An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models

High-resolution, ground-based and independent observations including co-located wind radiometer, lidar stations, and infrasound instruments are used to evaluate the accuracy of general circulation models and data-constrained assimilation systems in the middle atmosphere at northern hemisphere midlatitudes. Systematic comparisons between observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses including the recent Integrated Forecast System cycles 38r1 and 38r2, the NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses, and the free-running climate Max Planck Institute–Earth System Model–Low Resolution (MPI-ESM-LR) are carried out in both temporal and spectral dom ains. We find that ECMWF and MERRA are broadly consistent with lidar and wind radiometer measurements up to ~40 km. For both temperature and horizontal wind components, deviations increase with altitude as the assimilated observations become sparser. Between 40 and 60 km altitude, the standard deviation of the mean difference exceeds 5 K for the temperature and 20 m/s for the zonal wind. The largest deviations are observed in winter when the variability from large-scale planetary waves dominates. Between lidar data and MPI-ESM-LR, there is an overall agreement in spectral amplitude down to 15–20 days. At shorter time scales, the variability is lacking in the model by ~10 dB. Infrasound observations indicate a general good agreement with ECWMF wind and temperature products. As such, this study demonstrates the potential of the infrastructure of the Atmospheric Dynamics Research Infrastructure in Europe project that integrates various measurements and provides a quantitative understanding of stratosphere-troposphere dynamical coupling for numerical weather prediction applications.

Prediction of Psychosis by Mismatch Negativity

BACKGROUND: To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis. METHODS: At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model. RESULTS: Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves. CONCLUSIONS: Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Risk prediction of fever in neutropenia in children with cancer: a step towards individually tailored supportive therapy?

BACKGROUND: Fever in severe chemotherapy-induced neutropenia (FN) is the most frequent manifestation of a potentially lethal complication of current intensive chemotherapy regimens. This study aimed at establishing models predicting the risk of FN, and of FN with bacteremia, in pediatric cancer patients. METHODS: In a single-centre cohort study, characteristics potentially associated with FN and episodes of FN were retrospectively extracted from charts. Poisson regression accounting for chemotherapy exposure time was used for analysis. Prediction models were constructed based on a derivation set of two thirds of observations, and validated based on the remaining third of observations. RESULTS: In 360 pediatric cancer patients diagnosed and treated for a cumulative chemotherapy exposure time of 424 years, 629 FN were recorded (1.48 FN per patient per year, 95% confidence interval (CI), 1.37-1.61), 145 of them with bacteremia (23% of FN; 0.34; 0.29-0.40). More intensive chemotherapy, shorter time since diagnosis, bone marrow involvement, central venous access device (CVAD), and prior FN were significantly and independently associated with a higher risk to develop both FN and FN with bacteremia. The prediction models explained more than 30% of the respective risks. CONCLUSIONS: The two models predicting FN and FN with bacteremia were based on five easily accessible clinical variables. Before clinical application, they need to be validated by prospective studies.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models

BACKGROUND Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

Progenitor cell recruitment during individualized high-flow, very-large-volume apheresis for autologous transplantation improves collection efficiency

BACKGROUND: Individual adaptation of processed patient's blood volume (PBV) should reduce number and/or duration of autologous peripheral blood progenitor cell (PBPC) collections. STUDY DESIGN AND METHODS: The durations of leukapheresis procedures were adapted by means of an interim analysis of harvested CD34+ cells to obtain the intended yield of CD34+ within as few and/or short as possible leukapheresis procedures. Absolute efficiency (AE; CD34+/kg body weight) and relative efficiency (RE; total CD34+ yield of single apheresis/total number of preapheresis CD34+) were calculated, assuming an intraapheresis recruitment if RE was greater than 1, and a yield prediction models for adults was generated. RESULTS: A total of 196 adults required a total of 266 PBPC collections. The median AE was 7.99 x 10(6), and the median RE was 1.76. The prediction model for AE showed a satisfactory predictive value for preapheresis CD34+ only. The prediction model for RE also showed a low predictive value (R2 = 0.36). Twenty-eight children underwent 44 PBPC collections. The median AE was 12.13 x 10(6), and the median RE was 1.62. Major complications comprised bleeding episodes related to central venous catheters (n = 4) and severe thrombocytopenia of less than 10 x 10(9) per L (n = 16). CONCLUSION: A CD34+ interim analysis is a suitable tool for individual adaptation of the duration of leukapheresis. During leukapheresis, a substantial recruitment of CD34+ was observed, resulting in a RE of greater than 1 in more than 75 percent of patients. The upper limit of processed PBV showing an intraapheresis CD34+ recruitment is higher than in a standard large-volume leukapheresis. Therefore, a reduction of individually needed PBPC collections by means of a further escalation of the processed PBV seems possible.

