Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

Background

This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.

Methods and Materials

We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.

Conclusion

The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.     

Venous Thrombosis Risk after Cast Immobilization of the Lower Extremity: Derivation and Validation of a Clinical Prediction Score,L-TRiP(cast), in Three Population-Based Case–Control Studies

Background

Guidelines and clinical practice vary considerably with respect to thrombosis prophylaxis during plaster cast immobilization of the lower extremity. Identifying patients at high risk for the development of venous thromboembolism (VTE) would provide a basis for considering individual thromboprophylaxis use and planning treatment studies.The aims of this study were (1) to investigate the predictive value of genetic and environmental risk factors, levels of coagulation factors, and other biomarkers for the occurrence of VTE after cast immobilization of the lower extremity and (2) to develop a clinical prediction tool for the prediction of VTE in plaster cast patients.

Methods and Findings

We used data from a large population-based case–control study (MEGA study, 4,446 cases with VTE, 6,118 controls without) designed to identify risk factors for a first VTE. Cases were recruited from six anticoagulation clinics in the Netherlands between 1999 and 2004; controls were their partners or individuals identified via random digit dialing. Identification of predictor variables to be included in the model was based on reported associations in the literature or on a relative risk (odds ratio) > 1.2 and p ≤ 0.25 in the univariate analysis of all participants. Using multivariate logistic regression, a full prediction model was created. In addition to the full model (all variables), a restricted model (minimum number of predictors with a maximum predictive value) and a clinical model (environmental risk factors only, no blood draw or assays required) were created. To determine the discriminatory power in patients with cast immobilization (n = 230), the area under the curve (AUC) was calculated by means of a receiver operating characteristic. Validation was performed in two other case–control studies of the etiology of VTE: (1) the THE-VTE study, a two-center, population-based case–control study (conducted in Leiden, the Netherlands, and Cambridge, United Kingdom) with 784 cases and 523 controls included between March 2003 and December 2008 and (2) the Milan study, a population-based case–control study with 2,117 cases and 2,088 controls selected between December 1993 and December 2010 at the Thrombosis Center, Fondazione IRCCS Ca’ Granda–Ospedale Maggiore Policlinico, Milan, Italy.The full model consisted of 32 predictors, including three genetic factors and six biomarkers. For this model, an AUC of 0.85 (95% CI 0.77–0.92) was found in individuals with plaster cast immobilization of the lower extremity. The AUC for the restricted model (containing 11 predictors, including two genetic factors and one biomarker) was 0.84 (95% CI 0.77–0.92). The clinical model (consisting of 14 environmental predictors) resulted in an AUC of 0.77 (95% CI 0.66–0.87). The clinical model was converted into a risk score, the L-TRiP(cast) score (Leiden–Thrombosis Risk Prediction for patients with cast immobilization score), which showed an AUC of 0.76 (95% CI 0.66–0.86). Validation in the THE-VTE study data resulted in an AUC of 0.77 (95% CI 0.58–0.96) for the L-TRiP(cast) score. Validation in the Milan study resulted in an AUC of 0.93 (95% CI 0.86–1.00) for the full model, an AUC of 0.92 (95% CI 0.76–0.87) for the restricted model, and an AUC of 0.96 (95% CI 0.92–0.99) for the clinical model. The L-TRiP(cast) score resulted in an AUC of 0.95 (95% CI 0.91–0.99).Major limitations of this study were that information on thromboprophylaxis was not available for patients who had plaster cast immobilization of the lower extremity and that blood was drawn 3 mo after the thrombotic event.

Conclusions

These results show that information on environmental risk factors, coagulation factors, and genetic determinants in patients with plaster casts leads to high accuracy in the prediction of VTE risk. In daily practice, the clinical model may be the preferred model as its factors are most easy to determine, while the model still has good predictive performance. These results may provide guidance for thromboprophylaxis and form the basis for a management study.     

The Predictive Performance and Stability of Six Species Distribution Models

Background

Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.

Methodology

We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.

Results

The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).

Conclusions

According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.     

Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study

Models for prediction of allogeneic hematopoietic stem transplantation (HSCT) related mortality partially account for transplant risk. Improving predictive accuracy requires understating of prediction limiting factors, such as the statistical methodology used, number and quality of features collected, or simply the population size. Using an in-silico approach (i.e., iterative computerized simulations), based on machine learning (ML) algorithms, we set out to analyze these factors. A cohort of 25,923 adult acute leukemia patients from the European Society for Blood and Marrow Transplantation (EBMT) registry was analyzed. Predictive objective was non-relapse mortality (NRM) 100 days following HSCT. Thousands of prediction models were developed under varying conditions: increasing sample size, specific subpopulations and an increasing number of variables, which were selected and ranked by separate feature selection algorithms. Depending on the algorithm, predictive performance plateaued on a population size of 6,611–8,814 patients, reaching a maximal area under the receiver operator characteristic curve (AUC) of 0.67. AUCs’ of models developed on specific subpopulation ranged from 0.59 to 0.67 for patients in second complete remission and receiving reduced intensity conditioning, respectively. Only 3–5 variables were necessary to achieve near maximal AUCs. The top 3 ranking variables, shared by all algorithms were disease stage, donor type, and conditioning regimen. Our findings empirically demonstrate that with regards to NRM prediction, few variables “carry the weight” and that traditional HSCT data has been “worn out”. “Breaking through” the predictive boundaries will likely require additional types of inputs.     

Percentage of Body Fat Assessment Using Bioelectrical Impedance Analysis and Dual-Energy X-ray Absorptiometry in a Weight Loss Program for Obese or Overweight Chinese Adults

The current study aimed to compare the estimates of body fat percentage (%BF) by performing bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in a sample of obese or overweight Chinese adults who participated in a weight-loss randomized control trial stratified by gender to determine whether or not BIA is a valid measurement tool. Among 189 adults [73 males, 116 females; age  = 41 to 74 years; mean body mass index (BMI)  = 27.3 kg/m²], assessments of %BF at the baseline and six months from the baseline were conducted by performing BIA and DXA. Bland-Altman analyses and multiple regression analyses were used to assess the relationships between %BF_BIA and %BF_DXA. Compared with DXA, BIA underestimated %BF [in males: 4.6, –2.4 to 11.7 (mean biases, 95% limit of agreement) at the baseline, 1.4, –7.4 to 10.2 at the endpoint, and 3.2, –4.8 to 11.3 in changes; in females: 5.1, –2.4 to 12.7; 2.2, –6.1 to 10.4; and 3.0, –4.8 to 10.7, respectively]. For males and females, %BFDXA proved to be a significant predictor of the difference between DXA and BIA at the baseline, the endpoint, and in changes when BMI and age were considered (in males: p<0.01 and R ²  = 23.1%, 24.1%, 20.7%, respectively; for females: p<0.001 and R ²  = 40.4%, 48.8%, 25.4%, respectively). The current study suggests that BIA provides a relatively accurate prediction of %BF in individuals with normal weight, overweight, or obesity after the end of weight-loss program, but less accurate prediction of %BF in obese individuals at baseline or weight change during the weight-loss intervention program.     

GRACE Score among Six Risk Scoring Systems (CADILLAC,PAMI, TIMI,Dynamic TIMI,Zwolle) Demonstrated the Best Predictive Value for Prediction of Long-Term Mortality in Patients with ST-Elevation Myocardial Infarction

Aim

To compare the prognostic accuracy of six scoring models for up to three-year mortality and rates of hospitalisation due to acute decompensated heart failure (ADHF) in STEMI patients.

Methods and Results

A total of 593 patients treated with primary PCI were evaluated. Prospective follow-up of patients was ≥3 years. Thirty-day, one-year, two-year, and three-year mortality rates were 4.0%, 7.3%, 8.9%, and 10.6%, respectively. Six risk scores—the TIMI score and derived dynamic TIMI, CADILLAC, PAMI, Zwolle, and GRACE—showed a high predictive accuracy for six- and 12-month mortality with area under the receiver operating characteristic curve (AUC) values of 0.73–0.85. The best predictive values for long-term mortality were obtained by GRACE. The next best-performing scores were CADILLAC, Zwolle, and Dynamic TIMI. All risk scores had a lower prediction accuracy for repeat hospitalisation due to ADHF, except Zwolle with the discriminatory capacity for hospitalisation up to two years (AUC, 0.80–0.83).

Conclusions

All tested models showed a high predictive value for the estimation of one-year mortality, but GRACE appears to be the most suitable for the prediction for a longer follow-up period. The tested models exhibited an ability to predict the risk of ADHF, especially the Zwolle model.     

