Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn's disease for point of care risk evaluation.

BACKGROUND: Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk. METHODS: We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index. RESULTS: There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%. CONCLUSIONS: Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Neonatal phototherapy and risk of epilepsy-A Danish population based study.

To evaluate the risk of epilepsy in children who received neonatal phototherapy. A cohort of live singletons born at a Danish hospital (2002-2016) with a gestational age ≥ 35 weeks. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of epilepsy in children treated with neonatal phototherapy compared to children not treated with neonatal phototherapy in the general population, and in a subpopulation of children who had serum bilirubin measurement. Adjusted HRs (aHR) were computed using multivariable and propensity score matching models to take maternal and neonatal factors into consideration. Children were followed from day 29 after birth to diagnosis of epilepsy, death, emigration, or December 31, 2016. Among 65,365 children, 958 (1.5%) received neonatal phototherapy. Seven children (incidence rates (IRs): 10.8 /10,000 person-years) who received neonatal phototherapy and 354 children (IR: 7.7) who did not receive neonatal phototherapy were diagnosed with epilepsy. Neonatal phototherapy was not associated with an increased risk of epilepsy using the multivariable (aHR 0.95, 95% CI: 0.43-2.09) and propensity score matched (aHR 0.94, 95% CI: 0.39-2.28) models. In the subpopulation of 9,378 children with bilirubin measurement, 928 (9.9%) received neonatal phototherapy. In the analysis of the subpopulation in which bilirubin level and age at the time of bilirubin measurement were further taking into consideration, neonatal phototherapy was not associated with an increased risk of epilepsy using the multivariable (aHR 1.26, 95% CI: 0.54-2.97) and propensity score matched (aHR 1.24, 95% CI: 0.47-3.25) models,Conclusions: Neonatal phototherapy was not associated with an increased risk of epilepsy after taking maternal and neonatal factors into consideration. What is known: • A few studies have suggested that neonatal phototherapy for hyperbilirubinemia may increase the risk of childhood epilepsy. • Whether the observed associations contribute to hyperbilirubinemia, phototherapy, or underlying factors requires further investigation. What is new: • This study revealed no increased risk of epilepsy in children treated with neonatal phototherapy compared to children not treated with phototherapy after taking maternal and neonatal factors into consideration. • After further taking bilirubin level and age at the time of bilirubin measurement into consideration, neonatal phototherapy was not associated with an increased risk of epilepsy.

Relationships between cognition, function, and quality of life among HIV+ Canadian men.

OBJECTIVE: To estimate the extent to which HIV-related variables, cognition, and other brain health factors interrelate with other HIV-associated symptoms to influence function, health perception, and QOL in older HIV+ men in Canada. DESIGN: Cross-sectional structural equation modelling (SEM) of data from the inaugural visit to the Positive Brain Health Now Cohort. SETTING: HIV clinics at 5 Canadian sites. SUBJECTS: 707 men, age ≥ 35 years, HIV+ for at least one year, without clinically diagnosed dementia. MAIN OUTCOME MEASURES: Five latent and 21 observed variables from the World Health Organization's biopsychosocial model for functioning and disability and the Wilson-Cleary Model were analysed. SEM was used to link disease factors to symptoms, impairments, function, health perception, and QOL with a focus on cognition. RESULTS: QOL was explained directly by depression, social role, health perception, social support, and quality of the environment. Measured cognitive performance had direct effects on activity/function and indirect effects on participation, HP and QOL, acting through self-reported cognitive difficulties and meaningful activities. CONCLUSION: The biopsychosocial model showed good fit, with RMSEA < 0.05. This is the first time the full model has been tested in HIV. All of the domains included in the model are theoretically amenable to intervention and many have evidence-based interventions that could be harnessed to improve QOL.

Prediction equations of forced oscillation technique: the insidious role of collinearity.

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

Patient-reported outcomes in liver transplant recipients with hepatocellular carcinoma.

