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Purpose: To predict the probability of azoospermia without a semen analysis in men presenting with infertility by developing an azoospermia prediction model. Materials and Methods: Two predictive algorithms were generated, one with follicle stimulating hormone (FSH) as the only input and another logistic regression (LR) model with additional clinical inputs of age, luteinizing hormone, total testosterone, and bilateral testis volume. Men presenting between 01/2016 and 03/2020 with semen analyses, testicular ochiodemetry, and serum gonadotropin measurements collected within 120 days were included. An azoospermia prediction model was developed with multi-institutional two-fold external validation from tertiary urologic infertility clinics in Chicago, Miami, and Milan. Results: Total 3,497 participants were included (n=Miami 946, Milan 1,955, Chicago 596). Incidence of azoospermia in Miami, Milan, and Chicago was 13.8%, 23.8%, and 32.0%, respectively. Predictive algorithms were generated with Miami data. On Milan external validation, the LR and quadratic FSH models both demonstrated good discrimination with areas under the receiver-operating-characteristic (ROC) curve (AUC) of 0.79 and 0.78, respectively. Data from Chicago performed with AUCs of 0.71 for the FSH only model and 0.72 for LR. Correlation between the quadratic FSH model and LR model was 0.95 with Milan and 0.92 with Chicago data. Conclusions: We present and validate algorithms to predict the probability of azoospermia. The ability to predict the probability of azoospermia without a semen analysis is useful when there are logistical hurdles in obtaining a semen analysis or for reevaluation prior to surgical sperm extraction. [ABSTRACT FROM AUTHOR]

Objective To explore the diagnostic value of combining pediatric appendicitis score (PAS) with high-frequency ultrasound for appendicitis types in children over 5 years of age. Methods Retrospective analysis was performed for 394 hospitalized appendicitis children fulfilling the inclusion criteria from January 2018 to December 2020. There were 270 boys and 124 girls with a mean age of (8. 94 ± 2. 28) years. Depending on intraoperative finding and the final histology, patients were classified into two groups of simple appendicitis (n = 113) and non-simple appendicitis (n = 281). The effects of general data, PAS and ultrasonic images were compared between two groups. Fisher's stepwise discriminant analysis was employed for establishing a discriminant function by applying significant variables. Then the diagnostic efficacy of this model was verified by self-test, cross-validation and validation group. Results Univariate analysis indicated that 13 factors were statistically different between two group (P < 0. 05) . Seven parameters of PAS, illegibility of wall layers, count of leucocyte, duration of symptoms, periappendiceal fluid, enlarged appendix and thickened periappendiceal fat were crnsidered ultimately. The discriminant function equation was Y1 = - 14. 446 - 1. 312X3 + 1. 102X5 + 0. 481X2 + 0. 065X1 - 1. 884X6 + 4. 046X4 + 3. 878X7, Y2 = - 36. 834 + 1. 509X3 + 4. 297 X5 + 0. 687 X2 + 0. 110X1 -0. 167X6 + 3. 432X4 + 0. 874X7. The results obtained by self-test and cross-validation all indicated that the accuracy rate of discriminant function was 94. 7% (373/394); The results verified by validation group showed that the accuracy rate of discriminant function was 94. 4% (84/89). Conclusion Combining PAS with high-frequency ultrasound is useful in distinguishing the types of appendicitis in children aged above 5 years. Thus clinicians may formulate re111onahle treatments for acute appendicitis in children. [ABSTRACT FROM AUTHOR]