Evaluation of DNA Variants Associated with Androgenetic Alopecia and Their Potential to Predict Male Pattern Baldness

Androgenetic alopecia, known in men as male pattern baldness (MPB), is a very conspicuous condition that is particularly frequent among European men and thus contributes markedly to variation in physical appearance traits amongst Europeans. Recent studies have revealed multiple genes and polymorphisms to be associated with susceptibility to MPB. In this study, 50 candidate SNPs for androgenetic alopecia were analyzed in order to verify their potential to predict MPB. Significant associations were confirmed for 29 SNPs from chromosomes X, 1, 5, 7, 18 and 20. A simple 5-SNP prediction model and an extended 20-SNP model were developed based on a discovery panel of 305 males from various European populations fitting one of two distinct phenotype categories. The first category consisted of men below 50 years of age with significant baldness and the second; men aged 50 years or older lacking baldness. The simple model comprised the five best predictors: rs5919324 near AR, rs1998076 in the 20p11 region, rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9. The extended prediction model added 15 SNPs from five genomic regions that improved overall prevalence-adjusted predictive accuracy measured by area under the receiver characteristic operating curve (AUC). Both models were evaluated for predictive accuracy using a test set of 300 males reflecting the general European population. Applying a 65% probability threshold, high prediction sensitivity of 87.1% but low specificity of 42.4% was obtained in men aged <50 years. In men aged ≥50, prediction sensitivity was slightly lower at 67.7% while specificity reached 90%. Overall, the AUC=0.761 calculated for men at or above 50 years of age indicates these SNPs offer considerable potential for the application of genetic tests to predict MPB patterns, adding a highly informative predictive system to the emerging field of forensic analysis of externally visible characteristics.     

Non–Laboratory-Based Self-Assessment Screening Score for Non-Alcoholic Fatty Liver Disease: Development,Validation and Comparison with Other Scores

Background

Non-alcoholic fatty liver disease (NAFLD) is a prevalent and rapidly increasing disease worldwide; however, no widely accepted screening models to assess the risk of NAFLD are available. Therefore, we aimed to develop and validate a self-assessment score for NAFLD in the general population using two independent cohorts.

Methods

The development cohort comprised 15676 subjects (8313 males and 7363 females) who visited the National Health Insurance Service Ilsan Hospital in Korea in 2008–2010. Anthropometric, clinical, and laboratory data were examined during regular health check-ups and fatty liver diagnosed by abdominal ultrasound. Logistic regression analysis was conducted to determine predictors of prevalent NAFLD and to derive risk scores/models. We validated our models and compared them with other existing methods using an external cohort (N = 66868).

Results

The simple self-assessment score consists of age, sex, waist circumference, body mass index, history of diabetes and dyslipidemia, alcohol intake, physical activity and menopause status, which are independently associated with NAFLD, and has a value of 0–15. A cut-off point of ≥8 defined 58% of males and 36% of females as being at high-risk of NAFLD, and yielded a sensitivity of 80% in men (77% in women), a specificity of 67% (81%), a positive predictive value of 72% (63%), a negative predictive value of 76% (89%) and an AUC of 0.82 (0.88). Comparable results were obtained using the validation dataset. The comprehensive NAFLD score, which includes additional laboratory parameters, has enhanced discrimination ability, with an AUC of 0.86 for males and 0.91 for females. Both simple and comprehensive NAFLD scores were significantly increased in subjects with higher fatty liver grades or severity of liver conditions (e.g., simple steatosis, steatohepatitis).

Conclusions

The new non–laboratory-based self-assessment score may be useful for identifying individuals at high-risk of NAFLD. Further studies are warranted to evaluate the utility and feasibility of the scores in various settings.     

Effect of Larvae Treated with Mixed Biopesticide Bacillus thuringiensis - Abamectin on Sex Pheromone Communication System in Cotton Bollworm,Helicoverpa armigera

Third instar larvae of the cotton bollworm (Helicoverpa armigera) were reared with artificial diet containing a Bacillus thuringiensis - abamectin (BtA) biopesticide mixture that resulted in 20% mortality (LD₂₀). The adult male survivors from larvae treated with BtA exhibited a higher percentage of “orientation” than control males but lower percentages of “approaching” and “landing” in wind tunnel bioassays. Adult female survivors from larvae treated with BtA produced higher sex pheromone titers and displayed a lower calling percentage than control females. The ratio of Z-11-hexadecenal (Z11–16:Ald) and Z-9-hexadecenal (Z9–16:Ald) in BtA-treated females changed and coefficients of variation (CV) of Z11–16:Ald and Z9–16:Ald were expanded compared to control females. The peak circadian calling time of BtA-treated females occurred later than that of control females. In mating choice experiment, both control males and BtA-treated males preferred to mate with control females and a portion of the Bt-A treated males did not mate whereas all control males did. Our Data support that treatment of larvae with BtA had an effect on the sex pheromone communication system in surviving H.armigera moths that may contribute to assortative mating.     

