The use of decision-analytic models in Parkinson's disease: a systematic review and critical appraisal.

The aims of this review were to review decision-analytic models used to evaluate interventions in idiopathic Parkinson's disease (PD), and to consider the future directions for development of methods to model the progression of PD over time. A systematic search of the healthcare literature up to June 2010 identified model-based economic evaluations in PD. The modelling methods used in the identified studies were appraised using good practice guidelines for decision-analytic modelling. The review identified 18 model-based evaluations of interventions in PD. All models evaluated treatments targeted towards the motor symptoms of PD or the motor complications of PD treatment. There were no models identified that evaluated interventions targeted towards the non-motor symptoms of PD, such as neuropsychiatric problems or autonomic dysfunction. Consequently, models characterized disease progression in PD using clinical measures of motor functioning. Most studies (n = 13) evaluated medications, three evaluated diagnostic technologies and two examined surgical procedures. Overall, the models reported structural components and data inputs appropriately and clearly, although limited evidence was provided to support choices made on the structures used in the models or the data synthesis reported. Models did not adequately consider structural uncertainty or internal/external consistency. Modelling methods used to date do not capture the full impact of PD. The emphasis in the current literature is on the motor symptoms of PD, characterizing the clinical nature of disease progression, largely neglecting the important impacts of non-motor symptoms. Modelling methods reported for the motor symptoms of PD may not be suitable for future interventions targeted towards modifying disease progression in PD across the entire spectrum of PD. More comprehensive models of disease progression, including both motor and non-motor symptoms will be needed where it is important to capture the effects of interventions more broadly.

Shape analysis of 123I-N-omega-fluoropropyl-2-beta-carbomethoxy-3beta-(4-iodophenyl) nortropane single-photon emission computed tomography images in the assessment of patients with parkinsonian syndromes.

PURPOSE: The purpose of this study was to show the viability and performance of a shape-based pattern recognition technique applied to I-N-omega-fluoropropyl-2-beta-carbomethoxy-3beta-(4-iodophenyl) nortropane single-photon emission computed tomography (FP-CIT SPECT) in patients with parkinsonism. METHODS: A fully automated pattern recognition tool, based on the shape of FP-CIT SPECT images, was written using Java. Its performance was evaluated and compared with QuantiSPECT, a region-of-interest-based quantitation tool, and observer performance using receiver operating characteristic analysis and kappa statistics. The techniques were compared using a sample of patients and controls recruited from a prospective community-based study of first presentation of parkinsonian symptoms with longitudinal follow up (median 3 years). RESULTS: The shape-based technique as well as the conventional semiquantitative approach was performed by experienced observers. The technique had a high level of automation, thereby avoiding observer/operator variability. CONCLUSION: A pattern recognition approach is a viable alternative to traditional methods of analysis in FP-CIT SPECT and has additional advantages.

Developing a sensitive search strategy in MEDLINE to retrieve studies on assessment of the diagnostic performance of imaging techniques.

PURPOSE: To prospectively develop a search strategy in MEDLINE for identifying studies on the diagnostic performance of any imaging modality, with maximized and minimized retrieval of relevant and irrelevant studies, respectively. MATERIALS AND METHODS: Predefined inclusion criteria were used to conduct a hand search of two sets of radiologic journal articles for studies on assessment of the diagnostic performance of imaging techniques. These two sets of articles formed independent derivation and validation record sets for developing and evaluating the search strategy. The sensitivity and positive predictive values (PPVs) of search terms from the derivation reference-standard set of records were used to select terms and develop two components of the search strategy. The first component was used to identify any study (from the imaging literature) in which diagnostic test performance was assessed. The second component was used to identify studies of any imaging modality. The two components were combined in the final search strategy. The sensitivity, specificity, and PPV of the search strategy in the derivation and validation record sets were calculated. RESULTS: The final search strategy had a sensitivity of 92.8%, a specificity of 58.5%, and a PPV of 25.1% for retrieval of the derivation set of records. Validation with an independent set of records gave a sensitivity of 91.9% (95% confidence interval [CI]: 87.1%, 95.1%), a specificity of 52.2% (95% CI: 49.2%, 55.2%), and a PPV of 25.1% (95% CI: 22.0%, 28.5%). Removal of irrelevant publication types further improved specificity and PPV in the validation set: to 77.6% (95% CI: 75.0%, 80.0%) and 40.9% (95% CI: 36.2%, 45.7%), respectively. The volume of imaging literature retrieved from MEDLINE by using the described search strategy has tripled since 1975. CONCLUSION: A sensitive search strategy to identify studies of the diagnostic performance of any imaging test was developed and validated. The retrieval estimates of this strategy in MEDLINE are adequate to develop a register of studies.