Background: Hospital outbreaks of multidrug resistant Pseudomonas aeruginosa are often caused by Pseudomonas aeruginosa clones which produce metallo-β-lactamases, such as Verona Integron-encoded Metallo-β-lactamase (VIM). Although different sources have been identified, the exact transmission routes often remain unknown. However, quantifying the role of different transmission routes of VIM-PA is important for tailoring infection prevention and control measures. The aim of this study is to quantify the relative importance of different transmission routes by applying a mathematical transmission model using admission and discharge dates as well as surveillance culture data of patients. Methods: We analyzed VIM-PA surveillance data collected between 2010 and 2018 of two intensive-care unit (ICU) wards for adult patients of the Erasmus University Medical Center Rotterdam using a mathematical transmission model. We distinguished two transmission routes: direct cross-transmission and a persistent environmental route. Based on admission, discharge dates, and surveillance cultures, we estimated the proportion of transmissions assigned to each of the routes. Results: Our study shows that only 13.7% (95% CI 1.4%, 29%) of the transmissions that occurred in these two ICU wards were likely caused by cross-transmission, leaving the vast majority of transmissions (86.3%, 95% CI 71%, 98.6%) due to persistent environmental contamination. Conclusions: Our results emphasize that persistent contamination of the environment may be an important driver of nosocomial transmissions of VIM-PA in ICUs. To minimize the transmission risk from the environment, potential reservoirs should be regularly and thoroughly cleaned and disinfected, or redesigned. [ABSTRACT FROM AUTHOR]

Background: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms.Methods: We retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient's electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported.Results: The Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799).Conclusion: The Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance. [ABSTRACT FROM AUTHOR]

The CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist was created to provide methodological appraisals of predictive models, based on the best available scientific evidence and through systematic reviews. Our purpose is to give a general presentation on how to carry out a CHARMS analysis for prognostic multivariate models, making clear what the steps are and how they are applied individually to the studies included in the systematic review. This tutorial is aimed at providing such a resource. In addition to this explanation, we will apply the method to a real case: predictive models of atrial fibrillation in the community. This methodology could be applied to other predictive models using the steps provided in our review so as to have complete information for each included model and determine whether it can be implemented in daily clinical practice. [ABSTRACT FROM AUTHOR]

Objective To establish a clinical anatomic composite grading system and to evaluate the combined effects of the European Heart Surgery Risk Scoring System (EuroSCORE) Ⅱ and SYNTAX scoring system in predicting the prognosis of patients undergoing coronary artery bypass grafting (CABG). Methods The perioperative data of CABG patients in Tianjin chest hospital from July 2017 to December 2018 were retrospectively analyzed. EuroSCOREⅡ and SYNTAX were used to stratify the cardiovascular risk of patients. According to the classification of the two systems, they were arranged and redistributed to construct the clinical anatomy composite grading system. The discrimination and calibration of the system were evaluated compared by EuroSCOREⅡ and SYNTAX. Results The actual hospital mortality for 301 patients was 1.54% (20/1 301). According to the comparison of baseline data after grading by scoring system, there were significant differences between EuroSCORE Ⅱ groups except unstable angina pectoris and SYNTAX. There were significant differences in age, unstable angina pectoris and 30-day death between SYNTAX groups. There were significant differences in other variables except unstable angina pectoris, NYHA and peripheral arterial disease between composite grading system groups. The results of H-L goodness of fit test showed that EuroSCORE Ⅱ, SYNTAX and composite grading system had good fitting degree. ROC curve analysis showed that the accuracy of composite classification system was the best, (AUC=0.862). AUC of EuroSCORE Ⅱ and SYNTAX were 0.849 and 0.732 (P＜0.01). This indicates that the composite grading system can significantly improve the predictive ability of 30-day mortality after operation. Compared with EuroSCORE Ⅱ, NRI of composite grading system was 14.05% (Z=7.205, P＜0.01), compared with SYNTAX, NRI=37.85% (Z=4.240, P＜0.01). Conclusion The composite classification system of EuroSCORE Ⅱ and SYNTAX can improve the ability of single type scoring system to predict the short-term mortality of CABG. [ABSTRACT FROM AUTHOR]

