Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition.

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of 4 perfluoroalkyl and polyfluoroalkyl substances (PFAS) measured at 4 time points from gestation to age 12 years. We evaluated the relationships between profiles and z scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the receiver operating characteristic curve (AUC) for all PFAS. We identified 2 profiles: a higher PFAS profile (35% of sample) and a lower PFAS profile (relative to each other), based on their average PFAS concentrations at all time points. The higher PFAS profile had generally lower z scores for all outcomes, with somewhat larger effects for males, though all 95% CIs crossed the null. For example, the higher PFAS profile was associated with a 0.50-unit lower (ß = -0.50; 95% CI, -1.07 to 0.08) BMI z score among males but not among females (ß = 0.04; 95% CI, -0.45 to 0.54). We observed similar patterns with AUCs. We found that a higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence. This article is part of a Special Collection on Environmental Epidemiology.

Diagnostic performance of shear wave measurement in the detection of hepatic fibrosis: A multicenter prospective study.

AIM: This study aimed to establish the shear wave measurement (SWM) cut-off value for each fibrosis stage using magnetic resonance (MR) elastography values as a reference standard. METHODS: We prospectively analyzed 594 patients with chronic liver disease who underwent SWM and MR elastography. Correlation coefficients (were analyzed, and the diagnostic value was evaluated by the area under the receiver operating characteristic curve. Liver stiffness was categorized by MR elastography as F0 (<2.61 kPa), F1 (≥2.61 kPa, <2.97 kPa, any fibrosis), F2 (≥2.97 kPa, <3.62 kPa, significant fibrosis), F3 (≥3.62 kPa, <4.62 kPa, advanced fibrosis), or F4 (≥4.62 kPa, cirrhosis). RESULTS: The median SWM values increased significantly with increasing fibrosis stage (p < 0.001). The correlation coefficient between SWM and MR elastography values was 0.793 (95% confidence interval 0.761-0.821). The correlation coefficients between SWM and MR elastography values significantly decreased with increasing body mass index and skin-capsular distance; skin-capsular distance values were associated with significant differences in sensitivity, specificity, accuracy, or positive predictive value, whereas body mass index values were not. The best cut-off values for any fibrosis, significant fibrosis, advanced fibrosis, and cirrhosis were 6.18, 7.09, 8.05, and 10.89 kPa, respectively. CONCLUSIONS: This multicenter study in a large number of patients established SWM cut-off values for different degrees of fibrosis in chronic liver diseases using MR elastography as a reference standard. It is expected that these cut-off values will be applied to liver diseases in the future.

A comparative study of hepatic steatosis using two different qualitative ultrasound techniques measured based on magnetic resonance imaging-derived proton density fat fraction.

AIM: This study aimed to evaluate the diagnostic performance of attenuation measurement (ATT; dual-frequency method) and improved algorithm of ATT (iATT; reference method) for the assessment of hepatic steatosis using magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) as the reference standard. METHODS: We prospectively analyzed 427 patients with chronic liver disease who underwent ATT, iATT, or MRI-derived PDFF. Correlation coefficients were analyzed, and diagnostic values were evaluated by area under the receiver operating characteristic curve (AUROC). The steatosis grade was categorized as S0 (<5.2%), S1 (≥5.2%, <11.3%), S2 (≥11.3%, <17.1%), and S3 (≥17.1%) according to MRI-derived PDFF values. RESULTS: The median ATT and iATT values were 0.61 dB/cm/MHz (interquartile range 0.55-0.67 dB/cm/MHz) and 0.66 dB/cm/MHz (interquartile range 0.57-0.77 dB/cm/MHz). ATT and iATT values increased significantly as the steatosis grade increased in the order S0, S1, S2, and S3 (p < 0.001). The correlation coefficients between ATT or iATT values and MRI-derived PDFF values were 0.533 (95% confidence interval [CI] 0.477-0.610) and 0.803 (95% CI 0.766-0.834), with a significant difference between them (p < 0.001). For the detection of hepatic steatosis of ≥S1, ≥S2, and ≥S3, iATT yielded AUROCs of 0.926 (95% CI 0.901-0.951), 0.913 (95% CI 0.885-0.941), and 0.902 (95% CI 0.869-0.935), with significantly higher AUROC values than for ATT (p < 0.001, p < 0.001, p = 0.001). CONCLUSION: iATT showed excellent diagnostic performance for hepatic steatosis, and was strongly correlated with MRI-derived PDFF, with AUROCs of ≥0.900.

