Exploring the relationship between embolic acute stroke distribution and supra-aortic vessel patency: key findings from an in vitro model study.

BACKGROUND: We investigated differences in intracranial embolus distribution through communicating arteries in relation to supra-aortic vessel (SAV) patency. METHODS: For this experimental analysis, we created a silicone model of the extracranial and intracranial circulations using a blood-mimicking fluid under physiological pulsatile flow. We examined the sequence of embolus lodgment on injecting 104 frangible clot analogues (406 emboli) through the right internal carotid artery (CA) as SAV patency changed: (a) all SAV patent (baseline), (b) emboli from a CA occlusion, (c) emboli contralateral to a CA occlusion and (d) occlusion of the posterior circulation. The statistical analysis included a descriptive analysis of thrombi location after occlusion (absolute and relative frequencies). Sequences of occlusions were displayed in Sankey flow charts for the four SAV conditions. Associations between SAV conditions and occlusion location were tested by Fisher's exact test. Two-sided p values were compared with a significance level of 0.05. RESULTS: The total number of emboli was 406 (median fragments/clot: 4 (IQR: 3-5)). Embolus lodgment was dependent on SAV patency (p<0.0001). In all scenarios, embolism lodging in the anterior cerebral artery (ACA) occurred after a previous middle cerebral artery (MCA) embolism (MCA first lodge: 96%, 100/104). The rate of ipsilateral ACA embolism was 28.9% (28/97) at baseline, decreasing significantly when emboli originated from an occluded CA (16%, 14/88). There were more bihemispheric embolisations in cases of contralateral CA occlusion (37%, 45/122), with bilateral ACA embolisms preceding contralateral MCA embolism in 56% of cases (14/25 opposite MCA and ACA embolism). CONCLUSIONS: All emboli in the ACA occurred after a previous ipsilateral MCA embolism. Bihemispheric embolisms were rare, except when there was a coexisting occlusion in either CA, particularly in cases of a contralateral CA occlusion.

Making acute ischemic stroke thrombi visible in MRI imaging.

Knowledge of thrombus behavior and visualization on MRI in acute ischemic stroke is less than optimal. However, MRI sequences could be enhanced based on the typical T1 and T2 relaxation times of the target tissues, which mainly determine their signal intensities on imaging. We studied the relaxation times of a broad spectrum of clot analogs along with their image characteristics of three sequences analyzed: a T1-weighted turbo inversion-recovery sequence (T1w Turbo IR), a T1-weighted turbo spin echo with fat suppression (T1w TSE SPIR), and a T2-weighted 3D TSE with magnetization refocusing to remove T1 dependence (T2w TSE DRIVE). We compared their imaging behavior with the intensity values of normal brain tissue using the same imaging protocols as for clots. Each histological and biochemical clot component contributed to each of the relaxation times. Overall, histological composition correlated strongly with T1 times, and iron content, specifically, with T2 relaxation time. Using decision trees, fibrin content was selected as the primary biomarker for T1 relaxation times, inducing an increase. Up to four clot subgroups could be defined based on its distinctive T1 relaxation time. Clot signal intensity in the T1 and T2-weighted images varied significantly according to T1 and T2 relaxation times. Moreover, in comparison with normal brain tissue intensity values, T2w DRIVE images depict thrombi according to the principle of the more fibrin, the higher the intensity, and in T1w TSE, the more erythrocytes, the higher the intensity. These findings could facilitate improvements in MRI sequences for clot visualization and indicate that T2w DRIVE and T1w TSE sequences should depict the vast majority of acute ischemic stroke thrombi as more hyperintense than surrounding tissues.

Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.

BACKGROUND: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. METHODS: While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. RESULTS: Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. CONCLUSIONS: Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

There are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model ­ a type of computer model that learns from patterns in data without human input­to separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment.

Validation and refinement of the 2022 European LeukemiaNet genetic risk stratification of acute myeloid leukemia.

