Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Prognostic importance of anaemia in patients with acute pulmonary embolism

Although associated with adverse outcomes in other cardiopulmonary diseases, limited evidence exists on the prognostic value of anaemia in patients with acute pulmonary embolism (PE). We sought to examine the associations between anaemia and mortality and length of hospital stay in patients with PE. We evaluated 14,276 patients with a primary diagnosis of PE from 186 hospitals in Pennsylvania, USA. We used random-intercept logistic regression to assess the association between anaemia at the time of presentation and 30-day mortality and discrete-time logistic hazard models to assess the association between anaemia and time to hospital discharge, adjusting for patient (age, gender, race, insurance type, clinical and laboratory variables) and hospital (region, size, teaching status) factors. Anaemia was present in 38.7% of patients at admission. Patients with anaemia had a higher 30-day mortality (13.7% vs. 6.3%; p <0.001) and a longer length of stay (geometric mean, 6.9 vs. 6.6 days; p <0.001) compared to patients without anaemia. In multivariable analyses, anaemia remained associated with an increased odds of death (OR 1.82, 95% CI: 1.60-2.06) and a decreased odds of discharge (OR 0.85, 95% CI: 0.82-0.89). Anaemia is very common in patients presenting with PE and is independently associated with an increased short-term mortality and length of stay.

Hierarchical modeling gave plausible estimates of associations between metabolic syndrome and components of antiretroviral therapy

OBJECTIVE: Hierarchical modeling has been proposed as a solution to the multiple exposure problem. We estimate associations between metabolic syndrome and different components of antiretroviral therapy using both conventional and hierarchical models. STUDY DESIGN AND SETTING: We use discrete time survival analysis to estimate the association between metabolic syndrome and cumulative exposure to 16 antiretrovirals from four drug classes. We fit a hierarchical model where the drug class provides a prior model of the association between metabolic syndrome and exposure to each antiretroviral. RESULTS: One thousand two hundred and eighteen patients were followed for a median of 27 months, with 242 cases of metabolic syndrome (20%) at a rate of 7.5 cases per 100 patient years. Metabolic syndrome was more likely to develop in patients exposed to stavudine, but was less likely to develop in those exposed to atazanavir. The estimate for exposure to atazanavir increased from hazard ratio of 0.06 per 6 months' use in the conventional model to 0.37 in the hierarchical model (or from 0.57 to 0.81 when using spline-based covariate adjustment). CONCLUSION: These results are consistent with trials that show the disadvantage of stavudine and advantage of atazanavir relative to other drugs in their respective classes. The hierarchical model gave more plausible results than the equivalent conventional model.

Virologic and immunologic responses in treatment-naive patients to ritonavir-boosted atazanavir or efavirenz with a common backbone

Background: Atazanavir boosted with ritonavir (ATV/r) and efavirenz (EFV) are both recommended as first-line therapies for HIV-infected patients. We compared the 2 therapies for virologic efficacy and immune recovery. Methods: We included all treatment-naïve patients in the Swiss HIV Cohort Study starting therapy after May 2003 with either ATV/r or EFV and a backbone of tenofovir and either emtricitabine or lamivudine. We used Cox models to assess time to virologic failure and repeated measures models to assess the change in CD4 cell counts over time. All models were fit as marginal structural models using both point of treatment and censoring weights. Intent-to-treat and various as-treated analyses were carried out: In the latter, patients were censored at their last recorded measurement if they changed therapy or if they were no longer adherent to therapy. Results: Patients starting EFV (n = 1,097) and ATV/r (n = 384) were followed for a median of 35 and 37 months, respectively. During follow-up, 51% patients on EFV and 33% patients on ATV/r remained adherent and made no change to their first-line therapy. Although intent-to-treat analyses suggest virologic failure was more likely with ATV/r, there was no evidence for this disadvantage in patients who adhered to first-line therapy. Patients starting ATV/r had a greater increase in CD4 cell count during the first year of therapy, but this advantage disappeared after one year. Conclusions: In this observational study, there was no good evidence of any intrinsic advantage for one therapy over the other, consistent with earlier clinical trials. Differences between therapies may arise in a clinical setting because of differences in adherence to therapy.

Impact of anti-inflammatory treatment on the onset of uveitis in juvenile idiopathic arthritis: Longitudinal analysis from a nation-wide paediatric rheumatological database

PURPOSE Based on a nation-wide database, this study analysed the influence of methotrexate (MTX), TNF inhibitors and a combination of the two on uveitis occurrence in JIA patients. METHODS Data from the National Paediatric Rheumatological Database in Germany were used in this study. Between 2002 and 2013, data from JIA patients were annually documented at the participating paediatric rheumatological sites. Patients with JIA disease duration of less than 12 months at initial documentation and ≥2 years of follow-up were included in this study. The impact of anti-inflammatory treatment on the occurrence of uveitis was evaluated by discrete-time survival analysis. RESULTS A total of 3,512 JIA patients (mean age 8.3±4.8 years, female 65.7%, ANA-positive 53.2%, mean age at arthritis onset 7.8±4.8 years) fulfilled the inclusion criteria. Mean total follow-up time was 3.6±2.4 years. Uveitis developed in a total of 180 patients (5.1%) within one year after arthritis onset. Uveitis onset after the first year was observed in another 251 patients (7.1%). DMARD treatment in the year before uveitis onset significantly reduced the risk for uveitis: MTX (HR 0.63, p=0.022), TNF inhibitors (HR 0.56, p<0.001) and a combination of the two (HR 0.10, p<0.001). Patients treated with MTX within the first year of JIA had an even a lower uveitis risk (HR 0.29, p<0.001). CONCLUSION The use of DMARDs in JIA patients significantly reduced the risk for uveitis onset. Early MTX use within the first year of disease and the combination of MTX with a TNF inhibitor had the highest protective effect. This article is protected by copyright. All rights reserved.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.