Fit Of Logistic Regression Model Research Articles

Association between urinary arsenic levels and anemia among older adults in nine longevity areas of China

Objective: To investigate the association between urinary arsenic levels and anemia among older adults in nine longevity areas of China. Methods: A total of 1 896 subjects aged 65 years and above who participated in the Healthy Aging and Biomarkers Cohort Study (HABCS) in 2017-2018 were included. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information from the subjects. Through physical examination, data including height, weight and blood pressure were determined and body mass index (BMI) was calculated. Blood and urine samples were collected for the detection of hemoglobin (Hb), blood glucose, blood lipids, plasma vitamin B12 and urinary arsenic concentrations. The urinary arsenic levels were divided into four groups according to the quartiles of urinary arsenic concentrations (μg/g creatinine): Q1 (<18.7), Q2 (18.7-34.5), Q3 (34.6-69.5) and Q4(≥69.6). Multivariate logistic regression model and restricted cubic spline fitting logistic regression model were used to analyze the association between urinary arsenic levels and anemia. Results: The age of the 1 896 subjects (M (Q1, Q3)) was 83 (74, 92) years, including 952 females (50.21%), and the concentration of Hb (M (Q1, Q3)) was 135 (124, 147)g/L. The prevalence of anemia was 24.89% (472 cases). The geometric mean and M (Q1, Q3) of urinary arsenic concentrations were 37.5 and 34.6 (18.7, 69.6)μg/g creatinine, respectively. Multivariate logistic regression model analysis showed that after adjusting for age, gender, BMI, education level, smoking and drinking status, residence, economic level, ethnicity, the status of vitamin B12 deficiency, consumption frequency of aquatic products and meat, the prevalence of hypertension, diabetes and dyslipidemia, urinary arsenic levels were positively associated with anemia (Taking group Q1 as a reference, OR (95%CI) values in Q2, Q3 and Q4 groups were 1.73 (1.20-2.50), 2.08 (1.43-3.02) and 1.52 (1.02-2.28), respectively). The results of restricted cubic spline fitting logistic regression analysis showed a non-linear association between urinary arsenic concentrations and anemia (P<0.001). Subgroup analysis showed there was a negative multiplicative interaction between the prevalence of chronic diseases and urinary arsenic levels with OR (95%CI) was 0.55 (0.30-0.99), while no multiplicative interaction was found between age, gender, residence, smoking status, drinking status and urinary arsenic levels (P>0.05). There was a positive association between urinary arsenic levels and anemia in participants who were absence of chronic diseases,male, living in rural, smoking and drinking with OR (95%CI) values of 3.62 (1.30-10.06),2.46 (1.34-4.52), 1.70 (1.03-2.80), 2.21 (1.01-4.82) and 2.79 (1.23-6.33), respectively. Conclusion: There is a positive association between urinary arsenic levels and anemia among older adults in nine longevity areas of China.

Association between nutritional status and depression among centenarians in Hainan Province

Objective: To explore the relationship between nutritional status and depression of centenarians in Hainan Province. Methods: A total of 1 002 elderly people in Hainan Province who were 100 years of age or older on June 1, 2014 were included in the study. The basic condition questionnaire, Mini Nutritional Assessment Short-Form (MNA-SF), Instrumental Activities of Daily Living-Lawton scale (Lawton-IADL) and Geriatric Depression Scale (GDS-15) were used to collect the subjects' demographic characteristics, disease history, nutritional status, functional status of daily activities, and depression, respectively. The restricted cubic spline fitting logistic regression model was used to analyze the relationship between the scores of MNA-SF and depression. The multivariable logistic regression model was used to analyze the relationship between nutritional status and depression in the total population and different subgroups of daily activity function. Results: The M (Q1, Q3) of subjects' age was 102 (101, 104) years old, among which 82.0% (822) were women. The prevalence of malnutrition, impaired daily activities, and depression was 20.8% (208 cases), 64.7% (648 cases) and 28.5%(286 cases), respectively. Restricted cubic spline fitting logistic regression model showed a linear association between the scores of the MNA-SF and depression (P=0.251). The higher the MNA-SF score was, the lower the risk of depression was in centenarians. Multivariable logistic regression model analysis showed that after adjusting for sex, education level, diabetes, hypertension, coronary heart disease, visual function, hearing function, and functional status of daily activities, malnutrition was positively associated with the development of depression in the total population and the subgroup with impaired daily activities [OR (95%CI) was 1.50 (1.07-2.11) and 1.56 (1.09-2.24), respectively], but not in the subgroup with good daily activities [OR (95%CI): 0.77 (0.21-2.80)]. Conclusion: Malnutrition is positively associated with depression, especially in the centenarians with impaired daily activities.

