Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

PURPOSE OF REVIEW: Predicting asthma episodes is notoriously difficult but has potentially significant consequences for the individual, as well as for healthcare services. The purpose of this review is to describe recent insights into the prediction of acute asthma episodes in relation to classical clinical, functional or inflammatory variables, as well as present a new concept for evaluating asthma as a dynamically regulated homeokinetic system. RECENT FINDINGS: Risk prediction for asthma episodes or relapse has been attempted using clinical scoring systems, considerations of environmental factors and lung function, as well as inflammatory and immunological markers in induced sputum or exhaled air, and these are summarized here. We have recently proposed that newer mathematical methods derived from statistical physics may be used to understand the complexity of asthma as a homeokinetic, dynamic system consisting of a network comprising multiple components, and also to assess the risk for future asthma episodes based on fluctuation analysis of long time series of lung function. SUMMARY: Apart from the classical analysis of risk factor and functional parameters, this new approach may be used to assess asthma control and treatment effects in the individual as well as in future research trials.

Risk of severe asthma episodes predicted from fluctuation analysis of airway function

Asthma is an increasing health problem worldwide, but the long-term temporal pattern of clinical symptoms is not understood and predicting asthma episodes is not generally possible. We analyse the time series of peak expiratory flows, a standard measurement of airway function that has been assessed twice daily in a large asthmatic population during a long-term crossover clinical trial. Here we introduce an approach to predict the risk of worsening airflow obstruction by calculating the conditional probability that, given the current airway condition, a severe obstruction will occur within 30 days. We find that, compared with a placebo, a regular long-acting bronchodilator (salmeterol) that is widely used to improve asthma control decreases the risk of airway obstruction. Unexpectedly, however, a regular short-acting beta2-agonist bronchodilator (albuterol) increases this risk. Furthermore, we find that the time series of peak expiratory flows show long-range correlations that change significantly with disease severity, approaching a random process with increased variability in the most severe cases. Using a nonlinear stochastic model, we show that both the increased variability and the loss of correlations augment the risk of unstable airway function. The characterization of fluctuations in airway function provides a quantitative basis for objective risk prediction of asthma episodes and for evaluating the effectiveness of therapy.

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

OBJECTIVE Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. DESIGN, SETTING AND SUBJECTS We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. RESULTS The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). CONCLUSION The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

Twelve-month psychosis-predictive value of the ultra-high risk criteria in children and adolescents

Objective The validity of current ultra-high risk (UHR) criteria is under-examined in help-seeking minors, particularly, in children below the age of 12 years. Thus, the present study investigated predictors of one-year outcome in children and adolescents (CAD) with UHR status. Method Thirty-five children and adolescents (age 9–17 years) meeting UHR criteria according to the Structured Interview for Psychosis-Risk Syndromes were followed-up for 12 months. Regression analyses were employed to detect baseline predictors of conversion to psychosis and of outcome of non-converters (remission and persistence of UHR versus conversion). Results At one-year follow-up, 20% of patients had developed schizophrenia, 25.7% had remitted from their UHR status that, consequently, had persisted in 54.3%. No patient had fully remitted from mental disorders, even if UHR status was not maintained. Conversion was best predicted by any transient psychotic symptom and a disorganized communication score. No prediction model for outcome beyond conversion was identified. Conclusions Our findings provide the first evidence for the predictive utility of UHR criteria in CAD in terms of brief intermittent psychotic symptoms (BIPS) when accompanied by signs of cognitive impairment, i.e. disorganized communication. However, because attenuated psychotic symptoms (APS) related to thought content and perception were indicative of non-conversion at 1-year follow-up, their use in early detection of psychosis in CAD needs further study. Overall, the need for more in-depth studies into developmental peculiarities in the early detection and treatment of psychoses with an onset of illness in childhood and early adolescence was further highlighted.

