Invited speaker to the first IEEE-AMA Conference, found here:

http://ama-ieee.embs.org/overview/speakers/

I am into my 3rd day at a conference hosted by CONTINUA and CIMIT on medical device interoperability at the FDA. We are engaged in a 3 day communication, interaction, workshop on how to apply the existing 510(k), 513(g), IDE rules and processes to interoperable medical devices and those applications that fall into the domain of interoperability. This is a rather unique area as attributes that are affected are intended use, risks and hazard assessments. I am unsure as to outcome as of yet, but it is clear that a key purpose of the conference has been achieved: getting device manufacturers, EMR vendors, and the FDA in the same room to discuss the intended purpose. If nothing else, this is truly a feat.

So, the book continues… I have close to 40,000 words completed on my way to 92,000. I am running long, though, in terms of figures and words. Plus, I’ve included a lot of compute code that I’ve written to support the modeling practices and ideas throughout. All-in-all I am estimating a 300 page book. Target date is still June 30th, 2009. Publisher is Artech House.

Dear All–

Yes. It has been a while since I have posted anything. This is due to two reasons: first, I’ve been recovering from surgery. Second, I’ve been given a book contract to write a book on medical devices and modeling. My intention is to focus on the book for the next several months, as I do not have a ghost writer (I’m it), and making a living is necessary, as well. However, I will provide updates and details as I progress. As of this moment, chapter 1 is almost complete and I am about 4000 words along to a total of 92,000.

J. Zaleski

A web site covering medical applications related to Apple’s iPhone provides some interesting insight into little-published applications. An interesting set of statistics and use cases are illustrated related to appliance and brand loyalty from Flurry, with use of the device for medical applications in excess of 50%. A further illustration of the distribution of applications and uses is provided in the following figure, also from the Flurry blog.

Abstract

I present a concept for autonomic cardiac pacing as a method to augment existing physiological pacing for both ventricular assist devices (VAD) and heart transplantations. The following development represents a vision and reflects an area that has yet to be fully exploited in the field. Therefore, the analysis is meant to be a starting point for further study in this area. Furthermore, an automatic control system methodology for both heart rate and contractile force (stroke volume) of patients having either an artificial left ventricular assist device (LVAD) or who have experienced degenerative performance of the Sinoatrial node is suggested. The methodology is described both in terms of a device and associated operational framework, and is based on the use of the naturally-occurring hormones epinephrine, norepinephrine, and dopamine contained in the return blood flow through the superior vena cava. The quantities of these hormones measured in the blood stream are used to derive a proportional response in terms of contractile force and pacing of the Sinoatrial node. The method of control suggests features normally described using cyclic voltammetry, expert systems, and feedback to pacing an artificial assist device.

Nomenclature

AVN                 Atrioventricular Node

Ca                    Calcium

CO                   Cardiac Output

EPI                  Epinephrine

FDA                 Food and Drug Administration

K                     Potassium

LVAD               Left Ventricular Assist Device

Na                   Sodium

NE                   Norepinephrine

NHLBI National Heart, Lung, and Blood Institute

SAN                 Sinoatrial Node

SV                    Stroke Volume

TAH                 Total Artificial Heart

TCI                   Threshold Crossing Intervals

VAD                 Ventricular Assist Device

Background

Artificial assist devices that exist today normally operate by controlling heart rate based on Proprioceptors–position of limbs & muscles during physical activity, or Baroreceptors–monitoring blood pressure in major arteries & veins. None to date operate on the basis of Chemoreceptors: monitoring changes in chemical makeup of the blood stream in direct response to epinephrine production by the adrenal medulla. Such changes are, however, more consistent with the operation of the human heart. For instance, heart rate varies not only according to mechanical movement of limbs, but also as the result of changes in emotion. Such changes manifest themselves as increases and decreases in sympathetic and parasympathetic hormones. Sympathetic hormones (epinephrine, norepinephrine) tend to increase stroke work and heart rate, whereas parasympathetic hormones (acetylcholine) tend to lower heart rate. These two hormones operate to control the resting rate of the heart and its changes as a result of higher-brain center changes (including production of hormone by the adrenal medulla). Benefits of achieving this capability include more naturally-behaving artificial hearts, or, in the case in which a human heart is merely being paced by an assist device, to control normal heart function in relation to changes in hormone production. Thus, the controller described herein provides an adjunct to existing controllers. Specific methods outlining artificial control mechanisms for affecting heart rate and contractility have not specifically been described in the literature, although related concepts have been suggested [1].

Autonomic regulation of heart rate is controlled via one of the following specific systems within the body [2, 3, 4, 5, 6].

• Proprioceptors – monitor general movement—position of limbs & muscles during physical activity
• Chemoreceptors – monitor chemical changes in blood
• Baroreceptors – monitor blood pressure in major arteries & veins
• Chemical regulation (Hypoxia, Acidosis, Alkalosis)
• Hormones (Catecholamines & thyroid)
• Cations (balance of K⁺, Na⁺, Ca²⁺)

Current artificial heart assist devices operate using feedback from items (1) and (3) alone. Long-term (weeks to months) artificial heart assist devices popularly used for ventricular support, all under the auspices of NHLBI [7], include:

• Abiomed extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular, introduced in 1988, FDA approved for in-hospital use for low output syndrome.
• Thoratec extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular approved for in-hospital use for post cardiotomy low output and as a bridge to transplantation.
• TCI (Heart rate) implantable [8], [9], pulsatile, pneumatically-driven, approved for in-hospital use as a bridge to transplantation. The electrically powered totally implanted configuration with a wearable power source, transcutaneous power lead and vent, is approved for in-hospital as well as out-of-hospital use for bridging to transplantation and is currently used under IDE in the randomized REMATCH trial.
• Novacor [10] implantable electric, pulsatile, left ventricular (LV), wearable power source, transcutaneous power lead and vent, is approved for in-hospital and out-of-hospital use for bridge to transplantation.

