Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine
Open accessAbstract
Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some examples of how to map model risk to credibility goals for a specific COU have been provided in medical device applications.
“Some examples of how to map model risk to credibility goals for a specific COU has been provided in medical device applications.13, 14, 15”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global Sensitivity Analysis. The Primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2007) · doi:10.1002/9780470725184 · Cited by 6,430 · see citing works
Uncertainty Quantification: Theory, Implementation, and Applications
Smith, R. C. (2013) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611973228 · Cited by 1,179 · see citing works
The influence of age on propofol pharmacodynamics.
Schnider, T., Minto, C., Shafer, S., Gambús, P., Andresen, C., Goodale, D., & Youngs, E. (1999) · Anesthesiology · doi:10.1097/00000542-199906000-00003 · Cited by 1,141 · see citing works
<i>In Silico</i> Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes
Kovatchev, B. P., Breton, M., Dalla Man, C., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300106 · Cited by 760 · see citing works
Simple, accurate equations for human blood O2 dissociation computations
Severinghaus, J. W. (1979) · Journal of Applied Physiology · doi:10.1152/jappl.1979.46.3.599 · Cited by 751 · see citing works
High Level of Burnout in Intensivists
Embriaco, N., Azoulay, É., Barrau, K., Kentish‐Barnes, N., Pochard, F., Loundou, A., & Papazian, L. (2007) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200608-1184oc · Cited by 711 · see citing works
Population Pharmacokinetics of Propofol
Schüttler, J., & Ihmsen, H. (2000) · Anesthesiology · doi:10.1097/00000542-200003000-00017 · Cited by 438 · see citing works
Interaction between carotid baroregulation and the pulsating heart: a mathematical model
Ursino, M. (1998) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1998.275.5.h1733 · Cited by 362 · see citing works
Response Surface Model for Anesthetic Drug Interactions
Minto, C. F., Schnider, T. W., Short, T. G., Gregg, K. M., Gentilini, A., & Shafer, S. L. (2000) · Anesthesiology · doi:10.1097/00000542-200006000-00017 · Cited by 353 · see citing works
Mathematical and Computer Modeling of Physiological Systems
Rideout, V. C. (1991) · Cited by 165 · see citing works
Robust control of depth of anesthesia
Dumont, G. A., Martinez, A., & Ansermino, J. M. (2008) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.1087 · Cited by 161 · see citing works
An FDA Viewpoint on Unique Considerations for Medical-Device Clinical Trials
Faris, O., & Shuren, J. E. (2017) · New England Journal of Medicine · doi:10.1056/nejmra1512592 · Cited by 152 · see citing works
Struys, M. M. R. F., Smet, T. D., Greenwald, S. D., Absalom, A. R., Bingé, S., & Mortier, E. P. (2004) · Anesthesiology · doi:10.1097/00000542-200403000-00026 · Cited by 132 · see citing works
Predictive Capability Maturity Model for computational modeling and simulation.