Normal Thyroid Function and the Risk of Atrial Fibrillation: the Rotterdam Study

CONTEXT Hyperthyroidism is an established risk factor for atrial fibrillation (AF), but information concerning the association with variations within the normal range of thyroid function and subgroups at risk is lacking. OBJECTIVE This study aimed to investigate the association between normal thyroid function and AF prospectively and explore potential differential risk patterns. DESIGN, SETTING, AND PARTICIPANTS From the Rotterdam Study we included 9166 participants ≥ 45 y with TSH and/or free T4 (FT4) measurements and AF assessment (1997-2012 median followup, 6.8 y), with 399 prevalent and 403 incident AF cases. MAIN OUTCOME MEASURES Outcome measures were 3-fold: 1) hazard ratios (HRs) for the risk of incident AF by Cox proportional-hazards models, 2) 10-year absolute risks taking competing risk of death into account, and 3) discrimination ability of adding FT4 to the CHARGE-AF simple model, an established prediction model for AF. RESULTS Higher FT4 levels were associated with higher risks of AF (HR 1.63, 95% confidence interval, 1.19-2.22), when comparing those in the highest quartile to those in lowest quartile. Absolute 10-year risks increased with higher FT4 in participants ≤ 65 y from 1-9% and from 6-12% in subjects ≥ 65 y. Discrimination of the prediction model improved when adding FT4 to the simple model (c-statistic, 0.722 vs 0.729; P = .039). TSH levels were not associated with AF. CONCLUSIONS There is an increased risk of AF with higher FT4 levels within the normal range, especially in younger subjects. Adding FT4 to the simple model slightly improved discrimination of risk prediction.

Predicting adverse events in children with fever and chemotherapy-induced neutropenia: the prospective multicenter SPOG 2003 FN study

PURPOSE To develop a score predicting the risk of adverse events (AEs) in pediatric patients with cancer who experience fever and neutropenia (FN) and to evaluate its performance. PATIENTS AND METHODS Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of future AEs (ie, serious medical complication, microbiologically defined infection, radiologically confirmed pneumonia) was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. Results An AE was reported in 122 (29%) of 423 FN episodes. In 57 episodes (13%), the first AE was known only after reassessment after 8 to 24 hours of inpatient management. Predicting AE at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The score predicting future AE in 358 episodes without known AE at reassessment used the following four variables: preceding chemotherapy more intensive than acute lymphoblastic leukemia maintenance (weight = 4), hemoglobin > or = 90 g/L (weight = 5), leukocyte count less than 0.3 G/L (weight = 3), and platelet count less than 50 G/L (weight = 3). A score (sum of weights) > or = 9 predicted future AEs. The cross-validated performance of this score exceeded the performance of published risk prediction rules. At an overall sensitivity of 92%, 35% of the episodes were classified as low risk, with a specificity of 45% and a negative predictive value of 93%. CONCLUSION This score, based on four routinely accessible characteristics, accurately identifies pediatric patients with cancer with FN at risk for AEs after reassessment.

[Osteoporosis - whom to treat? The importance of FRAX® in Switzerland]