The revised 2022 European LeukemiaNet (ELN) AML risk stratification system requires validation in large, homogeneously treated cohorts. We studied 1118 newly diagnosed AML patients (median age, 58 years; range, 18-86 years) who received cytarabine-based induction chemotherapy between 1999 and 2012 and compared ELN-2022 to the previous ELN-2017 risk classification. Key findings were validated in a cohort of 1160 mostly younger patients. ELN-2022 reclassified 15% of patients, 3% into more favorable, and 12% into more adverse risk groups. This was mainly driven by patients reclassified from intermediate- to adverse-risk based on additional myelodysplasia-related mutations being included as adverse-risk markers. These patients (n = 79) had significantly better outcomes than patients with other adverse-risk genotypes (5-year OS, 26% vs. 12%) and resembled the remaining intermediate-risk group. Overall, time-dependent ROC curves and Harrel's C-index controlling for age, sex, and AML type (de novo vs. sAML/tAML) show slightly worse prognostic discrimination of ELN-2022 compared to ELN-2017 for OS. Further refinement of ELN-2022 without including additional genetic markers is possible, in particular by recognizing TP53-mutated patients with complex karyotypes as "very adverse". In summary, the ELN-2022 risk classification identifies a larger group of adverse-risk patients at the cost of slightly reduced prognostic accuracy compared to ELN-2017.

Different treatment strategies versus a common standard arm (CSA) in patients with newly diagnosed AML over the age of 60 years: a randomized German inter-group study.

A randomized inter-group trial comparing more intensive treatment strategies to a common standard arm 3 + 7 (CSA) was conducted in patients with non-M3 AML. Untreated patients ≥ 60 years were allocated to the CSA (n = 132) or to the study group arms (n = 1154) of the AMLCG (TAD/HAM versus HAM/HAM ± G-CSF followed by TAD and maintenance) and the OSHO (intermediate-dose ara-C/mitoxantrone followed by ara-C/mitoxantrone). Median age of the 1147 eligible patients was 69 (range 60-87) years. CR/CRi status at 90 days was not significantly different between the CSA (54% (95%CI: 45-64)) and the study group arms (53% (95%CI: 47-60) and 59% (95%CI: 58-63)). The five-year event-free survival (EFS) probability (primary endpoint) was 6.2% (95%CI: 2.7-14.0) in the CSA, 7.6% (95%CI: 4.5-12.8) in study group A and 11.1% (95%CI: 9.0-13.7) in B. The 5-year OS was 17.2% (95%CI: 11.0-26.9), 17.0% (95%CI: 2.0-23.9), and 19.5% (95%CI: 16.7-22.8) in CSA, study group A and B, respectively. Neither study group differed significantly from the CSA regarding EFS, OS, or relapse-free survival. In multivariate analyses, allocation to the treatment strategy was not significantly associated with the time-to-event endpoints. The evaluation of more intensive treatment strategies did not show clinically relevant outcome differences when compared to CSA.

Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.

Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77-0.86 and between 0.63-0.74, respectively in our test set, and between 0.71-0.80 and 0.65-0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.

Impact of IDH1 and IDH2 mutational subgroups in AML patients after allogeneic stem cell transplantation.

