BACKGROUND: The National Comprehensive Cancer Network (NCCN) guidelines recommend preoperative biopsy for diagnosing dermatofibrosarcoma protuberans (DFSP) but limited data support this approach. We characterized DFSP diagnostic practices and compared clinical outcomes based on technique. METHODS: Data were collected for adult patients who underwent resection for initial DFSP presentation between 2003 and 2021 at 10 international institutions. Patients were categorized by excisional versus preoperative biopsy (incisional, punch, core needle biopsies, or fine needle aspiration), and univariate and multivariable analyses were performed. RESULTS: The cohort included 321 patients, with excisional biopsy performed in 51.4% and preoperative biopsy performed in 48.6% of patients. Biopsy type was stable throughout the study period (p = 0.08). There were no differences in sex, disease presentation, or preoperative imaging. In unadjusted analysis, biopsy varied by practitioner specialty, with general surgeons performing nearly 50% of excisional biopsies. Despite similar planned circumferential margins and anatomic location, preoperative biopsy was associated with higher index R0 rate (60.1% vs. 78.6%), fewer total excisions, and fewer complications (38.2% vs. 25.6%, all p < 0.05). However, adjuvant radiotherapy (11.7% vs. 6.0%) and final R0 rates (91.5% vs. 88.4%) were comparable regardless of technique (p > 0.05). In adjusted analysis, excisional biopsy was associated with extremity tumors (odds ratio [OR] 1.79, confidence interval [CI] 1.21-2.66, p = 0.004), treatment in non-academic settings (OR 2.28, CI 1.10-4.73, p = 0.03), and inversely with preoperative imaging (OR 0.47, CI 0.24-0.93, p = 0.03). CONCLUSION: Preoperative biopsy is associated with margin-negative resection, fewer re-excisions, and reduced complications. Clinical suspicion of DFSP is paramount, and preoperative imaging may critically inform biopsy selection prior to index resection.

• MeSH
• Biopsy methods   MeSH
• Dermatofibrosarcoma * pathology   surgery   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Skin Neoplasms * pathology   surgery   MeSH
• Follow-Up Studies MeSH
• Prognosis MeSH
• Margins of Excision MeSH
• Retrospective Studies MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.

• MeSH
• Time Factors MeSH
• Electrocardiography * MeSH
• Phenotype * MeSH
• Risk Assessment MeSH
• Cardiovascular Diseases diagnosis   mortality   genetics   physiopathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Predictive Value of Tests * MeSH
• Prognosis MeSH
• Reproducibility of Results MeSH
• Risk Factors MeSH
• Aged MeSH
• Heart Rate MeSH
• Unsupervised Machine Learning MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Geographicals
• United States MeSH

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case-control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (pFWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (pFWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.

• MeSH
• Iron Deficiencies * MeSH
• Seizures, Febrile * diagnosis   etiology   MeSH
• Fever complications   MeSH
• Infant MeSH
• Humans MeSH
• Child, Preschool MeSH
• Case-Control Studies MeSH
• Iron MeSH
• Check Tag
• Infant MeSH
• Humans MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVES: Oral health-related quality of life (OHRQoL) is a multifaceted field involving many factors. The aim of our study was to assess whether implant therapy improves OHRQoL in dental patients. METHODS: Patients receiving at least one implant completed a health-related questionnaire before and after the implantation (minimum 1.5 months). Questions covered the functional and aesthetic scales (AS). Paired differences in individual scores were analysed using the Wilcoxon signed-rank test. A univariate analysis of covariance was used to relate overall and scale-specific average paired differences to age, gender, marital and educational status. Multivariate analysis of covariance was used to assess interactions between the covariates and different scales of outcome. All tests were performed at statistical significance level α = 0.05. RESULTS: All twelve Wilcoxon tests supported an improvement in OHRQoL after implant placement. On the AS, the mean difference in OHRQoL scores was found to be associated with marital status, presence of aesthetic reasons for undergoing the surgery and number of front teeth replaced by implants. On the functional scale (FS), most significant associations were observed with the number of front teeth replaced via implantation, followed by the presence of chewing problems and marital status. The multivariate analysis helped to identify the covariates that varied significantly over the two scales of interest. CONCLUSIONS: Effects of covariates responding significantly differently on different scales should not be summarized using an overall univariate analysis, using paired score differences averaged over all items. Such effect summary would be misleading. In the present study, significant implant-related improvements in OHRQoL were observed on both the aesthetic and FS in patients with at least one implant in the front dental area.

• MeSH
• Analysis of Variance MeSH
• Quality of Life MeSH
• Middle Aged MeSH
• Humans MeSH
• Marital Status MeSH
• Statistics, Nonparametric MeSH
• Surveys and Questionnaires MeSH
• Educational Status MeSH
• Dental Implants MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH