Paraplegic Patient with Metastatic Papillary Thyroid Cancer: A Multidisciplinary Approach to Radioactive Iodine Therapy Safety and Efficacy Strategy.

High-activity radioactive iodine (RAI) therapy for metastatic thyroid cancer (TC) requires isolation to minimize radiation exposure to third parties, thus posing challenges for patients needing hands-on care. There are limited data on the approach to high-activity RAI treatment in paraplegic patients. We report a state-of-the-art multidisciplinary approach to the management of bedbound patients, covering necessary radiation safety measures that lead to radiation exposure levels as low as reasonably achievable. Given the limited literature resources on standardized approaches, we provide a practical example of the safe and successful treatment of a woman with BRAFV600E-mutant tall-cell-variant papillary TC and pulmonary metastases, who underwent dabrafenib redifferentiation before RAI therapy. The patient was 69 y old and had become paraplegic because of a motor-vehicle accident. Since caring for a paraplegic patient with neurogenic bowel and bladder dysfunction poses radiation safety challenges, a multidisciplinary team comprising endocrinologists, nuclear medicine physicians, radiation safety specialists, and the nursing department developed a radiation mitigation strategy to ensure patient and staff safety during RAI therapy. The proposed standardized approach includes thorough monitoring of radiation levels in the workplace, providing additional protective equipment for workers who handle radioactive materials or are in direct patient contact, and implementing strict guidelines for safely disposing of radioactive waste such as urine collected in lead-lined containers. This approach requires enhanced training, role preparation, and practice; use of physical therapy equipment to increase the exposure distance; and estimation of the safe exposure time for caregivers based on dosimetry. The effective and safe treatment of metastatic TC in paraplegic patients can be successfully implemented with a comprehensive radiation mitigation strategy and thorough surveying of personnel for contamination.

Differential diagnosis of bone marrow failure syndromes guided by machine learning.

The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in patients with severe pancytopenia. Conversely, the misdiagnosis of inherited bone marrow failure (BMF) can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a 2-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Data sets were unbiasedly clustered, and an ensemble model was trained with cases from the largest cluster of a training cohort (n = 359) and validated with an independent cohort (n = 127). Cluster A, the largest group, was mostly immune or inherited aplastic anemia, whereas cluster B comprised underrepresented BMF phenotypes and was not included in the next step of data modeling because of a small sample size. The ensemble cluster A-specific model was accurate (89%) to predict BMF etiology, correctly predicting inherited and likely immune BMF in 79% and 92% of cases, respectively. Our model represents a practical guide for BMF diagnosis and highlights the importance of clinical and laboratory variables in the initial evaluation, particularly telomere length. Our tool can be potentially used by general hematologists and health care providers not specialized in BMF, and in under-resourced centers, to prioritize patients for genetic testing or for expeditious treatment.

Underperformance of clinical risk scores in identifying imaging-based high cardiovascular risk in psoriasis: results from two observational cohorts.

AIMS: We aimed to evaluate whether traditional risk scores [short-term, 'psoriasis-modified' (multiplied by 1.5) and lifetime] were able to capture high cardiovascular disease (CVD) risk as defined by the presence of atherosclerotic plaques in coronary, femoral, or carotid arteries in psoriasis. METHODS AND RESULTS: We used two prospectives obseravational cohorts. European cohort: femoral and carotid atherosclerotic plaques were evaluated by ultrasound in 73 psoriasis patients. Lifetime CVD risk (LTCVR) was evaluated with QRISK-LT; short-term CVD risk was evaluated with SCORE and psoriasis-modified SCORE. American cohort: 165 patients underwent coronary computed tomography angiography to assess presence of coronary plaques. LTCVR was evaluated with atherosclerotic cardiovascular disease (ASCVD-LT) lifetime; short-term CVD risk was evaluated with ASCVD and psoriasis-modified ASCVD. European cohort: subclinical atherosclerosis was present in 51% of patients. QRISK-LT identified 64% of patients with atherosclerosis missing a high proportion (35%) with atheroma plaque (P < 0.05). The percentage of patients with atherosclerosis identified by QRISK-LT was significantly higher than those detected by SCORE (0%) and modified SCORE (10%). American cohort: subclinical atherosclerosis was present in 54% of patients. ASCVD-LT captured 54% of patients with coronary plaques missing a high proportion (46%) with coronary plaque (P < 0.05). The percentage of patients with atheroma plaques detected with ASCVD and modified ASCVD were only 20% and 45%, respectively. CONCLUSIONS: Application of lifetime, short-term and 'psoriasis-modified' risk scores did not accurately capture psoriasis patients at high CVD risk.

Application of machine learning in understanding atherosclerosis: Emerging insights.

Biological processes are incredibly complex-integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.

BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effectively analyze well-characterized data sets. OBJECTIVE: In this study, we used machine learning algorithms to determine the top predictors of noncalcified coronary burden by CCTA in psoriasis. METHODS: The analysis included 263 consecutive patients with 63 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative. The random forest algorithm was used to determine the top predictors of noncalcified coronary burden by CCTA. We evaluated our results using linear regression models. RESULTS: Using the random forest algorithm, we found that the top 10 predictors of noncalcified coronary burden were body mass index, visceral adiposity, total adiposity, apolipoprotein A1, high-density lipoprotein, erythrocyte sedimentation rate, subcutaneous adiposity, small low-density lipoprotein particle, cholesterol efflux capacity and the absolute granulocyte count. Linear regression of noncalcified coronary burden yielded results consistent with our machine learning output. LIMITATION: We were unable to provide external validation and did not study cardiovascular events. CONCLUSION: Machine learning methods identified the top predictors of noncalcified coronary burden in psoriasis. These factors were related to obesity, dyslipidemia, and inflammation, showing that these are important targets when treating comorbidities in psoriasis.