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.     

Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor''s impact on prediction. Each model''s ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking''s impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.     

Salmo kottelati,a new species of trout from Alak?r Stream,draining to the Mediterranean in southern Anatolia,Turkey (Teleostei,Salmonidae)

Salmo kottelati sp. n., is described from Alakır Stream (Mediterranean basin) in Turkey. It is distinguished from other Anatolian Salmo species by a combination of the following characters (none unique to the species): general body colour greenish to silvery in life; 7–9 parr marks along lateral line; four dark bands on flank absent in both sexes; black ocellated spots few, present only on upper part of flank in individuals smaller than 160 mm SL but in larger both males and females black spots numerous and located on back and middle and upper part of flank; red spots few to numerous, scattered on median, and half of lower and upper part of flank; head long (length 29–33% SL in males, 26–32 in females); mouth large (length of mouth gape 13–19% SL in males, 12–15 in females); maxilla long (length 10–13% SL in males, 8–12 in females); 105–113 lateral line scales; 24–29 scale rows between lateral line and dorsal-fin origin, 17–19 scale rows between lateral line and anal-fin origin; 13–15 scales between lateral line and adipose-fin insertion.     

External Validation and Evaluation of Reliability and Validity of the S-ReSC Scoring System to Predict Stone-Free Status after Percutaneous Nephrolithotomy

Objectives

The Seoul National University Renal Stone Complexity (S-ReSC) scoring system was developed to predict the stone-free rate (SFR) after single-tract percutaneous nephrolithotomy (PCNL). This study is an external validation of this scoring system.

Materials and methods

A retrospective review included 327 patients who underwent PCNL at 2 tertiary referral centers. The S-ReSC score was assigned from 1 to 9 based on the number of sites involved. The stone free status was defined as either complete clearance or clinically insignificant residual fragments <4 mm in size at 1 month follow-up imaging. Inter-observer and test-retest reliabilities were evaluated. The statistical performance of the prediction model was assessed by its predictive accuracy, predictive probability, and clinical usefulness.

Results

The overall SFR was 65.4%. SFRs were 83.9%, 47.6%, and 21.4% in low (1–2), intermediate (3–4), and high (5–9) score groups, respectively, with significant differences (P<0.001). Inter-observer and test-retest reliabilities revealed almost perfect agreements. External validation of the S-ReSC scoring system revealed an AUC of 0.731 (95% CI 0.675–0.788). The AUC of 3-titered S-ReSC score groups was 0.691 (95% CI, 0.629–0.753). The calibration plot showed that the predicted probability of SFR had a concordance comparable to that of the observed frequency. The Hosmer–Lemeshow goodness-of-fit statistic revealed an adequate performance of the predictive model (P = 0.10). Inter-observer and test-retest reliability showed a good level of agreement.

Conclusions

The S-ReSC scoring system is useful in predicting the post-PCNL SFR and in describing the complexity of renal stones.     

Emergence,Development, and Maturity of the Gonad of Two Species of Chitons “Sea Cockroach” (Mollusca: Polyplacophora) through the Early Life Stages

This study describes and recognises, using histological and microscopical examinations on a morphometrical basis, several gonad traits through the early life stages of Chiton articulatus and C. albolineatus. Gonadal ontogenesis, gonad development stages, sexual differentiation, onset of the first sexual maturity, and growth sequences or “early life stages” were determined. In addition, allometry between lengths and body weight pooled for both sexes per each chiton were calculated using equation Y = aX^b. A total of 125 chitons (4≤TL≤40 mm, in total length “TL”) were used. All allometric relations showed a strong positive correlation (r), close to 1, with b-values above three, indicating an isometric growth. Gonadal ontogenesis and gonad development stages were categorised into three periods (“Pw” without gonad, “Pe” gonad emergence, and “Pf” gonadal sac formed) and four stages (“S0” gametocytogenesis, “S1” gametogenesis, “S2” mature, and “S3” spawning), respectively. Compound digital images were attained for each process. Periods and stages are overlapped among them and between species, with the following overall confidence intervals in TL: Pw 6.13–14.32 mm, Pe 10.32–16.93 mm, Pf 12.99–25.01 mm, S0 16.08–24.34 mm (females) and 19.51–26.60 mm (males), S1 27.15–35.63 mm (females) and 23.45–32.27 mm (males), S2 24.48–40.24 mm (females) and 25.45–32.87 mm (males). Sexual differentiation (in S0) of both chitons occurs first as a female then as a male; although, males reach the onset of the first sexual maturity earlier than females, thus for C. articulatus males at 17 mm and females at 32 mm, and for C. albolineatus males at 23.5 mm and females at 28 mm, all in TL. Four early life stages (i.e., subjuvenile, juvenile, subadult, and adult) are described and proposed to distinguish growth sequences. Our results may be useful to diverse disciplines, from developmental biology to fisheries management.     