Predicting the risk of hospital admission in older persons--validation of a brief self-administered questionnaire in three European countries

OBJECTIVES: To validate the Probability of Repeated Admission (Pra) questionnaire, a widely used self-administered tool for predicting future healthcare use in older persons, in three European healthcare systems. DESIGN: Prospective study with 1-year follow-up. SETTING: Hamburg, Germany; London, United Kingdom; Canton of Solothurn, Switzerland. PARTICIPANTS: Nine thousand seven hundred thirteen independently living community-dwelling people aged 65 and older. MEASUREMENTS: Self-administered eight-item Pra questionnaire at baseline. Self-reported number of hospital admissions and physician visits during 1 year of follow-up. RESULTS: In the combined sample, areas under the receiver operating characteristic curves (AUCs) were 0.64 (95% confidence interval (CI)=0.62-0.66) for the prediction of one or more hospital admissions and 0.68 (95% CI=0.66-0.69) for the prediction of more than six physician visits during the following year. AUCs were similar between sites. In comparison, prediction models based on a person's age and sex alone exhibited poor predictive validity (AUC or= 0.5) were 2.3 times as likely (95% CI=2.1-2.6) as low-risk individuals to have a hospital admission, and 2.1 times as likely (95% CI=2.0-2.2) to have more than six physician visits. CONCLUSION: The Pra instrument exhibits good validity for predicting future health service use on a population level in different healthcare settings. Administrative data have shown similar predictive validity, but in practice, such data are often not available. The Pra is likely of high interest to governments and health insurance companies worldwide as a basis for programs aimed at health risk management in older persons.

Admission blood glucose is an independent predictive factor for hospital mortality in polytraumatised patients

PURPOSE: The goal of this study was to analyse a possible association of admission blood glucose with hospital mortality of polytraumatised patients and to develop an outcome prediction model for this patient group. METHODS: The outcome of adult polytraumatised patients admitted to the University Hospital of Berne, Switzerland, between 2002 and 2004 with an ISS > or = 17, and more than one severely injured organ system was retrospectively analysed. RESULTS: The inclusion criteria were met by 555 patients, of which 108 (19.5%) died. Hyperglycaemia proved to be an independent predictor for hospital mortality (P < 0.0001), following multiple regression analysis. After inclusion of admission blood glucose, the calculated mortality prediction model performed better than currently described models (P < 0.0001, AUC 0.924). CONCLUSION: In this retrospective, single-centre study in polytraumatised patients, admission blood glucose proved to be an independent predictor of hospital mortality following regression analysis controlling for age, gender, injury severity and other laboratory parameters. A reliable admission blood glucose-based mortality prediction model for polytraumatised patients could be established. This observation may be helpful in improving the precision of future outcome prediction models for polytraumatised patients. These observations warrant further prospective evaluation.

