Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

ACL injuries identifiable for pre-participation imagiological analysis: Risk factors

Identification of pre-participation risk factors for noncontact anterior cruciate ligament (ACL) injuries has been attracting a great deal of interest in the sports medicine and traumatology communities. Appropriate methods that enable predicting which patients could benefit from pre- ventive strategies are most welcome. This would enable athlete-specific training and conditioning or tailored equipment in order to develop appropriate strategies to reduce incidence of injury. In order to accomplish these goals, the ideal system should be able to assess both anatomic and functional features. Complementarily, the screening method must be cost-effective and suited for widespread application. Anatomic study protocol requiring only standard X rays could answer some of such demands. Dynamic MRI/CT evaluation and electronically assisted pivot-shift evaluation can be powerful tools providing complementary information. These upcoming insights, when validated and properly combined, envision changing pre-participation knee examination in the near future. Herein different methods (validated or under research) aiming to improve the capacity to identify persons/athletes with higher risk for ACL injury are overviewed. 

Prodromal Questionnaire: translation, adaptation to Portuguese and preliminary results in ultra-high risk individuals and first episode psychosis

OBJECTIVE: The Prodromal Questionnaire (PQ) is a 92-item self-report screening tool for individuals at ultra-high risk (UHR) to develop psychosis. This study aims to present the translation to Portuguese and preliminary results in UHR and first episode (FE) psychosis in a Portuguese sample. METHODS: The PQ was translated from English to Portuguese by two bilingual researchers from the research program on early psychosis of the Instituto de Psiquiatria HCFMUSP, São Paulo, Brazil (ASAS - "Evaluation and Follow up of Adolescents and Young Adults in São Paulo") and back translated by two other researchers. The study participants (n = 11-) were evaluated through the Portuguese version of the Prodromal Questionnaire (PQ) and SIPS. RESULTS: The individuals at UHR (n = 7) presented a lower score than first episode patients (n = 4). The UHR mean scores and standard deviation on Portuguese version of the PQ were: 13.0 ± 10.0 points on positive symptoms subscale, and FE patients: 33.0 ± 10.0. CONCLUSION: The UHR and FE patients' of this study presented PQ scores similar to the ones found in the literature; what suggests that it is possible to use the PQ in Brazilian help-seeking individuals as a screening tool.

A study of risk-aware program transformation

In the trend towards tolerating hardware unreliability, accuracy is exchanged for cost savings. Running on less reliable machines, functionally correct code becomes risky and one needs to know how risk propagates so as to mitigate it. Risk estimation, however, seems to live outside the average programmer’s technical competence and core practice. In this paper we propose that program design by source-to-source transformation be risk-aware in the sense of making probabilistic faults visible and supporting equational reasoning on the probabilistic behaviour of programs caused by faults. This reasoning is carried out in a linear algebra extension to the standard, `a la Bird-Moor algebra of programming. This paper studies, in particular, the propagation of faults across standard program transformation techniques known as tupling and fusion, enabling the fault of the whole to be expressed in terms of the faults of its parts.

Subjective income expectations, canonical models and income risk

Expectations are central to behaviour. Despite the existence of subjective expectations data, the standard approach is to ignore these, to hypothecate a model of behaviour and to infer expectations from realisations. In the context of income models, we reveal the informational gain obtained from using both a canonical model and subjective expectations data. We propose a test for this informational gain, and illustrate our approach with an application to the problem of measuring income risk.

Biomedical risk assessment as an aid for smoking cessation.

