Publications by authors named "Frank C Bennis"

11 Publications

  • Page 1 of 1

Ratio of arterial blood pressures at borders of window surrounding systolic peak indicates patent ductus arteriosus in preterm infants.

Physiol Meas 2021 02 6;42(1):015005. Epub 2021 Feb 6.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Objective: Presence of a patent ductus arteriosus (PDA) in neonates is assessed by echocardiography. Echocardiographic assessment has disadvantages, primarily its discontinuous nature. We hypothesize that the continuously measured ratio of arterial blood pressures (ABP) at the borders of a window surrounding the systolic peak ratio discriminates non-PDA from PDA patients.

Approach: Preterm infants (gestational age <32 weeks) with and without PDA were included. Patients were divided into controls (n = 8) and PDA patients (n = 22), the latter with a subset of patients with closed PDA after three doses Ibuprofen (n = 10). For each patient, a six-hour ABP segment from 12 AM to 6 AM on the day of echocardiographic assessment patency or closure of the DA was selected. The mean ratio of the ABP values a samples before and p samples after the systolic peak (R ) was calculated for each segment. If R  < 1, the patient was predicted to have a PDA. The a and p with the least misclassifications were selected (-64 and +104 ms).

Main Results: R was significantly lower in PDA patients (median 0.95, IQR 0.06) compared to controls (median 1.05, IQR 0.10; p = 0.0024). R correctly predicted 19 out of 22 patients (86.4%) and six out of eight controls (75%). R increased after closure in nine out of 10 patients (median 1.01, IQR 0.04; p = 0. 0182).

Significance: R may discriminate preterm PDA patients from non-PDA patients and can be calculated continuously from clinical data measured during standard of care.
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http://dx.doi.org/10.1088/1361-6579/abd5aaDOI Listing
February 2021

Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course: design of the Maastricht Intensive Care COVID cohort (MaastrICCht).

BMJ Open 2020 09 29;10(9):e040175. Epub 2020 Sep 29.

Department of Intensive Care, Maastricht University Medical Center+, Maastricht, The Netherlands.

Introduction: The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with a SARS-CoV-2 infection.

Methods And Analysis: Mechanically ventilated patients admitted to the intensive care with a SARS-CoV-2 infection will be included. We will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, ECGs, echocardiography as well as other imaging modalities to assess heterogeneity of the course of a SARS-CoV-2 infection in critically ill patients. The MaastrICCht is also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national intensive care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence. The spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS-CoV-2 infection in mechanically ventilated patients. Our study design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of the MaastrICCht.

Ethics And Dissemination: Ethical approval has been obtained from the medical ethics committee (Medisch Ethische Toetsingscommissie 2020-1565/3 00 523) of the Maastricht University Medical Centre+ (Maastricht UMC+), which will be performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent to use the collected data and to store serum samples for COVID-19 research purposes. All study documentation will be stored securely for fifteen years after recruitment of the last patient. The results will be published in peer-reviewed academic journals, with a preference for open access journals, while particularly considering deposition of the manuscripts on a preprint server early.

Trial Registration Number: The Netherlands Trial Register (NL8613).
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http://dx.doi.org/10.1136/bmjopen-2020-040175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526030PMC
September 2020

Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model.

Neurocrit Care 2020 10;33(2):542-551

MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.

Background/objective: Current severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring.

Methods: Forty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0-6 h, 0-12 h, 0-18 h, 0-24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation.

Results: A logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0-6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices.

Conclusions: Current TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted.
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http://dx.doi.org/10.1007/s12028-020-00930-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505885PMC
October 2020

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart Rhythm 2020 05 7;17(5 Pt A):752-758. Epub 2020 Jan 7.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.

Background: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Objective: The purpose of this study was to improve LQTS diagnosis by combining new polynomial-based T-wave morphology parameters with the corrected QT interval (QTc), age, and sex in a model.

Methods: A retrospective cohort consisting of 333 patients with LQTS and 345 genotype-negative family members was used in this study. For each patient, a linear combination of the first 2 Hermite-Gauss (HG) polynomials was fitted to the STT segments of an average complex of all precordial leads and limb leads I and II. The weight coefficients as well as the error of the best fit were used to characterize T-wave morphology. Subjects were classified as patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine models fed with different features. An external cohort consisting of 72 patients and 45 controls was finally used to check the robustness of the models.

Results: Baseline QTc cutoffs were specific but had low sensitivity in diagnosing LQTS. The model with T-wave morphology features, QTc, age, and sex had the best overall accuracy (84%), followed by a model with QTc, age, and sex (79%). The model with T-wave morphology features especially performed better in LQTS type 3 patients (69%).

Conclusion: T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis.
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http://dx.doi.org/10.1016/j.hrthm.2019.12.020DOI Listing
May 2020

Artifacts in pulse transit time measurements using standard patient monitoring equipment.

PLoS One 2019 21;14(6):e0218784. Epub 2019 Jun 21.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Objective: Pulse transit time (PTT) refers to the time it takes a pulse wave to travel between two arterial sites. PTT can be estimated, amongst others, using the electrocardiogram (ECG) and photoplethysmogram (PPG). Because we observed a sawtooth artifact in the PTT while using standard patient monitoring equipment for ECG and PPG, we explored the reasons for this artifact.

Methods: PPG and ECG were simulated at a heartrate of both 100 and 160 beats per minute while using a Masimo PPG post-processing module and a Philips patient monitor setup at the neonatal intensive care unit. Two different post-processing modules were used. PTT was defined as the difference between the R-peak in the ECG and the point of 50% increase in the PPG.

