Use of a decision support system improves the management of ...

J Clin Monit Comput (2014) 28:41–47 DOI 10.1007/s10877-013-9466-1

ORIGINAL RESEARCH

Use of a decision support system improves the management of hemodynamic and respiratory events in orthopedic patients under propofol sedation and spinal analgesia: a randomized trial Cedrick Zaouter • Mohamad Wehbe • Shantale Cyr • Joshua Morse • Riccardo Taddei Pierre A. Mathieu • Thomas M. Hemmerling

•

Received: 11 December 2012 / Accepted: 1 April 2013 / Published online: 30 April 2013 Ó Springer Science+Business Media New York 2013

Abstract Decision support systems (DSSs) have been successfully implemented into clinical practice offering clinical suggestions and treatment options with excellent results in various clinical settings. Although their results appeared promising, showing that DSSs can increase anesthesiologists’ vigilance and patient safety during surgery, DSSs have never been used before to help anesthesiologists in identifying critical events in patients under spinal analgesia with sedation. We have developed and clinically evaluated a DSS for this specific task. The DSS was developed with the ability to indicate respiratory and hemodynamic critical events via audio–visual alarms and give decisional aid. Critical respiratory events were defined as SpO2 \92 % and/or respiratory rate \8/min. Critical hemodynamic events were defined as mean arterial pressure (MAP) \60 mmHg and/or heart rate \40 bpm. The objective of this trial was to determine the duration to detect and treat these critical events with the help of the DSS (DSS Group) compared with a standard Control Group where the system was not in place. One hundred and

C. Zaouter R. Taddei Department of Anesthesiology, University of Pisa, Pisa, Italy M. Wehbe J. Morse Department of Experimental Surgery, McGill University, Montreal, QC, Canada S. Cyr T. M. Hemmerling (&) Department of Anesthesia, McGill University, Montreal, QC, Canada e-mail: [email protected]; [email protected] P. A. Mathieu T. M. Hemmerling Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada

fifty orthopedic patients undergoing spinal analgesia with propofol sedation were enrolled in this randomized control trial, 75 each group. All respiratory and hemodynamic critical events were detected in the DSS Group, while in the Control Group 26 % of the events were not detected. The delay to detect and treat critical events was significantly shorter (P \ 0.0001) in the DSS Group at 9.1 ± 3.6 s, whereas 27.5 ± 18.9 s were necessary to identify them in the Control Group. There were no significant differences in physiological parameters in the two groups during surgery. The number of critical events/h occurring and the duration of surgery were similar in both groups. The number of hypoxemia episodes was significantly less (P = 0.036) in the DSS group (0.7 ± 1.0 vs. 1.4 ± 2.2 for the Control Group). The DSS tested in this trial could help the clinician to detect and treat critical events more efficiently and in a shorter length of time. Keywords Hybrid sedation system Regional anesthesia Decision support system Propofol sedation

1 Introduction Sedation with continuous infusion of propofol is very popular during spinal analgesia [1]. Sedation has been reported to provide comfort to the patient and to reduce the incidence of abrupt movements during surgery [2]. Spinal analgesia is a very common procedure. The reason for the use of this technique resides in possible faster recovery and better outcome [3, 4]. However, spinal analgesia with sedation can trigger critical respiratory and hemodynamic impairments [5, 6], especially in the elderly and sick population of patients undergoing hip or knee arthroplasty [7]. It is important to increase patient safety when

123

42

anesthesiologists perform spinal analgesia with sedation. Patient safety depends on rapid detection and action upon occurrence of critical events. Decision support systems (DSSs) have been successfully implemented into clinical practice offering clinical suggestions with treatment options showing excellent results in terms of patient care improvements in various clinical settings. Studies show that DSSs can increase the anesthesiologists’ vigilance and patient safety during surgery [8, 9]. In anesthesia, DSSs have never been used before to help identify critical events with smart alarms and treatment options. We developed a DSS with such characteristics to help the anesthesiologist to detect and treat critical respiratory and hemodynamic impairments. This study aims to perform a clinical evaluation of the DSS. The objective was to determine the amount of critical events detected by the anesthesiologist and the length of time to detect and treat these critical events in patients undergoing hip or knee arthroplasty under spinal analgesia with propofol sedation, with and without a DSS. Our hypothesis was that, when using a DSS, the time to detect and treat critical events is significantly shorter than without a DSS. The primary outcome of the study was the time taken to respond to critical events. As secondary outcome, the number of critical events not detected was recorded.

