PURPOSE: Minimum clinical standards for assessment and management of osteoporosis are needed in the Asia-Pacific (AP) region to inform clinical practice guidelines (CPGs) and to improve osteoporosis care. We present the framework of these clinical standards and describe its development.
METHODS: We conducted a structured comparative analysis of existing CPGs in the AP region using a "5IQ" model (identification, investigation, information, intervention, integration, and quality). One-hundred data elements were extracted from each guideline. We then employed a four-round Delphi consensus process to structure the framework, identify key components of guidance, and develop clinical care standards.
RESULTS: Eighteen guidelines were included. The 5IQ analysis demonstrated marked heterogeneity, notably in guidance on risk factors, the use of biochemical markers, self-care information for patients, indications for osteoporosis treatment, use of fracture risk assessment tools, and protocols for monitoring treatment. There was minimal guidance on long-term management plans or on strategies and systems for clinical quality improvement. Twenty-nine APCO members participated in the Delphi process, resulting in consensus on 16 clinical standards, with levels of attainment defined for those on identification and investigation of fragility fractures, vertebral fracture assessment, and inclusion of quality metrics in guidelines.
CONCLUSION: The 5IQ analysis confirmed previous anecdotal observations of marked heterogeneity of osteoporosis clinical guidelines in the AP region. The Framework provides practical, clear, and feasible recommendations for osteoporosis care and can be adapted for use in other such vastly diverse regions. Implementation of the standards is expected to significantly lessen the global burden of osteoporosis.
OBJECTIVE: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.
METHODS: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.
RESULTS: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).
CONCLUSIONS: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.
METHODS: LDA was applied to 6,328 Taiwanese clinical patients for classification purposes. Clustering method was used to identify the associated influential symptoms for each severity level.
RESULT: LDA shows only 36 HAICDDS questions are significant to distinguish the 5 severity levels with 80% overall accuracy and it increased to 85.83% when combining normal and MCI groups. Severe dementia patients have the most serious declination in most cognitive and functionality domains, follows by moderate dementia, mild dementia, MCI and normal patients.
CONCLUSION: HAICDDS is a reliable and time-saved diagnosis tool in classifying the severity of dementia before undergoing a more in-depth clinical examination. The modified CDR may be indicated for epidemiological study and provide a solid foundation to develop a machine-learning derived screening instrument to detect dementia symptoms.