Dietary changes and food habits: social and clinical determinants in a cohort of women diagnosed with breast cancer in Barcelona (DAMA cohort).

The aim of this study was to analyze the influence of social determinants on changes in dietary habits before and after diagnosis of breast cancer in women (Barcelona, 2003-2013). We performed a cohort study with 2,235 women diagnosed with breast cancer. The information was obtained from an ad hoc questionnaire based on recommendations from the Spanish Society of Community Nutrition. We conducted a descriptive bivariate analysis and fit logistic regression models. The dependent variable was the change in dietary habits (food groups) and the independent variables were a selection of social and clinical variables (age, social class, cohabitation, years since diagnosis, history of replaces, and treatment with chemotherapy). While 5.8% of women followed a healthy diet (consumption of vegetables, fruits, farinaceous, lean meat, and seafood) before diagnosis, 9.5% did so after diagnosis. We observed statistically significant changes in consumption of all food groups (p < 0.001) after diagnosis. The greatest change in consumption patterns was observed in women aged < 50years and those from non-manual classes (high classes) [e.g., legume consumption: OR<50years/>65years = 2.9 (95% CI 1.78-4.81); ORnon-manual/manual = 2.5 (95% CI 1.38-4.36)]. The occurrence of relapses and chemotherapy was associated with greater changes in dietary habits. Women with breast cancer change their eating habits after diagnosis, and these changes are conditioned by social and clinical determinants.

Modelling Interaction Effects by Using Extended WOE Variables with Applications to Credit Scoring

The term credit scoring refers to the application of formal statistical tools to support or automate loan-issuing decision-making processes. One of the most extended methodologies for credit scoring include fitting logistic regression models by using WOE explanatory variables, which are obtained through the discretization of the original inputs by means of classification trees. However, this Weight of Evidence (WOE)-based methodology encounters some difficulties in order to model interactions between explanatory variables. In this paper, an extension of the WOE-based methodology for credit scoring is proposed that allows constructing a new kind of WOE variable devised to capture interaction effects. Particularly, these new WOE variables are obtained through the simultaneous discretization of pairs of explanatory variables in a single classification tree. Moreover, the proposed extension of the WOE-based methodology can be complemented as usual by balance scorecards, which enable explaining why individual loans are granted or not granted from the fitted logistic models. Such explainability of loan decisions is essential for credit scoring and even more so by taking into account the recent law developments, e.g., the European Union’s GDPR. An extensive computational study shows the feasibility of the proposed approach that also enables the improvement of the predicitve capability of the standard WOE-based methodology.

Relationship of daily total somatic cell output with somatic cell concentration and clinical mastitis

Somatic cell count (SCC) measures the concentration of somatic cells in milk and is used as a mastitis diagnostic tool. It is plausible that variation in milk yield could alter the relationship between SCC and mastitis status. Our objective was to evaluate total daily SCC output as a predictor of clinical mastitis. Data included 37,035 test-day records from 4,179 lactations of 1,679 cows and 1,286 mastitis events from an experimental herd. Daily total SCC was derived by multiplying SCC by daily milk yield in milliliters and transformed to daily total somatic cell score (DTSCS) via a log2 transformation. Milk yield, SCS, and DTSCS were evaluated with mixed models that included the proximity of a mastitis event to the test date and days in milk as the main fixed effects. A second series of logistic regression was conducted that considered mastitis (1 = mastitis occurred during a test interval; 0 = no mastitis) as the dependent variable with milk yield, SCS, and DTSCS the main independent effects. Least squares means for test dates associated with mastitis-free lactations were 2.43 and 2.25 for SCS and DTSCS, respectively. The corresponding values were 5.96 for SCS and 5.66 for DTSCS for the week of a mastitis event. Whereas SCS declined rapidly in early lactation and then increased steadily thereafter, DTSCS was lowest in early lactation and increased by a proportionally smaller amount throughout lactation. Including both SCS and DTSCS in the same model improved the logistic regression model fit over a model with SCS only. Dilution effects from milk yield influence SCS, and consideration of DTSCS in management and genetic selection schemes could improve mastitis detection and resistance.