Results: A sawtooth artifact was seen in all simulations. Both length (59.2 to 72.4 s) and amplitude (30.8 to 36.0 ms) of the sawtooth were dependent on the post-processing module used. Furthermore, the absolute PTT value differed up to 250 ms depending on post-processing module and heart rate. The sawtooth occurred because the PPG wave continuously showed a minimal prolongation during the length of the sawtooth, followed by a sudden shortening. Both artifacts were generated in the post-processing module containing Masimo algorithms.

Conclusion: Post-processing of the PPG signal in the Masimo module of the Philips patient monitor introduces a sawtooth in PPG and derived PTT. This sawtooth, together with a large module-dependent absolute difference in PTT, renders the thus-derived PTT insufficient for clinical purposes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218784PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588249PMC
March 2020

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace 2018 Nov;20(suppl_3):iii113-iii119

Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

Aims: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine.

Methods And Results: A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (>480 ms), the extended model showed a major drop in false negative classifications of LQTS patients.

Conclusion: The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS.
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http://dx.doi.org/10.1093/europace/euy243DOI Listing
November 2018

Letter by Bennis et al Regarding Article, "Cerebral Near-Infrared Spectroscopy: A Potential Approach for Thrombectomy Monitoring".

Stroke 2018 03 9;49(3):e135. Epub 2018 Feb 9.

Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands.

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http://dx.doi.org/10.1161/STROKEAHA.117.020293DOI Listing
March 2018

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

Front Physiol 2017 5;8:1057. Epub 2018 Jan 5.

Department of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands.

In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. : volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. : sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91). The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.
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http://dx.doi.org/10.3389/fphys.2017.01057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761201PMC
January 2018

The development and validation of an easy to use automatic QT-interval algorithm.

PLoS One 2017 1;12(9):e0184352. Epub 2017 Sep 1.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Background: To evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.

Objective: To develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.

Methods: Additionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat. Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.

Results: After visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.

Conclusion: Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184352PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581168PMC
October 2017

The use of single bipolar scalp derivation for the detection of ictal events during long-term EEG monitoring.

Epileptic Disord 2017 Sep;19(3):307-314

Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede.

Epilepsy is difficult to diagnose using routine EEG recordings of short duration in patients who have low seizure frequency. Long-term EEG may be useful but is impractical in an out-of-hospital setting. We investigated whether single-channel scalp EEG placed behind the earlobe is suitable for seizure identification during prolonged EEG monitoring. Scalp EEG samples were selected from subjects over 15 years of age, and comprised two segments of either background followed by seizure or background followed by background. Bipolar EEG derivations in three directions (F8-T8, C4-T8 and T8-P8) were evaluated for the presence of a seizure by two experienced reviewers. For each EEG segment containing a seizure, one pair of electrodes was oriented towards the suspected region of seizure onset, while two pairs of electrodes were oriented elsewhere. The EEG data contained five frontally localized seizures, five parietal, five temporal, two occipital, and four primary or secondary generalized seizures. The sensitivity and specificity for recognition of seizures was 86% and 95% for Reviewer 1, and 79% and 99% for Reviewer 2, respectively. When identifying a seizure with the lead orientation towards the region of seizure onset, both reviewers identified 20 out of 21 seizures (95%). When the lead was not oriented towards the region of seizure onset, the reviewers identified 34 and 30 out of 42 ictal records correctly, respectively. These results suggest that it is possible to identify epileptic seizures by bipolar EEG derivation using only two scalp electrodes. Lead orientation towards the suspected region of seizure onset is important for optimal detection sensitivity.
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http://dx.doi.org/10.1684/epd.2017.0926DOI Listing
September 2017

A machine-learning based analysis for the recognition of progressive central hypovolemia.

Physiol Meas 2017 Aug 21;38(9):1791-1801. Epub 2017 Aug 21.

Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands. MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands.

Objective: Traditional patient monitoring during surgery includes heart rate (HR), blood pressure (BP) and peripheral oxygen saturation. However, their use as predictors for central hypovolemia is limited, which may lead to cerebral hypoperfusion. The aim of this study was to develop a monitoring model that can indicate a decrease in central blood volume (CBV) at an early stage.

Approach: Twenty-eight healthy subjects (aged 18-50 years) were included. Lower body negative pressure (-50 mmHg) was applied to induce central hypovolemia until the onset of pre-syncope. Ten beat-to-beat and four discrete parameters were measured, normalized, and filtered with a 30 s moving window. Time to pre-syncope was scaled from 100%-0%. A total of 100 neural networks with 5, 10, 15, 20, or 25 neurons in their respective hidden layer were trained by 10, 20, 40, 80, 160, or 320 iterations to predict time to pre-syncope for each subject. The network with the lowest average slope of a fitted line over all subjects was chosen as optimal.

Main Results: The optimal generalized model consisted of 10 hidden neurons, trained using 80 iterations. The slope of the fitted line on the average prediction was  -0.64 (SD 0.35). The model recognizes in 75% of the subjects the need for intervention at  >200 s before pre-syncope.

Significance: We developed a neural network based on a set of physiological variables, which indicates a decrease in CBV even in the absence of HR and BP changes. This should allow timely intervention and prevent the development of symptomatic cerebral hypoperfusion.
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http://dx.doi.org/10.1088/1361-6579/aa7d3dDOI Listing
August 2017
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