Ontogeny of Foraging Competence in Capuchin Monkeys (Cebus capucinus) for Easy versus Difficult to Acquire Fruits: A Test of the Needing to Learn Hypothesis

Which factors select for long juvenile periods in some species is not well understood. One potential reason to delay the onset of reproduction is slow food acquisition rates, either due to competition (part of the ecological risk avoidance hypothesis), or due to a decreased foraging efficiency (a version of the needing to learn hypothesis). Capuchins provide a useful genus to test the needing to learn hypothesis because they are known for having long juvenile periods and a difficult-to-acquire diet. Generalized, linear, mixed models with data from 609 fruit forage focal follows on 49, habituated, wild Cebus capucinus were used to test two predictions from the needing-to-learn hypothesis as it applies to fruit foraging skills: 1) capuchin monkeys do not achieve adult foraging return rates for difficult-to-acquire fruits before late in the juvenile period; and 2) variance in return rates for these fruits is at least partially associated with differences in foraging skill. In support of the first prediction, adults, compared with all younger age classes, had significantly higher foraging return rates when foraging for fruits that were ranked as difficult-to-acquire (return rates relative to adults: 0.30–0.41, p-value range 0.008–0.016), indicating that the individuals in the group who have the most foraging experience also achieve the highest return rates. In contrast, and in support of the second prediction, there were no significant differences between age classes for fruits that were ranked as easy to acquire (return rates relative to adults: 0.97–1.42, p-value range 0.086–0.896), indicating that strength and/or skill are likely to affect return rates. In addition, fruits that were difficult to acquire were foraged at nearly identical rates by adult males and significantly smaller (and presumably weaker) adult females (males relative to females: 1.01, p = 0.978), while subadult females had much lower foraging efficiency than the similarly-sized but more experienced adult females (subadults relative to adults: 0.34, p = 0.052), indicating that skill, specifically, is likely to have an effect on return rates. These results are consistent with the needing to learn hypothesis and indicate that long juvenile periods in capuchins may be the result of selection for more time to learn foraging skills for difficult-to-acquire fruits.     

External Validation of Prediction Models for Pneumonia in Primary Care Patients with Lower Respiratory Tract Infection: An Individual Patient Data Meta-Analysis

Background

Pneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.

Methods and Findings

S&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74–0.85) and 0.72 (0.68–0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61–0.68), 0.64 (0.61–0.67), 0.56 (0.49–0.63) and 0.53 (0.5–0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.

Conclusions

The model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Gastro-Esophageal Reflux Disease Symptoms and Demographic Factors as a Pre-Screening Tool for Barrett’s Esophagus

Background

Barrett’s esophagus (BE) occurs as consequence of reflux and is a risk factor for esophageal adenocarcinoma. The current “gold-standard” for diagnosing BE is endoscopy which remains prohibitively expensive and impractical as a population screening tool. We aimed to develop a pre-screening tool to aid decision making for diagnostic referrals.

Methodology/Principal Findings

A prospective (training) cohort of 1603 patients attending for endoscopy was used for identification of risk factors to develop a risk prediction model. Factors associated with BE in the univariate analysis were selected to develop prediction models that were validated in an independent, external cohort of 477 non-BE patients referred for endoscopy with symptoms of reflux or dyspepsia. Two prediction models were developed separately for columnar lined epithelium (CLE) of any length and using a stricter definition of intestinal metaplasia (IM) with segments ≥2 cm with areas under the ROC curves (AUC) of 0.72 (95%CI: 0.67–0.77) and 0.81 (95%CI: 0.76–0.86), respectively. The two prediction models included demographics (age, sex), symptoms (heartburn, acid reflux, chest pain, abdominal pain) and medication for “stomach” symptoms. These two models were validated in the independent cohort with AUCs of 0.61 (95%CI: 0.54–0.68) and 0.64 (95%CI: 0.52–0.77) for CLE and IM≥2 cm, respectively.

Conclusions

We have identified and validated two prediction models for CLE and IM≥2 cm. Both models have fair prediction accuracies and can select out around 20% of individuals unlikely to benefit from investigation for Barrett’s esophagus. Such prediction models have the potential to generate useful cost-savings for BE screening among the symptomatic population.     

Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10⁻³–10⁻²⁰) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.