Use of Neural Network model to examine post-operative infections following orthognathic surgeries in the United States

Objective: The objective of this report is to identify predictors of systemic infections following orthognathic surgeries. We test the hypothesis Neural Network models are better at predicting infections compared to traditional analytical models while using a large number of predictor variables. Methods: The Nationwide Inpatient Sample for the years 2006 to 2014 was used. All patients who had a jaw anomaly and underwent an orthognathic surgery were selected. Two models: Neural Network model and multivariable logistic regression model fit with the Maximum Likelihood method were used to predict systemic infections. Results: 55,839 orthognathic surgeries for maxillary/mandibular hyperplasia/hypoplasia required hospitalization in the United States. The overall systemic infection rate was 1.1%. The success rate for accurately predicting the occurrence of an infection was 98.7% by the Neural Network model. The top six predictors included: age, co-morbid condition of fluid and electrolyte disorders, year of procedure, race, deficiency anemia, and mandibular osteoplasty/segmental or subapical osteotomy. The multivariable logistic regression model had poor goodness of fit. Conclusions: Neural Network model is better at predicting systemic infections following orthognathic surgeries when compared to a traditional analytical model such as multivariable logistic regression approach while using a large set of predictor variables in a nationwide database.

Breastfeeding Is Associated With Lower Prevalence of Metabolic Syndrome in Women With Recent Gestational Diabetes in the Early Postpartum Period

Introduction: Women with gestational diabetes (GD) are at increased risk of future cardiovascular disease. The identification of factors that reduce metabolic syndrome (MetS) is important to improve cardiovascular outcomes. MetS has been shown to be associated with breastfeeding in women remote from pregnancy. We examined the association of breastfeeding with MetS in women with recent GD in the very early postpartum (pp) period. Methods: We performed a secondary analysis of the Balance After Baby (BAB) program which enrolled women with recent GD. GD was defined by Carpenter-Coustan criteria, a 50 gram glucose load test >200 mg/dL or by clinician diagnosis. Data collected during an early (~6 weeks) pp visit was used in this analysis. At this visit, weight, height, waist circumference (WC), blood pressure (BP), fasting plasma glucose (FPG) and lipid panel were obtained. MetS was classified per NCEP III. We defined breastfeeding as currently breastfeeding or not currently breastfeeding. We performed Student’s t-tests and Wilcoxon rank-sum tests as appropriate, and fit logistic and linear regression models. Models were adjusted for age, race/ethnicity, low household income, pre-pregnancy BMI, and weeks since delivery. An exploratory model further adjusted for postpartum weight retention. Results: Of 181 women enrolled in BAB, 178 were included in this analysis (3 excluded for missing lipid panels). The mean (± SD) age of participants was 33 ± 5 years and were 8.0 ± 1.8 weeks since delivery. Thirty-four % were Hispanic. Of non-Hispanics, 31.5% were White, 18.5% Asian and 12.9% Black/African American. The prevalence of MetS was 42.9% in women not breastfeeding versus 17.1% in women breastfeeding (P < 0.001; adjusted odds ratio [aOR] 0.16 [95% CI 0.06–0.41]). Breastfeeding women had significantly lower odds of FPG ≥100 mg/dL (aOR 0.36 [95% CI 0.14–0.95], p=0.039), HDL <50 mg/dL (aOR 0.19 [95% CI 0.08–0.46], p<0.001), and triglycerides (TG) ≥150 mg/dL (aOR 0.26 [95% CI 0.10–0.66], p=0.005). There was no significant difference in WC or BP between groups. All ORs remained significant after adjusting for weight retention. When evaluated as continuous variables, WC, FPG, and TG were significantly lower and HDL significantly higher in women breastfeeding in the early pp period (vs not breastfeeding). Conclusion: In a diverse population of women with recent GD, there was a lower prevalence of MetS in women breastfeeding compared to those not breastfeeding in the early postpartum period. This study extends the findings of an association of breastfeeding with MetS previously reported at times remote from pregnancy. Further studies are needed to determine if there is a protective role of breastfeeding on the risk of MetS.

Evaluating risk factors for acute graft versus host disease in pediatric hematopoietic stem cell transplant patients receiving tacrolimus.