Underlying Motivation for the Method

The suggested device measures the chemical content of the blood flow past the sinoatrial node, processes the chemical content via cyclic voltammetry [11, 12, 13, 14] to determine the amount of epinephrine (EPI) and norepinephrine (NE), and uses this information to establish the resting and various pacing heart rates of a left ventricular assist device. Furthermore, researchers [15] focusing on advances in heart assist devices have identified characteristics desired for upcoming artificial organs. These include (1) miniaturization, (2) interfaces with nerves for automatic control, (3) control systems that are acceptable for both the living body and the embedded artificial organs, and (4) harmonization with the living body in various ways, including interfaces with higher brain centers and reduction of thrombus (and the associated foreign body rejection issues).

In addition,

“the neuronal and hormonal control of the circulation, including the control of the heart, is mainly effectuated by the autonomic nervous system and its hormonal transmitters, the catecholamines. Autonomic control of the circulation primarily operates through the sympathetic system, though to a slight extent through parasympathetic signals to the heart. These have been lumped together, and there are basically three separate feedback mechanisms in this computational block. These are (1) feedback from the baroreceptor control system; (2) feedback from the peripheral chemoreceptors in the carotid and aortic bodies, and (3) feedback control of the circulator system caused by central nervous system ischemia, that is, ischemia of the vasomotor center in the brainstem. Another input that affects the autonomic nervous system is also included: The activation of the autonomic nervous system during exercise.” [16]

Methods for measuring serum epinephrine levels exist. WPI [17] suggests that sensitive, low noise carbon fiber (CF) and carbon disk (CD) electrodes can be employed in the electrochemical detection of catecholamines (e.g.: EPI, NE, Dopamine). As reported on their Web Site [18], the CF30-500 class of Carbon Fiber Disk Microelectrodes shows current output in Pico Amperes versus Dopamine concentration (nanograms/milliliter). In this analysis, WPI sites work by D. Yeomans and X. T. Wang of the University of Illinois. The analysis shows excellent linearity characteristics for CF filaments ranging in size from 10 to 30 microns to compounds with…detection limit[s] as low as 0.2 nanoMoles. Figure 1 illustrates this linear relationship [19].

Figure 1: Carbon Fiber Current Measurement versus Dopamine Concentration.

WPI reports [20] that “longer CF electrodes…provide higher sensitivity and larger signal to noise ration. They are hence very suitable for in vitro amperometric and differential pulse voltammetry (DPV) in which…voltage scan rates are much lower.”

WPI further suggests [21] “extracellular recordings using CD electrodes (CD-30) in CA1 region of the hippocampus in an anesthetized rat shows ultra-low noise (<5 microVolts).” Voltage response in same region varied between +50 and –100 microVolts with response time less than 5 milliseconds. This is important from the real-time implantable device perspective as changes in responses need to be measured rapidly in order to correctly mimic actual behavior. A suggested bio-sensing device based on those implemented by WPI and others [22, 23] is illustrated in Figure 2.

Figure 2: Author’s biosensing device based on technology suggested by WPI literature to measure current response to catecholamines using cyclic voltammetry.

While WPI stated [24] at the time of publication that there was no existing electrochemical method that exhibited selectivity among the members of the catecholamines or indolamines, others [25], reporting later, suggest that there may be ways to do this.

The Heart Conduction System

Now that the electrochemical mechanism has been suggested, let’s proceed with the automatic control methodology. Figure 3 indicates the hierarchy in which electrical signals and pacing are conducted throughout the human heart [26].

Figure 3: Major heart rate pacing centers.

The SAN, the primary pacemaker for the heart, is located in the rear wall of the right atrium near the opening of the superior vena cava. The SAN has the fastest rhythm producing, on average, from 60-70 action potentials per minute. SAN pacing overrides all others. The action potential originated within the SAN travels through walls of the atria causing contraction. Internodal pathways connect the SAN to the atrioventricular node (AVN). The AVN is the located in the right posterior portion of interatrial septum. In the absence of SAN pacing, the AVN can take over, although the resting rate is slower (40-60 action potentials per minute). Next, the AV Bundle of His divides into both right and left bundle branches in the ventricular septum and is the only electrical connection between atria & ventricles. The Bundle of His is capable of generating from 30-40 action potentials per minute. The Purkinje fibers (4) are distributed throughout the ventricular myocardium and synchronize ventricular contraction. Ventricular muscles can generate from 20-30 action potentials per minute.

The Mechanics of Catecholamine Measurement

The SAN is that which is affected by stimuli such as adrenaline, exercise, drugs, etc. As discussed, chemoreceptors that detect changes in catecholamine levels (EPI, NE, Dopamine) translate into changes in pacing and contractility. Studies of environmental stress on epinephrine levels in humans show rather discernable relationships between EPI levels and work-related stress [27]. Table 1 depicts the NE levels in male and female managers as a function of time in the workday [28].

Table 1: Norepinephrine Change Before & After Work.

Catecholamines affect the sinoatrial cells as illustrated within the simplified cartoon of Figure 4 [29]. The medullary cardiovascular control center in the brain contains both sympathetic and parasympathetic neurons that act as agonists to control contractility (ventricles), constriction (veins, arterioles), and control secretion of hormones to which the SAN responds. Carotid and aortic baroreceptors also respond to changes in blood pressure which provides feedback to the medullary control center that, in turn, affects contractility, pulse, and vasoconstriction.

Figure 4: Diagram depicting homeostatic control of pulse and contractility.

The sympathetic and parasympathetic hormones EPI and acetylcholine are secreted from the adrenal medulla and have the effect of causing increased and decreased pulse and contractility, respectively [30]. NE is a principal neurotransmitter in the sympathetic nervous system and is an a-adrenoceptor agonist [31], implying strong vasoconstrictor response, and, therefore, affects systolic and diastolic blood pressures as well as heart rate and contractility through b1-adrenoceptors [32]. Metabolism of EPI and acetylcholine at the cellular level is illustrated in the diagram of Figure 5. Relationships between stroke volume and contractile force are well known in the literature [33]. Thus, methods of measuring changes in catecholamine concentration must accommodate the combination of both pulsatile rate and contractility. The effects of sympathetic and parasympathetic nervous system on cardiovascular performance are known [34].