BACKGROUND: A possible strategy for increasing smoking cessation rates could be to provide smokers who have contact with healthcare systems with feedback on the biomedical or potential future effects of smoking, e.g. measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer. OBJECTIVES: To determine the efficacy of biomedical risk assessment provided in addition to various levels of counselling, as a contributing aid to smoking cessation. SEARCH STRATEGY: We systematically searched the Cochrane Collaboration Tobacco Addiction Group Specialized Register, Cochrane Central Register of Controlled Trials 2008 Issue 4, MEDLINE (1966 to January 2009), and EMBASE (1980 to January 2009). We combined methodological terms with terms related to smoking cessation counselling and biomedical measurements. SELECTION CRITERIA: Inclusion criteria were: a randomized controlled trial design; subjects participating in smoking cessation interventions; interventions based on a biomedical test to increase motivation to quit; control groups receiving all other components of intervention; an outcome of smoking cessation rate at least six months after the start of the intervention. DATA COLLECTION AND ANALYSIS: Two assessors independently conducted data extraction on each paper, with disagreements resolved by consensus. Results were expressed as a relative risk (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate a pooled effect was estimated using a Mantel-Haenszel fixed effect method. MAIN RESULTS: We included eleven trials using a variety of biomedical tests. Two pairs of trials had sufficiently similar recruitment, setting and interventions to calculate a pooled effect; there was no evidence that CO measurement in primary care (RR 1.06, 95% CI 0.85 to 1.32) or spirometry in primary care (RR 1.18, 95% CI 0.77 to 1.81) increased cessation rates. We did not pool the other seven trials. One trial in primary care detected a significant benefit of lung age feedback after spirometry (RR 2.12; 95% CI 1.24 to 3.62). One trial that used ultrasonography of carotid and femoral arteries and photographs of plaques detected a benefit (RR 2.77; 95% CI 1.04 to 7.41) but enrolled a population of light smokers. Five trials failed to detect evidence of a significant effect. One of these tested CO feedback alone and CO + genetic susceptibility as two different intervention; none of the three possible comparisons detected significant effects. Three others used a combination of CO and spirometry feedback in different settings, and one tested for a genetic marker. AUTHORS' CONCLUSIONS: There is little evidence about the effects of most types of biomedical tests for risk assessment. Spirometry combined with an interpretation of the results in terms of 'lung age' had a significant effect in a single good quality trial. Mixed quality evidence does not support the hypothesis that other types of biomedical risk assessment increase smoking cessation in comparison to standard treatment. Only two pairs of studies were similar enough in term of recruitment, setting, and intervention to allow meta-analysis.

The Inflation Risk Premium on Government Debt in an Overlapping Generations Model

This paper presents a general equilibrium model in which nominal government debt pays an inflation risk premium. The model predicts that the inflation risk premium will be higher in economies which are exposed to unanticipated inflation through nominal asset holdings. In particular, the inflation risk premium is higher when government debt is primarily nominal, steady-state inflation is low, and when cash and nominal debt account for a large fraction of consumers' retirement portfolios. These channels do not appear to have been highlighted in previous models or tested empirically. Numerical results suggest that the inflation risk premium is comparable in magnitude to standard representative agent models. These findings have implications for management of government debt, since the inflation risk premium makes it more costly for governments to borrow using nominal rather than indexed debt. Simulations of an extended model with Epstein-Zin preferences suggest that increasing the share of indexed debt would enable governments to permanently lower taxes by an amount that is quantitatively non-trivial.

A peripheral dose calculation model for estimating second cancer risk after breast radiotherapy