Diagnostic accuracy of community optometrists for age-related macular degeneration using colour fundus photographs: A pilot evaluation.

PURPOSE: The accurate diagnosis of age-related macular degeneration (AMD) represents an important step in delaying and preventing vision loss and achieving optimal patient care. Therefore, this pilot study aimed to estimate the diagnostic accuracy of community optometrists for identifying AMD using colour fundus photographs (CFPs) to support sample size calculations for subsequent definitive studies. METHODS: Five practising community optometrists were invited to classify a total of 1023 CFPs for the (1) presence of AMD, and, if applicable, (2) stage of AMD (early/intermediate/late geographic atrophy/late neovascular AMD). Diagnosis by referral centre clinicians formed the reference standard. Diagnostic accuracy was assessed by the area under the receiver operating characteristic curve (aROC). Sensitivity, specificity, positive and negative predictive values were also calculated. RESULTS: Of the 1023 CFPs included in the study, 226 images were of AMD and 797 images were of other ocular conditions or no abnormal findings. Participating community optometrists had a mean (SD) age of 30.2 (8.9) years, 60.0% (3/5) were female and the mean number of years practising in primary eye care was 5.4 (5.4) years. Community optometrists demonstrated excellent performance for diagnosing AMD, with an aROC of 0.86 (95% CI 0.83 to 0.89), sensitivity of 84.5% (95% CI 79.1 to 89.0) and specificity of 88.0% (95% CI 85.5 to 90.1). The aROC (95% CI) for diagnosing early, intermediate, late geographic atrophy and late neovascular AMD was 0.82 (0.73 to 0.91), 0.76 (0.72 to 0.81), 0.69 (0.49 to 0.90) and 0.55 (0.34 to 0.75), respectively. CONCLUSIONS: These results justify the need for an appropriately powered definitive study to assess community clinicians' diagnostic accuracy for AMD.

Validity of a combination of periodontal pathogens and salivary biomarkers as predictors of periodontitis.

OBJECTIVE: Chronic periodontitis is caused by multiple risk factors. To predict chronic periodontitis in older people, we evaluated the association between a combination of major periodontal pathogens and salivary biomarkers and the presence of periodontitis. METHODS: Stimulated saliva samples were collected to analyze the prevalence of Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Prevotella intermedia, as well as four biomarkers: interleukin (IL)-1ß, IL-6, tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE2). A total of 201 Japanese patients were recruited. Oral examinations ware performed to determine chronic periodontitis as measured by Community Periodontal Index. The sociodemographic and behavioral characteristics were also obtained, and the parameters were adjusted as potential confounders to employ statistical models. RESULTS: The odds ratio (OR) for the presence of P. gingivalis and the third tertile level of IL-1ß as compared with the absence of P. gingivalis and the lowest tertile of IL-1ß was highest in individuals with periodontitis (OR = 13.98; 95% confidence interval [CI] 3.87-50.52) with the best level (0.79) of area under the curve (AUC) based on the receiver operating characteristic curve. The OR for the presence of P. gingivalis and the third tertile of PGE2 was 7.76 (CI 1.89-31.91) with an AUC of 0.78. The coexistence of more than two periodontal bacteria and the third tertile of PGE2 was also strongly associated with chronic periodontitis (OR = 9.23, 95% CI 2.38-35.79) with an AUC of 0.76. CONCLUSIONS: The combined information of the presence of P. gingivalis in stimulated saliva, and higher levels of salivary IL-1ß may play a vital role in the detection and prediction of chronic periodontitis in older adults.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