BACKGROUND: The effect of awarding MELD exception points for hepatocellular carcinoma (HCC) on patient-reported outcomes (PROs) is unknown. We evaluated the physical and mental health-related quality of life (HRQOL) and symptoms of anxiety and depression in liver transplant recipients with HCC compared to patients without HCC. METHODS: The single-center sample measured PROs before and after transplant, which included 1521 multisurvey measurement points among 502 adults (67% male, 28% HCC, follow-up time: <1-131 months). Data were analyzed using multivariable mixed-effects models. RESULTS: Longitudinal PRO values did not differ between persons who received HCC exception points and those who did not have HCC. Patients with HCC who did not receive exception points had reduced physical HRQOL (P=.016), a late decline in mental HRQOL, and delayed reduction in anxiety (time-by-outcome interaction P<.050) compared to patients with HCC who received exception points. CONCLUSION: Transplant recipients who received HCC exception points had PROs that were comparable to those of patients without HCC, and reported better physical HRQOL and reduced symptoms of anxiety compared to patients with HCC who did not receive exception points. These analyses demonstrate the impact of HCC exception points on PROs, and may help inform policy regarding HCC exception point allocation.

Validation of prediction models for radiation-induced late rectal bleeding: Evidence from a large pooled population of prostate cancer patients.

PURPOSE: To validate published models for the risk estimate of grade ≥ 1 (G1+), grade ≥ 2 (G2+) and grade = 3 (G3) late rectal bleeding (LRB) after radical radiotherapy for prostate cancer in a large pooled population from three prospective trials. MATERIALS AND METHODS: The external validation population included patients from Europe, and Oceanian centres enrolled between 2003 and 2014. Patients received 3DCRT or IMRT at doses between 66-80 Gy. IMRT was administered with conventional or hypofractionated schemes (2.35-2.65 Gy/fr). LRB was prospectively scored using patient-reported questionnaires (LENT/SOMA scale) with a 3-year follow-up. All Normal Tissue Complication Probability (NTCP) models published until 2021 based on the Equivalent Uniform Dose (EUD) from the rectal Dose Volume Histogram (DVH) were considered for validation. Model performance in validation was evaluated through calibration and discrimination. RESULTS: Sixteen NTCP models were tested on data from 1633 patients. G1+ LRB was scored in 465 patients (28.5%), G2+ in 255 patients (15.6%) and G3 in 112 patients (6.8%). The best performances for G2+ and G3 LRB highlighted the importance of the medium-high doses to the rectum (volume parameters n = 0.24 and n = 0.18, respectively). Good performance was seen for models of severe LRB. Moreover, a multivariate model with two clinical factors found the best calibration slope. CONCLUSION: Five published NTCP models developed on non-contemporary cohorts were able to predict a relative increase in the toxicity response in a more recent validation population. Compared to QUANTEC findings, dosimetric results pointed toward mid-high doses of rectal DVH. The external validation cohort confirmed abdominal surgery and cardiovascular diseases as risk factors.

Dichotomous outcomes vs. survival regression models for identification of predictors of mortality among patients with severe acute respiratory illness during COVID-19 pandemics.

Introduction: As the studies predicting mortality in severe acute respiratory illness (SARI) have inferred associations either from dichotomous outcomes or from time-event models, we identified some clinical-epidemiological characteristics and predictors of mortality by comparing and discussing two multivariate models. Methods: To identify factors associated with death among all SARI hospitalizations occurred in Botucatu (Brazil)/regardless of the infectious agent, and among the COVID-19 subgroup, from March 2020 to 2022, we used a multivariate Poisson regression model with binomial outcomes and Cox proportional hazards (time-event). The performance metrics of both models were also analyzed. Results: A total of 3,995 hospitalized subjects were included, of whom 1338 (33%) tested positive for SARS-CoV-2. We identified 866 deaths, of which 371 (43%) were due to the COVID-19. In the total number of SARI cases, using both Poisson and Cox models, the predictors of mortality were the presence of neurological diseases, immunosuppression, obesity, older age, and need for invasive ventilation support. However, the Poisson test also revealed that admission to an intensive care unit and the COVID-19 diagnosis were predictors of mortality, with the female gender having a protective effect against death. Likewise, Poisson proved to be more sensitive and specific, and indeed the most suitable model for analyzing risk factors for death in patients with SARI/COVID-19. Conclusion: Given these results and the acute course of SARI and COVID-19, to compare the associations and their different meanings is essential and, therefore, models with dichotomous outcomes are more appropriate than time-to-event/survival approaches.

