scholarly journals Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up

2020 ◽  
Vol 10 (23) ◽  
pp. 8591
Author(s):  
Michael Saminsky ◽  
Anat Ben Dor ◽  
Jacob Horwitz
Keyword(s):  
Bone Loss ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Factors Associated ◽  
Implant Placement ◽  
Dental Records

The aim of this study is to evaluate factors associated with long-term peri-implant bone-loss and to create a statistical model explaining bone-loss. The dental records in a private periodontal practice were screened for implant-patients with a minimal follow-up period of 8 years with periapical radiographs at implant-placement (T0) and last follow-up (Tf). Collected data included demographics, general health, medications, periodontal parameters, implant parameters, bone augmentation procedures, restoration and antagonist data, number of supportive periodontal appointments (SPT), and radiographic bone-loss between T0 and Tf. Bivariate and Mixed Logistic Regression analyses were performed. “Goodness-of-fit” of the model was elaborated with Receiver Operating Characteristic Curve (ROC) analyses. Thirty-seven patients receiving 142 implants were included. Mean clinical follow-up period was 11.7 ± 3.7 years (range 8–23). Most implants 64.4% were SPT-maintained more than twice a year. Patients with osteoporosis and smokers were prone to increased radiographic peri-implant bone-loss. External-hex implants placed without guided bone regeneration (GBR) and implants 10–12 mm long and diameter of 3.7–4 mm showed less peri-implant bone-loss. The model’s Area Under the Curve (AUC) was 76.9% (Standard Error 4.6%, CI 67.8%–86%).

No evidence of disease activity is associated with reduced rate of axonal retinal atrophy in MS

Neurology ◽  
2017 ◽  
Vol 89 (24) ◽  
pp. 2469-2475 ◽  
Author(s):  
Marco Pisa ◽  
Simone Guerrieri ◽  
Giovanni Di Maggio ◽  
Stefania Medaglini ◽  
Lucia Moiola ◽  
...  
Keyword(s):  
Disease Activity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Fiber Layer ◽  
Additional Tool ◽  
Disability Status ◽  
Reduced Rate

Objective:To explore, in a longitudinal study, the usefulness of optical coherence tomography (OCT) in monitoring people with multiple sclerosis (MS) by testing the association between retinal nerve fiber layer (RNFL) thinning and clinical and brain MRI criteria of no evidence of disease activity (NEDA).Methods:OCT, visual evoked potentials (VEPs), and disability, using the Expanded Disability Status Scale (EDSS), were tested at baseline and after 2 years in 72 patients, 63 with routine yearly brain MRI.Results:Longitudinal mean binocular RNFL thinning, in absence of optic neuritis during follow-up, was correlated with EDSS worsening, also controlling for baseline EDSS, RNFL, disease duration, and MS subtype (Spearman ρ −0.462, p < 0.001; partial correlation coefficient −0.437, p < 0.001). At follow-up, patients classified as NEDA (20; 31.7%) had RNFL loss of −0.93 μm ± 1.35 SD, while patients with active disease had −2.83 μm ± 2 SD thinning (t test; p < 0.001). At logistic regression, mean RNFL reduction correctly classified 76.2% of patients as NEDA at 2 years (R2 0.355; p = 0.003). A cutoff of −1.25 μm RNFL loss classified NEDA status with specificity 81.4% and sensitivity 80% (receiver operating characteristic curve: area under the curve 0.8; p < 0.001). No significant longitudinal correlations were found between changes in RNFL and in VEP latencies or scores.Conclusions:NEDA is associated with a relatively preserved RNFL over 2 years. A greater neuroretinal loss was detected even in patients with clinical evidence of disease activity independently from changes in brain MRI lesions, prompting further validation of OCT as an additional tool in MS monitoring.

Factors Associated With Crestal Bone Loss Following Dental Implant Placement in a Longitudinal Follow-up Study

2015 ◽  
Vol 41 (5) ◽  
pp. 579-585 ◽  
Author(s):  
Sang Y. Kim ◽  
Thomas B. Dodson ◽  
Duy T. Do ◽  
Gary Wadhwa ◽  
Sung-Kiang Chuang
Keyword(s):  
Bone Loss ◽  
Dental Implant ◽  
Multivariate Model ◽  
Predictor Variables ◽  
Outcome Variable ◽  
Factors Associated ◽  
Implant Placement ◽  
Crestal Bone Loss ◽  
Bone Height

The purpose of this study is to estimate the magnitude of crestal bone loss and to identify factors associated with changes in crestal bone height following placement of dental implants. This was a retrospective cohort study, consisting of a sample derived from the population of patients who had at least 1 dental implant placed in a community practice over a 10-year period. A total of 11 predictor variables were grouped into demographic, related health status, anatomic, implant-specific, and operative categories. The primary outcome variable was a change in crestal bone height (mm) over the course of follow-up. The secondary outcome variable was crestal bone loss at 1 year grouped into 2 categories (bone loss &gt;1.5 mm and ≤1.5 mm). Univariate and multivariate regression mixed-effects models were developed to identify variables associated with crestal bone level changes over time. P values ≤.05 were considered statistically significant. The study sample was composed of 85 subjects who received 148 implants. The mean change of the crestal bone was −2.1 ± 1.5 mm (range = −12.5 to 0.5 mm; median = −1.77 mm). In the multivariate model, none of the variables studied were statistically associated with mean crestal bone loss. Among 84 (66.1%) implants with bone loss &gt;1.5 mm within 1 year, no variables were associated with bone loss in the multivariate model. Of the 11 predictor variables evaluated in this study, none were statistically significant with regard to an increased risk for crestal bone loss or for excessive bone loss within the first year after implant placement.

