Can infant lung function predict respiratory morbidity during the first year of life in preterm infants?

Compared with term-born infants, preterm infants have increased respiratory morbidity in the first year of life. We investigated whether lung function tests performed near term predict subsequent respiratory morbidity during the first year of life and compared this to standard clinical parameters in preterms.The prospective birth cohort included randomly selected preterm infants with and without bronchopulmonary dysplasia. Lung function (tidal breathing and multiple-breath washout) was measured at 44 weeks post-menstrual age during natural sleep. We assessed respiratory morbidity (wheeze, hospitalisation, inhalation and home oxygen therapy) after 1 year using a standardised questionnaire. We first assessed the association between lung function and subsequent respiratory morbidity. Secondly, we compared the predictive power of standard clinical predictors with and without lung function data.In 166 preterm infants, tidal volume, time to peak tidal expiratory flow/expiratory time ratio and respiratory rate were significantly associated with subsequent wheeze. In comparison with standard clinical predictors, lung function did not improve the prediction of later respiratory morbidity in an individual child.Although associated with later wheeze, noninvasive infant lung function shows large physiological variability and does not add to clinically relevant risk prediction for subsequent respiratory morbidity in an individual preterm.

Health-related quality of life in European women following myocardial infarction: A cross-sectional study

Background:Coronary heart disease is a major contributor to women's health problems.Design:Self-perceived social support, well-being and health-related quality of life (HRQL) were documented in the cross-sectional HeartQoL survey of European women one and six months after a myocardial infarction.Methods:European women were recruited in 18 European countries and grouped into four geographical regions (Southern Europe, Northern Europe, Western Europe and Eastern Europe). Continuous socio-demographic variables and categorical variables were compared by age and region with ANOVA and χ(2), respectively; multiple regression models were used to identify predictors of social support, well-being and HRQL.Results:Women living in the Eastern European region rated social support, well-being and HRQL significantly lower than women in the other regions. Older women had lower physical HRQL scores than younger women. Eastern European women rated social support, well-being and HRQL significantly lower than women in the other regions. Prediction of the dependent variables (social support, well-being and HRQL) by socio-demographic factors varied by total group, in the older age group, and by region; body mass index and managerial responsibility were the most consistent significant predictors.

Usefulness of electrocardiographic parameters for risk prediction in arrhythmogenic right ventricular dysplasia

The value of electrocardiographic findings predicting adverse outcome in patients with arrhythmogenic right ventricular dysplasia (ARVD) is not well known. We hypothesized that ventricular depolarization and repolarization abnormalities on the 12-lead surface electrocardiogram (ECG) predict adverse outcome in patients with ARVD. ECGs of 111 patients screened for the 2010 ARVD Task Force Criteria from 3 Swiss tertiary care centers were digitized and analyzed with a digital caliper by 2 independent observers blinded to the outcome. ECGs were compared in 2 patient groups: (1) patients with major adverse cardiovascular events (MACE: a composite of cardiac death, heart transplantation, survived sudden cardiac death, ventricular fibrillation, sustained ventricular tachycardia, or arrhythmic syncope) and (2) all remaining patients. A total of 51 patients (46%) experienced MACE during a follow-up period with median of 4.6 years (interquartile range 1.8 to 10.0). Kaplan-Meier analysis revealed reduced times to MACE for patients with repolarization abnormalities according to Task Force Criteria (p = 0.009), a precordial QRS amplitude ratio (∑QRS mV V1 to V3/∑QRS mV V1 to V6) of ≤ 0.48 (p = 0.019), and QRS fragmentation (p = 0.045). In multivariable Cox regression, a precordial QRS amplitude ratio of ≤ 0.48 (hazard ratio [HR] 2.92, 95% confidence interval [CI] 1.39 to 6.15, p = 0.005), inferior leads T-wave inversions (HR 2.44, 95% CI 1.15 to 5.18, p = 0.020), and QRS fragmentation (HR 2.65, 95% CI 1.1 to 6.34, p = 0.029) remained as independent predictors of MACE. In conclusion, in this multicenter, observational, long-term study, electrocardiographic findings were useful for risk stratification in patients with ARVD, with repolarization criteria, inferior leads TWI, a precordial QRS amplitude ratio of ≤ 0.48, and QRS fragmentation constituting valuable variables to predict adverse outcome.