Pelvic lymph node dissection in prostate cancer

CONTEXT: Pelvic lymph node dissection (PLND) is considered the most reliable procedure for the detection of lymph node metastases in prostate cancer (PCa); however, the therapeutic benefit of PLND in PCa management is currently under debate. OBJECTIVE: To systematically review the available literature concerning the role of PLND and its extent in PCa staging and outcome. All of the existing recommendations and staging tools determining the need for PLND were also assessed. Moreover, a systematic review was performed of the long-term outcome of node-positive patients stratified according to the extent of nodal invasion. EVIDENCE ACQUISITION: A Medline search was conducted to identify original and review articles as well as editorials addressing the significance of PLND in PCa. Keywords included prostate cancer, pelvic lymph node dissection, radical prostatectomy, imaging, and complications. Data from the selected studies focussing on the role of PLND in PCa staging and outcome were reviewed and discussed by all of the contributing authors. EVIDENCE SYNTHESIS: Despite recent advances in imaging techniques, PLND remains the most accurate staging procedure for the detection of lymph node invasion (LNI) in PCa. The rate of LNI increases with the extent of PLND. Extended PLND (ePLND; ie, removal of obturator, external iliac, hypogastric with or without presacral and common iliac nodes) significantly improves the detection of lymph node metastases compared with limited PLND (lPLND; ie, removal of obturator with or without external iliac nodes), which is associated with poor staging accuracy. Because not all patients with PCa are at the same risk of harbouring nodal metastases, several nomograms and tables have been developed and validated to identify candidates for PLND. These tools, however, are based mostly on findings derived from lPLND dissections performed in older patient series. According to these prediction models, a staging PLND might be omitted in low-risk PCa patients because of the low rate of lymph node metastases found, even after extended dissections (<8%). The outcome for patients with positive nodes is not necessarily poor. Indeed, patients with low-volume nodal metastases experience excellent survival rates, regardless of adjuvant treatment. But despite few retrospective studies reporting an association between PLND and PCa progression and survival, the exact impact of PLND on patient outcomes has not yet been clearly proven because of the lack of prospective randomised trials. CONCLUSIONS: On the basis of current data, we suggest that if a PLND is indicated, then it should be extended. Conversely, in view of the low rate of LNI among patients with low-risk PCa, a staging ePLND might be spared in this patient category. Whether this approach is also safe from oncologic perspectives is still unknown. Patients with low-volume nodal metastases have a good long-term prognosis; to what extent this prognosis is the result of a positive impact of PLND on PCa outcomes is still to be determined.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Understanding the performance of the FLake model over two African Great Lakes

The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from similar to 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

The ADO index as a predictor of two-year mortality in general practice-based chronic obstructive pulmonary disease cohorts.

BACKGROUND Existing prediction models for mortality in chronic obstructive pulmonary disease (COPD) patients have not yet been validated in primary care, which is where the majority of patients receive care. OBJECTIVES Our aim was to validate the ADO (age, dyspnoea, airflow obstruction) index as a predictor of 2-year mortality in 2 general practice-based COPD cohorts. METHODS Six hundred and forty-six patients with COPD with GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages I-IV were enrolled by their general practitioners and followed for 2 years. The ADO regression equation was used to predict a 2-year risk of all-cause mortality in each patient and this risk was compared with the observed 2-year mortality. Discrimination and calibration were assessed as well as the strength of association between the 15-point ADO score and the observed 2-year all-cause mortality. RESULTS Fifty-two (8.1%) patients died during the 2-year follow-up period. Discrimination with the ADO index was excellent with an area under the curve of 0.78 [95% confidence interval (CI) 0.71-0.84]. Overall, the predicted and observed risks matched well and visual inspection revealed no important differences between them across 10 risk classes (p = 0.68). The odds ratio for death per point increase according to the ADO index was 1.50 (95% CI 1.31-1.71). CONCLUSIONS The ADO index showed excellent prediction properties in an out-of-population validation carried out in COPD patients from primary care settings.

Cultural capital and smoking in young adults: applying new indicators to explore social inequalities in health behaviour.

BACKGROUND Associations between social status and health behaviours are well documented, but the mechanisms involved are less understood. Cultural capital theory may contribute to a better understanding by expanding the scope of inequality indicators to include individuals' knowledge, skills, beliefs and material goods to examine how these indicators impact individuals' health lifestyles. We explore the structure and applicability of a set of cultural capital indicators in the empirical exploration of smoking behaviour among young male adults. METHODS We analysed data from the Swiss Federal Survey of Adolescents (CH-X) 2010-11 panel of young Swiss males (n = 10 736). A set of nine theoretically relevant variables (including incorporated, institutionalized and objectified cultural capital) were investigated using exploratory factor analysis. Regression models were run to observe the association between factor scores and smoking outcomes. Outcome measures consisted of daily smoking status and the number of cigarettes smoked by daily smokers. RESULTS Cultural capital indicators aggregated in a three-factor solution representing 'health values', 'education and knowledge' and 'family resources'. Each factor score predicted the smoking outcomes. In young males, scoring low on health values, education and knowledge and family resources was associated with a higher risk of being a daily smoker and of smoking more cigarettes daily. CONCLUSION Cultural capital measures that include, but go beyond, educational attainment can improve prediction models of smoking in young male adults. New measures of cultural capital may thus contribute to our understanding of the social status-based resources that individuals can use towards health behaviours.