BACKGROUND: The role of allogeneic hematopoietic cell transplantation (alloHCT) in acute myeloid leukemia (AML) with mutated IDH1/2 has not been defined. Therefore, we analyzed a large cohort of 3234 AML patients in first complete remission (CR1) undergoing alloHCT or conventional chemo-consolidation and investigated outcome in respect to IDH1/2 mutational subgroups (IDH1 R132C, R132H and IDH2 R140Q, R172K). METHODS: Genomic DNA was extracted from bone marrow or peripheral blood samples at diagnosis and analyzed for IDH mutations with denaturing high-performance liquid chromatography, Sanger sequencing and targeted myeloid panel next-generation sequencing, respectively. Statistical as-treated analyses were performed using R and standard statistical methods (Kruskal-Wallis test for continuous variables, Chi-square test for categorical variables, Cox regression for univariate and multivariable models), incorporating alloHCT as a time-dependent covariate. RESULTS: Among 3234 patients achieving CR1, 7.8% harbored IDH1 mutations (36% R132C and 47% R132H) and 10.9% carried IDH2 mutations (77% R140Q and 19% R172K). 852 patients underwent alloHCT in CR1. Within the alloHCT group, 6.2% had an IDH1 mutation (43.4% R132C and 41.4% R132H) and 10% were characterized by an IDH2 mutation (71.8% R140Q and 24.7% R172K). Variants IDH1 R132C and IDH2 R172K showed a significant benefit from alloHCT for OS (p = .017 and p = .049) and RFS (HR = 0.42, p = .048 and p = .009) compared with chemotherapy only. AlloHCT in IDH2 R140Q mutated AML resulted in longer RFS (HR = 0.4, p = .002). CONCLUSION: In this large as-treated analysis, we showed that alloHCT is able to overcome the negative prognostic impact of certain IDH mutational subclasses in first-line consolidation treatment and could pending prognostic validation, provide prognostic value for AML risk stratification and therapeutic decision making.

Differential impact of IDH1/2 mutational subclasses on outcome in adult AML: results from a large multicenter study.

Mutations of the isocitrate dehydrogenase-1 (IDH1) and IDH2 genes are among the most frequent alterations in acute myeloid leukemia (AML) and can be found in ∼20% of patients at diagnosis. Among 4930 patients (median age, 56 years; interquartile range, 45-66) with newly diagnosed, intensively treated AML, we identified IDH1 mutations in 423 (8.6%) and IDH2 mutations in 575 (11.7%). Overall, there were no differences in response rates or survival for patients with mutations in IDH1 or IDH2 compared with patients without mutated IDH1/2. However, distinct clinical and comutational phenotypes of the most common subtypes of IDH1/2 mutations could be associated with differences in outcome. IDH1-R132C was associated with increased age, lower white blood cell (WBC) count, less frequent comutation of NPM1 and FLT3 internal tandem mutation (ITD) as well as with lower rate of complete remission and a trend toward reduced overall survival (OS) compared with other IDH1 mutation variants and wild-type (WT) IDH1/2. In our analysis, IDH2-R172K was associated with significantly lower WBC count, more karyotype abnormalities, and less frequent comutations of NPM1 and/or FLT3-ITD. Among patients within the European LeukemiaNet 2017 intermediate- and adverse-risk groups, relapse-free survival and OS were significantly better for those with IDH2-R172K compared with WT IDH, providing evidence that AML with IDH2-R172K could be a distinct entity with a specific comutation pattern and favorable outcome. In summary, the presented data from a large cohort of patients with IDH1/2 mutated AML indicate novel and clinically relevant findings for the most common IDH mutation subtypes.

A clinically applicable gene expression-based score predicts resistance to induction treatment in acute myeloid leukemia.

Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy using cytogenetic data and 29 gene expression markers (Predictive Score 29 Medical Research Council; PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by using the widely implemented NanoString platform and further validate the classifier in a more recently treated patient cohort. Analyses were performed on 351 patients with newly diagnosed AML intensively treated within the German AML Cooperative Group registry. As a continuous variable, PS29MRC performed best in predicting induction failure in comparison with previously published risk models. The classifier was strongly associated with overall survival. We were able to establish a previously defined cutoff that allows classifier dichotomization (PS29MRCdic). PS29MRCdic significantly identified induction failure with 59% sensitivity, 77% specificity, and 72% overall accuracy (odds ratio, 4.81; P = 4.15 × 10-10). PS29MRCdic was able to improve the European Leukemia Network 2017 (ELN-2017) risk classification within every category. The median overall survival with high PS29MRCdic was 1.8 years compared with 4.3 years for low-risk patients. In multivariate analysis including ELN-2017 and clinical and genetic markers, only age and PS29MRCdic were independent predictors of refractory disease. In patients aged ≥60 years, only PS29MRCdic remained as a significant variable. In summary, we confirmed PS29MRC as a valuable classifier to identify high-risk patients with AML. Risk classification can still be refined beyond ELN-2017, and predictive classifiers might facilitate clinical trials focusing on these high-risk patients with AML.