Clinical importance of risk variants in the dihydropyrimidine dehydrogenase gene for the prediction of early-onset fluoropyrimidine toxicity.

We investigated the clinical relevance of dihydropyrimidine dehydrogenase gene (DPYD) variants to predict severe early-onset fluoropyrimidine (FP) toxicity, in particular of a recently discovered haplotype hapB3 and a linked deep intronic splice site mutation c.1129-5923C>G. Selected regions of DPYD were sequenced in prospectively collected germline DNA of 500 patients receiving FP-based chemotherapy. Associations of DPYD variants and haplotypes with hematologic, gastrointestinal, infectious, and dermatologic toxicity in therapy cycles 1-2 and resulting FP-dose interventions (dose reduction, therapy delay or cessation) were analyzed accounting for clinical and demographic covariates. Fifteen additional cases with toxicity-related therapy delay or cessation were retrospectively examined for risk variants. The association of c.1129-5923C>G/hapB3 (4.6% carrier frequency) with severe toxicity was replicated in an independent prospective cohort. Overall, c.1129-5923G/hapB3 carriers showed a relative risk of 3.74 (RR, 95% CI = 2.30-6.09, p = 2 × 10(-5)) for severe toxicity (grades 3-5). Of 31 risk variant carriers (c.1129-5923C>G/hapB3, c.1679T>G, c.1905+1G>A or c.2846A>T), 11 (all with c.1129-5923C>G/hapB3) experienced severe toxicity (15% of 72 cases, RR = 2.73, 95% CI = 1.61-4.63, p = 5 × 10(-6)), and 16 carriers (55%) required FP-dose interventions. Seven of the 15 (47%) retrospective cases carried a risk variant. The c.1129-5923C>G/hapB3 variant is a major contributor to severe early-onset FP toxicity in Caucasian patients. This variant may substantially improve the identification of patients at risk of FP toxicity compared to established DPYD risk variants (c.1905+1G>A, c.1679T>G and c.2846A>T). Pre-therapeutic DPYD testing may prevent 20-30% of life-threatening or lethal episodes of FP toxicity in Caucasian patients.

Level of incongruence during cardiac rehabilitation and prediction of future CVD-related hospitalizations plus all-cause mortality

Independent of traditional risk factors, psychosocial risk factors increase the risk of cardiovascular disease (CVD). Studies in the field of psychotherapy have shown that the construct of incongruence (meaning a discrepancy between desired and achieved goals) affects the outcome of therapy. We prospectively measured the impact of incongruence in patients after undergoing a cardiac rehabilitation program. We examined 198 CVD patients enrolled in a 8–12 week comprehensive cardiac rehabilitation program. Patients completed the German short version of the Incongruence Questionnaire and the SF-36 Health Questionnaire to measure quality of life (QoL) at discharge of rehabilitation. Endpoints at follow-up were CVD-related hospitalizations plus all-cause mortality. During a mean follow-up period of 54.3 months, 29 patients experienced a CVD-related hospitalization and 3 patients died. Incongruence at discharge of rehabilitation was independent of traditional risk factors a significant predictor for CVD-related hospitalizations plus all-cause mortality (HR 2.03, 95% CI 1.29–3.20, p = .002). We also found a significant interaction of incongruence with mental QoL (HR .96, 95% CI .92–.99, p = .027), i.e. incongruence predicted poor prognosis if QoL was low (p = .017), but not if QoL was high (p = .74). Incongruence at discharge predicted future CVD-related hospitalizations plus all-cause mortality and mental QoL moderated this relationship. Therefore, incongruence should be considered for effective treatment planning and outcome measurement.

Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.