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In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing drug-induced QT prolongation is mainly executed by means of logistic regression; only one paper reported the use of machine learning techniques. In this paper, we compare the performance of both techniques on the same dataset. High risk for QT prolongation was defined as having a corrected QT interval (QTc) ≥ 450 ms or ≥ 470 ms for respectively male and female patients. Both conventional statistical methods (CSM) and machine learning techniques (MLT) were used. All algorithms were validated internally and with a hold-out dataset of respectively 512 and 102 drug-drug interactions with possible drug-induced QTc prolongation. MLT outperformed the best CSM in both internal and hold-out validation. Random forest and Adaboost classification performed best in the hold-out set with an equal harmonic mean of sensitivity and specificity (HMSS) of 81.2% and an equal accuracy of 82.4% in a hold-out dataset. Sensitivity and specificity were both high (respectively 75.6% and 87.7%). The most important features were baseline QTc value, C-reactive protein level, heart rate at baseline, age, calcium level, renal function, serum potassium level and the atrial fibrillation status. All CSM performed similarly with HMSS varying between 60.3% and 66.3%. The overall performance of logistic regression was 62.0%. MLT (bagging and boosting) outperform CSM in predicting drug-induced QTc prolongation. Additionally, 19.2% was gained in terms of performance by random forest and Adaboost classification compared to logistic regression (the most used technique in literature in estimating the risk for QTc prolongation). Future research should focus on testing the classification on fully external data, further exploring potential of other (new) machine and deep learning models and on generating data pipelines to automatically feed the data to the classifier used. [ABSTRACT FROM AUTHOR]

Background and Aim: Injury severity scores are important in the context of developing European and national goals on traffic safety, health-care benchmarking and improving patient communication. Various severity scores are available and are mostly based on Abbreviated Injury Scale (AIS) or International Classification of Diseases (ICD). The aim of this paper is to compare the predictive value for in-hospital mortality between the various severity scores if only International Classification of Diseases, 9th revision, Clinical Modification ICD-9-CM is reported.Methodology: To estimate severity scores based on the AIS lexicon, ICD-9-CM codes were converted with ICD Programmes for Injury Categorization (ICDPIC) and four AIS-based severity scores were derived: Maximum AIS (MaxAIS), Injury Severity Score (ISS), New Injury Severity Score (NISS) and Exponential Injury Severity Score (EISS). Based on ICD-9-CM, six severity scores were calculated. Determined by the number of injuries taken into account and the means by which survival risk ratios (SRRs) were calculated, four different approaches were used to calculate the ICD-9-based Injury Severity Scores (ICISS). The Trauma Mortality Prediction Model (TMPM) was calculated with the ICD-9-CM-based model averaged regression coefficients (MARC) for both the single worst injury and multiple injuries. Severity scores were compared via model discrimination and calibration. Model comparisons were performed separately for the severity scores based on the single worst injury and multiple injuries.Results: For ICD-9-based scales, estimation of area under the receiver operating characteristic curve (AUROC) ranges between 0.94 and 0.96, while AIS-based scales range between 0.72 and 0.76, respectively. The intercept in the calibration plots is not significantly different from 0 for MaxAIS, ICISS and TMPM.Discussion: When only ICD-9-CM codes are reported, ICD-9-CM-based severity scores perform better than severity scores based on the conversion to AIS. [ABSTRACT FROM AUTHOR]

