To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. ...Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors.
The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy.
Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN.
Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract It is still unclear how and why some patients develop painful and others painless polyneuropathy. The aim of this study was to identify multiple factors associated with painful ...polyneuropathies (NeuP). A total of 1181 patients of the multicenter DOLORISK database with painful (probable or definite NeuP) or painless (unlikely NeuP) probable or confirmed neuropathy were investigated clinically, with questionnaires and quantitative sensory testing. Multivariate logistic regression including all variables (demographics, medical history, psychological symptoms, personality items, pain-related worrying, life-style factors, as well as results from clinical examination and quantitative sensory testing) and machine learning was used for the identification of predictors and final risk prediction of painful neuropathy. Multivariate logistic regression demonstrated that severity and idiopathic etiology of neuropathy, presence of chronic pain in family, Patient-Reported Outcomes Measurement Information System Fatigue and Depression T-Score, as well as Pain Catastrophizing Scale total score are the most important features associated with the presence of pain in neuropathy. Machine learning (random forest) identified the same variables. Multivariate logistic regression archived an accuracy above 78%, random forest of 76%; thus, almost 4 out of 5 subjects can be classified correctly. This multicenter analysis shows that pain-related worrying, emotional well-being, and clinical phenotype are factors associated with painful (vs painless) neuropathy. Results may help in the future to identify patients at risk of developing painful neuropathy and identify consequences of pain in longitudinal studies.
We report here further development of the novel quasi-optical spatial power combining array for high power millimeter wave (MMW) traveling wave tubes (TWTs) by demonstrating a Ku-band high power TWT ...which covers 12-15 GHz and with 100 kilowatt (kW) output power. Specifically, a Ku-band high power TWT which consists of a quasi-optical spatial power combining array of fifteen beam-wave interaction circuit slow wave structures and, as a result, beam width/height aspect ratio of close to 85 was developed to achieve a combined output power of over 100 kW at Ku-band. The 15 individual beam-wave interaction structures in the quasi-optical spatial power combining array are arranged into a linear array. Instead of a single cathode, fifteen cathodes, each with its own focus electrode or, in other words, a total of 15 focus electrodes are also used to create a required large sheet of beam for the large quasi-optical spatial power combining array of 15 channels of individual beam-wave interaction structure. Although a single stage collector was initially designed, however, a multi-stage depressed collector will also be designed and implemented to improve the efficiency of this K-band high power TWT. The overall size of the Ku-band high power TWT is relatively small since the same vacuum envelope and electron beam focus optics are shared among the five beam-wave interaction structures. Design and fabrication of this Ku-band high power TWT will be presented to demonstrate the large quasi optical spatial power combining array for very high power MMW TWTs and with reasonable broad bandwidth.
The clinical, laboratory and cytological features of 2 Bahraini infants with Wolman's disease are described. While one of the cases showed the classical diagnostic features, the other case exhibited ...a few atypical features such as lack of adrenal calcification and unusual morphology of vacuolated marrow macrophages. Literature review shows that this disorder may not be rare in this region.
