scholarly journals How Do Biological Characteristics of Primary Intracranial Tumors Affect Their Clinical Presentation in Children and Young Adults?

2018 ◽  
Vol 33 (8) ◽  
pp. 503-511
Author(s):  
Thomas P. C. Chu ◽  
Anjali Shah ◽  
David Walker ◽  
Michel P. Coleman
Keyword(s):  
Primary Care ◽  
Rate Ratio ◽  
Malignant Tumors ◽  
Generalized Additive Models ◽  
Similar Rate ◽  
Population Based ◽  
Additive Models ◽  
Benign Tumors ◽  
Intracranial Tumors ◽  
Aggressive Tumors

We demonstrated the pattern in presentation of primary intracranial tumors in a population-based cohort of patients aged 0-24 years identified from the National Cancer Registry for England, using linked medical records from primary care and hospitals. We used generalized additive models to estimate temporal changes in presentation rates. Borderline and malignant tumors presented at a similar rate in primary care (6.4 and 6.6 consultations per 100 patients each month) and in hospital (3.4 and 3.6). Benign tumors presented earlier but less frequently (rate = 4.4 and rate ratio = 0.75, 95% CI = 0.60-0.93, in primary care; rate = 2.6 and rate ratio = 0.83, 95% CI = 0.77-0.89, in hospital). Many tumors began presenting shortly before their diagnosis, but less aggressive tumors were likely to present earlier in primary care. Earlier detection of less aggressive tumors in primary care may reduce the risk of complications and morbidity among survivors.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Generalized Additive Models ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Patient Health Status ◽  
Patient Health

Abstract Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.

scholarly journals Prenatal Manganese Exposure and Long-Term Neuropsychological Development at 4 Years of Age in a Population-Based Birth Cohort

2020 ◽  
Vol 17 (5) ◽  
pp. 1665
Author(s):  
Ainara Andiarena ◽  
Amaia Irizar ◽  
Amaia Molinuevo ◽  
Nerea Urbieta ◽  
Izaro Babarro ◽  
...  
Keyword(s):  
Motor Development ◽  
Generalized Additive Models ◽  
Epidemiological Studies ◽  
Population Based ◽  
Specific Pattern ◽  
Additive Models ◽  
Linear Regression Models ◽  
Neuropsychological Development ◽  
Manganese Exposure ◽  
Stratified Analysis

Background: Manganese (Mn) is an essential micronutrient for humans, the diet being the main source of exposure. Some epidemiological studies describe a negative association between prenatal Mn and later neuropsychological development, but results are inconsistent. The aim of this study was to explore the association between prenatal Mn exposure and neuropsychological development assessed at 4 years of age. Methods: Study subjects were 304 mother-child pairs from the Gipuzkoa cohort of the INMA (Environment and Childhood) Project. Mn was measured in newborns’ hair. Children’s neuropsychological development was assessed at 4 years of age using the McCarthy Scales of Children’s Abilities. Multivariate linear regression models were built. Stratified analysis by sex was performed. Generalized additive models were used to assess the shape of the relation. Results: The median Mn concentration in newborns’ hair was 0.42 μg/g (95% CI = 0.38, 0.46). The association between Mn levels and the neuropsychological development was not statistically significant for the general cognitive scale (β [95% CI] = 0.36 [−5.23, 5.95]), motor scale (β [95% CI] = 1.9 [−3.74, 7.55]) or any of the other outcomes. No sex-specific pattern was found. The best shape describing the relationship was linear for all the scales. Conclusion: Our results suggest that prenatal Mn concentrations measured in newborns’ hair do not affect cognitive or motor development at 4 years of age in boys or in girls at the observed Mn levels.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background: Understanding how comorbidity measures contribute to patient mortality are essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new population-based Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weightings in an adult in-patient population-based cohort of general hospitals. Methods: Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results: Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently, yet minimally higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862-0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups. The net reclassification improvement of new weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weightings.Conclusions: All weightings confirmed previous results with the national dataset. The new population-based weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

scholarly journals Hotspots of childhood obesity in a large metropolitan area: does neighbourhood social and built environment play a part?

2019 ◽  
Vol 49 (3) ◽  
pp. 934-943 ◽  
Author(s):  
Ana Isabel Ribeiro ◽  
Ana Cristina Santos ◽  
Verónica M Vieira ◽  
Henrique Barros
Keyword(s):  
Childhood Obesity ◽  
Built Environment ◽  
Metropolitan Area ◽  
Fast Food ◽  
Generalized Additive Models ◽  
Geographic Area ◽  
Population Based ◽  
Additive Models ◽  
Individual Level ◽  
Neighbourhood Characteristics

Abstract Background Effective place-based interventions for childhood obesity call for the recognition of the high-risk neighbourhoods and an understanding of the determinants present locally. However, such an approach is uncommon. In this study, we identified neighbourhoods with elevated prevalence of childhood obesity (‘hotspots’) in the Porto Metropolitan Area and investigated to what extent the socio-economic and built environment characteristics of the neighbourhoods explained such hotspots. Methods We used data on 5203 7-year-old children from a population-based birth cohort, Generation XXI. To identify hotspots, we estimated local obesity odds ratios (OR) and 95% confidence intervals (95%CI) using generalized additive models with a non-parametric smooth for location. Measures of the socio-economic and built environment were determined using a Geographic Information System. Associations between obesity and neighbourhood characteristics were expressed as OR and 95%CI after accounting for individual-level variables. Results At 7 years of age, 803 (15.4%) children were obese. The prevalence of obesity varied across neighbourhoods and two hotspots were identified, partially explained by individual-level variables. Adjustment for neighbourhood characteristics attenuated the ORs and further explained the geographic variation. This model revealed an association between neighbourhood socio-economic deprivation score and obesity (OR = 1.014, 95%CI 1.004–1.025), as well as with the presence of fast-food restaurants at a walkable distance from the residence (OR = 1.37, 1.06–1.77). Conclusions In our geographic area it was possible to identify neighbourhoods with elevated prevalence of childhood obesity and to suggest that targeting such high-priority neighbourhoods and their environmental characteristics may help reduce childhood obesity.