Background Clinicians need to rapidly and reliably diagnose coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions. Purpose To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 Reporting and Data System (CO-RADS). Materials and Methods This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT scans were obtained for primary work-up and scored using the five-point CO-RADS scheme for suspicion of COVID-19. CT was compared with severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (RT-PCR) assay and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing, and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both reference standards. Subgroup analysis was performed on the basis of symptom duration grouped presentations of less than 48 hours, 48 hours through 7 days, and more than 7 days. Results A total of 1070 patients (median age, 66 years; interquartile range, 54-75 years; 626 men) were included, of whom 536 (50%) had a positive RT-PCR result and 137 (13%) of whom were considered to have a possible or probable COVID-19 diagnosis based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR and 0.87 (95% CI: 0.85, 0.89) compared with the clinical reference standard. A CO-RADS score of 4 or greater yielded an odds ratio of 25.9 (95% CI: 18.7, 35.9) for a COVID-19 diagnosis with RT-PCR and an odds ratio of 30.6 (95% CI: 21.1, 44.4) with the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001). Conclusion Chest CT analysis using the coronavirus disease 2019 (COVID-19) Reporting and Data System enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

Evaluating the predictive performance of malaria antibodies and FCGR3B gene polymorphisms on Plasmodium falciparum infection outcome: a prospective cohort study.

BACKGROUND: Malaria antigen-specific antibodies and polymorphisms in host receptors involved in antibody functionality have been associated with different outcomes of Plasmodium falciparum infections. Thus, to identify key prospective malaria antigens for vaccine development, there is the need to evaluate the associations between malaria antibodies and antibody dependent host factors with more rigorous statistical methods. In this study, different statistical models were used to evaluate the predictive performance of malaria-specific antibodies and host gene polymorphisms on P. falciparum infection in a longitudinal cohort study involving Ghanaian children. METHODS: Models with different functional forms were built using known predictors (age, sickle cell status, blood group status, parasite density, and mosquito bed net use) and malaria antigen-specific immunoglobulin (Ig) G and IgG subclasses and FCGR3B polymorphisms shown to mediate antibody-dependent cellular functions. Malaria antigens studied were Merozoite surface proteins (MSP-1 and MSP-3), Glutamate Rich Protein (GLURP)-R0, R2, and the Apical Membrane Antigen (AMA-1). The models were evaluated through visualization and assessment of differences between the Area Under the Receiver Operating Characteristic Curve and Brier Score estimated by suitable internal cross-validation designs. RESULTS: This study found that the FCGR3B-c.233C>A genotype and IgG against AMA1 were relatively better compared to the other antibodies and FCGR3B genotypes studied in classifying or predicting malaria risk among children. CONCLUSIONS: The data supports the P. falciparum, AMA1 as an important malaria vaccine antigen, while FCGR3B-c.233C>A under the additive and dominant models of inheritance could be an important modifier of the effect of malaria protective antibodies.

Labor progress determined by ultrasound is different in women requiring cesarean delivery from those who experience a vaginal delivery following induction of labor.

BACKGROUND: The diagnosis of labor dystocia generally is determined by the deviation of labor progress, which is assessed by the use of a partogram. Recently, intrapartum transperineal ultrasound for the assessment of fetal head descent has been introduced to assess labor progress in the first stage of labor in a more objective and noninvasive way. OBJECTIVE: The objective of the study was to determine the differences in labor progress by the use of serial transperineal ultrasound assessment of fetal head descent between women having vaginal and cesarean delivery. STUDY DESIGN: This was a prospective longitudinal study performed in 315 women with singleton pregnancy who were undergoing labor induction at term between December 2016 and December 2017. Paired assessment of cervical dilation and fetal head station by vaginal examination and transperineal ultrasonographic assessment of parasagittal angle of progression and head-perineum distance were made serially after the commencement of labor induction. According to the hospital protocol, assessment was performed every 24 hours and 4 hours, respectively, during latent and active phases of labor. The researchers and the clinical team were blinded to each other's findings. The repeated measures data were analyzed by mixed effect models. To determine the effect of mode of delivery on the association between parasagittal angle of progression and head-perineum distance against fetal head station and cervical dilation, the significance of the interaction term between each mode of delivery and fetal head station or cervical dilation was determined, which accounted for parity and obesity. Area under receiver-operating characteristic curve was used to evaluate the performance of serial intrapartum sonography in predicting women with cesarean delivery because of failure to progress. RESULTS: The total number of paired vaginal examination and ultrasound assessments was 1198, with a median of 3 per woman. The median assessment-to-assessment interval was 4.6 hours (interquartile range, 4.3-5.1 hours). Women who achieved vaginal delivery (n=261) had steeper slopes of parasagittal angle of progression and head-perineum distance against fetal head station and cervical dilation than those who achieved cesarean delivery (n=54). Objectively, an additional decrease of 5.11 and 1.37 degrees in parasagittal angle of progression was observed for an unit increase in fetal head station and cervical dilation, respectively, in women who required cesarean delivery (P<.01; P=.01), compared with women who achieved vaginal delivery, after taking account of repeated measures from individuals and confounding factors. The respective additional increases in head-perineum distance for a unit increase in fetal head station and cervical dilation were 0.27 cm (P<.01) and 0.12 cm (P<.01). A combination of maternal characteristics with the temporal changes of parasagittal angle of progression for an unit increase in fetal head station achieved an area under receiver-operating characteristic curve of 0.85 (95% confidence interval, 0.76-0.94), with sensitivity of 79% and specificity of 80%, for the prediction of women who required cesarean delivery because of failure to progress. CONCLUSION: The differences in labor progress between vaginal and cesarean delivery have been illustrated objectively by serial intrapartum transperineal ultrasonographic assessment of fetal head descent. This tool is potentially predictive of women who will require cesarean delivery because of failure to progress.

