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We consider nonparametric prediction with multiple covariates, in particular categorical or functional predictors, or a mixture of both. The method proposed bases on an extension of the Nadaraya-Watson estimator where a kernel function is applied on a linear combination of distance measures each calculated on single covariates, with weights being estimated from the training data. The dependent variable can be categorical (binary or multi-class) or continuous, thus we consider both classification and regression problems. The methodology presented is illustrated and evaluated on artificial and real world data. Particularly it is observed that prediction accuracy can be increased, and irrelevant, noise variables can be identified/removed by ‘downgrading’ the corresponding distance measures in a completely data-driven way.

Background: Fatigue is common among patients with sarcoidosis. The etiology of this problem is unknown and multifactorial. Fatigue can be confounded with excessive daytime sleepiness (EDS). Fatigue and sleepiness have rarely been studied simultaneously in sarcoidosis patients. Objectives: The aim of this study was the confounder-adjusted estimation of risks for severe fatigue and EDS in a large population of sarcoidosis patients and the development of multivariate predictors from this population. Methods: 1,197 German sarcoidosis patients were examined using the Epworth Sleepiness Scale (ESS), the Fatigue Assessment Scale (FAS), the Hospital Anxiety and Depression Scale (HADS), and the Medical Research Council (MRC) dyspnea scale. Results: 16.5% (123 patients) had EDS (ESS ≥16), 16.4% had severe fatigue (FAS ≥35), and 6.3% had both extreme findings. In a multivariate logistic regression model, predictors of the risk of EDS were a history of sleep apnea (odds ratio [OR] 2.46, 95% confidence interval [CI] 1.5-3.9), dyspnea MRC grade ≥2 (OR 2.29, 95% CI 1.5-3.5), and organ involvement of 4-7 organs (OR 1.60, 95% CI 1.1-2.4). Significantly associated with higher risk of severe fatigue were the following: conspicuous depression (OR 5.95, 95% CI 4.1-8.7), conspicuous anxiety (OR 2.38, 95% CI 1.6-3.4), and muscle pain (OR 1.92, 95% CI 1.32-2.75). The logit models for severe fatigue with and without simultaneous EDS differed only slightly. Conclusion: An extreme form of fatigue and/or sleepiness was found in 27% of all sarcoidosis patients questioned. Because there is a certain overlap, both should be examined simultaneously to allow for a combined assessment.

BackgroundLeft main disease (LMD) and three-vessel disease (3VD) have important prognostic value in patients with coronary artery disease. However, uncertainties still exist about their prevalence and predictors in patients with acute coronary syndrome (ACS) and also in patients with stable coronary disease. Thus the aim of this study was to perform an international collaborative systematic review and meta-analysis to appraise the prevalence and predictors of LMD and 3VD.MethodsMedline/PubMed were systematically searched for eligible studies published up to 2010, reporting multivariate predictors of LMD or 3VD. Study features, patient characteristics, and prevalence and predictors of LMD and 3VD were abstracted and pooled with random-effect methods (95% CIs).Results17 studies (22 740 patients) were included, 11 focusing on ACS (17 896 patients) and six on stable coronary disease (4844 patients). In the ACS subgroup, LMD or 3VD occurred in 20% (95% CI 7.2% to 33.4%), LMD in 12% (95% CI 10.5% to 13.5%), and 3VD in 25% (95% CI 23.1% to 27.0%). Heart failure at admission and extent of ST-segment elevation in lead aVR on 12-lead ECG were the most powerful predictors of LMD or 3VD. In the stable disease subgroup, LMD or 3VD was found in 36% (95% CI 18.5% to 48.8%), with the most powerful predictors being transient ischaemic dilation during the imaging stress test, extent of ST-segment elevation in aVR and V1 during the stress test, and hyperlipidaemia.ConclusionsThis meta-analysis demonstrated that severe coronary disease—that is, LMD or 3VD—is more common in patients with ACS or stable coronary disease than generally perceived, and that simple and low-cost tools may help in the selection of the most appropriate therapeutic approach.