To identify the clinical and pharmacological risk factors associated with tacrolimus pharmacodynamics for acute graft‐versus‐host disease (aGVHD) in pediatric patients receiving allogeneic hematopoietic stem cell transplantation (HSCT) from a matched related donor. A retrospective cohort single center chart review study was conducted with pediatric patients who received tacrolimus prophylaxis after allogeneic HSCT between January 1, 2017, and December 31, 2019. Potential risk factors were tested separately between aGVHD and non‐aGVHD cohorts and were further analyzed in a logistic regression model with backward elimination and a partial least squares discriminant analysis. Thirty‐three patient cases were included in our study and 52% (17/33) developed aGVHD while on tacrolimus prophylaxis. When tested independently, donor age and sibling versus parent donor/recipient relation were shown to be statistically significant between aGVHD and non‐aGVHD patients (p < 0.005). Pharmacological factors associated with tacrolimus treatment failed to demonstrate a significant impact on patient’s risk of aGVHD. Using a best fit logistic regression model that tested all the variables together, donor age was the only significant variable predicting patient’s risk of aGVHD (p < 0.01). Donor relationship and donor age were unable to be evaluated separately and are therefore confounding variables. Among pediatric patients receiving allogeneic HSCT, aGVHD risk is significantly decreased by either sibling donor and/or younger donors. Although no conclusions were drawn on the effect of tacrolimus therapy (p = 0.08), results warrant additional research with a larger sample size to evaluate the accuracy of monitoring tacrolimus serum trough levels.

Mapping indoor radon hazard in Germany: The geogenic component.

Indoor radon is considered as an indoor air pollutant due to its carcinogenic effect. Since the main source of indoor radon is the ground beneath the house, we utilize the geogenic radon potential (GRP) and a geogenic radon hazard index (GRHI) for predicting the geogenic component of the indoor Rn hazard in Germany. For this purpose, we link indoor radon data (n=44,629) to maps of GRP and GRHI and fit logistic regression models to calculate the probabilities that indoor Rn exceeds thresholds of 100Bq/m3 and 300Bq/m3. The estimated probability was averaged for every municipality by considering only the estimates within the built-up area. Finally, the mean exceedance probability per municipality was coupled with the respective residential building stock for estimating the number of buildings with indoor Rn above 100Bq/m3 and 300Bq/m3 for each municipality. We found that (1) GRHI is a better predictor than GRP for indoor radon hazard in Germany, (2) the estimated number of buildings above 100Bq/m3 and 300Bq/m3 in Germany is ~2 million (11.6% of all residential buildings) and~350,000 (1.9%), respectively, (3) areas where 300Bq/m3 exceedance is greater than 10% comprise only 0.8% of the German building stock but 6.3% of buildings with indoor Rn exceeding 300Bq/m3, and (4) most urban areas and, hence, most buildings (77%) are located in low hazard regions. The implications for Rn protection are twofold: (1) the Rn priority area concept is cost-efficient in a sense that it allows to find the most buildings that exceed a threshold concentration with a given amount of resources, and (2) for an optimal reduction of lung cancer risk areas outside of Rn priority areas must be addressed since most hazardous indoor Rn concentrations occur in low to medium hazard areas.

Tuberculosis in the intensive care unit: alternative treatment regimens and association with mortality.

Adequate anti-tuberculosis (TB) treatment is an important factor that can affect the patient's outcome. Higher mortality is found in patients who do not receive optimal treatment that includes isoniazid and rifampicin. The objective of this study is to evaluate the association of use of alternative TB treatment regimens (without rifampicin and isoniazid) and mortality among patients requiring intensive care. Retrospective cohort study, from January 2010 to December 2018. Patients aged >18years with a TB diagnosis, admitted to the ICU of a general, tertiary care, university-affiliated hospital (Hospital de Clínicas de Porto Alegre - HCPA) were included. Data on TB treatment used and outcomes of treatment were collected. 462 patients met the inclusion criteria and were included in the analysis; 284 used the usual treatment regimen (rifampicin, isoniazid, pyrazinamide and ethambutol - all orally), and 178 used alternative treatment regimens (IV levofloxacin plus oral ethambutol plus IM streptomycin or IV amikacin, without rifampicin and isoniazid). The mortality was higher among users of alternative treatment regimens (63.5%) than among usual treatment regimen users (51.4%) (P=0.011). In a multivariate analysis, age, albumin and death were independently associated with alternative treatment regimens use. TB programmes in which IV rifampicin is not widely available should consider including it, especially for critically ill TB patients, for whom there may be improved survival.

