Mark van Gils

Spectral entropy monitoring is associated with reduced propofol use and faster emergence in propofol-nitrous oxide-alfentanil anesthesia.

Background:This multicenter study evaluated the effect of a new depth of anesthesia–monitoring device based on time-frequency–balanced spectral entropy of electroencephalogram monitoring (GE Healthcare Finland, Helsinki, Finland) on consumption of anesthetic drugs and recovery times after anesthesia. Methods:The study was a prospective, randomized, single-blind study performed in six hospitals in Finland, Sweden, and Norway. After institutional review board approval and written informed consent from each patient, the patients were randomly allocated to anesthesia with entropy values either shown (entropy group) or not shown (control group). Anesthesia was maintained with propofol, nitrous oxide, and alfentanil. In the entropy group, propofol was given to keep the state entropy value between 45 and 65, and alfentanil was given to keep the state entropy–response entropy difference below 10 units and heart rate and blood pressure within ±20% of the baseline values. The control group patients were anesthetized to keep heart rate and blood pressure within ±20% of the baseline values. Statistical methods included Mann–Whitney U test and unpaired t tests. Results:A total of 368 patients were studied. In the entropy group, entropy values were higher during the whole operation and especially during the last 15 min (P < 0.001). Consequently, propofol consumption was smaller in the entropy group during the whole anesthesia period (P < 0.001) and especially during the last 15 min (P < 0.001). This shortened the time delay in the early recovery parameters in the entropy group. Conclusion:Entropy monitoring assisted titration of propofol, especially during the last part of the procedures, as indicated by higher entropy values, decreased consumption of propofol, and shorter recovery times in the entropy group.

A Disease State Fingerprint for Evaluation of Alzheimer's Disease

Diagnostic processes of Alzheimers disease (AD) are evolving. Knowledge about disease-specific biomarkers is constantly increasing and larger volumes of data are being measured from patients. To gain additional benefits from the collected data, a novel statistical modeling and data visualization system is proposed for supporting clinical diagnosis of AD. The proposed system computes an evidence-based estimate of a patients AD state by comparing his or her heterogeneous neuropsychological, clinical, and biomarker data to previously diagnosed cases. The AD state in this context denotes a patients degree of similarity to previously diagnosed disease population. A summary of patient data and results of the computation are displayed in a succinct Disease State Fingerprint (DSF) visualization. The visualization clearly discloses how patient data contributes to the AD state, facilitating rapid interpretation of the information. To model the AD state from complex and heterogeneous patient data, a statistical Disease State Index (DSI) method underlying the DSF has been developed. Using baseline data from the Alzheimers Disease Neuroimaging Initiative (ADNI), the ability of the DSI to model disease progression from elderly healthy controls to AD and its ability to predict conversion from mild cognitive impairment (MCI) to AD were assessed. It was found that the DSI provides well-behaving AD state estimates, corresponding well with the actual diagnoses. For predicting conversion from MCI to AD, the DSI attains performance similar to state-of-the-art reference classifiers. The results suggest that the DSF establishes an effective decision support and data visualization framework for improving AD diagnostics, allowing clinicians to rapidly analyze large quantities of diverse patient data.

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimers disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimers Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimers disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.

Predicting AD Conversion: Comparison between Prodromal AD Guidelines and Computer Assisted PredictAD Tool

Purpose To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers. Methods Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1–42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later. Results The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician’s prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037). Conclusion With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.

Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 as Outcome Predictors in Traumatic Brain Injury

OBJECTIVE Biomarkers ubiquitin C-terminal hydrolase-L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) may help detect brain injury, assess its severity, and improve outcome prediction. This study aimed to evaluate the prognostic value of these biomarkers during the first days after brain injury. METHODS Serum UCH-L1 and GFAP were measured in 324 patients with traumatic brain injury (TBI) enrolled in a prospective study. The outcome was assessed using the Glasgow Outcome Scale (GOS) or the extended version, Glasgow Outcome Scale-Extended (GOSE). RESULTS Patients with full recovery had lower UCH-L1 concentrations on the second day and patients with favorable outcome had lower UCH-L1 concentrations during the first 2 days compared with patients with incomplete recovery and unfavorable outcome. Patients with full recovery and favorable outcome had significantly lower GFAP concentrations in the first 2 days than patients with incomplete recovery or unfavorable outcome. There was a strong negative correlation between outcome and UCH-L1 in the first 3 days and GFAP levels in the first 2 days. On arrival, both UCH-L1 and GFAP distinguished patients with GOS score 1-3 from patients with GOS score 4-5, but not patients with GOSE score 8 from patients with GOSE score 1-7. For UCH-L1 and GFAP to predict unfavorable outcome (GOS score ≤ 3), the area under the receiver operating characteristic curve was 0.727, and 0.723, respectively. Neither UCHL-1 nor GFAP was independently able to predict the outcome when age, worst Glasgow Coma Scale score, pupil reactivity, Injury Severity Score, and Marshall score were added into the multivariate logistic regression model. CONCLUSIONS GFAP and UCH-L1 are significantly associated with outcome, but they do not add predictive power to commonly used prognostic variables in a population of patients with TBI of varying severities.