Objectives: To determine trajectories of glycemic control and body mass index (BMI) z‐score in a large pediatric sample with type 1 diabetes (T1D) over a 38‐year period, and to evaluate sex differences and temporal changes in the prevalence of these trajectories. Methods: We conducted a longitudinal, retrospective study of 7002 2 to 18 year olds with T1D followed between 1978 and 2016 at a single center. Group‐based modeling was used to identify trajectories for hemoglobin A1c (HbA1c) and BMI z‐score. Multinomial logistic regression identified predictors of membership to less favorable glycemic trajectories. Results: Group‐based modeling yielded 5 HbA1c trajectories. A total of 86% of the sample fell within 3 trajectories that were largely stable across childhood and adolescence, and 14% fell within 2 trajectories characterized by marked deterioration beginning in pre‐adolescence. Girls were more likely to be in the HbA1c trajectory with the highest starting HbA1c and significant deterioration during adolescence, and in the highest two BMI z‐score trajectories. Patients with non‐white race had the highest odds of belonging to a less favorable HbA1c trajectory. Prevalence of the high stable HbA1c trajectory decreased and prevalence of the low stable HbA1c trajectory increased over the study period. Conclusions: A minority of youth with T1D experienced deterioration of glycemic control during adolescence. Girls were more likely to belong to the worst HbA1c trajectory and to BMI z‐score trajectories in the overweight/obese range, which may increase cardiometabolic risk. Addressing racial/ethnic disparities in glycemic control should remain a priority. Advances in T1D management correlated with favorable shifts in HbA1c trajectory prevalence. [ABSTRACT FROM AUTHOR]

Background: Bridging anticoagulation is commonly prescribed to patients with atrial fibrillation during initiation and interruption of warfarin. Guidelines recommend bridging patients at high risk of stroke, while a recent randomized trial demonstrated overall harm in a population at comparatively low risk of ischemic stroke. Theory suggests that patients at high risk of stroke and low risk of hemorrhage may benefit from bridging, but data informing patient selection are scant.Objective: To estimate the utility and cost-effectiveness of bridging anticoagulation among patients with nonvalvular atrial fibrillation, stratified by thromboembolic and hemorrhagic risk DESIGN: Cost-effectiveness analysis with lifelong time horizon, from the perspective of a third-party payer MAIN MEASURES: Quality-adjusted life years (QALYs) per bridged patient; US dollars per QALY gained KEY RESULTS: Unselected patients with nonvalvular atrial fibrillation may be harmed by bridging anticoagulation. Hospital admission for bridging is almost never cost-effective, and generally harmful. Among patients carefully selected by both thromboembolic and hemorrhagic risks, outpatient bridging can be beneficial and cost-effective. Results were sensitive to how effectively heparin products reduce stroke risk.Conclusions: Outpatient bridging anticoagulation can be beneficial and cost-effective for a subset of patients with nonvalvular atrial fibrillation during interruption or initiation of warfarin. Admission for bridging should be avoided. [ABSTRACT FROM AUTHOR]

Objectives: This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM).Methods: Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response.Results: 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05).Conclusion: Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies.Key Points: • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments. [ABSTRACT FROM AUTHOR]