AbstractBreast cancer is one of the most common cancers affecting one in eight women during their lives. Survival rates have increased steadily thanks to early diagnosis with mammography screening and more efficient treatment strategies. Post-operative radiation therapy is a standard of care in the management of breast cancer and has been shown to reduce efficiently both local recurrence rate and breast cancer mortality. Radiation therapy is however associated with some late effects for long-term survivors. Radiation-induced secondary cancer is a relatively rare but severe late effect of radiation therapy. Currently, radiotherapy plans are essentially optimized to maximize tumor control and minimize late deterministic effects (tissue reactions) that are mainly associated with high doses (» 1 Gy). With improved cure rates and new radiation therapy technologies, it is also important to evaluate and minimize secondary cancer risks for different treatment techniques. This is a particularly challenging task due to the large uncertainties in the dose-response relationship.In contrast with late deterministic effects, secondary cancers may be associated with much lower doses and therefore out-of-field doses (also called peripheral doses) that are typically inferior to 1 Gy need to be determined accurately. Out-of-field doses result from patient scatter and head scatter from the treatment unit. These doses are particularly challenging to compute and we characterized it by Monte Carlo (MC) calculation. A detailed MC model of the Siemens Primus linear accelerator has been thoroughly validated with measurements. We investigated the accuracy of such a model for retrospective dosimetry in epidemiological studies on secondary cancers. Considering that patients in such large studies could be treated on a variety of machines, we assessed the uncertainty in reconstructed peripheral dose due to the variability of peripheral dose among various linac geometries. For large open fields (> 10x10 cm2), the uncertainty would be less than 50%, but for small fields and wedged fields the uncertainty in reconstructed dose could rise up to a factor of 10. It was concluded that such a model could be used for conventional treatments using large open fields only.The MC model of the Siemens Primus linac was then used to compare out-of-field doses for different treatment techniques in a female whole-body CT-based phantom. Current techniques such as conformai wedged-based radiotherapy and hybrid IMRT were investigated and compared to older two-dimensional radiotherapy techniques. MC doses were also compared to those of a commercial Treatment Planning System (TPS). While the TPS is routinely used to determine the dose to the contralateral breast and the ipsilateral lung which are mostly out of the treatment fields, we have shown that these doses may be highly inaccurate depending on the treatment technique investigated. MC shows that hybrid IMRT is dosimetrically similar to three-dimensional wedge-based radiotherapy within the field, but offers substantially reduced doses to out-of-field healthy organs.Finally, many different approaches to risk estimations extracted from the literature were applied to the calculated MC dose distribution. Absolute risks varied substantially as did the ratio of risk between two treatment techniques, reflecting the large uncertainties involved with current risk models. Despite all these uncertainties, the hybrid IMRT investigated resulted in systematically lower cancer risks than any of the other treatment techniques. More epidemiological studies with accurate dosimetry are required in the future to construct robust risk models. In the meantime, any treatment strategy that reduces out-of-field doses to healthy organs should be investigated. Electron radiotherapy might offer interesting possibilities with this regard.RésuméLe cancer du sein affecte une femme sur huit au cours de sa vie. Grâce au dépistage précoce et à des thérapies de plus en plus efficaces, le taux de guérison a augmenté au cours du temps. La radiothérapie postopératoire joue un rôle important dans le traitement du cancer du sein en réduisant le taux de récidive et la mortalité. Malheureusement, la radiothérapie peut aussi induire des toxicités tardives chez les patients guéris. En particulier, les cancers secondaires radio-induits sont une complication rare mais sévère de la radiothérapie. En routine clinique, les plans de radiothérapie sont essentiellement optimisées pour un contrôle local le plus élevé possible tout en minimisant les réactions tissulaires tardives qui sont essentiellement associées avec des hautes doses (» 1 Gy). Toutefois, avec l'introduction de différentes nouvelles techniques et avec l'augmentation des taux de survie, il devient impératif d'évaluer et de minimiser les risques de cancer secondaire pour différentes techniques de traitement. Une telle évaluation du risque est une tâche ardue étant donné les nombreuses incertitudes liées à la relation dose-risque.Contrairement aux effets tissulaires, les cancers secondaires peuvent aussi être induits par des basses doses dans des organes qui se trouvent hors des champs d'irradiation. Ces organes reçoivent des doses périphériques typiquement inférieures à 1 Gy qui résultent du diffusé du patient et du diffusé de l'accélérateur. Ces doses sont difficiles à calculer précisément, mais les algorithmes Monte Carlo (MC) permettent de les estimer avec une bonne précision. Un modèle MC détaillé de l'accélérateur Primus de Siemens a été élaboré et validé avec des mesures. La précision de ce modèle a également été déterminée pour la reconstruction de dose en épidémiologie. Si on considère que les patients inclus dans de larges cohortes sont traités sur une variété de machines, l'incertitude dans la reconstruction de dose périphérique a été étudiée en fonction de la variabilité de la dose périphérique pour différents types d'accélérateurs. Pour de grands champs (> 10x10 cm ), l'incertitude est inférieure à 50%, mais pour de petits champs et des champs filtrés, l'incertitude de la dose peut monter jusqu'à un facteur 10. En conclusion, un tel modèle ne peut être utilisé que pour les traitements conventionnels utilisant des grands champs.Le modèle MC de l'accélérateur Primus a été utilisé ensuite pour déterminer la dose périphérique pour différentes techniques dans un fantôme corps entier basé sur des coupes CT d'une patiente. Les techniques actuelles utilisant des champs filtrés ou encore l'IMRT hybride ont été étudiées et comparées par rapport aux techniques plus anciennes. Les doses calculées par MC ont été comparées à celles obtenues d'un logiciel de planification commercial (TPS). Alors que le TPS est utilisé en routine pour déterminer la dose au sein contralatéral et au poumon ipsilatéral qui sont principalement hors des faisceaux, nous avons montré que ces doses peuvent être plus ou moins précises selon la technTque étudiée. Les calculs MC montrent que la technique IMRT est dosimétriquement équivalente à celle basée sur des champs filtrés à l'intérieur des champs de traitement, mais offre une réduction importante de la dose aux organes périphériques.Finalement différents modèles de risque ont été étudiés sur la base des distributions de dose calculées par MC. Les risques absolus et le rapport des risques entre deux techniques de traitement varient grandement, ce qui reflète les grandes incertitudes liées aux différents modèles de risque. Malgré ces incertitudes, on a pu montrer que la technique IMRT offrait une réduction du risque systématique par rapport aux autres techniques. En attendant des données épidémiologiques supplémentaires sur la relation dose-risque, toute technique offrant une réduction des doses périphériques aux organes sains mérite d'être étudiée. La radiothérapie avec des électrons offre à ce titre des possibilités intéressantes.