Standardized Handwriting to Assess Bradykinesia, Micrographia and Tremor in Parkinson's Disease

Objective To assess whether standardized handwriting can provide quantitative measures to distinguish patients diagnosed with Parkinsons disease from age- and gender-matched healthy control participants. Design Exploratory study. Pen tip trajectories were recorded during circle, spiral and line drawing and repeated character ‘elelelel’ and sentence writing, performed by Parkinson patients and healthy control participants. Parkinson patients were tested after overnight withdrawal of anti-Parkinsonian medication. Setting University Medical Center Groningen, tertiary care, the Netherlands. Participants Patients with Parkinsons disease (n = 10; mean age 69.0 years; 6 male) and healthy controls (n = 10; mean age 68.1 years; 6 male). Interventions Not applicable. Main Outcome Measures Movement time and velocity to detect bradykinesia and the size of writing to detect micrographia. A rest recording to investigate the presence of a rest-tremor, by frequency analysis. Results Mean disease duration in the Parkinson group was 4.4 years and the patients were in modified Hoehn-Yahr stages 1–2.5. In general, Parkinson patients were slower than healthy control participants. Median time per repetition, median velocity and median acceleration of the sentence task and median velocity of the elel task differed significantly between Parkinson patients and healthy control participants (all p<0.0014). Parkinson patients also wrote smaller than healthy control participants and the width of the ‘e’ in the elel task was significantly smaller in Parkinson patients compared to healthy control participants (p<0.0014). A rest-tremor was detected in the three patients who were clinically assessed as having rest-tremor. Conclusions This study shows that standardized handwriting can provide objective measures for bradykinesia, tremor and micrographia to distinguish Parkinson patients from healthy control participants.

Quantification of Epileptiform Electroencephalographic Activity during Sevoflurane Mask Induction

Background:Sevoflurane may induce epileptiform electroencephalographic activity leading to unstable Bispectral Index numbers, underestimating the hypnotic depth of anesthesia. The authors developed a method for the quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. Methods:Electroencephalographic data from 60 patients under sevoflurane mask induction were used in the analysis. Electroencephalographic data were visually classified. A novel electroencephalogram-derived quantity, wavelet subband entropy (WSE), was developed. WSE variables were calculated from different frequency bands. Performance of the WSE in detection and quantification of epileptiform electroencephalographic activity and the ability of the WSE to recognize misleading Bispectral Index readings caused by epileptiform activity were evaluated. Results:Two WSE variables were found to be sufficient for the quantification of epileptiform activity: WSE from the frequency bands 4–16 and 16–32 Hz. The lower frequency band was used for monophasic pattern monitoring, and the higher frequency band was used for spike activity monitoring. WSE values of the lower and higher bands followed the time evolution of epileptiform activity with prediction probabilities of 0.809 (SE, 0.007) and 0.804 (SE, 0.007), respectively. In deep anesthesia with epileptiform activity, WSE detected electroencephalographic patterns causing Bispectral Index readings greater than 60, with event sensitivity of 97.1%. Conclusions:The developed method proved useful in detection and quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. In the future, it may improve the understanding of electroencephalogram-derived information by assisting in recognizing misleading readings of depth-of-anesthesia monitors. The method also may assist in minimizing the occurrence of epileptiform activity and seizures during sevoflurane anesthesia.

The Levels of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 During the First Week After a Traumatic Brain Injury: Correlations With Clinical and Imaging Findings.