Oberkampf, W. L., Trucano, T. G., & Pilch, M. M. (2007) · doi:10.2172/976951 · Cited by 127 · see citing works
Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane
Gentilini, A., Rossoni-Gerosa, M., Frei, C. W., Wymann, R., Morari, M., Zbinden, A. M., & Schnider, T. W. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.936364 · Cited by 124 · see citing works
Validation of Biophysical Models: Issues and Methodologies
Bellocchi, G., Rivington, M., Donatelli, M., & Matthews, K. B. (2011) · Springer eBooks · doi:10.1007/978-94-007-0394-0_26 · Cited by 112 · see citing works
Nonlinear Identification of a Minimal Neuromuscular Blockade Model in Anesthesia
da Silva, M. M., Wigren, T., & Mendonça, T. (2011) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2107742 · Cited by 89 · see citing works
Friedrich, C. (2016) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12056 · Cited by 87 · see citing works
Validation of simple and complex models in physiology and medicine
Cobelli, C., Carson, E. R., Finkelstein, L., & Leaning, M. S. (1984) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.1984.246.2.r259 · Cited by 86 · see citing works
Rinehart, J. B., Alexander, B. S., Manach, Y. L., Hofer, C. K., Tavernier, B., Kain, Z. N., & Cannesson, M. P. (2011) · Critical Care · doi:10.1186/cc10562 · Cited by 84 · see citing works
A new paradigm for the closed-loop intraoperative administration of analgesics in humans
Gentilini, A., Schaniel, C., Morari, M., Bieniok, C., Wymann, R., & Schnider, T. W. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.991156 · Cited by 78 · see citing works
Parvinian, B., Scully, C. G., Wiyor, H. D., Kumar, A., & Weininger, S. (2017) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002329 · Cited by 76 · see citing works
Self-citationChiari, L., Avanzolini, G., & Ursino, M. (1997) · Annals of Biomedical Engineering · doi:10.1007/bf02684134 · Cited by 73 · see citing works
A Physiologically Based, Recirculatory Model of the Kinetics and Dynamics of Propofol in Man
Upton, R. N., & Ludbrook, G. (2005) · Anesthesiology · doi:10.1097/00000542-200508000-00018 · Cited by 73 · see citing works
A mathematical model of the human respiratory system
Fincham, W. F., & Tehrani, F. T. (1983) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(83)90030-4 · Cited by 69 · see citing works
Applicability Analysis of Validation Evidence for Biomedical Computational Models
Pathmanathan, P., Gray, R. A., Romero, V. J., & Morrison, T. M. (2017) · Journal of Verification Validation and Uncertainty Quantification · doi:10.1115/1.4037671 · Cited by 68 · see citing works
Self-citationPerspective: Validation—What Does It Mean?
Roache, P. J. (2009) · Journal of Fluids Engineering · doi:10.1115/1.3077134 · Cited by 67 · see citing works
Closed-Loop Control of Fluid Therapy for Treatment of Hypovolemia
Kramer, G. C., Kinsky, M. P., Prough, D. S., Salinas, J., Sondeen, J. L., Hazel, M., & Mitchell, C. E. (2008) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0b013e31816bf517 · Cited by 65 · see citing works
Held, C. M., & Roy, R. J. (1995) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.376130 · Cited by 65 · see citing works
An Intelligent Adaptive Control Scheme for Postsurgical Blood Pressure Regulation
Gao, Y., & Er, M. J. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.841798 · Cited by 64 · see citing works
Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology
Pathmanathan, P., & Gray, R. A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00106 · Cited by 63 · see citing works
Self-citationA framework to establish credibility of computational models in biology
Patterson, E. A., & Whelan, M. (2016) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2016.08.007 · Cited by 62 · see citing works
Ionescu, C. M., Naşcu, I., & KEYSER, R. D. (2013) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-013-9535-5 · Cited by 62 · see citing works
Systems biology and integrative physiological modelling
Hester, R. L., Iliescu, R., Summers, R., & Coleman, T. G. (2011) · The Journal of Physiology · doi:10.1113/jphysiol.2010.201558 · Cited by 58 · see citing works
A circulatory model for combined nitroprusside-dopamine therapy in acute heart failure.