Can the trabecular bone score be considered as a major clinical risk factor of osteoporotic fractures? A meta-like analysis.

To have an added value over BMD, a CRF of osteoporotic fracture must be predictable of the fracture, independent of BMD, reversible and quantifiable. Many major recognized CRF exist. Out of these factors many of them are indirect factor of bone quality. TBS predicts fracture independently of BMD as demonstrated from previous studies. The aim of the study is to verify if TBS can be considered as a major CRF of osteoporotic fracture. Existing validated datasets of Caucasian women were analyzed. These datasets stem from different studies performed by the authors of this report or provided to our group. However, the level of evidence of these studies will vary. Thus, the different datasets were weighted differently according to their design. This meta-like analysis involves more than 32000 women (≥50years) with 2000 osteoporotic fractures from two prospective studies (OFELY&MANITOBA) and 7 cross-sectional studies. Weighted relative risk (RR) for TBS was expressed for each decrease of one standard deviation as well as per tertile difference (TBS=1.300 and 1.200) and compared with those obtained for the major CRF included in FRAX®. Overall TBS RR obtained (adjusted for age) was 1.79 [95%CI-1.37-2.37]. For all women combined, RR for fracture for the lowest compared with the middle TBS tertile was 1.55[1.46-1.68] and for the lowest compared with the highest TBS tertile was 2.8[2.70-3.00]. TBS is comparable to most of the major CRF and thus could be used as one of them. Further studies have to be conducted to confirm these first findings.

Tibial or hip BMD predict clinical fracture risk equally well: results from a prospective study in 700 elderly Swiss women.

SUMMARY: In a randomly selected cohort of Swiss community-dwelling elderly women prospectively followed up for 2.8 +/- 0.6 years, clinical fractures were assessed twice yearly. Bone mineral density (BMD) measured at tibial diaphysis (T-DIA) and tibial epiphysis (T-EPI) using dual-energy X-ray absorptiometry (DXA) was shown to be a valid alternative to lumbar spine or hip BMD in predicting fractures. INTRODUCTION: A study was carried out to determine whether BMD measurement at the distal tibia sites of T-EPI and T-DIA is predictive of clinical fracture risk. METHODS: In a predefined representative cohort of Swiss community-dwelling elderly women aged 70-80 years included in the prospective, multi-centre Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture risk (SEMOF) study, fracture risk profile was assessed and BMD measured at the lumbar spine (LS), hip (HIP) and tibia (T-DIA and T-EPI) using DXA. Thereafter, clinical fractures were reported in a bi-yearly questionnaire. RESULTS: During 1,786 women-years of follow-up, 68 clinical fragility fractures occurred in 61 women. Older age and previous fracture were identified as risk factors for the present fractures. A decrease of 1 standard deviation in BMD values yielded a 1.5-fold (HIP) to 1.8-fold (T-EPI) significant increase in clinical fragility fracture hazard ratio (adjusted for age and previous fracture). All measured sites had comparable performance for fracture prediction (area under the curve range from 0.63 [LS] to 0.68 [T-EPI]). CONCLUSION: Fracture risk prediction with BMD measurements at T-DIA and T-EPI is a valid alternative to BMD measurements at LS or HIP for patients in whom these sites cannot be accessed for clinical, technical or practical reasons.

Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis.