Figure 5: Metabolism method of Epinephrine and Acetylcholine at cellular level.

Changes in sympathetic and parasympathetic hormone affect the arteriolar smooth muscle fibers, the ventricular myocardium, and SAN to affect (as described previously) vasodilation, contractility, and heart rate, respectively.  Studies have quantified the effects of changes in mean plasma catecholamine levels both in vivo and in vitro using techniques such as cyclic voltammetry and blood concentration measurement [35, 36]. Heart pacing set by SAN is regulated by antagonistic mechanisms: primarily through sympathetic innervations (release of NE, EPI – increases rate) and parasympathetic innervations (release of acetylcholine—lowers rate). These innervations act to regulate and control mean arterial pressure through heart rate, stroke volume, and constriction and dilation of arterioles, arteries, and veins. Measuring the amount of EPI, NE, and dopamine in vitro has been performed [37]. One measurement approach is via cyclic voltammetry, details of which are available in the literature. Figure 6 depicts the author’s reconstruction of the relationship between carbon fiber peak anode current and Dopamine levels obtained via in vitro measurement using gold-tipped nanotube electrodes [38]. NE and EPI are released from chromaffin cells. Discrimination between NE and EPI from the same cell has been reported to be possible using slow-scan cyclic voltammetry [39]. Furthermore, the use of amperometry enables sub-millisecond release events to be measured [40]. The mechanism for EPI and NE measurement via chromaffin cell ordinarily involves contacting the cell surface via microelectrode. Furthermore, placing microelectrodes at a distance exceeding several microns can result in significant signal loss [41].

Figure 6: Carbon Fiber Anode Current versus Dopamine Concentration.

Suggested Controller Implementation

A methodology is suggested that provides an initial point-of-departure for refinement and iteration. The starting point depends upon the relationship between plasma concentrations of the sympathetic and parasympathetic hormones. Figure 7, Figure 8 and Figure 9 derive a rather simple relationship between HR and NE levels [42].

Figure 7: Heart rate variability with NE concentration, 13 patients prior to, during, and post sleep measurement of plasma NE levels.

Figure 8: Heart rate variability with EPI concentration, same sample as in previous figure.

Figure 9: Plasma NE versus EPI relationship.

The relationship is somewhat misleading in that the implication is that NE is causative with respect to HR. This is only partially true, Figure 9 illustrates with respect to EPI. Again, the relationship provides only a partial picture. Heart rate is only one component affected. It is worthwhile to point out again that these relationships are not necessarily causative. The association between NE and EPI plasma concentrations is shown in Figure 9. Analysis indicates a linear relationship exists in EPI the ranges specified, and the correlation appears to be quite good, suggesting a relatively simple predictive model. While HR relationship to hormone level suggests a linear dependency within the specified NE range, another study [43], Figure 10 shows a more nonlinear relationship between cardiac output and EPI. This relationship is suggestive of an optimum level of CO change with respect to EPI concentration in dogs. While not conclusive, this relationship serves to illustrate the point that a nonlinear relationship can exist that must be represented in the modeling of plasma hormone levels and the effect on contractility and pulse.

Figure 10: Relationship between cardiac output and EPI levels as a percentage from baseline between 10 & 240 minutes from start of infusion in dogs.

Model Training:

1. Determine the resting pulse and cardiac output of a patient, where pulse (/min) x SV (liters) = CO (liters/min).
2. Measure the blood plasma EPI, NE, acetylcholine, and dopamine levels. This establishes the baseline state of the patient.
3. Measure the patient’s pulse and CO and draw blood samples associated with the plasma levels of the hormones during specific activities, including vigorous exercise, sleeping & awakening. This establishes the training set of inputs (i.e., hormone levels) and outputs (i.e., pulse, CO).
4. Build the expert system training model that establishes the inclusive range on hormonal input versus output parameters.

Calibrate the voltammetric sensor (invasive component) for measuring real-time anode current versus hormonal concentration. Anode current levels correlate to different voltage samples using cyclic voltammetry. Peak anode currents vary according to hormone level.

Thus, the specific level of each hormone level would be identified on the basis of the sample voltage value. The training mechanism involved relies on taking known inputs (e.g.: catecholamine levels) and measuring outputs, then using these values to develop a training matrix that establishes the transformation between the input and output (e.g., pulse, stroke volume). So, very crudely, this might be represented as follows:

wherein the xform(training) matrix is determined based on the input and output. Note that this is not a single matrix and not this simplistic in representation: an array of inputs and matching outputs will need to be determined that will translate into classes of transformation matrices. Of course, the viability of this approach would need to be determined. Furthermore, the outputs would provide only one component of input determinant to cardiac behavior. The effects of vasoconstriction, for example, must also be accommodated in terms of its effect on arterial pressure and loading.

In Vivo Operations:

1. Hormonal concentration derived value from cyclic voltammetry defines the input parameters (test parameters) used as input to the feed-forward expert system trained using the training set developed above.

Output pulse and CO in terms of pacing trigger voltage to SAN defines the derived pulse and, thus, the appropriate rate for patient heart function based on catecholamine levels.

This new pacing relationship between input hormonal levels and output pacing can be maintained for a specific patient within a processing chip associated or in proximity to a pacemaker unit. The trained relationship then establishes the expected behavior for a cardiovascular pacing or left ventricular assist device. The equation relating pacing to hormone level can be stored in a secure electronic patient record (for instance) for recall, updated training, or for use in data mining to compare and develop more complex relationships with those of other patients. While this methodology does indeed require validation and refinement, it defines a vision for possible implementation. The very nonlinear relationships among the input and output variables cannot be simply represented using one-dimensional mathematical relationships. Furthermore, mechanical and physical issues remain that will be challenging. For example, biofouling of in vivo electrodes must be overcome and represents a formidable technological challenge [44].