scholarly journals A Low Level of Darunavir Resistance–Associated Mutation Emergence in Patients With Virological Failure During Long-term Use of Darunavir in People With HIV. The ANRS CO3 Aquitaine Cohort

2020 ◽  
Vol 7 (12) ◽  
Author(s):  
Hélène Chaussade ◽  
Camille Tumiotto ◽  
Fabien Le Marec ◽  
Olivier Leleux ◽  
Lucile Lefèvre ◽  
...  
Keyword(s):  
Protease Inhibitor ◽  
Virological Failure ◽  
Resistance Test ◽  
Resistance Testing ◽  
Factors Associated ◽  
Viral Loads ◽  
Hiv Resistance ◽  
Baseline Characteristics

Abstract Background Ritonavir-boosted darunavir (DRV/r) is a protease inhibitor (PI) indicated for the treatment of naïve and pretreated HIV-infected patients since 2007. Our study aims to describe DRV/r-treated patients experiencing virological failure (VF) documented with HIV resistance testing. Methods Data from patients belonging to the ANRS CO3 Aquitaine Cohort treated with a regimen including DRV/r between February 2007 and December 2015 were analyzed. Baseline characteristics of patients experiencing VF (defined by 2 consecutive plasma viral loads &gt;50 copies/mL) were compared with those without VF. We then described factors associated with VF as emergence of IAS DRV resistance–associated mutations (RAMs). Results Among the 1458 patients treated at least once with a DRV/r-based regimen, 270 (18.5%) patients experienced VF during follow-up, including 240 with at least 1 genotype resistance test (GRT). DRV RAMs were detected in 29 patients (12%). Among them, 25/29 patients had ≥2 DRV RAMs before DRV/r initiation, all of whom had experienced VF during previous PI treatments. For 18/29, DRV/r was maintained after VF, and controlled viremia was restored after modification of DRV-associated antiretroviral molecules or increased DRV dose. Finally, only 6/29 patients selected new DRV RAMs after DRV/r initiation. All of these experienced previous VFs while on other PIs. Conclusions These results highlight the efficacy and robustness of DRV/r, as the emergence of DRV RAMs appeared in &lt;0.4% of patients receiving a DRV/r-based regimen in our large cohort.

scholarly journals Soluble SIGLEC5: A New Prognosis Marker in Colorectal Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3896
Author(s):  
Karla Montalbán-Hernández ◽  
Ramón Cantero-Cid ◽  
Roberto Lozano-Rodríguez ◽  
Alejandro Pascual-Iglesias ◽  
José Avendaño-Ortiz ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Operating Characteristic ◽  
Prognostic Markers ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Significant Heterogeneity ◽  
Kaplan Meier ◽  
Prognosis Marker ◽  
Colorectal Cancer Patients ◽  
Healthy Participants

Colorectal cancer (CRC) is the second most deadly and third most commonly diagnosed cancer worldwide. There is significant heterogeneity among patients with CRC, which hinders the search for a standard approach for the detection of this disease. Therefore, the identification of robust prognostic markers for patients with CRC represents an urgent clinical need. In search of such biomarkers, a total of 114 patients with colorectal cancer and 67 healthy participants were studied. Soluble SIGLEC5 (sSIGLEC5) levels were higher in plasma from patients with CRC compared with healthy volunteers. Additionally, sSIGLEC5 levels were higher in exitus than in survivors, and the receiver operating characteristic curve analysis revealed sSIGLEC5 to be an exitus predictor (area under the curve 0.853; cut-off > 412.6 ng/mL) in these patients. A Kaplan–Meier analysis showed that patients with high levels of sSIGLEC5 had significantly shorter overall survival (hazard ratio 15.68; 95% CI 4.571–53.81; p ≤ 0.0001) than those with lower sSIGLEC5 levels. Our study suggests that sSIGLEC5 is a soluble prognosis marker and exitus predictor in CRC.

scholarly journals Cancer incidence and factors associated with malignancies in coeliac disease during long‐term follow‐up

GastroHep ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 107-115
Author(s):  
Inka Koskinen ◽  
Kaisa Hervonen ◽  
Eero Pukkala ◽  
Timo Reunala ◽  
Katri Kaukinen ◽  
...  
Keyword(s):  
Coeliac Disease ◽  
Cancer Incidence ◽  
Factors Associated ◽  
Long Term Follow Up

scholarly journals Transalveolar nasal floor elevation and implant placement: Long term follow-up case report and description of the technique

