescription data[11,17,21] and seven PPV estimates and two sensitivity estimates from combining datasets from different sources[12,13,22–24]. There were no sensitivity estimates from primary care or prescription data.…”

“…PD was evaluated in 13 articles, with eight estimating PPV[13,14,16–18,20,21,24], four estimating sensitivity[25–28] and one estimating both[12]. Parkinsonism was evaluated by seven articles, of which six estimated PPV[11,15,19,21–23] and one assessed both PPV and sensitivity[12].…”

“…The diagnostic coding position assessed also varied. Three studies assessed primary diagnoses alone[18,24,25], eight used any diagnostic position[12,19,26–28], while 13 did not specify the coding position[11,13–17,22,23]. Diagnostic position was not applicable in the studies of primary care and prescription data due to the nature of these datasets[11,17,20,21].…”

“…For PD, there were 17 PPV estimates in total (Fig 2)[12–14,16–18,20,21,24]. These comprised seven PPV estimates of hospital data alone[12–14,16–18], one of mortality data alone[12], two for prescription data alone[17,21], one of primary care data alone[20], one of prescription data and primary care data in combination[20], and five of datasets used in combination[13,24]. PPVs ranged from 36-90% across all studies.…”