Yu, C., Roy, R., & Kaufman, H. (1990) · PubMed · Cited by 56 · see citing works
Summers, R. L., Ward, K. R., Witten, T. M., Convertino, V. A., Ryan, K. L., Coleman, T. G., & Hester, R. L. (2009) · Resuscitation · doi:10.1016/j.resuscitation.2009.09.001 · Cited by 55 · see citing works
Mathematical analysis and computer simulation of the respiratory system in the newborn infant
Tehrani, F. T. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.243414 · Cited by 50 · see citing works
First closed-loop goal directed fluid therapy during surgery: A pilot study
Rinehart, J. B., Manach, Y. L., Douiri, H., Lee, C., Lilot, M., Le, K. T., Canales, C., & Cannesson, M. P. (2014) · Annales Françaises d Anesthésie et de Réanimation · doi:10.1016/j.annfar.2013.11.016 · Cited by 49 · see citing works
Adaptive fuzzy predictive controller for anesthesia delivery
Pérez, J. A. M., Marrero, A., Reboso, J. A., & León, A. M. (2015) · Control Engineering Practice · doi:10.1016/j.conengprac.2015.09.009 · Cited by 49 · see citing works
Improvement in Arteral Oxygen Control Using Multiple-Model Adaptive Control Procedures
Yu, C. K.-M., He, W. G., So, J. M., Roy, R. J., Kaufman, H., & NEWELL, J. C. (1987) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1987.326067 · Cited by 44 · see citing works
Multiple-drug hemodynamic control using fuzzy decision theory
Huang, J., & Roy, R. J. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.661269 · Cited by 44 · see citing works
A closed-loop anesthetic delivery system for real-time control of burst suppression
Liberman, M., Ching, S., Chemali, J. J., & Brown, E. N. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046004 · Cited by 43 · see citing works
Robust control design for automatic regulation of blood pressure
Malagutti, N., Dehghani, A., & Kennedy, R. A. (2013) · IET Control Theory and Applications · doi:10.1049/iet-cta.2012.0254 · Cited by 43 · see citing works
Closed-Loop Fluid Resuscitation
Rinehart, J. B., Lee, C., Cannesson, M. P., & Dumont, G. A. (2013) · Anesthesia & Analgesia · doi:10.1213/ane.0b013e3182930050 · Cited by 42 · see citing works
Martin, I. F., Schneider, A., Quinn, M. L., & Smith, N. T. (1992) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.126610 · Cited by 41 · see citing works
Jin, X., Bighamian, R., & Hahn, J. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2880927 · Cited by 40 · see citing works
Chen, C.-T., Lin, W., Kuo, T., & Wang, C.-Y. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.594901 · Cited by 40 · see citing works
Unconstrained and constrained generalised predictive control of depth of anaesthesia during surgery
Mahfouf, M., Asbury, A. J., & Linkens, D. A. (2003) · Control Engineering Practice · doi:10.1016/s0967-0661(03)00075-3 · Cited by 39 · see citing works
Model‐based control of mechanical ventilation: design and clinical validation
Martinoni, E. P., Pfister, C. A., Stadler, K. S., Schumacher, P. M., Leibundgut, D., Bouillon, T. W., Böhlen, T., & Zbinden, A. M. (2004) · British Journal of Anaesthesia · doi:10.1093/bja/aeh145 · Cited by 39 · see citing works
Ursino, M., & Magosso, E. (2003) · Journal of Integrative Neuroscience · doi:10.1142/s0219635203000275 · Cited by 38 · see citing works
Modeling and control of pharmacodynamics
Beck, C. L. (2015) · European Journal of Control · doi:10.1016/j.ejcon.2015.04.006 · Cited by 38 · see citing works
On the Use of Multivariable Piecewise-Linear Models for Predicting Human Response to Anesthesia
Lin, H.-H., Beck, C. L., & Bloom, M. J. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.831541 · Cited by 38 · see citing works
Burnout Syndrome in the Intensive Care Unit. Future Directions for Research
Mealer, M. L. (2016) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201604-280ed · Cited by 36 · see citing works
Kashihara, K., Kawada, T., Uemura, K., Sugimachi, M., & Sunagawa, K. (2004) · Annals of Biomedical Engineering · doi:10.1114/b:abme.0000042225.19806.34 · Cited by 35 · see citing works
Tehrani, F. T. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.09.007 · Cited by 35 · see citing works
Controlling the depth of anesthesia by a novel positive control strategy