BACKGROUND: Nerve root compression of lumbar disc herniation is difficult to diagnose. Pattern recognition technology combined with surface electromyography will provide new ideas for improving the diagnostic accuracy of compressed nerve roots. OBJECTIVE: To establish the recognition model of the nerve root compression of lumbar disc herniation through three kinds of pattern recognition methods, and to analyze the diagnostic accuracy of the models. METHODS: Twenty-four cases of disc herniation at L4/L5 segments combined with L5 nerve root compression and 23 cases of disc herniation at L5/S1 segments combined with S1 nerve root compression from October 2015 to October 2016 were enrolled. The surface electromyography parameters were collected and the Logistic regression equation, decision tree and artificial neural network were used to establish the identification model of compressed nerve roots. The sensitivity, specificity and diagnosis accuracy of the three models were calculated. The diagnosis accuracy was compared by receiver operating characteristic curve. REEULTS AND CONCLUSSION: (1) The logistic regression model had established the three models and the accuracy increased from 85.7% to 100%, with an average of 93.6%, and the sensitivity and specificity of the model was 0.98 and 0.92, respectively. (2) The Chi-squared Automatic Interaction Detector showed an accuracy of 42.86%-85.71%, with an average of 66.43%, the sensitivity and specificity of the model was 0.77 and 0.56, respectively. (3) The Classification and Regression Tree showed an accuracy of 57.14%-85.71%, with an average of 72.14%, the sensitivity and specificity of the model was 0.71 and 0.73, respectively. (4) The neural network model showed an accuracy of 85.7%-100%, with an average of 92.14%, and the sensitivity and specificity of the model were 0.93 and 0.92, respectively. (5) The area under the Receiver Operating Characteristic Curve was used to evaluate the three models, and the neural network was 0.98, the logistic regression was 0.97, and the decision tree was 0.90. (6) These results indicate that both neural network model and the logic regression model show satisfactory results in recognition of the compressed nerve roots, which are superior to MRI. The neural network model is more stable and it may be a more suitable auxiliary method for the diagnosis of nerve root compression. The Logistic regression model is suitable when no neural network diagnostic model is established. The decision tree shows a good performance in the screening of risk factors, and which can be combined with other methods to improve the recognition accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Only one prognostic model for laryngeal cancer has been published, but it has not been properly validated and is only applicable to patients treated with radiotherapy. Consequently, we constructed, internally validated and implemented in an App (Android), a predictive model of 5‐year mortality in patients with glottic cancer in a cohort study of 189 patients with glottic cancer in 2004–2016 in Spain. The main variable was time‐to‐death. Secondary variables were age, gender, TNM, stage, smoking, alcohol consumption, histology and treatment. A scoring system to predict mortality at 5 years was constructed, validated internally by bootstrapping and then integrated into an Android app. In all, 70 patients died (37.0%, 76 deaths per 1,000 patient‐years). The predictive model had the following prognostic factors: larger tumour size, greater degree of lymph node metastasis, higher stage, smoking and alcohol consumption. The internal validation of the model through bootstrapping was satisfactory. In conclusion, a points system to predict mortality at 5 years in patients with glottic cancer has been constructed, internally validated and integrated into an Android application. External validation is suggested to make available a quick and simple tool to establish the prognosis for these patients. [ABSTRACT FROM AUTHOR]

Building upon evidence linking socio-economic position (SEP) in childhood and adulthood with health-related behaviours (HRB) in adulthood, we examined how pre-adolescent SEP predicted membership of three HRB clusters: "Risky", "Moderate Smokers" and "Mainstream" (the latter pattern consisting of more beneficial HRBs), that were detected in our previous work. Data were taken from two British cohorts (born in 1958 and 1970) in pre-adolescence (age 11 and 10, respectively) and adulthood (age 33 and 34). SEP constructs in pre-adolescence and adulthood were derived through Confirmatory Factor Analysis. Conceptualised paths from pre-adolescent SEP to HRB cluster membership via adult SEP in our path models were tested for statistical significance separately by gender and cohort. Adult SEP mediated the path between pre-adolescent SEP and adult HRB clusters. More disadvantaged SEP in pre-adolescence predicted more disadvantaged SEP in adulthood which was associated with membership of the "Risky" and "Moderate Smokers" clusters compared to the "Mainstream" cluster. For example, large positive indirect effects between pre-adolescent SEP and adult HRB via adult SEP were present (coefficient 1958 Women = 0.39; 1970 Women = 0.36, 1958 Men = 0.51; 1970 Men = 0.39; p < 0.01) when comparing "Risky" and "Mainstream" cluster membership. Amongst men we found a small significant direct association (p < 0.001) between pre-adolescent SEP and HRB cluster membership. Our findings suggest that associations between adult SEP and HRBs are not likely to be pre-determined by earlier social circumstances, providing optimism for interventions relevant to reducing social gradients in HRBs. Observing consistent findings across the cohorts implies the social patterning of adult lifestyles may persist across time. [ABSTRACT FROM AUTHOR]