BACKGROUND Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) are promising biomarkers of traumatic brain injury (TBI). OBJECTIVE We investigated the relation of the GFAP and UCH-L1 levels to the severity of TBI during the first week after injury. METHODS Plasma UCH-L1 and GFAP were measured from 324 consecutive patients with acute TBI and 81 control subject enrolled in a 2-center prospective study. The baseline measures included initial Glasgow Coma Scale (GCS), head computed tomographic (CT) scan at admission, and blood samples for protein biomarkers that were collected at admission and on days 1, 2, 3, and 7 after injury. RESULTS Plasma levels of GFAP and UCH-L1 during the first 2 days after the injury strongly correlated with the initial severity of TBI as assessed with GCS. Additionally, levels of UCH-L1 on the seventh day after the injury were significantly related to the admission GCS scores. At admission, both biomarkers were capable of distinguishing mass lesions from diffuse injuries in CT, and the area under the curve of the receiver-operating characteristic curve for prediction of any pathological finding in CT was 0.739 (95% confidence interval, 0.636-0.815) and 0.621 (95% confidence interval, 0.517-0.713) for GFAP and UCH-L1, respectively. CONCLUSION These results support the prior findings of the potential role of GFAP and UCH-L1 in acute-phase diagnostics of TBI. The novel finding is that levels of GFAP and UCH-L1 correlated with the initial severity of TBI during the first 2 days after the injury, thus enabling a window for TBI diagnostics with latency. ABBREVIATIONS AUC, area under the curveCI, confidence intervalED, emergency departmentGCS, Glasgow Coma ScaleGRAP, glial fibrillary acidic proteinIMPACT, International Mission for Prognosis and Clinical TrialROC, receiver-operating characteristicTBI, traumatic brain injuryTRACK-TBI, Transforming Research and Clinical Knowledge in Traumatic Brain InjuryUCH-L1, ubiquitin C-terminal hydrolase-L1.

Weight rhythms: weight increases during weekends and decreases during weekdays.

Background/Aims: The weeks cycle influences sleep, exercise, and eating habits. An accurate description of weekly weight rhythms has not been reported yet - especially across people who lose weight versus those who maintain or gain weight. Methods: The daily weight in 80 adults (BMI 20.0-33.5 kg/m2; age, 25-62 years) was recorded and analysed to determine if a group-level weekly weight fluctuation exists. This was a retrospective study of 4,657 measurements during 15-330 monitoring days. Semi-parametric regression was used to model the rhythm. Results: A pattern of daily weight changes was found (p < 0.05), with higher weight early in the week (Sunday and Monday) and decreasing weight during the week. Increases begin on Saturday and decreases begin on Tuesday. This compensation pattern was strongest for those who lost or maintained weight and weakest for those who slowly gained weight. Conclusion: Weight variations between weekends and weekdays should be considered as normal instead of signs of weight gain. Those who compensate the most are most likely to either lose or maintain weight over time. Long-term habits may make more of a difference than short-term splurges. People prone to weight gain could be counselled about the importance of weekday compensation.

Quantitative Evaluation of Disease Progression in a Longitudinal Mild Cognitive Impairment Cohort

Several neuropsychological tests and biomarkers of Alzheimers disease (AD) have been validated and their evolution over time has been explored. In this study, multiple heterogeneous predictors of AD were combined using a supervised learning method called Disease State Index (DSI). The behavior of DSI values over time was examined to study disease progression quantitatively in a mild cognitive impairment (MCI) cohort. The DSI method was applied to longitudinal data from 140 MCI cases that progressed to AD and 149 MCI cases that did not progress to AD during the follow-up. The data included neuropsychological tests, brain volumes from magnetic resonance imaging, cerebrospinal fluid samples, and apolipoprotein E from the Alzheimers Disease Neuroimaging Initiative database. Linear regression of the longitudinal DSI values (including the DSI value at the point of MCI to AD conversion) was performed for each subject having at least three DSI values available (147 non-converters, 126 converters). Converters had five times higher slopes and almost three times higher intercepts than non-converters. Two subgroups were found in the group of non-converters: one group with stable DSI values over time and another group with clearly increasing DSI values suggesting possible progression to AD in the future. The regression parameters differentiated between the converters and the non-converters with classification accuracy of 76.9% for the slopes and 74.6% for the intercepts. In conclusion, this study demonstrated that quantifying longitudinal patient data using the DSI method provides valid information for follow-up of disease progression and support for decision making.