Nogueira, F. N., Mendonça, T., & Rocha, P. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.12.016 · Cited by 33 · see citing works
Indirect adaptive nonlinear control of drug delivery systems
Polycarpou, M. M., & Conway, J. (1998) · IEEE Transactions on Automatic Control · doi:10.1109/9.679024 · Cited by 33 · see citing works
Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation
Bighamian, R., Parvinian, B., Scully, C. G., Kramer, G. C., & Hahn, J. (2018) · Control Engineering Practice · doi:10.1016/j.conengprac.2018.01.008 · Cited by 32 · see citing works
Self-citationOn using a self-tuning controller for blood pressure regulation during surgery in man
Millard, R. K., Hutton, P., Pereira, E., & Roberts, C. P. (1987) · Computers in Biology and Medicine · doi:10.1016/0010-4825(87)90029-1 · Cited by 31 · see citing works
Frei, C. W., Derighetti, M., Morari, M., Glattfelder, A. H., & Zbinden, A. M. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.880097 · Cited by 30 · see citing works
Upton, R. N., & Ludbrook, G. (2005) · BMC Pharmacology · doi:10.1186/1471-2210-5-5 · Cited by 28 · see citing works
Robust control of burst suppression for medical coma
Westover, M. B., Kim, S., Ching, S., Purdon, P. L., & Brown, E. N. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046004 · Cited by 27 · see citing works
Batzel, J. J., Baselli, G., Mukkamala, R., & Chon, K. H. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0266 · Cited by 26 · see citing works
Simultaneous Regulation of Hemodynamic and Anesthetic States: A Simulation Study
Rao, R. R., Bequette, B. W., & Roy, R. J. (2000) · Annals of Biomedical Engineering · doi:10.1114/1.255 · Cited by 25 · see citing works
A model for the design and evaluation of algorithms for closed-loop cardiovascular therapy
Woodruff, E. A., Martin, J. F., & Omens, M. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.605426 · Cited by 25 · see citing works
Robust linear parameter-varying control of blood pressure using vasoactive drugs
Luspay, T., & Grigoriadis, K. M. (2015) · International Journal of Control · doi:10.1080/00207179.2015.1027953 · Cited by 24 · see citing works
Silva, M. M., Lemos, J. M., Coito, A. L., Costa, B. A., Wigren, T., & Mendonça, T. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.07.020 · Cited by 22 · see citing works
Pharmacokinetic models of propofol for TCI
Schnider, T. W., & Minto, C. F. (2008) · Anaesthesia · doi:10.1111/j.1365-2044.2007.05419_1.x · Cited by 22 · see citing works
Self-tuning control of hypotension during ENT surgery using a volatile anaesthetic
Millard, R. K., Monk, C. R., & Roberts, C. P. (1988) · IEE Proceedings D Control Theory and Applications · doi:10.1049/ip-d.1988.0014 · Cited by 22 · see citing works
Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia
Elkfafi, M., Shieh, J.-S., Linkens, D. A., & Peacock, J. E. (1998) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(97)00175-9 · Cited by 20 · see citing works
Simulations for Mechanical Ventilation in Children: Review and Future Prospects
Fléchelles, O., Ho, A., Hernert, P., Émériaud, G., Zaglam, N., Farida Cheriet, A., & Jouvet, P. A. (2013) · Critical Care Research and Practice · doi:10.1155/2013/943281 · Cited by 18 · see citing works
Bibian, S., Dumont, G. A., Huzmezan, M., & Ries, C. R. (2006) · IFAC Proceedings Volumes · doi:10.3182/20060920-3-fr-2912.00097 · Cited by 18 · see citing works
Sreenivas, Y., Yeng, T. W., Rangaiah, G. P., & Lakshminarayanan, S. (2009) · Industrial & Engineering Chemistry Research · doi:10.1021/ie800927u · Cited by 17 · see citing works
Generalized predictive control with feedforward (GPCF) for multivariable anaesthesia
Linkens, D. A., & Mahfouf, M. (1992) · International Journal of Control · doi:10.1080/00207179208934356 · Cited by 16 · see citing works
The benefits of using Guyton's model in a hypotensive control system
Nguyen, C.-N., Simanski, O., Kähler, R., Schubert, A. U., Janda, M., Bajorat, J., & Lampe, B. P. (2007) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2007.03.005 · Cited by 16 · see citing works
On the Identification of the Propofol PK/PD Model Using BIS Measurements