Objective: Acute infection is the most common presentation of children to hospital. A minority of these infections are serious, but early recognition and adequate management are essential. We aimed to develop improved tools to assess children attending ambulatory hospital care, integrating clinical features with point-of-care C reactive protein (CRP).Design: Prospective observational diagnostic study.Setting and Patients: 5517 acutely ill children (1 month-16 years) presenting to 106 paediatricians at six outpatient clinics and six emergency departments in Belgium.Index Test: Point-of-care CRP alongside vital signs and objective symptoms measurements.Main Outcome: Hospital admission for >24 hours with a serious infection <5 days after presentation.Results: An algorithm was developed consisting of clinical features and CRP. This achieved 97.1% (95% CI 94.3% to 98.7%) sensitivity and 99.6% (95% CI 99.2% to 99.8%) negative predictive value, excluding serious infections in 36.4% of children. It stratifies patients into three groups based on CRP level: high-risk group with CRP >75 mg/L (26.8% risk of infection), intermediate-risk group with CRP 20-75 mg/L and at least one of seven clinical features (8.1%), and lower risk group with CRP <20 mg/L with at least one of the 11 features (3.8%). Children in intermediate-risk or low-risk groups with normal clinical assessment have 0.6% and 0.4% risk of serious infections, respectively.Conclusions: Conducting a CRP test may first enable children to be stratified into three risk groups, guiding assessment of clinical features that could be performed by junior doctors or nurses. In one-third of acutely ill children, the algorithm could exclude serious infection. Prospective validation of the algorithm is needed.Clinical Trial Registration: NCT02024282 (post-results). [ABSTRACT FROM AUTHOR]

Objective: Observational study of patients with chest pain in primary care: determination of incidence, referral rate, diagnostic tests and (agreement between) working and final diagnoses.Methods: 118 general practitioners (GPs) in the Netherlands and Belgium recorded all patient contacts during  2weeks. Furthermore, patients presenting with chest pain were registered extensively. A follow-up form was filled in after 30 days.Results: 22 294 patient contacts were registered. In 281 (1.26%), chest pain was a reason for consulting the GP (mean age for men 54.4/women 53 years). In this cohort of 281 patients, in 38.1% of patients, acute coronary syndrome (ACS) was suspected at least temporarily during consultation, 40.2% of patients were referred to secondary care and 512 diagnostic tests were performed by GPs and consulted specialists. Musculoskeletal pain was the most frequent working (26.1%) and final diagnoses (33.1%). Potentially life-threatening diseases as final diagnosis (such as myocardial infarction) accounted for 8.4% of all chest pain cases. In 23.1% of cases, a major difference between working and final diagnoses was found, in 0.7% a severe disease was initially missed by the GP.Conclusion: Chest pain was present in 281 patients (1.26% of all consultations). Final diagnoses were mostly non-life-threatening. Nevertheless, in 8.4% of patients with chest pain, life-threatening underlying causes were identified. This seems reflected in the magnitude and wide variety of diagnostic tests performed in these patients by GPs and specialists, in the (safe) overestimation of life-threatening diseases by GPs at initial assessment and in the high referral rate we found. [ABSTRACT FROM AUTHOR]

Background. Chiropractors throughout the world by and large focus on patients with musculoskelctal complaints who are generally in good health. Currently, it is widely accepted that neck pain and low back pain are best understood as biopsychosocial phenomena. Objective. The purpose of this study was to determine whether certain psychosocial factors provide added value for predicting recovery. Design. This was a prospective, multicenter, chiropractic, practice-based cohort study in Belgium and the Netherlands. Methods. A total of 917 participants, 326 with neck pain and 591 with low back pain, completed self-administered questionnaires at baseline and at 3, 6, and 12 months. They provided information on several demographic, biomedical, and psychosocial variables. Lasting perceived recovery was used as the outcome measure, that is, recovery at all follow-up assessments from 3 months on. Twenty-seven potential predictors of outcome were used to build the predictive model. Stepwise, backward generalized estimating equation regression models were used to take into account the clustering of participants within practices. For assessment of the added value of psychosocial variables, 2 model fit indexes were compared. Results. After the addition of psychosocial variables, predictors in the final model for neck pain included occupational status, body mass index, duration of complaints, previous treatment, and participant expectations (the model fit was marginally improved from 0.684 to 0.695 for the area under the curve and from 65.0% to 66.1% for the percentage correctly predicted). In the final model for low back pain, the selected predictors included country of treatment, age, duration of complaints, previous imaging, and somatization (the area under the curve changed from 0.669 to 0.715, and the percentage correctly predicted changed from 68.6% to 69.5%). Only a minority of participants had high scores on psychological variables. Limitations. The reliability and validity of lasting recovery as an outcome measure have not been tested. The cohort needs to be seen as a convenience sample. Selection bias, therefore, not be ruled out. There are no indications, however, that patients with complex psychosocial profiles were excluded from this study. Conclusions. Psychosocial variables provided little added value for predicting outcome in people who had neck pain or low back pain and sought chiropractic care. Therefore, chiropractors should not screen extensively for them at baseline. With regard to the identification of the small subgroup of people with high scores on psychosocial variables and a high risk for chronic pain, further investigation is needed. [ABSTRACT FROM AUTHOR]