Background: Lung transplantation (LTx) is a well-accepted treatment that can prolong survival of patients with advanced lung disease. Objective: To evaluate the association between serum cyclosporine level (SCL) pattern and mortality of LTx recipients. Methods: This retrospective cohort study included 1019 observations on 38 patients who underwent LTx in Masih Daneshvari Hospital from 2000 to 2013. The analysis applied a joint model with shared random effects. Results: The mean±SD age of the recipients was 36±14.5 years. The findings indicated that sex, age, body mass index (BMI), the underlying disease, and cytomegalovirus infection were not associated with mortality. The mortality risk for the recipients with acute rejection (AR) history was 1.54 times that of the recipients who had none (95% CI: 1.08-2.19). The association parameter in the joint model (a = -0.8) showed that higher SCL was associated with lower mortality risk (95% CI: 1-1.011). A slightly linear decreasing trend in SCL mean was found after 10 months post-LTx; a significant 2% per month (95% CI: -0.03 to -0.019). Conclusion: AR history was found to be a risk factor in mortality in Iranian LTx recipients. Given the association between the higher SCL and lower mortality found in this study, it is recommended to pay serious attention to SCL changes in the overall post-transplantation survival assessment in Iranian LTx recipients. [ABSTRACT FROM AUTHOR]

We evaluated the association between white blood cell counts and long-term mortality rates in 2,129 patients (mean age, 65.3 ± 13.5 yr; 69% men) who had survived acute myocardial infarction. We obtained white blood cell counts and differential counts of white blood cell subtypes within the first 72 hours of hospital admission. The primary outcome was all-cause death at 1, 5, and 10 years after acute myocardial infarction. In regard to death in the long term, we found significant negative linear associations (lymphocytes), positive linear associations (neutrophils and the neutrophil-to-lymphocyte ratio), and nonlinear U-shaped associations (basophils, eosinophils, monocytes, and total white blood cell count). After multivariate adjustment for the Soroka Acute Myocardial Infarction risk score, lymphocytes (strongest association), neutrophil-to-lymphocyte ratio, and eosinophils were independently associated with death for up to 10 years after hospital discharge. The independent associations weakened over time. We conclude that lymphocyte count, neutrophil-to-lymphocyte ratio, and eosinophil count are independently and incrementally associated with death in the long term after acute myocardial infarction. [ABSTRACT FROM AUTHOR]

Vascular markers such as pulse-wave velocity and carotid intima-media thickness (CIMT) might improve the prediction of incident cardiovascular disease beyond traditional risk factors. These vascular markers have not been well characterized in minority populations and might be more useful than inflammatory biomarkers. We conducted a prospective, longitudinal cohort study among hypertensive patients in an urban safety-net hospital. We evaluated inflammatory biomarkers, arterial pulse-wave velocity, and carotid intima-media thickness at baseline, 1 year, and 2 years. The primary outcome variable was CIMT. Generalized linear mixed-effects models were used to evaluate associations between CIMT and predictive variables accounting for the correlation of multiple measurements within subjects over time. For our secondary outcome, we used administrative and National Death Index data to determine all-cause death, and univariate relationships were evaluated. Among 175 subjects, 117 were Latino (67%) and 117 were female (67%). Pulse-wave velocity and CIMT regressed over time (both P <0.001) and were highly correlated (P <0.001). Only pulse-wave velocity (P=0.002) and total cholesterol (P=0.03) were associated with CIMT in time-varying covariate analysis. At a median follow-up period of 80 months, 17 of 175 subjects had died (10%). Higher baseline CIMT and pulse-wave velocity were associated with increased mortality rates (both P <0.01). No serum inflammatory marker was significantly correlated with longitudinal changes in CIMT or death. In conclusion, both arterial stiffness and preclinical carotid atherosclerosis were associated with increased mortality rates and might be useful risk-stratification markers among this minority population. [ABSTRACT FROM AUTHOR]