Merigo, L., Padula, F., Latronico, N., Mendonça, T., Paltenghi, M., Rocha, P., & Visioli, A. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.261 · Cited by 16 · see citing works
Silva, M. M., Paz, L., Wigren, T., & Mendonça, T. (2014) · Asian Journal of Control · doi:10.1002/asjc.981 · Cited by 15 · see citing works
A Cascade Feedback Control Approach for Hypnosis
Puebla, H., & Álvarez‐Ramírez, J. (2005) · Annals of Biomedical Engineering · doi:10.1007/s10439-005-6490-4 · Cited by 15 · see citing works
Bridging different perspectives of the physiological and mathematical disciplines
Batzel, J. J., Hinghofer-Szalkay, H. G., Kappel, F., Schneditz, D., Kenner, T., & Goswami, N. (2012) · AJP Advances in Physiology Education · doi:10.1152/advan.00074.2012 · Cited by 14 · see citing works
Oximetry feedback flow control simulation for oxygen therapy
Iobbi, M. G., Simonds, A. K., & Dickinson, R. J. (2007) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-006-9064-6 · Cited by 14 · see citing works
AUTOMATIC CONTROL OF ARTERIAL PRESSURE FOR HYPOTENSIVE PATIENTS USING PHENYLEPHRINE
Wassar, T., Luspay, T., Kallu, U. R., Moisi, M. D., Voigt, R. B., Marques, N. R., Khan, M. N., Grigoriadis, K. M., Franchek, M. A., & Kramer, G. C. (2014) · International Journal of Modelling and Simulation · doi:10.2316/journal.205.2014.4.205-6087 · Cited by 14 · see citing works
Hahn, J., Dumont, G. A., & Ansermino, J. M. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2204067 · Cited by 13 · see citing works
PATIENT VARIABILITY AND UNCERTAINTY QUANTIFICATION IN ANESTHESIA: PART II – PKPD UNCERTAINTY
Bibian, S., Dumont, G. A., Huzmezan, M., & Ries, C. R. (2006) · IFAC Proceedings Volumes · doi:10.3182/20060920-3-fr-2912.00098 · Cited by 13 · see citing works
Kim, C., Ansermino, J. M., & Hahn, J. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00008 · Cited by 12 · see citing works
A new approach to the modeling and control of postoperative pain
Liu, F.-Y., & Northrop, R. B. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.64459 · Cited by 12 · see citing works
Design of L1 adaptive controllers for human patient anesthesia
Kharisov, E., Beck, C. L., & Bloom, M. J. (2015) · Control Engineering Practice · doi:10.1016/j.conengprac.2015.06.003 · Cited by 11 · see citing works
An enriched simulation environment for evaluation of closed-loop anesthesia
Fang, M., Yuan, T., & Wang, Y. (2013) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-013-9483-0 · Cited by 10 · see citing works
Modeling and Control of Anesthetic Pharmacodynamics
Beck, C. L., Lin, H.-H., & Bloom, M. J. (2007) · Springer eBooks · doi:10.1007/978-3-540-71988-5_11 · Cited by 10 · see citing works
Automatic control of blood oxygen saturation in premature infants
Morozoff, P. E., Evans, R. W., & Smyth, J. A. (2002) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.1993.348256 · Cited by 10 · see citing works
Mean arterial pressure‐embedded model for real‐time simulation and controller validation
Silva, S. J., Scardovelli, T. A., Neto, H. M., Boschi, S. R. M. D. S., & da Silva, A. P. (2017) · Electronics Letters · doi:10.1049/el.2016.4788 · Cited by 10 · see citing works
Pomprapa, A., Leonhardt, S. K., & Misgeld, B. J. E. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.07.014 · Cited by 9 · see citing works
Teixeira, M., Mendonça, T., Rocha, P., & Rabiço, R. (2013) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-013-9500-3 · Cited by 8 · see citing works
Zhusubaliyev, Z. T., Medvedev, A. V., & da Silva, M. M. (2014) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2014.6858674 · Cited by 8 · see citing works
Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications
Jin, X., Kim, C., Shipley, S. T., Dumont, G. A., & Hahn, J. (2017) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.2832 · Cited by 7 · see citing works
Modeling and Simulation of an Electronic Oxygen Regulator Based on All-Coefficient Adaptive Control
Jiang, Y., Sun, Q., Tan, P., & Chen, Z. (2016) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4033413 · Cited by 7 · see citing works
ROBUST CONTROL OF END-TIDAL CO2 USING THE H∞ LOOP-SHAPING APPROACH