Machine learning applications for the prediction of surgical site infection in neurological operations.

OBJECTIVE: Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations. METHODS: A retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed. RESULTS: Data were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%. CONCLUSIONS: The naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics.

OBJECTIVE: Incidental aneurysms pose a challenge for physicians, who need to weigh the rupture risk against the risks associated with treatment and its complications. A statistical model could potentially support such treatment decisions. A recently developed aneurysm rupture probability model performed well in the US data used for model training and in data from two European cohorts for external validation. Because Japanese and Finnish patients are known to have a higher aneurysm rupture risk, the authors' goals in the present study were to evaluate this model using data from Japanese and Finnish patients and to compare it with new models trained with Finnish and Japanese data. METHODS: Patient and image data on 2129 aneurysms in 1472 patients were used. Of these aneurysm cases, 1631 had been collected mainly from US hospitals, 249 from European (other than Finnish) hospitals, 147 from Japanese hospitals, and 102 from Finnish hospitals. Computational fluid dynamics simulations and shape analyses were conducted to quantitatively characterize each aneurysm's shape and hemodynamics. Next, the previously developed model's discrimination was evaluated using the Finnish and Japanese data in terms of the area under the receiver operating characteristic curve (AUC). Models with and without interaction terms between patient population and aneurysm characteristics were trained and evaluated including data from all four cohorts obtained by repeatedly randomly splitting the data into training and test data. RESULTS: The US model's AUC was reduced to 0.70 and 0.72, respectively, in the Finnish and Japanese data compared to 0.82 and 0.86 in the European and US data. When training the model with Japanese and Finnish data, the average AUC increased only slightly for the Finnish sample (to 0.76 ± 0.16) and Finnish and Japanese cases combined (from 0.74 to 0.75 ± 0.14) and decreased for the Japanese data (to 0.66 ± 0.33). In models including interaction terms, the AUC in the Finnish and Japanese data combined increased significantly to 0.83 ± 0.10. CONCLUSIONS: Developing an aneurysm rupture prediction model that applies to Japanese and Finnish aneurysms requires including data from these two cohorts for model training, as well as interaction terms between patient population and the other variables in the model. When including this information, the performance of such a model with Japanese and Finnish data is close to its performance with US or European data. These results suggest that population-specific differences determine how hemodynamics and shape associate with rupture risk in intracranial aneurysms.

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis.