Risk of tumor recurrence in intracranial meningiomas: comparative analyses of the predictive value of the postoperative tumor volume and the Simpson classification.

OBJECTIVE: In meningiomas, the Simpson grading system is applied to estimate the risk of postoperative recurrence, but might suffer from bias and limited overview of the resection cavity. In contrast, the value of the postoperative tumor volume as an objective predictor of recurrence is largely unexplored. The objective of this study was to compare the predictive value of residual tumor volume with the intraoperatively assessed extent of resection (EOR). METHODS: The Simpson grade was determined in 939 patients after surgery for initially diagnosed intracranial meningioma. Tumor volume was measured on initial postoperative MRI within 6 months after surgery. Correlation between both variables and recurrence was compared using a tree-structured Cox regression model. RESULTS: Recurrence correlated with Simpson grading (p = 0.003). In 423 patients (45%) with available imaging, residual tumor volume covered a broad range (0-78.5 cm3). MRI revealed tumor remnants in 8% after gross-total resection (Simpson grade I-III, range 0.12-33.5 cm3) with a Cohen's kappa coefficient of 0.7153. Postoperative tumor volume was correlated with recurrence in univariate analysis (HR 1.05 per cm3, 95% CI 1.02-1.08 per cm3, p < 0.001). A tree-structured Cox regression model revealed any postoperative tumor volume > 0 cm3 as a critical cutoff value for the prediction of relapse. Multivariate analysis confirmed the postoperative tumor volume (HR 1.05, p < 0.001) but not the Simpson grading (p = 0.398) as a predictor for recurrence. CONCLUSIONS: EOR according to Simpson grading was overrated in 8% of tumors compared to postoperative imaging. Because the predictive value of postoperative imaging is superior to the Simpson grade, any residual tumor should be carefully considered during postoperative care of meningioma patients.

Accuracy of intraoral scans in the mixed dentition: a prospective non-randomized comparative clinical trial.

OBJECTIVE: In-vivo accuracy of intraoral scans of complete mixed dentitions of patients in active treatment have not yet been investigated. The aim was to test the hypothesis that dimensional differences between intraoral scans and conventional alginate impressions in the mixed dentition are clinically irrelevant. METHODS: Trial design: Prospective non-randomized comparative clinical trial. Based on sample size calculation 44 evaluable mixed dentition jaws of patients in active orthodontic treatment were included. Each patient received an alginate impression following an intraoral scan (TRIOS® Ortho). Plaster cast was fabricated and scanned with an external scanner (ATOS-SO®). Both STL datasets were analyzed with the 3D inspection and mesh processing software GOM Inspect®. Statistical analysis comprised sample size calculation, t-test as well as nonparametric tests. RESULTS: The absolute mean difference between digital plaster casts and intraoral scans is 0.022 mm ± 0.027 mm (median 0.015 mm). The obtained measurements are in the range of comparable studies on full arch permanent dentitions. Gender, the size of the jaw represented by the dentition stage and upper respectively lower jaw, as well the malocclusion have no effect on the total deviations between digital plaster casts and intraoral scans. Detectable impression errors were bubbles in fissures and marginal ridges as well as incomplete alginate flow and detachment from the tray. Detectable scanning errors were incomplete distal surface of the most distal molar. CONCLUSION: Dimensional differences between intraoral scans and conventional alginate impressions in the mixed dentition are clinically irrelevant for orthodontic purposes. In all clinical situations of active treatment in the mixed dentition, the intraoral scans are more detailed and less error-prone.

Predictors of Successful First-Pass Thrombectomy with a Balloon Guide Catheter: Results of a Decision Tree Analysis.