Pomprapa, A., Misgeld, B. J. E., Sorgato, V., Stollenwerk, A., Walter, M., & Leonhardt, S. K. (2013) · Acta Polytechnica · doi:10.14311/ap.2013.53.0895 · Cited by 6 · see citing works
Morozoff, E., & Saif, M. (2008) · Control and Intelligent Systems · doi:10.2316/journal.201.2008.3.201-1651 · Cited by 6 · see citing works
Nonlinear closed-loop control system for intracranial pressure regulation
Coté, G. L., Durai, R., & Zoghi, B. (1995) · Annals of Biomedical Engineering · doi:10.1007/bf02584475 · Cited by 5 · see citing works
Burn fluid resuscitation: Let the autopilot do it!*
Bracco, D. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e31821f030d · Cited by 5 · see citing works
Scully, C. G., Pathmanathan, P., Daluwatte, C., Yaghouby, F., Gray, R. A., Weininger, S., Morrison, T. M., & Parvinian, B. (2018) · IEEE Life Sciences Conference (LSC) · doi:10.1109/lsc.2018.8572270 · Cited by 4 · see citing works
Self-citationAdaptive regulation of arterial gas pressures by using robust adaptive control scheme
Sano, A., Ohkubo, K., Ohmori, H., & Kikuchi, M. (2003) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.1988.194317 · Cited by 4 · see citing works
FDA Seminar on Computational Modeling for Medical Devices
Morrison, T. M., Angelone, L. M., Myers, M., Soneson, J. E., Wang, Q., Weaver, J., Hariharan, P., V&V40, A., Dharia, M. A., & Reiterer, M. W. (2017) · Figshare · doi:10.6084/m9.figshare.5018783 · Cited by 4 · see citing works
Self-citationController design for neuromuscular blockade level tracking based on optimal control
Almeida, J., Mendonça, T., Rocha, P., & Rodrigues, L. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.08.019 · Cited by 4 · see citing works
Fernando, T. L., Packer, J. S., & Cade, J. F. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)00146-l · Cited by 2 · see citing works
Nonlinear controller for bispectral index tracking: Robustness and on-line retuning
Nogueira, F. N., Mendonça, T., & Rocha, P. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.03.021 · Cited by 2 · see citing works
Urzúa, J., Sauma, E. E., Cipriano, A. Z., Guarini, M. W., & Zapata, P. (1999) · PubMed · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Kharisov E. Beck C. L. Bloom M. (2012). “ Control of patient response to anesthesia using L1 adaptive methods,” in Proceedings of the 8th Annual IFAC Symposium on Biological and Medical Systems, Budapest.
Cited by
Sorted by citations
Viceconti, M., Pappalardo, F., Rodriguez, B., Horner, M., Bischoff, J., & Musuamba Tshinanu, F. (2021) · Methods · doi:10.1016/j.ymeth.2020.01.011 · Cited by 361 · see citing works
Kuemmel, C., Yang, Y., Zhang, X., Florian, J., Zhu, H., Tegenge, M., Huang, S., Wang, Y., Morrison, T., & Zineh, I. (2020) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12479 · Cited by 177 · see citing works
Self-citationClosed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives
Ghita, M., Neckebroek, M. M., Muresan, C. I., & Copoţ, D. (2020) · IEEE Access · doi:10.1109/access.2020.3037725 · Cited by 117 · see citing works
In silico modelling of cancer nanomedicine, across scales and transport barriers
Stillman, N., Kovačević, M., Balaž, I., & Hauert, S. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00366-8 · Cited by 99 · see citing works
Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation
Khodaei, M. J., Candelino, N. W., Mehrvarz, A., & Jalili, N. (2020) · IEEE Access · doi:10.1109/access.2020.2968440 · Cited by 66 · see citing works
Credibility assessment of in silico clinical trials for medical devices
Pathmanathan, P., Aycock, K., Badal, A., Bighamian, R., Bodner, J., Craven, B. A., & Niederer, S. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012289 · Cited by 62 · see citing works
Self-citationUse of Computational Modeling to Study Joint Degeneration: A Review
Mukherjee, S., Nazemi, M., Jonkers, I., & Geris, L. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00093 · Cited by 58 · see citing works
Peri‐operative considerations for sedation‐analgesia during cataract surgery: a narrative review