The probability of indolent versus aggressive histology based on renal tumor size: Implications for surveillance and treatment.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 704-704
Author(s):  
Bimal Bhindi ◽  
Robert Houston Thompson ◽  
Christine M. Lohse ◽  
Ross Mason ◽  
Igor Frank ◽  
...  
Keyword(s):  
Decision Making ◽  
Tumor Size ◽  
Clear Cell ◽  
Renal Tumor ◽  
Malignant Tumors ◽  
Benign Tumors ◽  
Low Grade ◽  
Patient Counseling ◽  
Aggressive Histology ◽  
Aggressive Tumors

704 Background: While the probability of benign versus malignant histology based on renal tumor size has been described, this alone does not sufficiently inform decision-making in the modern era since indolent malignant tumors can be surveilled. Thus, we sought to characterize the probability of indolent versus aggressive histology based on radiographic tumor size. Methods: We evaluated patients who underwent radical or partial nephrectomy at Mayo Clinic for a pT1-2, pNx/0, M0 solid renal tumor between 1990-2010. Pathology was reviewed by one genitourinary pathologist. Benign tumors, low grade (1-2) clear cell and papillary renal cell carcinoma (RCC), and any chromophobe, clear cell papillary, mucinous tubular and spindle cell, SDH-B deficient, and tubulocystic RCC were considered indolent. All other histologies were considered aggressive, as were any malignancies with necrosis or sarcomatoid differentiation. Cancer-specific survival (CSS) was estimated using the Kaplan Meier method. Logistic regression models were used to estimate the probability of malignant and aggressive histology based on tumor size. Sex-stratified analyses were also performed. Results: Of the 2650 patients included, there were 1773 patients with indolent tumors (303 benign; 1470 malignant) and 877 with aggressive tumors. Ten-year CSS was 96% for indolent malignant tumors and 82% for aggressive tumors. The predicted probabilities of any malignant histology and aggressive malignant histology increased with tumor size (Table; 1-7cm point estimates shown). For example, a 3 cm tumor had an 87% probability of malignancy and a 27% probability of being aggressive. For any given tumor size, men had a greater probability of aggressive histology than women. Conclusions: We present tumor size-based estimates of the probability of aggressive histology for renal masses. This information should be useful for patient counseling and treatment decision-making. [Table: see text]

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background: Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weightings in an adult in-patient population-based cohort of general hospitals. Methods: Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results: Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI: 0.865–0.868) and van Walraven’s weights (0.863, 95% CI: 0.862-0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI: 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weightings.Conclusions: All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

scholarly journals Incidence of Benign and Malignant Tumors in Patients with Acromegaly is Increased: a Nationwide Population-Based Study

2021 ◽  
Author(s):  
Daniela Esposito ◽  
Oskar Ragnarsson ◽  
Gudmundur Johannsson ◽  
Daniel S Olsson
Keyword(s):  
General Population ◽  
Anal Cancer ◽  
Malignant Tumors ◽  
Population Based ◽  
Benign Tumors ◽  
Medical Surveillance ◽  
Population Based Study ◽  
Increased Risk ◽  
National Patient ◽  
Standardized Mortality Ratios

Abstract Context Whether cancer risk in acromegaly is increased remains controversial. Also, the risk of benign tumors has been little studied. Objective To investigate the incidence of benign and malignant tumors in acromegaly in a nationwide population-based study. Design Adult patients diagnosed with acromegaly between 1987 and 2017 were identified in the Swedish National Patient Registry. The diagnoses of benign and malignant tumors were recorded. Standardized incidence ratios (SIRs) and standardized mortality ratios (SMRs) for neoplasms with 95% confidence intervals (CIs) were calculated using the Swedish general population as reference. Results The study included 1296 patients (52% women). Mean (SD) age at diagnosis was 51.6 (14.7) years. Median (range) follow-up time was 11.7 (0-31) years. Overall, 186 malignancies were identified in acromegalic patients compared to 144 expected in the general population (SIR 1.3; 95% CI, 1.1-1.5). The incidence of colorectal and anal cancer (SIR 1.5; 95% CI, 1.0-2.2), and renal and ureteral cancer (SIR 4.0; 95% CI, 2.3-6.5) was increased, whereas the incidence of malignancies of the respiratory system, brain, prostate, and breast was not. Only three cases of thyroid cancer were recorded. Mortality due to malignancies was not increased (SMR 1.1; 95% CI, 0.9-1.4). Incidence of benign tumors was increased more than 2-fold (SIR 2.4; 95% CI, 2.1-2.7). Conclusions Patients with acromegaly had an increased risk of both benign and malignant tumors including colorectal and anal cancer, and renal and ureteral cancer. Whether this is associated with acromegaly itself or due to more intensive medical surveillance remains to be shown.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2020 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background: Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods: Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results: Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI: 0.865–0.868) and van Walraven’s weights (0.863, 95% CI: 0.862-0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI: 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights.Conclusions: All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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