Discussion

The preceding describes a rough and partially complete model based on lab research that is suggestive of baroceptor measurement of in vivo catecholamine levels. The motivation behind the approach is the lack of capability in current LVAD technologies that focus on this aspect of autonomic pacing. The methodology and concept would also apply in those cases in which patients may have damaged SANs.

As pointed out, detailed training issues related to generalization to any LVAD, validation of the range of catecholamine concentrations and impacts on pacing is that pulse and contractility are well behaved and do not pose a hazard to the patient, issues related to biofouling of sensors and calibration must be addressed. However, even before considering implementation, concept and technology proof-of-principle must be validated. This will require both human and non-human trials. Operationally, manufacturing and implementation challenges must be overcome.

References:

[1] Y.E. Earm, Y. Shimoni, A.J. Spindler, “A Pace-Maker-Like Current In The Sheep Atrium And Its Modulation By Catecholamines,” J. Physiology (1983), 342, 589-590.

[2] University of South Australia—online learning environment–www.unisanet.unisa.edu.au/Information/12925info/Lecture%20Presentation%20-%20The%20Heart.ppt

[3] I. Kestin, “Control of Heart Rate,” Physiology, 1993, Issue 3, Article 3.

[4] http://courses.washington.edu/conj/bess/spindle/proprioceptors.html

[5] http://en.wikipedia.org/wiki/Chemoreception

[6] http://medical-dictionary.thefreedictionary.com/baroreceptor

[7] NHLBI: “Expert Panel Review of the NHLBI Total Artificial Heart (TAH) Program: June 1998 – November 1999).

[8] CT Lewis et al., “The use of an implantable left ventricular assist device following irreversible ventricular fibrillation secondary to massive myocardial infarction,”  European Journal of Cardio-Thoracic Surgery, Vol 4, 54-56, Copyright 1990 by European Association of Cardio-thoracic Surgery.

[9] Todd J. Cohen, “A Theoretical Right Atrial Pressure Feedback Heat Rate Control System to Restore Physiologic Control to the Rate-limited Heart,” Pacing and Clinical Electrophysiology 7 (4), 671-677, July 1984.

[10] Worldheart Novacor LVAS http://www.worldheart.com/products/novacor_lvas.cfm

[11] http://en.wikipedia.org/wiki/Cyclic_voltammetry

[12] R. Mark Wightman, “Probing Cellular Chemistry in Biological Systems with Microelectrodes,” Science 17 March 2006: Vol. 311 no. 5767, pp. 1570-1574.

[13] Jinwoo Park, et al., “Diamond microelectrodes for use in biological environments,” Journal of Electroanalytical Chemistry, Volume 583, Issue 1, 1 September 2005, pp. 56-68.

[14] D. Bhaskarab, CR Freed, “Changes in arterial blood pressure lead to baroreceptor-mediated changes in norepinephrine and 5-hydroxyindoleacetic acid in rat nucleus tractus solitarius,” Pharmacology And Experimental Therapeutics, Volume 245, Issue 1, pp 356-262, 04/01/1988.

[15] 6th International Micromachine Symposium Special Lecture: “Artificial Heart Research by the Use of Micromachines.” Lecture by Sinichi Nitta, Vice President of Tohoku University and Professor of the Institute of Development for Aging and Cancer

[16] E. Naujokat, U. Kiencke,  “Neuronal and hormonal cardiac control processes in a model of the human circulatory system,” International Journal of Bioelectromagnetism, 2000, Volume 2, Number 2.

[17] “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording.”  World Precision Instruments, March 1998. http://www.wpiinc.com/products/biosensing/carbon-elec/CFM_AppNotes.pdf

[18] http://www.wpiinc.com/products/biosensing/carbon-elec

[19] WPI, “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording,” page 3, Figure 1.

[20] Ibid. page 4

[21] Ibid., page 8

[22] Xueji Zhang, et al., “An Integrated Nitric Oxide Sensor Based on Carbon Fiber Coated with Selective Membranes,” Electroanalysis 2000, 12, No. 14.

[23] Xueji Zhang, Mark Broderick, “Amperometric Detection of Nitric Oxide,” Mod. Asp. Immunibiol 1 (4), 160-165, 2000.

[24] WPI, “Carbon Fiber and Carbon Disk Microelectrodes for Electrochemical Analysis and Electrophysiological Recording,” page 10.

[25] Yi-Xin Sun, Sheng-Fu Weng, Xiu Hua Zhang, Yin-Fang Huang,” Simultaneous determination of epinephrine and ascorbic acid at the electrochemical sensor of triazole SAM modified gold electrode,” Sensors and Actuators B: Chemical, Volume 133, Issue 1, 17 January 2006, pages 156-161.

[26] Diagram Copyright Marquette Electronics, 1996.

[27] Ulf Lundberg, “Catecholamines and Environmental Stress,” Summary prepared for the Allostatic Load notebook. Last revised September, 2003. Author sites L. Forsman, “Individual and group differences in psychophysiological responses to stress-with emphasis on sympathetic-adrenal medullary and pituitary-adrenal cortical responses.” Doctoral Dissertation, Department of Psychology, Stockholm University, 1983.

[28] Adapted from U. Lundberg, M. Frankenhauser, “Stress and workload of men and women in high ranking positions,” Journal of Occupational Health Psychology, 4, 142-151, 1999.

[29] G. Monreal, Staff, Cardiothoracic Surgery, The Ohio State University,: MadSci Network: General Biology “How and why does caffeine affect the pulse rate of a person?”, Michael Onken, Washington University, February 2000,

[30] Vicki R. Kee, “Hemodynamic Pharmacology of Intravenous Vasopressors,” Critical Care Nurse, Vol. 23, No. 4, August 2003.

[31] “A drug that binds a receptor of a cell and triggers a response by the cell…Often mimics the action of a naturally occurring substance.” Source: MedicineNet.com

[32] Ibid.

[33] Jeff Isaacson,  “Mammalian Physiology 1”, Lecture 11, UC SanDiego, Lecture 11, Fall 2006. Source Text: Human Physiology, 4th Edition (2006).

