Predicting sentinel node positivity in melanoma patients: external validation of a risk‐prediction calculator (the MIA nomogram) using a large European population‐based patient cohort

2021 ◽  
Author(s):  
M.A. El Sharouni ◽  
A.H.R. Varey ◽  
A.J. Witkamp ◽  
T. Ahmed ◽  
V. Sigurdsson ◽  
...  
Keyword(s):  
Sentinel Node ◽  
Risk Prediction ◽  
External Validation ◽  
Population Based ◽  
European Population ◽  
Patient Cohort ◽  
Melanoma Patients

scholarly journals Protocol for the derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery (PAUSE score—Probability of mortality Associated with Urgent/emergent general Surgery in oldEr patients score)

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e034060
Author(s):  
Simon Feng ◽  
Carl Van Walraven ◽  
Manoj Lalu ◽  
Husein Moloo ◽  
Reilly Musselman ◽  
...  
Keyword(s):  
Research Ethics ◽  
Risk Prediction ◽  
General Surgery ◽  
Older Patients ◽  
Prediction Models ◽  
External Validation ◽  
Population Based ◽  
Future Research ◽  
Emergency General Surgery ◽  
Improvement Program

IntroductionPeople 65 years and older represent the fastest growing segment of the surgical population. Older age is associated with doubling of risk when undergoing emergency general surgery (EGS) procedures and often coexists with medical complexity and considerations of end-of-life care, creating prognostic and decisional uncertainty. Combined with the time-sensitive nature of EGS, it is challenging to gauge perioperative risk and ensure that clinical decisions are aligned with the patient values. Current preoperative risk prediction models for older EGS patients have major limitations regarding derivation and validation, and do not address the specific risk profile of older patients. Accurate and externally validated models specific to older patients are needed to inform care and decision making.Methods and analysisWe will derive, internally and externally validate a multivariable model to predict 30-day mortality in EGS patients >65 years old. Our derivation sample will be individuals enrolled in the National Surgical Quality Improvement Program (NSQIP) database between 2012 and 2016 having 1 of 7 core EGS procedures. Postulated predictor variables have been identified based on previous research, clinical and epidemiological knowledge. Our model will be derived using logistic regression penalised with elastic net regularisation and ensembled using bootstrap aggregation. The resulting model will be internally validated using k-fold cross-validation and bootstrap validation techniques and externally validated using population-based health administrative data. Discrimination and calibration will be reported at each step.Ethics and disseminationEthics for NSQIP data use was obtained from the Ottawa Hospital Research Ethics Board; external validation will use routinely collected anonymised data legally exempt from research ethics review. The final risk score will be published in a peer-reviewed journal. We plan to further disseminate the model as an online calculator or application for clinical use. Future research will be required to test the clinical application of the final model.

scholarly journals Diabetes after pregnancy: a study protocol for the derivation and validation of a risk prediction model for 5-year risk of diabetes following pregnancy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Stephanie H. Read ◽  
Laura C. Rosella ◽  
Howard Berger ◽  
Denice S. Feig ◽  
Karen Fleming ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Study Protocol ◽  
Large Population ◽  
External Validation ◽  
Population Based ◽  
Lifestyle Changes ◽  
Risk Prediction Model ◽  
Type 2 Dm