Association of state vision screening requirements with parent-reported vision testing in young children

To examine the relationship between state school vision screening requirements and the likelihood that children 3-5years of age receive vision testing. We obtained nationally representative data from the 2016 National Survey of Children's Health on children ages 3-5 (n=7,567) and used available sources to compile state policies that mandate childhood vision testing. We calculated the rates of parent-reported vision testing for each state and fit logistic regression models using survey-based estimation methods with nationally representative weights. Our models controlled for factors such as age, sex, race/ethnicity, and insurance coverage. Additional analyses added comorbidities that may lead to an eye care provider referral. Parent-reported vision testing rates by state ranged from 41% to 84%. A significant association was found between the presence of state-level vision screening requirements and parent-reported vision testing, which remained after controlling for comorbidities (aOR=1.374; P=0.016). Of these comorbidities, arthritis, blindness, and very low birth weight were associated with a higher rate of vision screening (all P<0.05). The presence of a state-level school vision screening requirement is associated with increased parent-reported vision testing in children 3-5years of age. This suggests that state policy may ensure timely screening for amblyopia and other sight-threatening complications.

Multi-level analysis of symptoms of poison exposure reported to the Japanese Poison Information Center.

Two types of analytical multi-level approach to analyse symptoms after exposure to poisons were performed using numerical data extracted from reports received by the JAPAN POISON INFORMATION CENTER on the number "Poisoning 110" during 1998-2017. In the first approach, data collected in 2017, 2012, 2007 and 2002 were used, while in the second approach, all data collected during 1999-2017 were used. In the first approach, according to the odds ratio, the order of causative agents was industrial drugs>home drugs>general drugs and in the second approach, it was industrial drugs>foods/natural poisoning>agricultural drugs. However, in the first approach, the order based on the 95% confidence interval (CI) and profile likelihood was general drugs>home drugs>industrial drugs and in the second approach, the order based on 95% CI, profile likelihood and Wald value was agricultural drugs>foods/natural poisoning>industrial drugs. These multi-prospects were developed using a multilevel approach and the most optimized model was selected from the number of numerical data and the adaptability of fit of the multi-level logistic regression models in this report.

Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test.

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300000 observations.

Rejoinder to "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test".

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.

Discussion on "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test" by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow.

This work was supported by the Marsden Fund (Award Num-ber E2987-3648) administrated by the Royal Society of NewZealand and also supported by grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovacion (MCI,Spain), by the Agencia Estatal de Investigacion (AEI, Spain),and by the European Regional Development Fund FEDER(FEDER, UE). We are grateful to Dr Louise McMillan for helpful comments.

Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow

This work was supported by the Marsden Fund (Award Num-ber E2987-3648) administrated by the Royal Society of NewZealand and also supported by grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovacion (MCI,Spain), by the Agencia Estatal de Investigacion (AEI, Spain),and by the European Regional Development Fund FEDER(FEDER, UE). We are grateful to Dr Louise McMillan for helpful comments.

Discussion on "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test" by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow.

Discussion on "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test" by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow.

FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study

SummaryBackgroundThe burden of non-alcoholic fatty liver disease (NAFLD) is increasing globally, and a major priority is to identify patients with non-alcoholic steatohepatitis (NASH) who are at greater risk of progression to cirrhosis, and who will be candidates for clinical trials and emerging new pharmacotherapies. We aimed to develop a score to identify patients with NASH, elevated NAFLD activity score (NAS≥4), and advanced fibrosis (stage 2 or higher [F≥2]).MethodsThis prospective study included a derivation cohort before validation in multiple international cohorts. The derivation cohort was a cross-sectional, multicentre study of patients aged 18 years or older, scheduled to have a liver biopsy for suspicion of NAFLD at seven tertiary care liver centres in England. This was a prespecified secondary outcome of a study for which the primary endpoints have already been reported. Liver stiffness measurement (LSM) by vibration-controlled transient elastography and controlled attenuation parameter (CAP) measured by FibroScan device were combined with aspartate aminotransferase (AST), alanine aminotransferase (ALT), or AST:ALT ratio. To identify those patients with NASH, an elevated NAS, and significant fibrosis, the best fitting multivariable logistic regression model was identified and internally validated using boot-strapping. Score calibration and discrimination performance were determined in both the derivation dataset in England, and seven independent international (France, USA, China, Malaysia, Turkey) histologically confirmed cohorts of patients with NAFLD (external validation cohorts). This study is registered with ClinicalTrials.gov, number NCT01985009.FindingsBetween March 20, 2014, and Jan 17, 2017, 350 patients with suspected NAFLD attending liver clinics in England were prospectively enrolled in the derivation cohort. The most predictive model combined LSM, CAP, and AST, and was designated FAST (FibroScan-AST). Performance was satisfactory in the derivation dataset (C-statistic 0·80, 95% CI 0·76–0·85) and was well calibrated. In external validation cohorts, calibration of the score was satisfactory and discrimination was good across the full range of validation cohorts (C-statistic range 0·74–0·95, 0·85; 95% CI 0·83–0·87 in the pooled external validation patients' cohort; n=1026). Cutoff was 0·35 for sensitivity of 0·90 or greater and 0·67 for specificity of 0·90 or greater in the derivation cohort, leading to a positive predictive value (PPV) of 0·83 (84/101) and a negative predictive value (NPV) of 0·85 (93/110). In the external validation cohorts, PPV ranged from 0·33 to 0·81 and NPV from 0·73 to 1·0.InterpretationThe FAST score provides an efficient way to non-invasively identify patients at risk of progressive NASH for clinical trials or treatments when they become available, and thereby reduce unnecessary liver biopsy in patients unlikely to have significant disease.FundingEchosens and UK National Institute for Health Research.