Kumar, C. M., Seet, E. C. P., Eke, T., Irwin, M. G., & Joshi, G. P. (2019) · Anaesthesia · doi:10.1111/anae.14845 · Cited by 49 · see citing works
Toward Good Simulation Practice
Viceconti, M., & Emili, L. (2024) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-48284-7 · Cited by 47 · see citing works
Santiago, A., Butakoff, C., Eguzkitza, B., Gray, R. A., May‐Newman, K., Pathmanathan, P., Vu, V., & Vázquez, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010141 · Cited by 36 · see citing works
Self-citationAdvances in automated anesthesia: a comprehensive review
Cai, X., Wang, X., Zhu, Y., Yao, Y., & Chen, J. (2025) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-024-00085-z · Cited by 40 · see citing works
Towards in silico Models of the Inflammatory Response in Bone Fracture Healing
Lafuente-Gracia, L., Borgiani, E., Nasello, G., & Geris, L. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.703725 · Cited by 32 · see citing works
Evaluation of Fluid Resuscitation Control Algorithms via a Hardware-in-the-Loop Test Bed
Mirinejad, H., Parvinian, B., Ricks, M., Zhang, Y., Weininger, S., Hahn, J., & Scully, C. G. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2915526 · Cited by 30 · see citing works
Self-citationTivay, A., Kramer, G. C., & Hahn, J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3103141 · Cited by 21 · see citing works
Physiological closed-loop control in critical care: opportunities for innovations
Hahn, J., & Inan, O. T. (2022) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ac6d36 · Cited by 21 · see citing works
Berti, F., Antonini, L., Poletti, G., Fiuza, C., Vaughan, T. J., Migliavacca, F., Petrini, L., & Pennati, G. (2021) · Frontiers in Medical Technology · doi:10.3389/fmedt.2021.702656 · Cited by 20 · see citing works
Arnal, J.-M., Katayama, S., & Howard, C. (2022) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000001012 · Cited by 19 · see citing works
Closed-Loop Control for Fluid Resuscitation: Recent Advances and Future Challenges
Gholami, B., Haddad, W. M., Bailey, J. M., & Muir, W. W. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.642440 · Cited by 16 · see citing works
Bighamian, R., Hahn, J., Kramer, G. C., & Scully, C. G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251001 · Cited by 11 · see citing works
Self-citationDevelopment and validation of a mathematical model of heart rate response to fluid perturbation
Kanal, V., Pathmanathan, P., Hahn, J.-O., Kramer, G., Scully, C., & Bighamian, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-25891-y · Cited by 10 · see citing works
Self-citationBranco, L. R. F., Ehteshami, A., Azgomi, H. F., & Faghih, R. T. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.747735 · Cited by 10 · see citing works
Advancing treatment of retinal disease through in silico trials
Hernandez, R. J., Roberts, P. A., & El‐Bouri, W. K. (2023) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/acc8a9 · Cited by 10 · see citing works
Arabidarrehdor, G., Tivay, A., Meador, C., Kramer, G. C., Hahn, J., & Salinas, J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3094515 · Cited by 9 · see citing works
Courcelles, E., Horner, M., Afshari, P., Kulesza, A. J., Curreli, C., Vaghi, C., Orcajo, E. M., Pappalardo, F., Maquer, G., Russo, G. L., et al. (2024) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-48284-7_4 · Cited by 7 · see citing works
Santiago, A., Butakoff, C., Eguzkitza, B., Gray, R. A., May‐Newman, K., Pathmanathan, P., Vu, V., & Vázquez, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.11.468169 · Cited by 7 · see citing works
Self-citationScuoppo, R., Catalano, C., Turgut, T., Götzen, N., Cannata, S., Gentile, G., Gandolfo, C. M., & Pasta, S. (2025) · APL Bioengineering · doi:10.1063/5.0280959 · Cited by 6 · see citing works
Parvinian, B., Bighamian, R., Scully, C. G., Hahn, J., & Pathmanathan, P. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.705222 · Cited by 4 · see citing works
Self-citationRobust closed-loop control of systemic oxygenation in acute lung injury
von Platen, P., Abdelsamed, A., Lohse, A., Russ, M., Wolters, C., Pickerodt, P. A., Francis, R. C. E., Leonhardt, S. K., & Walter, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105532 · Cited by 3 · see citing works