Complete recanalization after a single retrieval maneuver is an interventional goal in acute ischemic stroke and an independent factor for good clinical outcome. Anatomical biomarkers for predicting clot removal difficulties have not been comprehensively analyzed and await unused. We retrospectively evaluated 200 consecutive patients who suffered acute stroke and occlusion of the anterior circulation and were treated with mechanical thrombectomy through a balloon guide catheter (BGC). The primary objective was to evaluate the influence of carotid tortuosity and BGC positioning on the one-pass Modified Thrombolysis in Cerebral Infarction Scale (mTICI) 3 rate, and secondarily, the influence of communicating arteries on the angiographic results. After the first-pass mTICI 3, recanalization fell from 51 to 13%. The regression models and decision tree (supervised machine learning) results concurred: carotid tortuosity was the main constraint on efficacy, reducing the likelihood of mTICI 3 after one pass to 30%. BGC positioning was relevant only in carotid arteries without elongation: BGCs located in the distal internal carotid artery (ICA) had a 70% probability of complete recanalization after one pass, dropping to 43% if located in the proximal ICA. These findings demonstrate that first-pass mTICI 3 is influenced by anatomical and interventional factors capable of being anticipated, enabling the BGC technique to be adapted to patient's anatomy to enhance effectivity.

Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning.

Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom and covering a wide range of histologies, we analyzed 80 clot analogs with respect to X-ray attenuation at 24 and 48 h after production. The mean, maximum, and minimum HU values for the axial and coronal reconstructions were recorded. Each thrombus underwent a corresponding histological analysis, together with a laboratory analysis of water and iron contents. Decision trees, a type of supervised machine learning, were used to select the primary variable altering attenuation and the best parameter for predicting histology. The decision trees selected red blood cells (RBCs) for correlation with all attenuation parameters (p < 0.001). Conversely, maximum attenuation on axial CT offered the greatest accuracy for discriminating up to four groups of clot histology (p < 0.001). Similar RBC-rich thrombi displayed variable imaging associated with different iron (p = 0.023) and white blood cell contents (p = 0.019). Water content varied among the different histologies but did not in itself account for the differences in attenuation. Independent factors determining clot attenuation were the RBCs (ß = 0.33, CI = 0.219-0.441, p < 0.001) followed by the iron content (ß = 0.005, CI = 0.0002-0.009, p = 0.042). Our findings suggest that it is possible to extract more and valuable information from NCCT that can be extrapolated to provide insights into clot histological and chemical composition.

Asymmetry-index and orthodontic facial analysis of children with foetal alcohol syndrome using 3D-facial scans.

BACKGROUND: The foetal alcohol spectrum disorder (FASD) is a complex and heterogenic disorder, caused by gestational exposure to alcohol. Patients with foetal alcohol syndrome (FAS-most severe form) show abnormal facial features. Our study aims at finding additional reliable and objective parameters for FAS diagnosis. METHODS: Facial three-dimensional scans of 30 children with FAS and 30 controls were analysed. Orthodontic profile analysis (concerning position of upper and lower jaw) was performed. Vertical facial proportions were taken and facial asymmetry index (right to left side) was calculated. RESULTS: Profile type was significantly different for children with FAS (p = 0.001) with lower jaws more frequently in a retral position. Profile angle was significantly larger in the group with FAS (p = 0.009). Children with FAS had shorter middle thirds and longer lower thirds of the face (p < 0.001). Stomion (point between upper and lower lip) was located significantly more caudally in the FAS group (p < 0.001). Facial asymmetry index was not significantly different. CONCLUSIONS: Children with FAS differ significantly from controls in vertical and sagittal facial measurements. Profile analysis and measurement of vertical proportions are easy to apply standard procedures in everyday orthodontic practice and could be time-saving and objective means for additional verification of FAS.

Children with Fetal Alcohol Syndrome (FAS): 3D-Analysis of Palatal Depth and 3D-Metric Facial Length.

