OBJECTIVE: We sought to assess dermatologists' experiences and perceptions toward using teledermatology for HS relative to atopic dermatitis (AD) as a comparison.
METHODS: A survey was disseminated electronically to practicing dermatologists in the Asia-Pacific region between February and June 2022. Differences in attitudes and perceptions between HS and AD were compared using random-effects ordered logistic regression, controlling for demographics.
RESULTS: A total of 100 responses were obtained comprising of 76 (81.7%) dermatologists and 17 (18.3%) dermatology trainees; 62.6% (62/98) of physicians were uncomfortable with using teledermatology for HS. Multivariable regression confirmed increased perceived challenges with managing HS using teledermatology compared to AD. These challenges include the need for photography of hard-to-reach or sensitive areas (odds ratio [OR] 4.71, 95% CI 2.44-9.07; P
METHODS: An Internet-based, cross-sectional survey was administered on 29 January 2020. A total of 4393 adults ≥18 y of age and residing or working in the province of Hubei, central China were included in the study.
RESULTS: The majority of the participants expressed a great degree of trust in the information and preventive instructions provided by the central government compared with the local government. Being under quarantine (adjusted odds ratio [OR] 2.35 [95% confidence interval {CI} 1.80 to 3.08]) and having a high institutional trust score (OR 2.23 [95% CI 1.96 to 2.53]) were both strong and significant determinants of higher preventive practices scores. The majority of study participants (n=3640 [85.7%]) reported that they would seek hospital treatment if they suspected themselves to have been infected with COVID-19. Few of the participants from Wuhan (n=475 [16.6%]) and those participants who were under quarantine (n=550 [13.8%]) expressed an unwillingness to seek hospital treatment.
CONCLUSIONS: Institutional trust is an important factor influencing adequate preventive behaviour and seeking formal medical care during an outbreak.
METHOD: For 293 consecutive patients admitted to our hospital via the emergency department for COVID-19 between 01/03/20 -18/05/20, demographic data, laboratory findings, admission electrocardiograph and clinical observations were compared in those who survived and those who died within 6 weeks. Hospital records were reviewed for prior electrocardiograms for comparison with those recorded on presentation with COVID-19.
RESULTS: Patients who died were older than survivors (82 vs 69.8 years, p 455 ms (males) and >465 ms (females) (p = 0.028, HR 1.49 [1.04-2.13]), as predictors of mortality. QTc prolongation beyond these dichotomy limits was associated with increased mortality risk (p = 0.0027, HR 1.78 [1.2-2.6]).
CONCLUSION: QTc prolongation occurs in COVID-19 illness and is associated with poor outcome.
RESEARCH DESIGN AND METHODS: The China Kadoorie Biobank recruited 512,891 adults (59% women) aged 30-79 from 10 regions of China during 2004-2008. At baseline survey, and subsequent resurveys of a random subset of survivors, participants were interviewed and measurements collected, including on-site RPG testing. Cause of death was ascertained via linkage to local mortality registries. Cox regression yielded adjusted HR for all-cause and cause-specific mortality associated with usual levels of RPG.
RESULTS: During median 11 years' follow-up, 37,214 deaths occurred among 452,993 participants without prior diagnosed diabetes or other chronic diseases. There were positive log-linear relationships between RPG and all-cause, cardiovascular disease (CVD) (n=14,209) and chronic kidney disease (CKD) (n=432) mortality down to usual RPG levels of at least 5.1 mmol/L. At RPG <11.1 mmol/L, each 1.0 mmol/L higher usual RPG was associated with adjusted HRs of 1.14 (95% CI 1.12 to 1.16), 1.16 (1.12 to 1.19) and 1.44 (1.22 to 1.70) for all-cause, CVD and CKD mortality, respectively. Usual RPG was positively associated with chronic liver disease (n=547; 1.45 (1.26 to 1.66)) and cancer (n=12,680; 1.12 (1.09 to 1.16)) mortality, but with comparably lower risks at baseline RPG ≥11.1 mmol/L. These associations persisted after excluding participants who developed diabetes during follow-up.
CONCLUSIONS: Among Chinese adults without diabetes, higher RPG levels were associated with higher mortality risks from several major diseases, with no evidence of apparent thresholds below the cut-points for diabetes diagnosis.
METHODS: We performed whole genome sequencing and analyzed 6 199 696 common variants among 113 aromatic ASM-induced SJS/TEN cases and 84 tolerant controls of Han Chinese ethnicity.
RESULTS: In the primary analysis, nine variants reached genome-wide significance (p
OBJECTIVE: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.
EXPOSURES: One of 7 antiseizure medications.
MAIN OUTCOMES AND MEASURES: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.
RESULTS: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.
CONCLUSIONS AND RELEVANCE: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.