Meta-analysis of prospective studies shows that quantitative ultrasound of the heel using validated devices predicts risk of different types of fracture with similar performance across different devices and in elderly men and women. These predictions are independent of the risk estimates from hip DXA measures.Introduction Clinical utilisation of heel quantitative ultrasound (QUS) depends on its power to predict clinical fractures. This is particularly important in settings that have no access to DXA-derived bone density measurements. We aimed to assess the predictive power of heel QUS for fractures using a meta-analysis approach.Methods We conducted an inverse variance random effects meta-analysis of prospective studies with heel QUS measures at baseline and fracture outcomes in their follow-up. Relative risks (RR) per standard deviation (SD) of different QUS parameters (broadband ultrasound attenuation [BUA], speed of sound [SOS], stiffness index [SI], and quantitative ultrasound index [QUI]) for various fracture outcomes (hip, vertebral, any clinical, any osteoporotic and major osteoporotic fractures) were reported based on study questions.Results Twenty-one studies including 55,164 women and 13,742 men were included in the meta-analysis with a total follow-up of 279,124 person-years. All four QUS parameters were associated with risk of different fracture. For instance, RR of hip fracture for 1 SD decrease of BUA was 1.69 (95% CI 1.43-2.00), SOS was 1.96 (95% CI 1.64-2.34), SI was 2.26 (95%CI 1.71-2.99) and QUI was 1.99 (95% CI 1.49-2.67). There was marked heterogeneity among studies on hip and any clinical fractures but no evidence of publication bias amongst them. Validated devices from different manufacturers predicted fracture risks with similar performance (meta-regression p values > 0.05 for difference of devices). QUS measures predicted fracture with a similar performance in men and women. Meta-analysis of studies with QUS measures adjusted for hip BMD showed a significant and independent association with fracture risk (RR/SD for BUA = 1.34 [95%CI 1.22-1.49]).Conclusions This study confirms that heel QUS, using validated devices, predicts risk of different fracture outcomes in elderly men and women. Further research is needed for more widespread utilisation of the heel QUS in clinical settings across the world.

A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation

This paper analyses the impact of using different correlation assumptions between lines of business when estimating the risk-based capital reserve, the Solvency Capital Requirement (SCR), under Solvency II regulations. A case study is presented and the SCR is calculated according to the Standard Model approach. Alternatively, the requirement is then calculated using an Internal Model based on a Monte Carlo simulation of the net underwriting result at a one-year horizon, with copulas being used to model the dependence between lines of business. To address the impact of these model assumptions on the SCR we conduct a sensitivity analysis. We examine changes in the correlation matrix between lines of business and address the choice of copulas. Drawing on aggregate historical data from the Spanish non-life insurance market between 2000 and 2009, we conclude that modifications of the correlation and dependence assumptions have a significant impact on SCR estimation.

How to use the standard model with own data?

In this work discuss the use of the standard model for the calculation of the solvency capital requirement (SCR) when the company aims to use the specific parameters of the model on the basis of the experience of its portfolio. In particular, this analysis focuses on the formula presented in the latest quantitative impact study (2010 CEIOPS) for non-life underwriting premium and reserve risk. One of the keys of the standard model for premium and reserves risk is the correlation matrix between lines of business. In this work we present how the correlation matrix between lines of business could be estimated from a quantitative perspective, as well as the possibility of using a credibility model for the estimation of the matrix of correlation between lines of business that merge qualitative and quantitative perspective.

Efavirenz in an obese HIV-infected patient - a report and an in vitro-in vivo extrapolation model indicate risk of underdosing.

Here, we report suboptimal efavirenz exposure in an obese patient treated with the standard 600 mg dose. Tripling the dose allowed attainment of therapeutic efavirenz concentrations. We developed an in vitro-in vivo extrapolation model to quantify dose requirements in obese individuals. Obesity represents a risk factor for antiretroviral therapy underdosing.

What was your fracture risk evaluated by FRAX(®) the day before your osteoporotic fracture?

Osteoporotic fracture (OF) is one of the major causes of morbidity and mortality in industrialized countries. Switzerland is among the countries with the greatest risk. Our aim was (1) to calculate the FRAX(®) in a selected Swiss population the day before the occurrence of an OF and (2) to compare the results with the proposed Swiss FRAX(®) thresholds. The Swiss Association Against Osteoporosis proposed guidelines for the treatment of osteoporosis based on age-dependent thresholds. To identify a population at a very high risk of osteoporotic fracture, we included all consecutive patients in the active OF pathway cohort from the Lausanne University Hospital, Switzerland. FRAX(®) was calculated with the available data the day before the actual OF. People with a FRAX(®) body mass index (BMI) or a FRAX(®) (bone mineral density) BMD lower than the Swiss thresholds were not considered at high risk. Two-hundred thirty-seven patients were included with a mean age of 77.2 years, and 80 % were female. Major types of fracture included hip (58 %) and proximal humerus (25 %) fractures. Mean FRAX(®) BMI values were 28.0, 10.0, 13.0, 26.0, and 37.0 % for age groups 50-59, 60-69, 70-79, and 80-89 years old, respectively. Fifty percent of the population was not considered at high risk by the FRAX(®) BMI. FRAX(®) BMD was available for 95 patients, and 45 % had a T score < -2.5 standard deviation. Only 30 % of patients with a normal or osteopenic BMD were classified at high risk by FRAX(®) BMD. The current proposed Swiss thresholds were not able to classify at high risk in 50 to 70 % of the studied population the day before a major OF.