The Centers for Disease Control and Prevention currently recommends a 2-tier serologic approach to Lyme disease laboratory diagnosis, comprised of an initial serum enzyme immunoassay (EIA) for antibody to Borrelia burgdorferi followed by supplementary IgG and IgM Western blotting of EIA-positive or -equivocal samples. Western blot accuracy is limited by subjective interpretation of weakly positive bands, false-positive IgM immunoblots, and low sensitivity for detection of early disease. We developed an objective alternative second-tier immunoassay using a multiplex microsphere system that measures VlsE1-IgG and pepC10-IgM antibodies simultaneously in the same sample. Our study population comprised 79 patients with early acute Lyme disease, 82 patients with early-convalescent-phase disease, 47 patients with stage II and III disease, 34 patients post-antibiotic treatment, and 794 controls. A bioinformatic technique called partial receiver-operator characteristic (ROC) regression was used to combine individual antibody levels into a single diagnostic score with a single cutoff; this technique enhances test performance when a high specificity is required (e.g., >95%). Compared to Western blotting, the multiplex assay was equally specific (95.6%) but 20.7% more sensitive for early-convalescent-phase disease (89.0% versus 68.3%, respectively; 95% confidence interval [95% CI] for difference, 12.1% to 30.9%) and 12.5% more sensitive overall (75.0% versus 62.5%, respectively; 95% CI for difference, 8.1% to 17.1%). As a second-tier test, a multiplex assay for VlsE1-IgG and pepC10-IgM antibodies performed as well as or better than Western blotting for Lyme disease diagnosis. Prospective validation studies appear to be warranted.
Although decision models can provide a formal foundation for guideline development and clinical decision support, their widespread use is often limited by the lack of platform-independent software that geographically dispersed users can access and use easily without extensive training. To address these limitations the authors developed a World Wide Web-based interface for previously developed decision models. They describe the use and functionality of the interface using a decision model that evaluates the cost-effectiveness of strategies for preventing sudden cardiac death. The system allows an analyst to use a web browser to interact with the decision model and to change the values of input variables within pre-specified ranges, to specify sensitivity or threshold analyses, to evaluate the decision model, and to view the results generated dynamically. The web site also provides linkages to an explanation of the model, and evidence tables for input variables. The system demonstrates a method for providing distributed decision support to remote users such as guideline developers, decision analysts, and potentially practicing physicians. The web interface provides platform-independent and almost universal access to a decision model. This approach can make distributed decision support both practical and economical, and has the potential to increase the usefulness of decision models by enabling a broader audience to incorporate systematic analyses into both policy and clinical decisions.
Clinical application of decision analysis has been limited by unfamiliarity of clinicians with the technique, large data requirements, and the length of time needed to construct models. In order to make decision modeling more accessible to clinicians, the authors developed a computer program to construct decision models automatically. The system contains two separate knowledge bases. One contains frames encoding knowledge of the medical domain, the evaluation of pulmonary disease in patients infected with the human immunodeficiency virus (HIV). The other contains rules of correct decision model construction that guide the selection of items from the domain knowledge base and their insertion into the decision model. The system can create either a tree or an influence diagram that satisfies previously published critiquing rules. The system has the potential to enable novices to construct useful decision models and to provide individualized decision-analytic advice to clinicians in real time.
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