Risk, Resilience, and Smoking in a National, Probability Sample of Sexual and Gender Minority Adults, 2017, USA.

Background. There are well-documented inequities in smoking between sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, and transgender [LGBT]) and straight and cisgender people. However, there is less information about risk for and resilience against smoking among SGM people. Such information is critical for understanding etiology and developing interventions. Aims. To conduct a within-group assessment of risks and resiliencies relating to smoking status. Method. In 2017, we conducted a cross-sectional telephone survey with a national, probability-based sample of SGM adults (N = 453). We assessed theory-informed risks (adverse childhood events, substance use-oriented social environment, mental distress, stigma, discrimination, social isolation, and identity concealment) and resiliencies (advertising skepticism, identity centrality, social support, and SGM community participation). We applied survey weights, standardized predictor variables, and fit logistic regression models predicting smoking status. We stratified by age and SGM identity. Results. Patterns of risk and resilience differ by age and identity. Effects were consistently in the same direction for all groups for participating in substance use-oriented social environments, pointing to a potential risk factor for all groups. Advertising skepticism and having people you can talk to about being LGBTQ were potential protective factors. Discussion. Intervention development should address risk and resilience that differs by SGM identity. Additionally, our findings suggest interventionists should consider theoretical frameworks beyond minority stress. Conclusion. While much of the literature has focused on the role of stress from stigma and discrimination in tobacco use, addressing social norms and bolstering protective factors may also be important in SGM-targeted interventions.

Validation of ART Calculator for Predicting the Number of Metaphase II Oocytes Required for Obtaining at Least One Euploid Blastocyst for Transfer in Couples Undergoing in vitro Fertilization/Intracytoplasmic Sperm Injection.

This multicenter study evaluated the reliability of the recently published ART calculator for predicting the minimum number of metaphase II (MII) oocytes (MIImin) to obtain at least one euploid blastocyst in patients undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). We used clinical and embryonic retrospective data of 1,464 consecutive infertile couples who underwent IVF/ICSI with the intention to have preimplantation genetic testing for aneuploidy. The validation procedure followed a stepwise approach. Firstly, we assessed the distribution of euploid blastocysts per patient and found that it followed a negative binomial distribution. Secondly, we used generalized linear models and applied the Lasso procedure–including MII oocytes to adjust the data–to select the factors predicting the response variable “euploid blastocyst.” Third, a logistic regression model–fit to the binomial response euploid (yes/no) for each MII oocyte–was built using the relevant factors. The observational unit was the “woman” whereas the response was the pair (m, n), where n is the number of retrieved MII oocytes and m the corresponding number of euploid blastocysts. The model was internally validated by randomly splitting the data into training and validation sets. The R-squares (~0.25) and the area under the ROC curve (~0.70) did not differ between the training and validation datasets. Fourth, mathematical equations and the calculated probabilities generated by the validation model were used to determine the MIImin required for obtaining at least one euploid blastocyst according to different success probabilities. Lastly, we compared the fittings generated by the validation model and the ART calculator and assessed the predictive value of the latter using the validation dataset. The fittings were sufficiently close for both the estimated probabilities of blastocyst euploid per MII oocyte (r = 0.91) and MIImin (r = 0.88). The ART calculator positive predictive values, i.e., the frequency of patients with at least one euploid blastocyst among those who achieved the estimated MIImin, were 84.8%, 87.5%, and 90.0% for 70%, 80%, and 90% predicted probabilities of success, respectively. The ART calculator effectively predicts the MIImin needed to achieve at least one euploid blastocyst in individual patients undergoing IVF/ICSI. The prediction tool might be used for counseling and planning IVF/ICSI treatments.