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient's clinical prognosis. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided. [ABSTRACT FROM AUTHOR]

Objective: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.Design: Observational cohort study.Setting: Five hospitals, from November 2008 until January 2013.Patients: Hospitalized ward patientsInterventions: NoneMeasurements and Main Results: Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]).Conclusions: In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards. [ABSTRACT FROM AUTHOR]

Greater understanding of the factors associated with a protective response to influenza vaccine in older adults could have tremendous public health benefits. We studied 158 participants age 50–74 years vaccinated with 2010–2011 inactivated influenza vaccine and performed innate immunity and humoral immunity assays directed against influenza A/California/2009 (H1N1) as measured through hemagglutination inhibition (HAI), microneutralization, and B cell ELISPOT at days 0, 3, and 28 postvaccination. We report the results of statistical modeling using Day 3 cytokines, chemokines, and innate cell populations to model Day 0 to Day 28 HAI seroconversion, viral neutralization seroconversion, and B cell ELISPOT results. [ABSTRACT FROM AUTHOR]

The article discusses whether the meta-analyses are a form of fake news in medical research field. Topics discussed include the critical examination of the research methodology used in meta-analyses by physicians, the issues with the meta-analysis of randomized controlled trials, and the need for meta-analyses to stop relying on a small number of events.

The article discusses a prospective study to construct a model to predict nonadherence to guidelines for prescribing antiplatelet therapy (NGAT) to hypertensive patients. The study was performed in 2007-2009 to determine whether hypertensive patients were or were not being prescribed antiplatelet therapy. It concludes that a tool was constructed to predict NGAT and that the associated factors were related with cardiovascular risk.

The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use such methods, and those that do correct for optimism use diverse methods, some of which are known to be biased. We used clinical data sets (United Kingdom Down syndrome screening data from Glasgow (1991–2003), Edinburgh (1999–2003), and Cambridge (1990–2006), as well as Scottish national pregnancy discharge data (2004–2007)) to evaluate different approaches to adjustment for optimism. We found that sample splitting, cross-validation without replication, and leave-1-out cross-validation produced optimism-adjusted estimates of the C statistic that were biased and/or associated with greater absolute error than other available methods. Cross-validation with replication, bootstrapping, and a new method (leave-pair-out cross-validation) all generated unbiased optimism-adjusted estimates of the C statistic and had similar absolute errors in the clinical data set. Larger simulation studies confirmed that all 3 methods performed similarly with 10 or more events per variable, or when the C statistic was 0.9 or greater. However, with lower events per variable or lower C statistics, bootstrapping tended to be optimistic but with lower absolute and mean squared errors than both methods of cross-validation. [ABSTRACT FROM PUBLISHER]

Objective: To explore the application of the RCOG (Royal College of Obstetricians and Gynaecologists) model of thrombosis risk assessment in the prevention of maternity venous thromboembolism (VTE). Methods: A total of 2 089 puerperas delivered in our hospital were included from May 2015 to June 2016. They were randomly divided into the control group and the observation group. Puerperas in the control group were treated by traditional nursing to prevent VTE. Based on the RCOG model, those puerperas in the observation group were classified and treated by corresponding intervention. The levels of prenatal and postnatal blood D -dim er in two groups, and the morbidity of VTE, were compared. Results: There was not significant difference in the level of prenatal blood D-dim er between two groups (P> 0.05). The level and the positive rate of postnatal D-dim er in the observation group were significantly lower than those in the control group (P< 0.05). The ratio of puerperas with the increased postnatal D -dim er level in the observation group was much lower than that in the control group (P〈 0.05). No puerpera in the observation group had VTE during the 6-week follow-up period, while 4 puerperas with the high VTE risk in the control groups had VTE, who were classified into the high risk or highest risk group following RCOG assessment model. The morbidity of VTE in the control group was higher than it in the observation group, but the difference was not significant (P> 0.05). Conclusions: The RCOG model of thrombosis risk assessment was effective in the classification of puerperas VTE risk, the decreased D -dim er level, and the prevention of VTE by corresponding intervention. [ABSTRACT FROM AUTHOR]