The WHO fracture risk assessment tool FRAX® is a computer based algorithm that provides models for the assessment of fracture probability in men and women. The approach uses easily obtained clinical risk factors (CRFs) to estimate 10-year probability of a major osteoporotic fracture (hip, clinical spine, humerus or wrist fracture) and the 10-year probability of a hip fracture. The estimate can be used alone or with femoral neck bone mineral density (BMD) to enhance fracture risk prediction. FRAX® is the only risk engine which takes into account the hazard of death as well as that of fracture. Probability of fracture is calculated in men and women from age, body mass index, and dichotomized variables that comprise a prior fragility fracture, parental history of hip fracture, current tobacco smoking, ever long-term use of oral glucocorticoids, rheumatoid arthritis, other causes of secondary osteoporosis, daily alcohol consumption of 3 or more units daily. The relationship between risk factors and fracture probability was constructed using information of nine population-based cohorts from around the world. CRFs for fracture had been identified that provided independent information on fracture risk based on a series of meta-analyses. The FRAX® algorithm was validated in 11 independent cohorts with in excess of 1 million patient-years, including the Swiss SEMOF cohort. Since fracture risk varies markedly in different regions of the world, FRAX® models need to be calibrated to those countries where the epidemiology of fracture and death is known. Models are currently available for 31 countries across the world. The Swiss-specific FRAX® model was developed very soon after the first release of FRAX® in 2008 and was published in 2009, using Swiss epidemiological data, integrating fracture risk and death hazard of our country. Two FRAX®-based approaches may be used to explore intervention thresholds. They have recently been investigated in the Swiss setting. In the first approach the guideline that individuals with a fracture probability equal to or exceeding that of women with a prior fragility fracture should be considered for treatment is translated into thresholds using 10-year fracture probabilities. In that case the threshold is age-dependent and increases from 16 % at the age of 60 ys to 40 % at the age of 80 ys. The second approach is a cost-effectiveness approach. Using a FRAX®-based intervention threshold of 15 % for both, women and men 50 years and older, should permit cost-effective access to therapy to patients at high fracture probability in our country and thereby contribute to further reduce the growing burden of osteoporotic fractures.

Prediction of first coronary events with the Framingham score: a systematic review

BACKGROUND: Uncertainty exists about the performance of the Framingham risk score when applied in different populations. OBJECTIVE: We assessed calibration of the Framingham risk score (ie, relationship between predicted and observed coronary event rates) in US and non-US populations free of cardiovascular disease. METHODS: We reviewed studies that evaluated the performance of the Framingham risk score to predict first coronary events in a validation cohort, as identified by Medline, EMBASE, BIOSIS, and Cochrane library searches (through August 2005). Two reviewers independently assessed 1496 studies for eligibility, extracted data, and performed quality assessment using predefined forms. RESULTS: We included 25 validation cohorts of different population groups (n = 128,000) in our main analysis. Calibration varied over a wide range from under- to overprediction of absolute risk by factors of 0.57 to 2.7. Risk prediction for 7 cohorts (n = 18658) from the United States, Australia, and New Zealand was well calibrated (corresponding figures: 0.87-1.08; for the 5 biggest cohorts). The estimated population risks for first coronary events were strongly associated (goodness of fit: R2 = 0.84) and in good agreement with observed risks (coefficient for predicted risk: beta = 0.84; 95% CI 0.41-1.26). In 18 European cohorts (n = 109499), the corresponding figures indicated close association (R2 = 0.72) but substantial overprediction (beta = 0.58, 95% CI 0.39-0.77). The risk score was well calibrated on the intercept for both population clusters. CONCLUSION: The Framingham score is well calibrated to predict first coronary events in populations from the United States, Australia, and New Zealand. Overestimation of absolute risk in European cohorts requires recalibration procedures.

Predictors of attempted suicide among Swiss patients with alcohol-use disorders

Objective: Suicide attempts are common in patients being treated for alcohol-use disorders (AUDs). However, clinical assessment of suicide risk is difficult. In this Swiss multisite study, we propose a decision tree to facilitate identification of profiles of AUD patients at high risk for suicidal behavior. Method: In this retrospective study, we used a sample of 700 patients (243 female), attending 1 of 12 treatment programs for AUDs in the German-speaking part of Switzerland. Sixty-nine patients who reported a suicide attempt in the 3 months before the index treatment were compared using risk factors with 631 patients without a suicide attempt. Receiver operating characteristic (ROC) analyses were used to identify patients at risk of having had a suicide attempt in the previous 3 months. Results: Consistent with previous empirical findings in AUD patients, a prior history of attempted suicide and severe symptoms of depression and aggression considerably increased the risk of a suicide attempt and, in combination, raised the likelihood of a prior suicide attempt to 52%. In addition, one third of AUD patients who had a history of suicide attempts and previous inpatient psychiatric treatment, or who were male and had previous inpatient psychiatric treatment, also reported a suicide attempt. Conclusions: The empirically supported decision tree helps to identify profiles of suicidal AUD patients in Switzerland and supplements clinicians' judgments in making triage decisions for suicide management.