Background: Drinking alcohol during pregnancy can result in severe developmental disorders in the child. Symptoms of the fetal alcohol spectrum disorder (FASD) comprise growth deficiencies, abnormal facial phenotype and damage or dysfunction of the central nervous system. Numerous diagnostic methods for facial phenotyping in FASD exist, but diagnoses are still difficult. Our aim was to find additional and objective methods for the verification of FAS(D). Methods: Three-dimensional dental models of 60 children (30 FAS and 30 controls) were used to metrically determine maximum palatal depths at the median palatine raphe. Three-dimensional facial scans were taken, and vertical distances of the face were measured at five defined facial landmarks (FP1-FP5) for each child. Results: Mean palatal height, total facial length (FP1-FP5) as well as FP4-FP5 did not significantly differ between the FAS group and the control group. Comparing vertical facial subdivisions, however, resulted in significant differences for distances FP1 to FP2 (p = 0.042, FAS > controls), FP2 to FP3 (p < 0.001, FAS < controls), FP3 to FP4 (p < 0.001, FAS > controls) and FP3 to FP5 (p = 0.007, FAS > controls). Conclusions: Metric vertical measurements of the face can be used as additional objective criteria for FAS diagnoses. However, no significant differences were reported for palatal depth evaluation in the specific age range tested in the present study.

Comparing Plasma Exchange to Escalated Methyl Prednisolone in Refractory Multiple Sclerosis Relapses.

Intravenous methyl prednisolone (IVMPS) represents the standard of care for multiple sclerosis (MS) relapses, but fail to improve symptoms in one quarter of patients. In this regard, apart from extending steroid treatment to a higher dose, therapeutic plasma exchange (TPE) has been recognized as a treatment option. The aim of this retrospective, monocentric study was to investigate the efficacy of TPE versus escalated dosages of IVMPS in refractory MS relapses. An in-depth medical chart review was performed to identify patients from local databases. Relapse recovery was stratified as "good/full", "average" and "worst/no" according to function score development. In total, 145 patients were analyzed. Good/average/worst recovery at discharge was observed in 60.9%/32.6%/6.5% of TPE versus 15.2%/14.1%/70.7% of IVMPS patients, respectively. A total of 53.5% of IVMPS patients received TPE as rescue treatment and 54.8% then responded satisfactorily. The multivariable odds ratio (OR) for worst/no recovery was 39.01 (95%-CI: 10.41-146.18; p ≤ 0.001), favoring administration of TPE as first escalation treatment. The effects were sustained at three-month follow-ups, as OR for further deterioration was 6.48 (95%-CI: 2.48-16.89; p ≤ 0.001), favoring TPE. In conclusion, TPE was superior over IVMPS in the amelioration of relapse symptoms at discharge and follow-up. This study provides class IV evidence supporting the administration of TPE as the first escalation treatment to steroid-refractory MS relapses.

Tooth Malformations, DMFT Index, Speech Impairment and Oral Habits in Patients with Fetal Alcohol Syndrome.

Background: Fetal alcohol spectrum disorder (FASD) is a developmental disorder with severe negative lifetime consequences. Although knowledge about the harmfulness of alcohol consumption during pregnancy has spread, the prevalence of fetal alcohol spectrum disorder is very high. Our study aims at identifying fetal alcohol syndrome (FAS)-associated dental anomalies or habits, which need early attention. Methods: Sixty children (30 FAS; 30 controls) were examined prospectively. Swallowing pattern, oral habits, breastfeeding, speech therapy, ergotherapy, physiotherapy, exfoliation of teeth, DMFT (decayed, missing, filled teeth) index, modified DDE (developmental defects of enamel) index and otitis media were recorded. Results: Swallowing pattern, exfoliation of teeth, and otitis media were not significantly different. Significant differences could be found concerning mouthbreathing (p = 0.007), oral habits (p = 0.047), age at termination of habits (p = 0.009), speech treatment (p = 0.002), ergotherapy, physiotherapy, and breastfeeding (p ≤ 0.001). DMFT (p ≤ 0.001) and modified DDE (p = 0.001) index showed significantly higher values for children with fetal alcohol syndrome. Conclusions: Children with fetal alcohol syndrome have a higher need for early developmental promotion such as speech treatment, ergotherapy, and physiotherapy. Mouthbreathing, habits, and lack of breastfeeding may result in orthodontic treatment needs. High DMFT and modified DDE indexes hint at a higher treatment and prevention need in dentistry.