Predictive Models for Diabetes Patients in Medicaid

A novel method for detecting inpatient pediatric asthma encounters using administrative data

Multiple methods for detecting asthma encounters are used today in public surveillance, quality reporting, and clinical research. Failure to detect asthma encounters can make it difficult to measure the scope and effectiveness of hospital or community-based interventions important in comparative effectiveness research and accountable care. Given the pairing of asthma with certain respiratory conditions, the objective of this study was to develop and test an asthma detection algorithm with specificity and sensitivity using 2 criteria: (1) principal discharge diagnosis and (2) asthma diagnosis code position. A medical record review was conducted (n=191) as the gold standard for identifying asthma encounters given objective criteria. The study team observed that for certain principal respiratory diagnoses (n=110), the observed odds ratio that encounters were for asthma when asthma was coded in the second or third code position was not significantly different than when asthma was coded as the principal diagnosis, 0.36 (P=0.42) and 0.18 (P=0.14), respectively. In contrast, the observed odds ratio was significantly different when asthma was coded in the fourth or fifth positions (P<.001). This difference remained after adjusting for covariates. Including encounters with asthma in 1 of the 3 first positions increased the detection sensitivity to 0.84 [95% confidence interval (CI): 0.76-0.92] while increasing the false positive rate to 0.19 [95% CI: 0.07-0.31]. Use of the proposed algorithm significantly improved the reporting accuracy [0.83 95%CI:0.76-0.90] over use of (1) the principal diagnosis alone [0.55 95% CI:0.46-0.64] or (2) all encounters with asthma 0.66 [95% CI:0.57-0.75]. Bed days resulting from asthma encounters increased 64% over use of the principal diagnosis alone. Given these findings, an algorithm using certain respiratory principal diagnoses and asthma diagnosis code position can reliably improve asthma encounter detection for population-based health impact measurement.

Applications of ROC analysis in medical research: recent developments and future directions

With the growing focus on comparative effectiveness research and personalized medicine, receiver-operating characteristic analysis can continue to play an important role in health care decision making. Specific applications of receiver-operating characteristic analysis include predictive model assessment and validation, biomarker diagnostics, responder analysis in patient-reported outcomes, and comparison of alternative treatment options. The authors present a survey of the potential applications of the method and briefly review several relevant extensions. Given the level of attention paid to biomarker validation, personalized medicine and comparative effectiveness research, it is highly likely that the receiver-operating characteristic analysis will remain an important visual and analytic tool for medical research and evidence-based medicine in the foreseeable future.