Gessa, F., Asare, P., Bray, A., Clipp, R. B., & Poler, S. M. (2019) · ACM SIGBED Review · doi:10.1145/3357495.3357499 · Cited by 3 · see citing works
Schmal, M., Haueisen, J., Männel, G., Rostalski, P., Kircher, M., Bluth, T., de Abreu, M. G., & Stender, B. (2020) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2020-3080 · Cited by 2 · see citing works
Personalised Fractional-Order Autotuner for the Maintenance Phase of Anaesthesia Using Sine-Tests
Mihai, M. D., Birs, I., Badau, N. E., Hegedüs, E. T., Ynineb, A. R., & Muresan, C. I. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9050317 · Cited by 2 · see citing works
Wanki, G., Ekwaro-Osire, S., Dias, J. P., & Cunha, A. (2020) · Journal of Verification Validation and Uncertainty Quantification · doi:10.1115/1.4048749 · Cited by 2 · see citing works
Restrepo, F. S., & Hernández, A. M. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09810-2 · Cited by 2 · see citing works
Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside
Rinehart, J. B., Coeckelenbergh, S., Srivastava, I., Cannesson, M. P., & Joosten, A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13071101 · Cited by 2 · see citing works
Cámara, Ó. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39074-7_35 · Cited by 2 · see citing works
Perspectives on Hybrid Control of the Anesthesia-Hemodynamic System in the Pandemic Context
Ghita, M., Neckebroek, M. M., Copoţ, D., Ghita, M., Visioli, A., & Ionescu, C. M. (2021) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med51440.2021.9480209 · Cited by 1 · see citing works
Development of a Virtual Patient Generator for Simulation of Vasopressor Resuscitation
Kao, Y.-M., Chalumuri, Y. R., Sampson, C. M., Shah, S. A., Salsbury, J. R., Tivay, A., Kinsky, M. P., Kramer, G. C., & Hahn, J. (2024) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4066394 · Cited by 1 · see citing works
Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
Li, Z., Liu, J., & Liang, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8202869 · Cited by 2 · see citing works
A Generative Approach to Testing the Performance of Physiological Control Algorithms
Tivay, A., Bighamian, R., Hahn, J., & Scully, C. G. (2024) · ASME Letters in Dynamic Systems and Control · doi:10.1115/1.4065934 · Cited by 1 · see citing works
Self-citationRecommendations for the Development of CFD Model to Assess the Thrombogenic Potential of Stents
Stiehm, M., Borowski, F., Oldenburg, J., Öner, A. Ö., & Schmitz, K. (2022) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2022-1133
Mulpuru, V., & Mishra, N. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815196986123010005
Kulesza, A. J., Loewe, A., Stenti, A., Nicolò, C., Orcajo, E. M., Courcelles, E., Sips, F. L. P., Pappalardo, F., Russo, G. L., Horner, M., et al. (2024) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-48284-7_3
FDA regulation of anesthesia and critical care medical devices
Parvinian, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-818381-6.00035-8
Self-citation
- Authors
- Parvinian, B., Pathmanathan, P., Daluwatte, C., Yaghouby, F., Gray, R. A., Weininger, S., Morrison, T. M., & Scully, C. G.
- Affiliations
- United States Food and Drug Administration (US)
- Corresponding author
- Parvinian, Bahram
- ORCID
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2019-03-26
- Volume
- 10
- Pages
- 220-220
- ISSN
- 1664-042X (ISSN-L)
- PMC
- PMC6445134
- OpenAlex
- W2922820107
- Cited by
- 44
- References
- 106
- Citations by year
- 2025: 3 · 2024: 7 · 2023: 4 · 2022: 8 · 2021: 9 · 2020: 8 · 2019: 5
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Physiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Energy
- U.S. Food and Drug Administration
- Oak Ridge Institute for Science and Education
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
- Keywords
- intensive care, model credibility, verification and validation, uncertainty quantification, hemodynamic stability, mechanical ventilation, anesthetic delivery, fluid resuscitation, medical device development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