[34] University of California at Berkeley lectures on cardiovascular system and heart, 2004.  http://mcb.berkeley.edu/courses/mcb136/topic/Muscle_Cardiovascular/SlideSet2/cardiac.pdf

[35] Christoph Dodt, Ulrike Breckling, Inge Derad, Horst Lorenz Fehm, Jan Born, “Plasma  Epinephrine Concentrations of Healthy Humans Associated with Nighttime Sleep and Morning Arousal,“ Hypertension 1997; 30:71-76.

[36] Spencer E. Hochstetler and R. Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill.

[37] Spencer E. Hochstetler and R. Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill.

[38] Y-H Yun et al., “A nanotube composite microelectrode for monitoring dopamine levels using cyclic voltammetry and differential pulse voltammetry,” Prec. IMechE Vol. 220 Part N: J. Nanoengineering and Nanosystems, 2007.

[39] Spencer E. Hochstetler and Mark Wightman, “Detection of Secretion with Electrochemical Methods,” Department of Chemistry, University of North Carolina at Chapel Hill, pages 13-20. Biophysics Textbook On-Line, Victor Bloomfield, editor, submitted February 18, 1998.

[40] Ibid., page 13

[41] Ibid., page 22.

[42] Christoph Dodt et al., “Plasma Epinephrine and Norepinephrine Concentrations of Healthy Humans Associated with Nightime Sleep and Morning Arousal,”  Hypertension. 1997;30:71-76.

[43] Michael B. Maron, “Dose-response relationship between plasma epinephrine concentration and alveolar liquid clearance in dogs,” J. Appl Physiol 85:1702-1707, 1998.

[44] Y-H Yun et al., “A nanotube composite microelectrode for monitoring dopamine levels using cyclic voltammetry and differential pulse voltammetry,” Proc. IMechE Vol. 220 Part N: J. Nanoengineering and Nanosystems, 2007.

Highly reliable, available, and performing health information software and systems may be noteworthy accomplishments in and of themselves. In a clinical environment at the point-of-care, reliability and availability are expected and assumed. These performance characteristics,  considered salient selling points in other industries, must be common characteristics in the healthcare environment to ensure clinical usability.

Inasmuch as performance and reliability are fundamental to any health information system in clinical practice, the mandate to “do no harm” must be observed. Developing and fielding a clinical information system involves risk and hazard assessment and mitigation as well as regulatory evaluation to establish the potential likelihood of adverse events. Hazard analysis ranks potential adverse events from the incredible to the likely, and the potential degree of harm that can be caused from the negligible to the catastrophic. The span of the hazard mitigation envelope in these two dimensions is represented by the risk assessment graphic shown in Fig. 1.

Figure 1: Hazard likelihood versus severity assessment for evaluating the potential impact of adverse events associated with medical hardware and software.

Green areas identify regions of acceptable risk, either from the perspective of incredibility of occurrence or negligibility of effect. Red areas identify intolerable regions. Yellow areas must be evaluated individually to assess their overall impact. As software continue to be developed and deployed throughout the healthcare enterprise it becomes exceedingly important to adhere to standards that ensure its safe use and application in patient care. Herein defines the context for techniques to mitigate data collection errors, and the associated clinical import.

In the areas of ambulatory and critical care monitoring error reduction is of key import. For instance, a typical task in medical / surgical wards is collecting patient vitals. The frequency with which vitals data (or findings) are collected in these wards is usually three times daily—once at each shift change—although, variability exists. These include blood pressure (systolic, diastolic), O₂ saturation, pulse, temperature, and respiratory rate.

These six parameters are collected during a single encounter between an allied health professional (e.g.: nurse, nurses aid) and a patient. The approach to data collection historically involved writing the findings in a patient chart. This manual process also carried with it the likelihood that errors would be introduced both in the transcription process and in positively identifying the patient from whom the findings were taken. Based on 6 parameters collected during one encounter the total number of vitals retrieved in a given year compute to:

[6 parameters / encounter] x

[3 encounters / day] x

[365 days / year] = 6,270 entries (or findings)

that are transcribed by allied health professionals during the course of a year.

Errors can occur during transcription in several ways:

• Recording findings in the wrong patient record;
• Incorrectly recording findings on the correct patient;
• Omitting or overlooking recorded values.

If even 1% of findings were entered incorrectly in a given year, that translates into ~60 errors, despite the use of an electronic medical record.

How might these errors be mitigated? The following techniques are being applied today to reduce the likelihood of or otherwise mitigate errors in the clinical environment.

Figure 2: Example RFID reader and patient wristband (Precision Dynamics Corporation DR1000 RFID Reader).

Barcode and RFID technologies have enjoyed more widespread use in the area of drug administration (i.e., medication administration checking and infusion pump drug validation) by verifying the identity of the patient via a UPC or RFID embedded patient bracelet—example shown in Fig. 2. Both methods provide a means of positive identity as the bracelets are attached during the in-patient registration process and are not removed until discharge. RFID technologies have the added benefit over barcode in that more information may be stored in the same physical space as well as added reliability: the UPC symbol is absent and, therefore, no risk of smudging can occur, and the RFID reader need not maintain line-of-sight with the UPC symbol in order to read the bracelet.

Both barcode and RFID tags are created using standard, off-the-shelf technology that can be linked directly to electronic medical record registration systems. Many RFID readers can actually read through flesh and bone.

The use of RFID and barcodes to verify patient identity is necessary to reduce identity errors that can be incurred at the bedside. However, in order to ensure that results and observations are posted correctly to the patient record it is necessary to go a step farther and provide a mechanism to link, or associate, patient identity directly with the findings collected on the patient. Findings can then be transmitted directly to the medical record, removing the process of patient selection and results transcription entirely. This is achieved by passing patient context (i.e., patient identifying information) from the barcode or RFID reader to the health information system. This context is then used to display patient records directly without need for selection from a patient census list. This methodology not only reduces the likelihood of selecting the wrong patient (effectively making the likelihood non-existent), but also provides the framework for enabling direct collection of patient information at the bedside. This leads to the next technique.