2021 ◽  
pp. 100222
Author(s):  
Eduardo Anitua ◽  
Beatriz Anitua ◽  
Mohammad Hamdan Alkhraisat ◽  
Laura Piñas ◽  
Asier Eguia
Keyword(s):  
Case Report ◽  
Implant Placement ◽  
Nasal Floor ◽  
Long Term Follow Up

scholarly journals Prediction of anemia at delivery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Enav Yefet ◽  
Avishag Yossef ◽  
Zohar Nachum
Keyword(s):  
Risk Factors ◽  
Operating Characteristic ◽  
Complete Blood Count ◽  
Characteristic Curve ◽  
Secondary Analysis ◽  
Area Under The Curve ◽  
Iron Supplement ◽  
Independent Risk Factors ◽  
Operating Characteristic Curve ◽  
Study Database

AbstractWe aimed to assess risk factors for anemia at delivery by conducting a secondary analysis of a prospective cohort study database including 1527 women who delivered vaginally ≥ 36 gestational weeks. Anemia (Hemoglobin (Hb) < 10.5 g/dL) was assessed at delivery. A complete blood count results during pregnancy as well as maternal and obstetrical characteristics were collected. The primary endpoint was to determine the Hb cutoff between 24 and 30 gestational weeks that is predictive of anemia at delivery by using the area under the curve (AUC) of the receiver operating characteristic curve. Independent risk factors for anemia at delivery were assessed using stepwise multivariable logistic regression. Hb and infrequent iron supplement treatment were independent risk factors for anemia at delivery (OR 0.3 95%CI [0.2–0.4] and OR 2.4 95%CI [1.2–4.8], respectively; C statistics 83%). Hb 10.6 g/dL was an accurate cutoff to predict anemia at delivery (AUC 80% 95%CI 75–84%; sensitivity 75% and specificity 74%). Iron supplement was beneficial to prevent anemia regardless of Hb value. Altogether, Hb should be routinely tested between 24 and 30 gestational weeks to screen for anemia. A flow chart for anemia screening and treatment during pregnancy is proposed in the manuscript.Trial registration: ClinicalTrials.gov Identifier: NCT02434653.

scholarly journals Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shulun Nie ◽  
Yufang Zhu ◽  
Jia Yang ◽  
Tao Xin ◽  
Song Xue ◽  
...  
Keyword(s):  
Dna Methyltransferase ◽  
Goodness Of Fit ◽  
Clinical Target Volume ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
High Grade Glioma ◽  
Target Volume ◽  
Tumor Location ◽  
High Grade ◽  
Clinical Trial Registry

Abstract Introduction In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy. Materials and methods The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model. Results Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O6-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: YME = 0.672 + 0.513XGrade + 0.380XSVZ + 0.439XMGMT + 0.320XIDH + 0.333X1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001). Conclusion ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients. Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

scholarly journals Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

2020 ◽  
Vol 23 (4) ◽  
pp. 140-145
Author(s):  
Chenlu Li ◽  
Delia A Gheorghe ◽  
John E Gallacher ◽  
Sarah Bauermeister
Keyword(s):  
Machine Learning ◽  
Cognitive Decline ◽  
Psychiatric Disorders ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Imaging Study ◽  
Cognitive Change ◽  
Poor Mental Health ◽  
Uk Biobank

BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

scholarly journals The Optimal Cutoff Value of D-dimer Levels to Predict in Hospital Mortality in Severe Cases of Coronavirus Disease 2019

2021 ◽  
Vol 9 (B) ◽  
pp. 1561-1564
Author(s):  
Ngakan Ketut Wira Suastika ◽  
Ketut Suega
Keyword(s):  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Optimal Cutoff ◽  
Cutoff Value ◽  
Operating Characteristic Curve ◽  
D Dimer

Introduction: Coronavirus disease 2019 (Covid-19) can cause coagulation parameters abnormalities such as an increase of D-dimer levels especially in severe cases. The purpose of this study is to determine the differences of D-dimer levels in severe cases of Covid-19 who survived and non-survived and determine the optimal cut-off value of D-dimer levels to predict in-hospital mortality. Method: Data were obtained from confirmed Covid-19 patients who were treated from June to September 2020. The Mann-Whitney U test was used to determine differences of D-dimer levels in surviving and non-surviving patients. The optimal cut-off value and area under the curve (AUC) of the D-dimer level in predicting mortality were obtained by the receiver operating characteristic curve (ROC) method. Results: A total of 80 patients were recruited in this study. Levels of D-dimer were significantly higher in non-surviving patients (median 3.346 mg/ml; minimum – maximum: 0.939 – 50.000 mg/ml) compared to surviving patients (median 1.201 mg/ml; minimum – maximum: 0.302 – 29.425 mg/ml), p = 0.012. D-dimer levels higher than 1.500 mg/ml are the optimal cut-off value for predicting mortality in severe cases of Covid-19 with a sensitivity of 80.0%; specificity of 64.3%; and area under the curve of 0.754 (95% CI 0.586 - 0.921; p = 0.010). Conclusions: D-dimer levels can be used as a predictor of mortality in severe cases of Covid-19.

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