Background: Researchers often misinterpret and misrepresent statistical outputs. This abuse has led to a large literature on modification or replacement of testing thresholds and P-values with confidence intervals, Bayes factors, and other devices. Because the core problems appear cognitive rather than statistical, we review some simple methods to aid researchers in interpreting statistical outputs. These methods emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions.Methods: We use the Shannon transform of the P-value p, also known as the binary surprisal or S-value s = -log2(p), to provide a measure of the information supplied by the testing procedure, and to help calibrate intuitions against simple physical experiments like coin tossing. We also use tables or graphs of test statistics for alternative hypotheses, and interval estimates for different percentile levels, to thwart fallacies arising from arbitrary dichotomies. Finally, we reinterpret P-values and interval estimates in unconditional terms, which describe compatibility of data with the entire set of analysis assumptions. We illustrate these methods with a reanalysis of data from an existing record-based cohort study.Conclusions: In line with other recent recommendations, we advise that teaching materials and research reports discuss P-values as measures of compatibility rather than significance, compute P-values for alternative hypotheses whenever they are computed for null hypotheses, and interpret interval estimates as showing values of high compatibility with data, rather than regions of confidence. Our recommendations emphasize cognitive devices for displaying the compatibility of the observed data with various hypotheses of interest, rather than focusing on single hypothesis tests or interval estimates. We believe these simple reforms are well worth the minor effort they require. [ABSTRACT FROM AUTHOR]

Question In patients with suspected angina, does the Prognosis In Suspected Angina (PISA) model, which uses clinical variables available at presentation, predict 10-year coronary mortality? Methods Design Cohort study with derivation of the PISA model in 1 clinic (n = 4412) and validation across 5 clinics (n = 4350). Median follow-up was 10 years. Setting 6 chest pain clinics in England, UK. Patients 8762 consecutive patients (mean age 55 y, 52% men) who had a first suspected angina event with chest pain on presentation between 1996 and 2002, and complete follow-up. Exclusion criteria included previous coronary artery disease (CAD) or revascularization procedures, or diagnosis of an acute coronary syndrome (ACS) on the day of presentation. Description of prediction guide The PISA model is based on 7 variables available at patient presentation: age, sex, chest pain typicality (atypical, typical, noncardiac), pulse rate, smoking status, diabetes, and electrocardiography (ECG) findings (normal vs abnormal). An online calculator for the PISA model is available at www.sealedenvelope.com/trials/pisa. Outcome Primary outcome was coronary mortality, identified by linkage to a national registry and International Classification of Diseases-10 codes. Main results 233 (2.7%) patients died due to coronary causes. The c-statistic for the PISA model discriminating between patients who had a coronary death and those who did not was 0.84 {95% CI 0.80 to 0.87}* in the derivation cohort, 0.82 {CI 0.79 to 0.86}* in the validation cohort, and 0.83 {CI 0.81 to 0.86}* in the combined derivation and validation cohort (Table). The PISA model had good calibration {Hosmer-Lemeshow goodness-of-fit test P = 0.87}* in the combined cohort; observed and predicted event rates by PISA risk quartiles are shown in the Table. Conclusion In patients with a first suspected angina event, the Prognosis In Suspected Angina model used 7 clinical variables available at presentation to predict 10-year risk for coronary mortality. [ABSTRACT FROM AUTHOR]