Once a mechanism is in place to positively identify a patient the next step involves collecting and validating vitals data. Patient demographic information taken from the barcode or RFID bracelet becomes a prerequisite to collecting patient findings from the bedside. Devices for bedside measurement of findings are readily available. These devices also provide data download mechanisms. Software applications can retrieve these findings automatically and present them to clinicians for validation. These applications also associate patient identity with the findings to ensure that they are routed to the correct patient. Both patient identifying information and findings can be transmitted directly to the medical record using a standardized language (such as Health Level 7). Thus, patient selection, findings collection, and storage within the electronic medical record are accomplished. The likelihood of incorrect patient identification or data entry is negated.

Automated data collection is only one component in the clinical monitoring process. Once collected, findings must be transmitted to the correct computerized patient record. The most reliable way to do this is by associating every finding with patient specific information—i.e., medical record number, account number, name, etc. This information can be taken from the clinical record automatically prior to transmitting findings. However, this in itself is not foolproof unless the allied health professional ensures that the correct clinical record has been selected. For example, a nurse or other allied health professional could unwittingly select the wrong patient from the census. Findings would then be associated with the wrong patient—a hazard with the potential to cause a patient serious harm. This example of human error can be addressed using workflow solutions at the bedside, such as barcode and RFID. For instance, the workflow could restrict the ability to collect findings until the patient was positively identified via the wrist bracelet.

Access to patient records must also be restricted to individuals in specific roles on a need-to-know basis. Applications will display patient information to allied health professionals out of necessity. Thus, user interfaces must feature time-outs and automatic logoff so that patient information does not remain visible should a clinician step away from the computer. Enforcement methods include RFID-enabled identification cards that must be inserted into a card reader and that, once removed, immediately lock the computer or cause automatic logoff. Network security using virtual private networks and encryption are also a necessary part of these solutions to ensure patient privacy, user security, and patient safety.

The case for automating vitals collection and employing positive identification techniques was presented. These are key techniques in mitigating medical errors. The approaches described have been applied clinically, are available commercially, and have been demonstrated to reduce errors during vitals collection and to decrease the time from when data are collected to when they are posted within the electronic medical record. Such methods will become more widespread in the future, and increasingly will involve integrating medical devices, medical software, and other technologies to improve medicine at the point of care.

HealthCare IT News recently reported the recent award of a $730k grant by the US Army Telemedicine and Advanced Technology Research Center (TATRC) to “pursue standardization of information integration technologies” [1].

The grant will be used to develop and further evolve the integrated clinical environment (ICE) of the Center for Integration of Medicine and Innovative Technology’s Medical Device Plug-and-Play (MD PnP) organization. There are a number of efforts focused on standardization of interfaces among medical devices and electronic health records. However, key to the ICE activity is the focus on interaction and interoperability among medical devices using a standardized application interface (API). The standard belying the ICE activity is a modified (enhanced) ISO/IEEE 11073 protocol. This protocol allows for near real-time interaction among devices.

The initial focus of the activity will be in the surgical (operating room) space. However, this is only the beginning. A benefit of standardized interoperability is improved safety and an increased quantity of common interface-capable medical devices. The activity with TATRC is being funded through the small business innovations research (SBIR) program.

This is the direction that the industry is taking and the ability to support seamless integration of medical devices with each other and with health information technology (e.g.: electronic health records) is necessary for future evolution of the field and industry. I urge those in the field who have not already done so to research and inform themselves of the possible workflow impacts and benefits that medical device connectivity and seamless integration with electronic media would have within the scope of existing healthcare infrastructure. Access to data is the first necessary step towards its effective use in clinical environments.

[1] “The ICEMAN cometh, and it’s plug-and-play,” http://www.healthcareitnews.com/news/iceman-cometh-and-its-plug-and-play accessed October 7th, 2009.

Here is a link back to an article I wrote on Tim Gee’s blog on how medical device connectivity can improve outcomes in a surgical intensive care unit (SICU).

In this article I walk through decision-making processes within SICU related to respiratory weaning. I illustrate the importance of medical device connectivity in acute care environments as a necessary adjunct and enabler for complete documentation and clinical decision making at the bedside.

Let me know what you think.

John R Zaleski, PhD, CPHIMS

In this article I wish to provide some comments and highlight the functional requirements associated with high-acuity medical device communication in relation to the American Health Information Community (AHIC) priorities on common device connectivity (CDC) to electronic health records (EHRs).

The US Department of Health and Human Services Office of the National Coordinator for Health Information Technology has chartered the development of a series of documents to represent American Health Information Community (AHIC) priorities for national health information activities. The 2009 Common Device Connectivity (CDC) Extension/Gap document, as requested by AHIC, was commissioned to address information transfer from “high-acuity and inpatient diagnostic/therapeutic medical devices…into electronic health records.”

As stated in the scope of the CDC AHIC Extension/Gap, section 2.2, Common device connectivity is

“…the means by which high-acuity and inpatient clinical device information such as settings, measurements, and monitoring values are communicate to and from [the electronic health record] and other specialized clinical information systems.”

Examples cited include vital signs monitors, mechanical ventilators, anesthesia, and infusion pumps. Radiological devices are explicitly excluded from consideration. As such, single- and multi-parameter data from such devices are assumed the primary sources of data for communication to EHRs and clinical information systems (CISs).

It is noted, as stated within section 1.2 of the CDC AHIC Extension/Gap document, that as of the publication date of this CDC document, available at

http://healthit.hhs.gov/portal/server.pt/gateway/PTARGS_0_10731_848117_0_0_18/ComDevFinalExtGap.pdf, the national health agenda

“…has not formally addressed the interoperability considerations for connectivity between medical devices and EHRs.”

Progress has been made through the spring and summer of this year via the Tiger Team efforts initiated earlier this year, with focus on the Remote Monitoring Use Case. The 2008 Remote Monitoring (RMON) Use Case highlights communication from ambulatory settings to EHR and PHR. The functional requirements associated with CDC related to communication and exchange of information between medical devices and the electronic health record are highlighted in section 3.0 of the subject document. As is pointed out in the preamble to section 3.0, it is implicit in these functional needs that what are described are key capabilities and not detailed, explicit functional requirements, representative of those expected in a mission, software, and interface requirements specification. Rather, the focus is on the high-level functional needs.

