Predicting mobilization failure before it starts may enable patient-tailored strategies. Although consensus criteria for predicted PM (pPM) are available, their predictive performance has never been measured on real data. We retrospectively collected and analyzed 1318 mobilization procedures performed for MM and lymphoma patients in the plerixafor era. In our sample, 180/1318 (13.7%) were PM. The score resulting from published pPM criteria had sufficient performance for predicting PM, as measured by AUC (0.67, 95%CI: 0.63-0.72). We developed a new prediction model from multivariate analysis whose score (pPM-score) resulted in better AUC (0.80, 95%CI: 0.76-0.84, p < 0001). pPM-score included as risk factors: increasing age, diagnosis of NHL, positive bone marrow biopsy or cytopenias before mobilization, previous mobilization failure, priming strategy with G-CSF alone, or without upfront plerixafor. A simplified version of pPM-score was categorized using a cut-off to maximize positive likelihood ratio (15.7, 95%CI: 9.9-24.8); specificity was 98% (95%CI: 97-98.7%), sensitivity 31.7% (95%CI: 24.9-39%); positive predictive value in our sample was 71.3% (95%CI: 60-80.8%). Simplified pPM-score can "rule in" patients at very high risk for PM before starting mobilization, allowing changes in clinical management, such as choice of alternative priming strategies, to avoid highly likely mobilization failure.
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http://dx.doi.org/10.1038/s41409-017-0051-y | DOI Listing |
Intern Emerg Med
December 2024
Department of Respiratory Medicine and Allergology, University Hospital, Goethe University, Frankfurt, Germany.
The aim was to identify predictors for early identification of HFNC failure risk in patients with severe community-acquired (CAP) pneumonia or COVID-19. Data from adult critically ill patients admitted with CAP or COVID-19 and the need for ventilatory support were retrospectively analysed. HFNC failure was defined as the need for invasive ventilation or death before intubation.
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December 2024
Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, 1060 William Moore Dr, Raleigh, NC, 27607, USA.
Hypertrophic cardiomyopathy (HCM) afflicts humans, cats, pigs, and rhesus macaques. Disease sequelae include congestive heart failure, thromboembolism, and sudden cardiac death (SCD). Sarcomeric mutations explain some human and cat cases, however, the molecular basis in rhesus macaques remains unknown.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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December 2024
Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Sepsis is defined as a dysfunctional, life-threatening response to infection leading to multiorgan dysfunction and failure. During the past decade, studies have highlighted the relationship between sepsis and aging. However, the role of aging-related mechanisms in the progression and prognosis of sepsis remains unclear.
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December 2024
Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.
The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance.
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