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There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (ß = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.
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ABSTRACT: Despite predictions that the United States will experience a growing physician shortage in the coming decades, there remains a population of would-be clinicians who are barred from becoming licensed physicians because of their immigration status. Undocumented medical students who do not have DACA have to overcome many barriers to gain admittance to medical school, but because they lack work authorization in the United States, they are unable to enter residency training. In this article, the authors highlight the implications of this dilemma for academic medicine. After detailing the experiences of an undocumented medical student, they examine the historical and policy background of this barrier to licensure and board certification. The authors consider the most immediate option available to academic medical institutions: to create a viable training program that is an alternative to residency. Such an alternative training program would need to be equivalent in quality to an accredited residency program and enable its trainees to become board-certified but not be a form of employment. Finally, the authors conclude with a call for academic medicine to engage in advocacy in support of legislative solutions for undocumented young people.
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BACKGROUND: Transcriptomic kidney profile testing and donor-derived cell-free DNA (dd-cfDNA) testing are new methods shown to provide early markers of graft inflammation during the post-transplant period. This study focused on utilizing clinical data to evaluate the application of these tests in detecting transplant rejection by comparing tests results to biopsy reports. MATERIAL AND METHODS: We conducted a retrospective analysis of a prospectively collected database of all adult kidney transplant patients at SUNY Upstate Medical Hospital from 1 January 2014 to 1 December 2022. Inclusion criteria were patients with concurrent transcriptomic kidney profile test and kidney biopsy results. RESULTS: Biopsies identified 33 kidney transplant rejections. For diagnosis of kidney rejection, transcriptomic kidney profile testing had a 52.83% positive predictive value and 92.77% negative predicative value, while dd-cfDNA testing had a 54.83% positive predictive value and 86.45% negative predictive value. Transcriptomic kidney profile testing showed an 82.35% sensitivity and 75.49% specificity, while dd-cfDNA testing showed a 56.66% sensitivity and 85.56% specificity. Positive transcriptomic kidney profile and dd-cfDNA tests detected 51.51% of rejections. Combined negative tests were observed in 70.21% of biopsies without rejection. CONCLUSIONS: Despite certain discrepancies and limitations, we believe transcriptomic profile testing and dd-cfDNA testing are useful for detecting early-stage rejections and can guide patient care. Additionally, dd-cfDNA testing avoids invasive screening biopsies. Following negative test results, the probability patients are not having rejection is 86.45%. The transcriptomic profile test's high sensitivity and specificity allow possible detection of transplant rejections that may have otherwise not been identified by biopsy.