My approach is to list each of the explicit functional needs (as quoted directly from the document—in italics) and then provide my feedback on the potential implications of each.

3.0 Functional Needs

A. The ability to configure and register a device to communicate with an EHR or other system.

i. When a device is set up within an organization to communicate measurement information, the device is configured and registered within the organization’s electronic health record to uniquely identify the device and enable connectivity between the device and system.

Zaleski:

This is usually a manual process today. The ability to associate a device with a patient is normally managed through the clinical software. One method is through the manual assignment via the clinical information system, such as through the critical care flow sheet. A pick list may be shown in which a clinician assigns the available devices to patients. Other approaches to associating medical devices with patients are being evaluated by various interoperability vendors, including the automated association via barcode or radio frequency identification token through a common device token, similar to a serial number.

B. The ability to associate patient identification and device information with an EHR.

i. Patient registration, location, and identification information available within the EHR is uniquely associated with the patient’s monitoring device using standardized mechanisms for admission, transfer, and discharge from beds, units, wards, and entities within the facility.

Zaleski:

Many vital signs monitoring systems provide methods for associating monitors in individual patient rooms with patients within a flow sheet, in the format of a bed board. Patients are assigned by nursing upon admission to the unit. The key identifying information can include a medical record number and visit identifier. Upon discharge, patients are disassociated using the bed board mechanism again. This process, while manual, addresses the point identified above.

ii. In the event patient identification information is associated with a device in error, the device can be disassociated with the current patient within the EHR and associated with the correct patient.

Zaleski:

Some clinical information systems (CISs) today provide the capability to disassociate the medical device from the patient through a flow sheet user interface. Those CISs that provide the equivalent of “bed boards” whereby patients are manually assigned to monitors (in rooms) via this user interface is one mechanism by which this can be accomplished.

iii. A patient may be placed on a monitoring device prior to the completion of patient registration or the availability of patient identification information within the EHR, especially in emergent or critical situations. The measurement information is available in the EHR upon initiation of the monitoring function or medical device initiation, and can be reconciled with patient registration or patient identification information within the EHR when available. Data collected prior to patient registration should be buffered and retained for a reasonable period of time sufficient to complete the registration process.

Zaleski:

Again, certain CIS flow sheets support this, with association or linkage to patient done after admission to the unit. The HL7 admission, discharge and transfer (ADT) messages arriving from an existing master registration system to the unit can then be used to link the patient-specific identifying information to the vitals data measured from the bedside equipment. Once linked, the observations and measurements can be sent back to electronic health records.

iv. Organizational policies and procedures may require medical device measurement values within a patient’s record to be validated by a licensed clinician prior to being stored within a patient’s record. This function may prevent the charting of erroneous values within a patient’s permanent medical record.

Zaleski:

The validation step is key to ensure that the data are indeed a true and accurate representation of the measurements from the patient. Furthermore, context added to the measurements (for example, clinical notes or text) that establish conditions at the bedside that may impact or influence the measured observations are also critical and necessary to communicate to the electronic health record.

C. The ability to associate patient identification and device information with an EHR.

i. Measurement and device information generated by the medical device is communicated to the EHR. Measurement information such as device settings, parameters, values, and units may be utilized by the EHR and/or clinical decision support (CDS) systems to support patient management.

ii. The devices should communicate state, error conditions, and user selections to support the analysis of adverse events.

Zaleski:

This causes me to think about the work I conducted when I was at PENN in the early 90s, and why I became involved in the field of medical devices and clinical decision making in healthcare. Having a complete and accurate record of the settings, parameter values, error conditions, etc. is certainly important for documentation purposes. But, moreover, it is essential to the “art” of making clinical decisions in an advisory role to the bedside clinician. What also speaks to me here is the necessity to begin thinking in terms of real-time management and monitoring of data—through the electronic health record! Again, it is certainly necessary to have a complete and accurate record of information from the documentation perspective. However, we should bear in mind that manual recording of this information has been done for decades. Presumably, benefits in terms of reduced errors, improved quality, and interventional guidance can be offered to the clinician by monitoring and recording this information in a timely manner. It would seem to follow that as much of this information is available in real- or near real-time, it would be beneficial to the patient to record information in as high a frequency as possible and practicable. Furthermore, status and error information that are logged could be made available to biomedical and IT departments for servicing and quality control purposes.

D. The ability to support point-of-care integration to uniquely identify a device and related components, communicate device setting and detailed device information, associated with each measurement value, to the EHR.

i. When a patient device is replaced by another device of the same type, measurement information may seamlessly populate the EHR. The devices may be from different manufacturers, but communicate the same information to the EHR. The EHR recognizes the measurement parameters and is able to represent the measurement values consistently within the EHR. Device information, settings, and metadata specific to each device is associated with each measurement value and is accessible within the EHR. This is accomplished via a standards-based first communication link interface between the point-of-care device and the EHR, device intermediary, or device gateway.

ii. A patient placed on multiple monitoring and patient care devices that need to be associated with the patient within the EHR. When multiple devices are capturing the same measurement or monitoring parameter, the information available within the EHR enables clinicians to distinguish between the measurements and determine the measurements that are captured from each device.

iii. Device data should be uniquely associated with the device, the patient, and the date and time the data was acquired, sent, and received.

Zaleski:

Standards-based communication from instrument or device gateways is typically accomplished using an HL7 result transaction. While the specific segment syntax can vary depending on the peculiarities of the device and the manufacturers’ objectives, this is more often than not the case. When device gateways do not exist, then the form of communication can be rather proprietary. Those in the device community are engaged in a continuing dialog on how to address this situation. Yet, from a clinical perspective, if two devices are interchanged (measuring the same parameter), then it may be in the interest to note the change in device as variations in device sensitivity, behavior, and manufacturing may result in some slight variation or difference in reported output. Yet, such variation should be well within the range of clinical significance so as not to raise a question as to the veracity of the result. Furthermore, one poor practice I have seen is representing two of the same values as two separate entries in flow sheets. For example, oxygen saturation from two different SpO2 cuffs. If these represent the same value (and not, for example, SpO2 and SaO2), then the values shown multiple times or presented in parallel with one another can cause confusion. While certain measurements can vary depending upon where measured (left arm versus right arm blood pressure measurement), and the differential is indeed necessary for clinical decision making, diagnosis and treatment, care must be taken so as not to present redundant measurements before the eyes of the clinician that may in fact be the same in every respect except for name. This simply will serve to confuse.