Abstract Background Pregnancy offers a unique opportunity to identify women at higher future risk of type 2 diabetes mellitus (DM). In pregnancy, a woman has greater engagement with the healthcare system, and certain conditions are more apt to manifest, such as gestational DM (GDM) that are important markers for future DM risk. This study protocol describes the development and validation of a risk prediction model (RPM) for estimating a woman’s 5-year risk of developing type 2 DM after pregnancy. Methods Data will be obtained from existing Ontario population-based administrative datasets. The derivation cohort will consist of all women who gave birth in Ontario, Canada between April 2006 and March 2014. Pre-specified predictors will include socio-demographic factors (age at delivery, ethnicity), maternal clinical factors (e.g., body mass index), pregnancy-related events (gestational DM, hypertensive disorders of pregnancy), and newborn factors (birthweight percentile). Incident type 2 DM will be identified by linkage to the Ontario Diabetes Database. Weibull accelerated failure time models will be developed to predict 5-year risk of type 2 DM. Measures of predictive accuracy (Nagelkerke’s R2), discrimination (C-statistics), and calibration plots will be generated. Internal validation will be conducted using a bootstrapping approach in 500 samples with replacement, and an optimism-corrected C-statistic will be calculated. External validation of the RPM will be conducted by applying the model in a large population-based pregnancy cohort in Alberta, and estimating the above measures of model performance. The model will be re-calibrated by adjusting baseline hazards and coefficients where appropriate. Discussion The derived RPM may help identify women at high risk of developing DM in a 5-year period after pregnancy, thus facilitate lifestyle changes for women at higher risk, as well as more frequent screening for type 2 DM after pregnancy.

scholarly journals Comparing the characteristics and predicting the survival of patients with head and neck melanoma versus body melanoma: a population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Health Management ◽  
External Validation ◽  
Area Under The Curve ◽  
Population Based ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Cox Models ◽  
Lentigo Maligna Melanoma ◽  
Clinical Tools

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.

scholarly journals A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Janhavi R. Raut ◽  
Ben Schöttker ◽  
Bernd Holleczek ◽  
Feng Guo ◽  
Megha Bhardwaj ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Prediction ◽  
Risk Score ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Population Based ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Polygenic Scores

AbstractCirculating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50–75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498–0.616) for the ERS and 0.622 (0.564–0.681) for the PRS.

scholarly journals Prenatal and postnatal exposure to acetaminophen in relation to autism spectrum and attention-deficit and hyperactivity symptoms in childhood: Meta-analysis in six European population-based cohorts

2021 ◽  
Author(s):  
Silvia Alemany ◽  
Claudia Avella-García ◽  
Zeyan Liew ◽  
Raquel García-Esteban ◽  
Kosuke Inoue ◽  
...  
Keyword(s):  
Attention Deficit ◽  
Clinical Symptoms ◽  
Meta Analysis ◽  
Collaborative Study ◽  
Population Based ◽  
Autism Spectrum ◽  
European Population ◽  
Autism Spectrum Conditions ◽  
Adhd Symptoms ◽  
Postnatal Exposure

AbstractThe potential etiological role of early acetaminophen exposure on Autism Spectrum Conditions (ASC) and Attention-Deficit/Hyperactivity Disorder (ADHD) is inconclusive. We aimed to study this association in a collaborative study of six European population-based birth/child cohorts. A total of 73,881 mother–child pairs were included in the study. Prenatal and postnatal (up to 18 months) acetaminophen exposure was assessed through maternal questionnaires or interviews. ASC and ADHD symptoms were assessed at 4–12 years of age using validated instruments. Children were classified as having borderline/clinical symptoms using recommended cutoffs for each instrument. Hospital diagnoses were also available in one cohort. Analyses were adjusted for child and maternal characteristics along with indications for acetaminophen use. Adjusted cohort-specific effect estimates were combined using random-effects meta-analysis. The proportion of children having borderline/clinical symptoms ranged between 0.9 and 12.9% for ASC and between 1.2 and 12.2% for ADHD. Results indicated that children prenatally exposed to acetaminophen were 19% and 21% more likely to subsequently have borderline or clinical ASC (OR = 1.19, 95% CI 1.07–1.33) and ADHD symptoms (OR = 1.21, 95% CI 1.07–1.36) compared to non-exposed children. Boys and girls showed higher odds for ASC and ADHD symptoms after prenatal exposure, though these associations were slightly stronger among boys. Postnatal exposure to acetaminophen was not associated with ASC or ADHD symptoms. These results replicate previous work and support providing clear information to pregnant women and their partners about potential long-term risks of acetaminophen use.

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