Background Bronchiolitis is considered one of the earliest and most common causes of hospitalisation in young children. Development of molecular technologies allowed a better understanding of bronchiolitis aetiology. Results from cohort studies evaluating the association between single, multiple viral infections and clinical outcomes are conflicting. Data on viral bronchiolitis in children were found to be limited in Qatar. This study aimed to determine frequency and seasonal trends of viral pathogens causing acute bronchiolitis, and to explore association between viral pathogens, disease severity and length of stay (LOS). Methods This is a retrospective descriptive study, including children admitted in 2010 and 2011 with acute bronchiolitis. Presenting history, physical examination and respiratory viral co-infections as detected by molecular assays were analysed. Results At least one virus was detected in 315/369 (85.4%) of included children with single and multiple viruses in 67 and 33% of cases respectively. Respiratory syncytial virus (RSV) was the most detected virus, accounting for 51.2% followed by rhinovirus (RV) in 25.5% of cases. Fall and summer admissions were associated with longer LOS. On multivariate logistic regression analysis, retraction (OR 3.96; 95% CI 1.64,9.59) and age group 1–3 months (OR 3.09; 95% CI 1.06,9.05) were associated with longer LOS. Crepitation (OR 9.15; 95% CI 1.58,53.13), retraction (OR 4.10; 95% CI 1.05,16.12) and respiratory rate (OR 1.46; 95% CI 1.28,1.66) were associated with moderate to severe bronchiolitis. Identifying the viral agent did not influence disease severity or LOS. Conclusion Clinical presentation is of more relevance to LOS and disease severity than the detected viruses. Future studies should investigate the interplay between climate characteristics, population’s factors and the most detectable circulating viruses.

Background Preoperative anxiety comprising anesthesia and surgery related anxiety is common and perceived by many patients as the worst aspect of the surgical episode. The aim of this study was to identify independent predictors of these three anxieties dimensions and to quantify the relevance of specific fears particularly associated with anesthesia. Methods This study was part of a cross-sectional survey in patients scheduled to undergo elective surgery. Anxiety levels were measured with the Amsterdam Preoperative Anxiety and Information Scale (APAIS). Modified numeric rating scales (mNRS, range 0–10) were used to assess the severity of eight selected specific fears which were predominantly analyzed descriptively. Multivariate stepwise linear regression was applied to determine independent predictors of all three anxiety dimensions (APAIS anxiety subscales). Results 3087 of the 3200 enrolled patients were analyzed. Mean (SD) total preoperative anxiety (APAIS-A-T, range 4–20) was 9.9 (3.6). High anxiety (APAIS-A-T > 10) was reported by 40.5% of subjects. Mean (SD) levels of concern regarding the eight studied specific fears ranged from 3.9 (3.08) concerning “Anesthesiologist error” to 2.4 (2.29) concerning “Fatigue and drowsiness” with an average of 3.2 (2.84) concerning all specific fears. Ranking of all specific fears according to mean mNRS scores was almost identical in patients with high versus those with low anxiety. Among nine independent predictors of anxiety, only 3 variables (female gender, negative and positive anesthetic experience) independently predicted all three APAIS anxiety subscales. Other variables had a selective impact on one or two APAIS anxiety subscales only. Female gender had the strongest impact on all three APAIS anxiety subscales. Adjusted r 2 values of the three models were all below 13%. Conclusions The high variability of importance assigned to all specific fears suggests an individualized approach is advisable when support of anxious patients is intended. Considering independent predictors of anxiety to estimate each patient’s anxiety level is of limited use given the very low predictive capacity of all three models. The clinical benefit of dividing patients into those with high and low anxiety is questionable. Trial registration German Registry of Clinical Trials ( DRKS00016725 ), retrospectively registered.

Background: vaccine hesitancy is defined as a delay in the acceptance or refusal of vaccination, even though immunisation is a determinant in reducing the mortality and morbidity associated with Coronavirus Disease 2019 (COVID-19). Aim: to identify and analyse the predictors of COVID-19 vaccine acceptance and/or hesitancy. Methods: a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Keywords: vaccine and (COVID or SARS) and (acceptance or acceptability or willingness or hesitancy or refusal) and (multivariate or regression) and (questionnaire or survey) and national. Databases/resources: PubMed, DOAJ, SciELO and b-on. Timeframe: March 2020–2022. Inclusion criteria: general population, questionnaire-based, calculation of a multivariate regression model and national studies. Quality assessment: application of the National Heart, Lung, and Blood institute (NHLBI) tool. Results: a total of 37 studies were selected, whose overall rate was fair. The most predominant predictors of vaccine hesitancy were a lower perceived risk of getting infected, a lower level of institutional trust, not being vaccinated against influenza, lower levels of perceived severity of COVID-19, or stronger beliefs that the vaccination would cause side effects or be unsafe. Discussion and conclusion: the identified predictors can be used to design tailored health policies and/or public health interventions, or to evaluate subjects’ vaccine hesitancy.