E. The ability to communicate measurement intervals and device setting information within the EHR.

i. When a patient is placed on a medical device, the clinician’s order details may specify measurement intervals for patient information to be communicated to the EHR.

ii. Depending upon patient acuity and monitoring needs, measurement intervals may need to be modified during the course of care. A clinician may modify the measurement parameters and intervals via the EHR or by modifying the device directly. Measurement interval information is communicated from the device to the EHR so the clinician may access this information.

iii. Inbound device settings and controls from the EHR may be subject to clinical oversight, validation and verification at the point of care prior to execution on the instrument itself.

iv. Measurement intervals are reconciled against the system time available from the EHR to ensure consistent and accurate identification of time intervals in absolute time.

v. The communication of multiple interval types should be supported (e.g. episodic, regular, quasi-continuous, sampled waveform, continuous waveform).

Zaleski:

An example that is used in clinical practice is the ordering of initial support on mechanical ventilation upon admission to an intensive care unit (ICU) of a post-operative coronary bypass graft patient. For instance, upon admission, initial ventilator settings of intermittent mandatory ventilation (IMV) at 12 breaths per minute, with a forced inspired oxygen of 100% and a positive end expiratory pressure (PEEP) of 5 cmH2O might be ordered. Then, as the patient is weaned down, the order is changed over time.

F. The ability to query the device or device intermediary [Zaleski adds: I interpret this as the device gateway] for additional information captured by the device that may not have been communicated to the EHR.

i. A clinician may request certain intervals for viewing device measurements or information within the EHR. If a patient event occurs that requires further investigation, the clinician may utilize the EHR to query for additional retrospective device information or measurement details that were not initially communicated to the EHR based upon the data intervals set for the patient.

Zaleski:

Clinical information systems that support automated collection at the bedside typically display automated vital signs information in discrete intervals. These intervals can vary. Typical ranges are one set of parameters every several minutes to once an hour, with typical values being in the quarter-hour range (i.e., once every 15 minutes, or q15). Flow sheets and their supporting medical device interoperability software need to allow for more or less frequent collection of information. The challenge remains that unless medical devices at the point of care provide for local storage of their data (possibly through their intermediaries), there may be no possibility to recall retrospective data on a patient.

G. The ability to communicate device and measurement information to the EHR when there is a lapse in EHR connectivity.

i. If a break in network connectivity occurs, or other factors prevent device communication to the EHR, device and measurement information is communicated to the EHR when connectivity is restored. Upon establishing or re-establishing this connectivity, there is no loss of measurement information in the EHR. In addition, details associated with measurement or device settings are communicated with the appropriate timestamp and patient parameters (e.g., identification, device settings) present at the time of information capture at the device.

ii. A notification may be sent to the EHR notifying of the event in which data transmission or communications are lost between the EHR and medical device. This notification consists of a standard health and status message that confirms device connectivity and general operation.

Zaleski:

A necessary requirement and cannot be overemphasized. Quality of Service undergirds this. Assured delivery of medical device data must occur if we are to use such data for intervention. As manufacturers of medical devices evolve more towards a plug and play paradigm, perhaps analogous to USB 2.0, this will assist in achieving this requirement. What we are talking about here is intelligent connectivity: devices “know” to whom they were attached; their data are not lost in the event of inadvertent loss of connectivity; the data can be picked up from where it was lost upon reconnection. Some monitoring systems provide what is typically called a “full disclosure database” which, in many instances, can store up to 72 hours of moment-by-moment data on any given patient until that patient is discharged. However, this is done at a much higher frequency than is normally stored within electronic health records.

H. The ability to communicate standardized alarm types and alarm violation types to the EHR in near real-time.

i. If a medical device generates an alarm, the alarm information and details are communicated to the EHR in time to support clinician life support efforts and critical care activities. Both text-based and audible alarm information should be communicated. For example, when a clinician or patient modifies device settings such as patient-controlled analgesics that are out of range and generates an alarm, the alarm and associated device details are communicated to the EHR.

Zaleski:

If we expect episodic, regular, quasi-continuous, sampled waveforms, continuous waveforms to somehow be supported, then near real-time implies real-time to me. This presents an interesting quandary: if we are to communicate interventional information to the EHR, is it not implied that this information will, somehow, be used for interventional guidance? To me, this further implies a medical device, possibly requiring pre-market notification and substantial equivalent to existing monitoring systems and full-disclosure databases (Class-II regulatory implication).

I. The ability to set and communicate limits and safeguards for device settings from the EHR to a device.

i. Evidence-based guidelines or clinician preferences for device parameters or alarms may be communicated from the EHR or other systems to the device. For example, this would enable an infusion pump to be interrupted or paused based upon EHR information. Interrupts and pauses are not intended to be or imply closed loop control.

Zaleski:

Presumably, we’re talking about communicating notifications to clinicians who would then intervene and stop or adjust the device, since we’re not talking about closed loop control.

J. The ability to wirelessly communicate point of care device information from the device to a device intermediary or EHR.

i. Wireless communication of high-acuity and inpatient medical device information may require specifications for wireless networking that supports the critical nature of this information and can co-exist with other medical devices and wireless applications.

Zaleski:

Clearly key: high quality of service, secure, available, reliable wireless infrastructure. This is the subject of an entirely new discussion, and one that I will be bringing up in the future. From iPhones™ to BlackBerrys™, clinicians of the future will be relying on mobile device technology and will expect them to support their clinical workflow in ways we have not yet even considered.