People's affective forecasts are often inaccurate because they tend to overestimate how they will feel after an event. As life decisions are often based on affective forecasts, it is crucial to find ways to manage forecasting errors. We examined the impact of a fair treatment on forecasting errors in candidates in a Belgian reality TV talent show. We found that perceptions of fair treatment increased the forecasting error for losers (a negative audition decision) but decreased it for winners (a positive audition decision). For winners, this effect was even more pronounced when candidates were highly invested in their self-view as a future pop idol whereas for losers, the effect was more pronounced when importance was low. The results in this study point to a potential paradox between maximizing happiness and decreasing forecasting errors. A fair treatment increased the forecasting error for losers, but actually made them happier. [ABSTRACT FROM AUTHOR]

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Background: Asthma and chronic obstructive pulmonary disease (COPD) have major symptoms in common. However, the mode of the underlying chronic airway inflammation is different. There is still no single diagnostic test that can be considered a gold standard to distinguish asthma from COPD. Objectives: To determine the diagnostic accuracy for asthma and COPD of a series of diagnostic steps in a population older than 40 years with probable obstructive airway disease (OAD) in primary care. Methods: In this prospective cohort study, patients without a certain diagnosis underwent a work-up, including office spirometry by their general practitioner (GP). They were then referred to a pulmonologist, and they had control visits with their GP. The diagnostic gain of subsequent steps was calculated for 2 endpoints, namely the specialist's opinion and the GP's final opinion. Results: Up to 60% of the patients failed to consult with the pulmonologist. For this subgroup, the office spirometry induced significantly more diagnostic congruency than any other diagnostic step. The specialists rejected 44.5% of the diagnoses made by the GPs, including spirometry. High values of diagnostic gain were found after the office spirometry and after the specialist's advice. Up to 25% of the population taking bronchodilators were judged not to suffer from OAD. Conclusions: Office spirometry added significantly more to the diagnostic certainty of the GPs than questionnaires, history and clinical examination. A pulmonologist's advice contributed more to diagnostic certainty than any other diagnostic step. Nevertheless, 26% of the diagnoses made by the chest physicians were reconsidered by the GPs. Copyright © 2011 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

This study evaluated the convergent and predictive validity of the Sex Offender Risk Appraisal Guide (SORAG; V. L. Quinsey, G. T. Harris, M. E. Rice, & C. A. Cormier, 1998) and the Static-99 (R. K. Hanson & D. Thornton, 1999, 2000) among 147 male sex offenders committed to a high-security hospital in Belgium (Centre de Défense Sociale "Les Marronniers"). Of the sample, 63.8% were child abusers (victims under 14 years of age), 24.6% were rapists (victims aged 14 years or more), and 11.5% were mixed-victim offenders (victims less than 14 years of age and victims aged 14 years or more). After an average follow-up period of 4.2 years (SD = 3.4 years), the sexual recidivism rate was 25.2%, the violent recidivism rate was 17.1%, and the general (any) recidivism rate was 33.1%. The SORAG and the Static-99 were moderately correlated with each other (r = .55), and both showed strong predictive validity for general and violent recidivism (ROC AUC's ranging from .68 to .72 for the total sample). Both instruments showed moderate predictive validity for sexual recidivism (AUC of .64 for SORAG and .66 for Static-99). [ABSTRACT FROM AUTHOR]