What causes the death of patients with cutaneous squamous cell carcinoma? A prospective analysis in 1400 patients.

INTRODUCTION: Cutaneous squamous cell carcinoma (cSCC) is a common tumor of elderly Caucasian patients. METHODS: Competing multivariable risk models to analyze different types of cSCC associated death (local infiltration [LI], locoregional [LR], and distant metastases [DM]) in terms of prognostic factors. RESULTS AND DISCUSSION: 1400 patients were analyzed. In the adjusted multivariable subdistribution hazard approach for tumor volume, the best model for death of cSCC overall revealed the presence of desmoplasia (HR 4.52; p < 0.001), bone invasion (HR 10.06; p < 0.01), and immunosuppression (HR 3.19; p = 0.003) as significant factors. Death due to LI indicated desmoplasia (HR 15.39; p < 0.01) and bone invasion (HR 16.9; p < 0.001) as significant factors. For death by LM, immunosuppression with a HR of 3.27; p = 0.004 was the only significant prognostic factors as well as in death by DM with a HR of 4.54; p = 0.02. CONCLUSIONS: The three types of death caused by cSCC can be distinguished based on risk factors with different weights. Patients with these factors should be monitored closely.

Prediction models for child and adolescent mental health: A systematic review of methodology and reporting in recent research.

Background: There has been a rapid growth in the publication of new prediction models relevant to child and adolescent mental health. However, before their implementation into clinical services, it is necessary to appraise the quality of their methods and reporting. We conducted a systematic review of new prediction models in child and adolescent mental health, and examined their development and validation. Method: We searched five databases for studies developing or validating multivariable prediction models for individuals aged 18 years old or younger from 1 January 2018 to 18 February 2021. Quality of reporting was assessed using the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis checklist, and quality of methodology using items based on expert guidance and the PROBAST tool. Results: We identified 100 eligible studies: 41 developing a new prediction model, 48 validating an existing model and 11 that included both development and validation. Most publications (k = 75) reported a model discrimination measure, while 26 investigations reported calibration. Of 52 new prediction models, six (12%) were for suicidal outcomes, 18 (35%) for future diagnosis, five (10%) for child maltreatment. Other outcomes included violence, crime, and functional outcomes. Eleven new models (21%) were developed for use in high-risk populations. Of development studies, around a third were sufficiently statistically powered (k = 16%, 31%), while this was lower for validation investigations (k = 12, 25%). In terms of performance, the discrimination (as measured by the C-statistic) for new models ranged from 0.57 for a tool predicting ADHD diagnosis in an external validation sample to 0.99 for a machine learning model predicting foster care permanency. Conclusions: Although some tools have recently been developed for child and adolescent mental health for prognosis and child maltreatment, none can be currently recommended for clinical practice due to a combination of methodological limitations and poor model performance. New work needs to use ensure sufficient sample sizes, representative samples, and testing of model calibration.

Statistical Models in Clinical Studies.

Although statistical models serve as the foundation of data analysis in clinical studies, their interpretation requires sufficient understanding of the underlying statistical framework. Statistical modeling is inherently a difficult task because of the general lack of information of the nature of observable data. In this article, we aim to provide some guidance when using regression models to aid clinical researchers to better interpret results from their statistical models and to encourage investigators to collaborate with a statistician to ensure that their studies are designed and analyzed appropriately.

Environmental and trophic determinism of fruit abscission and outlook with climate change in tropical regions.

Fruit abscission facilitates the optimal conditions and timing of seed dispersal. Environmental regulation of tropical fruit abscission has received little attention, even though climate change may have its strongest impacts in tropical regions. In this study, oil palm fruit abscission was monitored during multiple years in the Benin Republic to take advantage of the climatic seasonality and the continuous fruit production by this species. An innovative multivariable statistical method was used to identify the best predictors of fruit abscission among a set of climate and ecophysiological variables, and the stage of inflorescence and fruit bunch development when the variables are perceived. The effects of climate scenarios on fruit abscission were then predicted based on the calibrated model. We found complex regulation takes place at specific stages of inflorescence and bunch development, even long before the fruit abscission zone is competent to execute abscission. Among the predictors selected, temperature variations during inflorescence and fruit bunch development are major determinants of the fruit abscission process. Furthermore, the timing of ripe fruit drop is determined by temperature in combination with the trophic status. Finally, climate simulations revealed that the abscission process is robust and is more affected by seasonal variations than by extreme scenarios. Our investigations highlighted the central function of the abscission zone as the sensor of environmental signals during reproductive development. Coupling ecophysiological and statistical modeling was an efficient approach to disentangle this complex environmental regulation.

Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This review aims to systematically review the existing multivariable prognostic models for the recurrence of CVD or major adverse cardiovascular events in adults with established CVD diagnosis. METHODS AND ANALYSIS: Bibliographic databases (Ovid MEDLINE, EMBASE, PsycINFO and Web of Science) will be searched, from database inception to April 2020, using terms relating to the clinical area and prognosis. A hand search of the reference lists of included studies will also be done to identify additional published studies. No restrictions on language of publications will be applied. Eligible studies present multivariable models (derived or validated) of adults (aged 16 years and over) with an established diagnosis of CVD, reporting at least one of the components of the primary outcome of major adverse cardiovascular events (defined as either coronary heart disease, stroke, peripheral artery disease, heart failure or CVD-related mortality). Reviewing will be done by two reviewers independently using the pre-defined criteria. Data will be extracted for included full-text articles. Risk of bias will be assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Prognostic models will be summarised narratively. If a model is tested in multiple validation studies, the predictive performance will be summarised using a random-effects meta-analysis model to account for any between-study heterogeneity. ETHICS AND DISSEMINATION: Ethics approval is not required. The results of this study will be submitted to relevant conferences for presentation and a peer-reviewed journal for publication. PROSPERO REGISTRATION NUMBER: CRD42019149111.

Footbathing, formalin and foot trimming: The 3Fs associated with granulomas and shelly hoof in sheep.

Granulomas and shelly hoof (SH), are lesions of sheep feet. Our objective was to use data from four questionnaires on lameness sent to English sheep farmers in 2004, 2013, 2014 and 2015 to further understanding of the risks and aetiologies of both lesions. Granulomas were more likely in flocks where routine foot trimming (odds ratio [OR]=3.17; 95% confidence intervals [CI] 1.11-11.47) and routine footbathing (OR=2.38; 95% CI 1.19-4.83) were practised than where these management protocols were not. SH was more likely in flocks that were footbathed in formalin compared with not footbathing (OR=1.65; 95% CI 1.19-2.30), and was less common in flocks that stocked ewes at more than eight vs. four per acre (OR=0.34; 95% CI 0.17-0.68). There were weak associations between SH and foot trimming. In 2004 only, SH was more likely in flocks where therapeutic foot trimming was practised than not practised (OR=2.24; 95% CI 1.12-4.68). In 2014 only, SH was marginally less likely in flocks where no feet bled during trimming, compared with flocks not routinely trimmed (OR=0.55; CI 0.30-1.00); SH was not related to foot trimming once severe footrot was included. We propose that flocks with granulomas and SH would decrease if farmers stopped footbathing in general, in particular with formalin, and avoided foot trimming whether as a therapeutic or routine practice. Further work is needed to understand the role of stocking density.

The history, current status, and possible future of precision mental health.

In evidence-based mental health practice, decisions must often be made for which there is little or no empirical basis. A common example of this is when there are multiple empirically supported interventions for a person with a given diagnosis, where the aim is to recommend the treatment most likely to be effective for that person. Data obtained from randomized clinical trials allow for the identification of patient characteristics that could be used to match patients to treatments. Historically, researchers have focused on individual moderators, single variables that interact statistically with treatment type, but these have rarely proved powerful enough to inform treatment decisions. Recently, researchers have begun to explore ways in which the use of multivariable algorithms might improve clinical decision-making. Common pitfalls have been identified, including the use of methods that provide overoptimistic estimates of the gains that can be expected from the applications of an algorithm in a clinical setting. It is too early to tell if these efforts will pay off and, if so, how much their use can increase the efficiency and effectiveness of mental health systems. It behooves the field to continue to learn and develop the most powerful methods that can produce generalizable knowledge that will advance the aims of precision mental health.

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Hypoglycemia Early Alarm Systems Based On Multivariable Models.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.