Risk scoring for setting priorities in a monitoring of antimicrobial resistance in meat and meat products

Meat and meat products can be contaminated with different species of bacteria resistant to various antimicrobials. The human health risk of a type of meat or meat product carry by emerging antimicrobial resistance depends on (i) the prevalence of contamination with resistant bacteria, (ii) the human health consequences of an infection with a specific bacterium resistant to a specific antimicrobial and (iii) the consumption volume of a specific product. The objective of this study was to compare the risk for consumers arising from their exposure to antibiotic resistant bacteria from meat of four different types (chicken, pork, beef and veal), distributed in four different product categories (fresh meat, frozen meat, dried raw meat products and heat-treated meat products). A semi-quantitative risk assessment model, evaluating each food chain step, was built in order to get an estimated score for the prevalence of Campylobacter spp., Enterococcus spp. and Escherichia coli in each product category. To assess human health impact, nine combinations of bacterial species and antimicrobial agents were considered based on a published risk profile. The combination of the prevalence at retail, the human health impact and the amount of meat or product consumed, provided the relative proportion of total risk attributed to each category of product, resulting in a high, medium or low human health risk. According to the results of the model, chicken (mostly fresh and frozen meat) contributed 6.7% of the overall risk in the highest category and pork (mostly fresh meat and dried raw meat products) contributed 4.0%. The contribution of beef and veal was of 0.4% and 0.1% respectively. The results were tested and discussed for single parameter changes of the model. This risk assessment was a useful tool for targeting antimicrobial resistance monitoring to those meat product categories where the expected risk for public health was greater.

Neural correlates of anticipation risk reflect risk preferences

Individual risk preferences have a large influence on decisions, such as financial investments, career and health choices, or gambling. Decision making under risk has been studied both behaviorally and on a neural level. It remains unclear, however, how risk attitudes are encoded and integrated with choice. Here, we investigate how risk preferences are reflected in neural regions known to process risk. We collected functional magnetic resonance images of 56 human subjects during a gambling task (Preuschoff et al., 2006). Subjects were grouped into risk averters and risk seekers according to the risk preferences they revealed in a separate lottery task. We found that during the anticipation of high-risk gambles, risk averters show stronger responses in ventral striatum and anterior insula compared to risk seekers. In addition, risk prediction error signals in anterior insula, inferior frontal gyrus, and anterior cingulate indicate that risk averters do not dissociate properly between gambles that are more or less risky than expected. We suggest this may result in a general overestimation of prospective risk and lead to risk avoidance behavior. This is the first study to show that behavioral risk preferences are reflected in the passive evaluation of risky situations. The results have implications on public policies in the financial and health domain.

Prediction of arrhythmic events by Wedensky modulation in patients with coronary artery disease.

