Do intensive care data on respiratory infections reflect influenza epidemics?

Objectives Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. Methods We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003–2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Results Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. Conclusions ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities

Subchondral Bone Trabecular Integrity Predicts and Changes Concurrently with Radiographic and MRI Determined Knee Osteoarthritis Progression

OBJECTIVE: To evaluate subchondral bone trabecular integrity (BTI) on radiographs as a predictor of knee osteoarthritis (OA) progression. METHODS: Longitudinal (baseline, 12-month, and 24-month) knee radiographs were available for 60 female subjects with knee OA. OA progression was defined by 12- and 24-month changes in radiographic medial compartment minimal joint space width (JSW) and medial joint space area (JSA), and by medial tibial and femoral cartilage volume on magnetic resonance imaging. BTI of the medial tibial plateau was analyzed by fractal signature analysis using commercially available software. Receiver operating characteristic (ROC) curves for BTI were used to predict a 5% change in OA progression parameters. RESULTS: Individual terms (linear and quadratic) of baseline BTI of vertical trabeculae predicted knee OA progression based on 12- and 24-month changes in JSA (P < 0.01 for 24 months), 24-month change in tibial (P < 0.05), but not femoral, cartilage volume, and 24-month change in JSW (P = 0.05). ROC curves using both terms of baseline BTI predicted a 5% change in the following OA progression parameters over 24 months with high accuracy, as reflected by the area under the curve measures: JSW 81%, JSA 85%, tibial cartilage volume 75%, and femoral cartilage volume 85%. Change in BTI was also significantly associated (P < 0.05) with concurrent change in JSA over 12 and 24 months and with change in tibial cartilage volume over 24 months. CONCLUSION: BTI predicts structural OA progression as determined by radiographic and MRI outcomes. BTI may therefore be worthy of study as an outcome measure for OA studies and clinical trials. Copyright 2013 by the American College of Rheumatology

Quantum decoherence and measurement in small spin systems

Contains fulltext : 197505.pdf (publisher's version ) (Open Access)Radboud University, 27 november 2018Promotor : Katsnelson, M.I.x, 170 p

Quantum dynamics of a small symmetry breaking measurement device

Contains fulltext : 196427.pdf (preprint version ) (Open Access

Temporal cross-correlation between influenza-like illnesses and invasive pneumococcal disease in the Netherlands

BACKGROUND: While the burden of community-acquired pneumonia and invasive pneumococcal disease (IPD) is still considerable, there is little insight in the factors contributing to disease. Previous research on the lagged relationship between respiratory viruses and pneumococcal disease incidence is inconclusive, and studies correcting for temporal autocorrelation are lacking. OBJECTIVES: To investigate the temporal relation between influenza-like illnesses (ILI) and IPD, correcting for temporal autocorrelation. METHODS: Weekly counts of ILI were obtained from the Sentinel Practices of NIVEL Primary Care Database. IPD data were collected from the Dutch laboratory-based surveillance system for bacterial meningitis from 2004 to 2014. We analysed the correlation between time series, pre-whitening the dependent time series with the best-fit seasonal autoregressive integrated moving average (SARIMA) model to the independent time series. We performed cross-correlations between ILI and IPD incidences, and the (pre-whitened) residuals, in the overall population and in the elderly. RESULTS: We found significant cross-correlations between ILI and IPD incidences peaking at lags -3 overall and at 1 week in the 65+ population. However, after pre-whitening, no cross-correlations were apparent in either population group. CONCLUSION: Our study suggests that ILI occurrence does not seem to be the major driver of IPD incidence in The Netherlands. (aut. ref.

Do intensive care data on respiratory infections reflect influenza epidemics?

Objectives: Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. Methods: We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003–2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Results: Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. Conclusions: ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities. (aut.ref.

Recognition of anxiety disorders by family physicians after rigorous medical record case extraction Results of the Netherlands Study of Depression and Anxiety

Objective: Previous studies reported low and inconsistent rates of recognition of anxiety disorders by family physicians (FPs). Our objectives were to examine (a) which combination of indications within medical records most accurately reflects recognition of anxiety disorders and (b) whether patient and FP characteristics were related to recognition. Method: A cross-sectional comparison was made between FPs' registration and a structured diagnostic interview, the Composite International Diagnostic Interview, according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria. Seven definitions of recognition were tested using diagnostic codes, medication data, referral data and free text in medical records. Data were derived from the Netherlands Study of Depression and Anxiety. A total of 816 patients were included. Results: Recognition ranged between 9.1% and 85.8%. A broader definition was associated with a higher recognition rate, but led to more false positives. The best definition comprised diagnostic codes for anxiety disorders and symptoms, strong free-text indications, medication and referral to mental health care. Generalized anxiety disorder was best recognized by this definition. Recognition was better among patients with increased severity, comorbid depression and older age. Conclusion: FPs recognized anxiety disorders better than previously reported when all medical record data were taken into account. However, most patients were nonspecifically labeled as having a mental health problem. (C) 2012 Elsevier Inc. All rights reserved