Integrated sampling-and-sensing using microdialysis and biosensing by particle motion for continuous cortisol monitoring

Microdialysis catheters are small probes that allow sampling from biological systems and human subjects with minimal perturbation. Traditionally, microdialysis samples are collected in vials, transported to a laboratory, and analysed with typical turnaround times of hours to days. To realize a continuous sampling-and-sensing methodology with minimal time delay, we studied the integration of microdialysis sampling with a sensor for continuous biomolecular monitoring based on Biosensing by Particle Motion (BPM). A microfluidic flow cell was designed with a volume of 12 μl in order to be compatible with flowrates of microdialysis sampling. The analyte recovery and the time characteristics of the sampling-and-sensing system were studied using a food colorant in buffer and using cortisol in buffer and in blood plasma. Concentration step functions were applied, and the system response was measured using optical absorption and a continuous BPM cortisol sensor. The cortisol recovery was around 80% for a 30 mm microdialysis membrane with a 20 kDa molecular weight cut-off and a flowrate of 2 μl min−1. The concentration-time data could be fitted with a transport delay time and single-exponential relaxation curves. The total delay time of the sampling-and-sensing methodology was about 15 minutes. Continuous sampling-and-sensing was demonstrated over a period of 5 hours. These results represent an important step toward integrated sampling-and-sensing for the continuous monitoring of a wide variety of low-concentration biomolecular substances for applications in biological and biomedical research.</p

Integrated sampling-and-sensing using microdialysis and biosensing by particle motion for continuous cortisol monitoring

Microdialysis catheters are small probes that allow sampling from biological systems and human subjects with minimal perturbation. Traditionally, microdialysis samples are collected in vials, transported to a laboratory, and analysed with typical turnaround times of hours to days. To realize a continuous sampling-and-sensing methodology with minimal time delay, we studied the integration of microdialysis sampling with a sensor for continuous biomolecular monitoring based on Biosensing by Particle Motion (BPM). A microfluidic flow cell was designed with a volume of 12 μl in order to be compatible with flowrates of microdialysis sampling. The analyte recovery and the time characteristics of the sampling-and-sensing system were studied using a food colorant in buffer and using cortisol in buffer and in blood plasma. Concentration step functions were applied, and the system response was measured using optical absorption and a continuous BPM cortisol sensor. The cortisol recovery was around 80% for a 30 mm microdialysis membrane with a 20 kDa molecular weight cut-off and a flowrate of 2 μl min−1. The concentration-time data could be fitted with a transport delay time and single-exponential relaxation curves. The total delay time of the sampling-and-sensing methodology was about 15 minutes. Continuous sampling-and-sensing was demonstrated over a period of 5 hours. These results represent an important step toward integrated sampling-and-sensing for the continuous monitoring of a wide variety of low-concentration biomolecular substances for applications in biological and biomedical research.</p

The prognostic value of the hamstring outcome score to predict the risk of hamstring injuries

OBJECTIVES: Hamstring injuries are common among soccer players. The hamstring outcome score (HaOS) might be useful to identify amateur players at risk of hamstring injury. Therefore the aims of this study were: To determine the association between the HaOS and prior and new hamstring injuries in amateur soccer players, and to determine the prognostic value of the HaOS for identifying players with or without previous hamstring injuries at risk of future injury. DESIGN: Cohort study. METHODS: HaOS scores and information about previous injuries were collected at baseline and new injuries were prospectively registered during a cluster-randomized controlled trial involving 400 amateur soccer players. Analysis of variance and t-tests were used to determine the association between the HaOS and previous and new hamstring injury, respectively. Logistic regression analysis indicated the prognostic value of the HaOS for predicting new hamstring injuries. RESULTS: Analysis of data of 356 players indicated that lower HaOS scores were associated with more previous hamstring injuries (F=17.4; p=0.000) and that players with lower HaOS scores sustained more new hamstring injuries (T=3.59, df=67.23, p=0.001). With a conventional HaOS score cut-off of 80%, logistic regression models yielded a probability of hamstring injuries of 11%, 18%, and 28% for players with 0,1, or 2 hamstring injuries in the previous season, respectively. CONCLUSIONS: The HaOS is associated with previous and future hamstring injury and might be a useful tool to provide players with insight into their risk of sustaining a new hamstring injury risk when used in combination with previous injuries

Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis

INTRODUCTION: The individual prognostic factors for COVID-19 are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients. METHODS: We systematically reviewed PubMed from January 1, 2020 to July 26, 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19. Random-effects meta-analysis was performed, and between-study heterogeneity was quantified using I2 metric. Presence of small-study effects was assessed by applying the Egger's regression test. RESULTS: We identified 428 eligible articles, which were used in a total of 263 meta-analyses examining the association of 91 unique prognostic factors with 11 outcomes. Angiotensin-converting enzyme inhibitors, obstructive sleep apnea, pharyngalgia, history of venous thromboembolism, sex, coronary heart disease, cancer, chronic liver disease, chronic obstructive pulmonary disease, dementia, any immunosuppressive medication, peripheral arterial disease, rheumatological disease and smoking were associated with at least one outcome and had >1000 events, p-value <0.005, I2 <50%, 95% prediction interval excluding the null value, and absence of small-study effects in the respective meta-analysis. The risk of bias assessment using the Quality In Prognosis Studies tool indicated high risk of bias in 302 of 428 articles for study participation, 389 articles for adjustment for other prognostic factors, and 396 articles for statistical analysis and reporting. CONCLUSIONS: Our findings could be used for prognostic model building and guide patients' selection for randomised clinical trials

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.Clinical epidemiolog

Comment on Williamson et al. (OpenSAFELY): The Table 2 Fallacy in a Study of COVID-19 Mortality Risk Factors

To the Editor: We write with respect to the recently published work by Williamson et al. “OpenSAFELY: factors associated with COVID-19 death in 17 million patients.” We have serious concerns about both the way these results are presented, and how they are likely to be interpreted. Our specific concerns revolve around whether the work is intended by the authors to estimate causal effects, or not—and how, regardless of their intent, it seems likely to us that their work will be interpreted as causal

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425-436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).Clinical epidemiolog

Regression shrinkage methods for clinical prediction models do not guarantee improved performance: simulation study

When developing risk prediction models on datasets with limited sample size, shrinkage methods are recommended. Earlier studies showed that shrinkage results in better predictive performance on average. This simulation study aimed to investigate the variability of regression shrinkage on predictive performance for a binary outcome. We compared standard maximum likelihood with the following shrinkage methods: uniform shrinkage (likelihood-based and bootstrap-based), penalized maximum likelihood (ridge) methods, LASSO logistic regression, adaptive LASSO, and Firth's correction. In the simulation study, we varied the number of predictors and their strength, the correlation between predictors, the event rate of the outcome, and the events per variable. In terms of results, we focused on the calibration slope. The slope indicates whether risk predictions are too extreme (slope 1). The results can be summarized into three main findings. First, shrinkage improved calibration slopes on average. Second, the between-sample variability of calibration slopes was often increased relative to maximum likelihood. In contrast to other shrinkage approaches, Firth's correction had a small shrinkage effect but showed low variability. Third, the correlation between the estimated shrinkage and the optimal shrinkage to remove overfitting was typically negative, with Firth's correction as the exception. We conclude that, despite improved performance on average, shrinkage often worked poorly in individual datasets, in particular when it was most needed. The results imply that shrinkage methods do not solve problems associated with small sample size or low number of events per variable.Development and application of statistical models for medical scientific researc