OBJECTIVEPatient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The authors aim to evaluate the feasibility of predicting short- and long-term PROMs, reoperations, and perioperative parameters by machine learning (ML) methods.METHODSData were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. The prediction models were trained using various ML-based algorithms to predict the endpoints of interest. Models were selected by area under the receiver operating characteristic curve (AUC). The endpoints were dichotomized by minimum clinically important difference (MCID) and included 6-week and 12-month numeric rating scales for back pain (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), as well as prolonged surgery (> 45 minutes), extended length of hospital stay (> 28 hours), and reoperations.RESULTSA total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85%, and with AUCs of 0.75, 0.79, and 0.92. At 12 months, 66%, 63%, and 51% of patients reported MCID; the observed accuracies were 62%, 74%, and 66%, with AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) occurred at the index level. Overall and index-level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61. In 15%, a length of surgery greater than 45 minutes was observed and predicted with 78% accuracy and AUC of 0.54. Only 15% of patients were admitted to the hospital for longer than 28 hours. The developed ML-based model enabled prediction of extended hospital stay with an accuracy of 77% and AUC of 0.58.CONCLUSIONSPreoperative prediction of a range of clinically relevant endpoints in decompression surgery for LSS using ML is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with LSS.

Responses in the crucian carp (Carassius auratus) exposed to environmentally relevant concentration of 17α-Ethinylestradiol based on metabolomics.

17α-ethynylestradiol (EE2), a ubiquitous synthetic endocrine disrupting chemical, was the principal component of contraceptive drugs and one of common hormone medications. The detrimental impact of EE2 on the reproduction of organisms was widely recognized. However, the underlying mechanisms of physiological and metabolome effects of EE2 on freshwater fish are still unclear. This study investigated the toxic effects and related mechanisms of EE2 on freshwater fish crucian carp (Carassius auratus) based on metabolomics. Crucian carp were exposed to EE2 at environmentally relevant concentration for 9 days, 18 days, and 27 days, and the biological responses were explored through analysis of the physiological endpoints, steroid hormones, and metabolome. The physiological endpoints of crucian carp had no distinct change after EE2 exposure. However, metabolomics analysis probed significant deviation based on chemometrics, indicating that the metabolomics approach was more sensitive to the effects of EE2 at environmentally relevant concentration to freshwater fish than the traditional endpoints. The alterations of 24 metabolites in gonad and 16 metabolites in kidney were induced by treatment with EE2, respectively, which suggesting the perturbations in amino acid metabolism, lipid metabolism, energy metabolism, and oxidative stress. Moreover, EE2 exposure could induce the disruption of lipid metabolism and then broke the homeostasis of endogenous steroid hormones. Metabolomics provided a new strategy for the studies on contaminant exposure at a low dose in the short term and gave important information for the toxicology and mechanism of EE2.

Inference for the difference in the area under the ROC curve derived from nested binary regression models.

The area under the curve (AUC) statistic is a common measure of model performance in a binary regression model. Nested models are used to ascertain whether the AUC statistic increases when new factors enter the model. The regression coefficient estimates used in the AUC statistics are computed using the maximum rank correlation methodology. Typically, inference for the difference in AUC statistics from nested models is derived under asymptotic normality. In this work, it is demonstrated that the asymptotic normality is true only when at least one of the new factors is associated with the binary outcome. When none of the new factors are associated with the binary outcome, the asymptotic distribution for the difference in AUC statistics is a linear combination of chi-square random variables. Further, when at least one new factor is associated with the outcome and the population difference is small, a variance stabilizing reparameterization improves the asymptotic normality of the AUC difference statistic. A confidence interval using this reparameterization is developed and simulations are generated to determine their coverage properties. The derived confidence interval provides information on the magnitude of the added value of new factors and enables investigators to weigh the size of the improvement against potential costs associated with the new factors. A pancreatic cancer data example is used to illustrate this approach.

Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients.

OBJECTIVEPseudarthrosis can occur following adult spinal deformity (ASD) surgery and can lead to instrumentation failure, recurrent pain, and ultimately revision surgery. In addition, it is one of the most expensive complications of ASD surgery. Risk factors contributing to pseudarthrosis in ASD have been described; however, a preoperative model predicting the development of pseudarthrosis does not exist. The goal of this study was to create a preoperative predictive model for pseudarthrosis based on demographic, radiographic, and surgical factors.METHODSA retrospective review of a prospectively maintained, multicenter ASD database was conducted. Study inclusion criteria consisted of adult patients (age ≥ 18 years) with spinal deformity and surgery for the ASD. From among 82 variables assessed, 21 were used for model building after applying collinearity testing, redundancy, and univariable predictor importance ≥ 0.90. Variables included demographic data along with comorbidities, modifiable surgical variables, baseline coronal and sagittal radiographic parameters, and baseline scores for health-related quality of life measures. Patients groups were determined according to their Lenke radiographic fusion type at the 2-year follow-up: bilateral or unilateral fusion (union) or pseudarthrosis (nonunion). A decision tree was constructed, and internal validation was accomplished via bootstrapped training and testing data sets. Accuracy and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model.RESULTSA total of 336 patients were included in the study (nonunion: 105, union: 231). The model was 91.3% accurate with an AUC of 0.94. From 82 initial variables, the top 21 covered a wide range of areas including preoperative alignment, comorbidities, patient demographics, and surgical use of graft material.CONCLUSIONSA model for predicting the development of pseudarthrosis at the 2-year follow-up was successfully created. This model is the first of its kind for complex predictive analytics in the development of pseudarthrosis for patients with ASD undergoing surgical correction and can aid in clinical decision-making for potential preventative strategies.

Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data.

The Cox proportional hazards cure model is a survival model incorporating a cure rate with the assumption that the population contains both uncured and cured individuals. It contains a logistic regression for the cure rate, and a Cox regression to estimate the hazard for uncured patients. A single predictive model for both the cure and hazard can be developed by using a cure model that simultaneously predicts the cure rate and hazards for uncured patients; however, model selection is a challenge because of the lack of a measure for quantifying the predictive accuracy of a cure model. Recently, we developed an area under the receiver operating characteristic curve (AUC) for determining the cure rate in a cure model (Asano et al., 2014), but the hazards measure for uncured patients was not resolved. In this article, we propose novel C-statistics that are weighted by the patients' cure status (i.e., cured, uncured, or censored cases) for the cure model. The operating characteristics of the proposed C-statistics and their confidence interval were examined by simulation analyses. We also illustrate methods for predictive model selection and for further interpretation of variables using the proposed AUCs and C-statistics via application to breast cancer data.

Relative lumbar lordosis and lordosis distribution index: individualized pelvic incidence-based proportional parameters that quantify lumbar lordosis more precisely than the concept of pelvic incidence minus lumbar lordosis.

OBJECTIVE The subtraction of lumbar lordosis (LL) from the pelvic incidence (PI) offers an estimate of the LL required for a given PI value. Relative LL (RLL) and the lordosis distribution index (LDI) are PI-based individualized measures. RLL quantifies the magnitude of lordosis relative to the ideal lordosis as defined by the magnitude of PI. LDI defines the magnitude of lower arc lordosis in proportion to total lordosis. The aim of this study was to compare RLL and PI - LL for their ability to predict postoperative complications and their correlations with health-related quality of life (HRQOL) scores. METHODS Inclusion criteria were ≥ 4 levels of fusion and ≥ 2 years of follow-up. Mechanical complications were proximal junctional kyphosis/proximal junctional failure, distal junctional kyphosis/distal junctional failure, rod breakage, and implant-related complications. Correlations between PI - LL, RLL, PI, and HRQOL were analyzed using the Pearson correlation coefficient. Mechanical complication rates in PI - LL, RLL, LDI, RLL, and LDI interpreted together, and RLL subgroups for each PI - LL category were compared using chi-square tests and the exact test. Predictive models for mechanical complications with RLL and PI - LL were analyzed using binomial logistic regressions. RESULTS Two hundred twenty-two patients (168 women, 54 men) were included. The mean age was 52.2 ± 19.3 years (range 18-84 years). The mean follow-up was 28.8 ± 8.2 months (range 24-62 months). There was a significant correlation between PI - LL and PI (r = 0.441, p < 0.001), threatening the use of PI - LL to quantify spinopelvic mismatch for different PI values. RLL was not correlated with PI (r = -0.093, p > 0.05); therefore, it was able to quantify divergence from ideal lordosis for all PI values. Compared with PI - LL, RLL had stronger correlations with HRQOL scores (p < 0.05). Discrimination performance was better for the model with RLL than for PI - LL. The agreement between RLL and PI - LL was high (κ = 0.943, p < 0.001), moderate (κ = 0.455, p < 0.001), and poor (κ = -0.154, p = 0.343), respectively, for large, average, and small PI sizes. When analyzed by RLL, each PI - LL category was further divided into distinct groups of patients who had different mechanical complication rates (p < 0.001). CONCLUSIONS Using the formula of PI - LL may be insufficient to quantify normolordosis for the whole spectrum of PI values when applied as an absolute numeric value in conjunction with previously reported population-based average thresholds of 10° and 20°. Schwab PI - LL groups were found to constitute an inhomogeneous group of patients. RLL offers an individualized quantification of LL for all PI sizes. Compared with PI - LL, RLL showed a greater association with both mechanical complications and HRQOL. The use of RLL and LDI together, instead of PI - LL, for surgical planning may result in lower mechanical complication rates and better long-term HRQOL.

Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.

The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R.

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

BACKGROUND: Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. METHODS: Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r2) for each model. RESULTS: Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. CONCLUSIONS: Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Reproducibility in the global indices for multifocal visual evoked potentials and Humphrey visual fields in controls and glaucomatous eyes within a 2-year period.

PURPOSE: In previous studies, we applied receiver operating characteristic curve analysis to the signal-to-noise ratio distributions in the signal and noise windows of multifocal VEP (mfVEP) response. The areas under the curve thus obtained (SNR-AUC) were found to quantitatively detect glaucomatous visual field damage. The present study evaluated the reproducibility of SNR-AUC and the Humphrey visual field (HVF) global indices in 37 eyes with primary open angle glaucoma (POAG; POAG group) and in 30 controls (control group) within a 2-year period. METHODS: The HVF SITA standard 24-2 and mfVEP were recorded at three separate sessions for each individual. The intersession variability for SNR-AUC, mean deviation (MD), and pattern standard deviation (PSD) was evaluated using the repeated measures of analysis of variance and Bland-Altman plots. The logarithmically converted coefficients of variation (CV) of PSD and SNR-AUC were compared between the control and POAG groups. Linear regression analyses were performed on the logarithmic CV of SNR-AUC against the average MD, PSD, and SNR-AUC. RESULTS: SNR-AUC in the POAG group was significantly lower and its CV was greater compared with the control group (P < 0.0001). MD value recorded at the third visit had significantly improved than that at the first visit in the control group (analysis of variance, P = 0.03), whereas PSD value was significantly worse in the POAG group (P = 0.024). In the POAG group, SNR-AUC CV increased as the glaucoma stage became more advanced when evaluated by any functional parameters tested (i.e., MD, PSD, or SNR-AUC). CONCLUSIONS: The SNR-AUC of mfVEP showed a high reproducibility in control group, whereas it fluctuated more in the POAG group according to the disease severity. MD in the control group and PSD in POAG group fluctuated among sessions during the 2-year period.

A simple asthma prediction tool for preschool children with wheeze or cough.

BACKGROUND: Many preschool children have wheeze or cough, but only some have asthma later. Existing prediction tools are difficult to apply in clinical practice or exhibit methodological weaknesses. OBJECTIVE: We sought to develop a simple and robust tool for predicting asthma at school age in preschool children with wheeze or cough. METHODS: From a population-based cohort in Leicestershire, United Kingdom, we included 1- to 3-year-old subjects seeing a doctor for wheeze or cough and assessed the prevalence of asthma 5 years later. We considered only noninvasive predictors that are easy to assess in primary care: demographic and perinatal data, eczema, upper and lower respiratory tract symptoms, and family history of atopy. We developed a model using logistic regression, avoided overfitting with the least absolute shrinkage and selection operator penalty, and then simplified it to a practical tool. We performed internal validation and assessed its predictive performance using the scaled Brier score and the area under the receiver operating characteristic curve. RESULTS: Of 1226 symptomatic children with follow-up information, 345 (28%) had asthma 5 years later. The tool consists of 10 predictors yielding a total score between 0 and 15: sex, age, wheeze without colds, wheeze frequency, activity disturbance, shortness of breath, exercise-related and aeroallergen-related wheeze/cough, eczema, and parental history of asthma/bronchitis. The scaled Brier scores for the internally validated model and tool were 0.20 and 0.16, and the areas under the receiver operating characteristic curves were 0.76 and 0.74, respectively. CONCLUSION: This tool represents a simple, low-cost, and noninvasive method to predict the risk of later asthma in symptomatic preschool children, which is ready to be tested in other populations.