We investigate a class of partially linear functional additive models (PLFAM) that predicts a scalar response by both parametric effects of a multivariate predictor and nonparametric effects of a multivariate functional predictor. We jointly model multiple functional predictors that are cross-correlated using multivariate functional principal component analysis (mFPCA), and model the nonparametric effects of the principal component scores as additive components in the PLFAM. To address the high-dimensional nature of functional data, we let the number of mFPCA components diverge to infinity with the sample size, and adopt the component selection and smoothing operator (COSSO) penalty to select relevant components and regularize the fitting. A fundamental difference between our framework and the existing high-dimensional additive models is that the mFPCA scores are estimated with error, and the magnitude of measurement error increases with the order of mFPCA. We establish the asymptotic convergence rate for our estimator, while allowing the number of components diverge. When the number of additive components is fixed, we also establish the asymptotic distribution for the partially linear coefficients. The practical performance of the proposed methods is illustrated via simulation studies and a crop yield prediction application. Supplementary materials for this article are available online.

Purpose This study aimed to analyze baseline predictors of functional outcomes six weeks and at one year follow-up in patients older than 60 years with complex regional pain syndrome type 1 (CRPS I) after distal radius fracture (DRF). Methods A total of 120 patients with CRPS I after DRF were prospectively recruited. Presumptive relevant factors were collected and analyzed as potential baseline predictors. Additionally, functional outcomes were assessed at the beginning of physiotherapy treatment, at six weeks after finishing physiotherapy treatment, and at one year follow-up. Patient-Rated Wrist Evaluation; Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire; Jamar dynamometer; and visual analog scale (VAS) were assessed. Results All participants completed the study. At 6 weeks, the main results showed an association of lower values of grip strength with female sex ( p = 0.010), intra-articular DRFs ( p = 0.030), longer immobilization time ( p = 0.040), lower levels of physical activity ( p < 0.001), higher levels of kinesiophobia ( p = 0.010), and anxiety ( p = 0.020). At 1-year follow-up, the results showed an association of lower values of DASH with higher BMI ( p < 0.001) and longer immobilization time ( p < 0.001); and higher values of VAS showed an association with older age ( p = 0.010), higher BMI ( p = 0.010), and lower levels of physical activity ( p = 0.040). Conclusion At six weeks, factors such as BMI, immobilization time, physical activity, and kinesiophobia are associated with lower functional outcomes. Additionally, at one year follow-up, BMI, immobilization time, and physical activity continue to be associated with lower functional outcomes in patients with CRPS I after DRF treated conservatively.

Regression analyses are commonly used for selecting determinants to target in behavior change interventions, but the aim of this article is to explain why regression analyses are not suitable for this purpose (i.e. the regression trap). This aim is achieved by providing (1) a theoretical rationale based on overlap among determinants; (2) a mathematical rationale based on the formulas that are used to calculate regression coefficients; and (3) examples based on real-world data. First, the meaning of regression coefficients is commonly explained as expressing the association between a determinant and a target behavior 'holding all other predictors constant.' We explain that this often boils down to 'neglecting a part of the psyche.' Second, we demonstrate that the interpretation of regression coefficients is distorted by correlations between determinants. Third, the examples provided demonstrate the impact this has in practice. This results in interventions targeting determinants that are less relevant and, thereby, have less impact on behavior change. There are theoretical, mathematical, and practical reasons why regression analyses, and by extension multivariate analyses relying on correlations, are not suitable to select determinants to target in behavior change interventions. Instead, intervention developers should consider univariate distributions and bivariate association estimates simultaneously and there are freely accessible tools available to do so.