PRINCIPLES Prediction of arrhythmic events (AEs) has gained importance with the availability of implantable cardioverter-defibrillators (ICDs), but is still imprecise. This study evaluated the innovative Wedensky modulation index (WMI) as predictor of AEs. METHODS In this prospective cohort, 179 patients with coronary artery disease (CAD) referred for AE risk assessment underwent baseline evaluation including measurement of R-/T-wave WMI (WMI(RT)) and left ventricular ejection fraction (LVEF). Two endpoints were assessed 3 years after the baseline evaluation: sudden cardiac death or appropriate ICD event (EP1) and any cardiac death or appropriate ICD event (EP2). Associations between baseline predictors (WMI(RT) and LVEF) and endpoints were evaluated in regression models. RESULTS Only three patients were lost to follow-up. EP1 and EP2 occurred in 24 and 27 patients, respectively. WMI(RT) (odds ratio [OR] per 1 point increase for EP1 20.1, 95% confidence interval [CI] 1.8-221.4, p = 0.014, and for EP2 73.3, 95% CI 6.6-817.7, p <0.001) and LVEF (OR per 1% increase for EP1 0.94, 95% CI 0.90-0.99, p = 0.013, and for EP2 0.93, 95% CI 0.89-0.97, p = 0.002) were significantly associated with both endpoints. In bivariable regression controlled for LVEF, WMI(RT) was independently associated with EP1 (p = 0.047) and EP2 (p = 0.007). The combination of WMI(RT) ≥0.60 and LVEF ≤30% resulted in a positive predictive value of 36% for EP1 and 50% for EP2. CONCLUSIONS WMI(RT) is a significant predictor of AEs independent of LVEF and has potential to improve AE risk prediction in CAD patients. However, WMI(RT) should be evaluated in larger and independent samples before recommendations for clinical routine can be made.

Are suicide attempters wired differently?

Objective: Limited research exists on internal risk processes in suicide attempters and factors that distinguish them from non-suicidal, depressive individuals. In this qualitative study, we investigated Plans, motives, and underlying self-regulatory processes of the two groups and conducted a comparative analysis. Methods: We analyzed narrative interviews of 17 suicide attempters and intake interviews of 17 non-suicidal, depressive patients using Plan Analysis. Then, we developed a prototypical Plan structure for both groups. Results: Suicidal behavior serves various Plans only found in suicide attempters. Plans of this group are especially related to social perfectionism and withdrawal in order to protect their self-esteem. Depressive patients employ several interpersonal control and coping strategies, which might help prevent suicidal behavior. Conclusion: The prototypical Plan structure of suicide attempters may be a valuable tool for clinicians to detect critical Plans and motives in their interaction with patients, which are related to suicide risk.

Very long-term survival patterns of young patients treated with radical prostatectomy for high-risk prostate cancer

OBJECTIVE In patients with a long life expectancy with high-risk (HR) prostate cancer (PCa), the chance to die from PCa is not negligible and may change significantly according to the time elapsed from surgery. The aim of this study was to evaluate long-term survival patterns in young patients treated with radical prostatectomy (RP) for HRPCa. MATERIALS AND METHODS Within a multiinstitutional cohort, 600 young patients (≤59 years) treated with RP between 1987 and 2012 for HRPCa (defined as at least one of the following adverse characteristics: prostate specific antigen>20, cT3 or higher, biopsy Gleason sum 8-10) were identified. Smoothed cumulative incidence plot was performed to assess cancer-specific mortality (CSM) and other cause mortality (OCM) rates at 10, 15, and 20 years after RP. The same analyses were performed to assess the 5-year probability of CSM and OCM in patients who survived 5, 10, and 15 years after RP. A multivariable competing risk regression model was fitted to identify predictors of CSM and OCM. RESULTS The 10-, 15- and 20-year CSM and OCM rates were 11.6% and 5.5% vs. 15.5% and 13.5% vs. 18.4% and 19.3%, respectively. The 5-year probability of CSM and OCM rates among patients who survived at 5, 10, and 15 years after RP, were 6.4% and 2.7% vs. 4.6% and 9.6% vs. 4.2% and 8.2%, respectively. Year of surgery, pathological stage and Gleason score, surgical margin status and lymph node invasion were the major determinants of CSM (all P≤0.03). Conversely, none of the covariates was significantly associated with OCM (all P≥ 0.09). CONCLUSIONS Very long-term cancer control in young high-risk patients after RP is highly satisfactory. The probability of dying from PCa in young patients is the leading cause of death during the first 10 years of survivorship after RP. Thereafter, mortality not related to PCa became the main cause of death. Consequently, surgery should be consider among young patients with high-risk disease and strict PCa follow-up should enforce during the first 10 years of survivorship after RP.

Computational fluid dynamics modelling in cardiovascular medicine.

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.