2 Materials and methods The DSS is a part of a hybrid sedation system (HSS), an automated, closed-loop drug delivery system for sedation used in conjunction with spinal analgesia. A closed-loop system is composed of a control variable, actuator, and controller. For the HSS, these components were a BIS monitor (BIS Vista, Aspect Medical Systems, MA, USA), a vital signs monitor (CASMED 740, CAS Medical Systems Inc., Brandford, CT, USA), a standard syringe pump (Graseby 3400, Graseby Medical, UK), and a PC running the control algorithm, respectively. The software was also designed to provide an intuitive user interface based on a study previously conducted in our lab to identify the elements of an intuitive anesthesia interface [10]. Additionally, the software controlled the communication between the different components of the system using the RS-232 serial protocol. The control software is written in LabVIEW (National Instruments, TX, USA). It contains interactive graphics, charts and numerical elements. The user interface of the HSS is shown in Fig. 1. 2.1 Decision support system The DSS and smart alarm monitoring were integrated in the system to assist the anesthesiologists in rapidly processing

123

J Clin Monit Comput (2014) 28:41–47

the vast amount of information available from monitoring devices. The DSS is knowledge-driven, integrating information from multiple signals and their reactions to medication and modeling complex associations similar to experienced anesthesiologists’ reasoning. Since false positive alarms cannot be totally excluded, the alarm system indicates the cause of the alarm generated, allowing the anesthesiologist to check its validity [11]. The DSS integrates alarms for low heart rate (HR), low respiratory rate (RR), low MAP and low peripheral oxygen saturation (SpO2). These alarms are modal dialogs and are accompanied by a description of possible reasons for their occurrence and provide the anesthesiologist with several options pertaining to the affected vital sign. The flowchart in Fig. 2 describes the DSS. A HR alarm is produced whenever the patient’s HR is lower than 40 bpm and the time elapsed from the last HR alarm is more than 90 s. The alarm pop-up menu asks the anesthesiologist to either perform a manual check or give 0.6 mg of atropine. The MAP alarm is displayed whenever the patient’s MAP is less than 60 and the time elapsed from the last MAP alarm is more than 90 s. With this alarm, the anesthesiologist is asked to check the pressure cuff and redo the MAP measurement, insert an arterial line or order blood in case of active bleeding, give 5 mg of ephedrine in case of bradycardia, or give 40 lg of phenylephrine in case of normal HR. The RR alarm has two versions, a basic alarm that pops up whenever the RR is below 8 and the time from the last event is more than 90 s, and an advanced alarm (with more options) that pops up whenever more than 4 basic events have occurred during a period of 15 min. The basic alarm asks the anesthesiologist to either perform a manual check or decrease the propofol dose. If the anesthesiologist presses the ‘‘Decrease propofol dose’’ button, the system will reduce the propofol dose by 10 %. In addition to options provided in the basic alarm, the advanced alarm asks the anesthesiologist to either do a jaw thrust and chin lift or place a nasopharyngeal airway. Similar to the RR alarm, the SpO2 alarm also has two versions, a basic one that pops up whenever the peripheral saturation is below 92 % and the time from the last event is more than 90 s, and an advanced alarm that pops up whenever more than 4 basic events have occurred during a period of 15 min. The basic alarm asks the anesthesiologist to perform a manual check, increase the oxygen flow, or decrease the propofol dose. In addition to options provided in the basic alarm, the advanced alarm asks the anesthesiologist to either do a jaw thrust and chin lift or place a nasopharyngeal airway. In case the event detection was a false positive, there is an option on all the smart alarm pop ups to let the system know that the detection was a false positive. An example


43

Fig. 1 Graphical user interface of the hybrid sedation system

Fig. 2 Flowchart illustrating the decision support system. RR respiratory rate, HR heart rate, MAP mean arterial pressure

123

44

Fig. 3 Pop-up alarm for a low oxygen saturation critical event

pop-up menu, appearing on the interface when a critical event occurred, is depicted in Fig. 3. 2.2 Clinical study In this randomized control trial, patients enrolled were the same as another study conducted by the same authors, data not published yet. The rational to write a second paper with the data obtained from the same study patients lies in the fact that the results, acquired from the DSS, need to be highlighted in an effort to outline its clinical relevance in anesthesia practice. The study received approval from the local Institutional Ethics Committee (McGill University Health Center, Montreal General Hospital, Montreal, Quebec, Canada). The investigation was conducted in patients requiring spinal analgesia for elective lower-limb surgery with propofol sedation. The study was registered on clinicaltrials.gov (NCT: 01020643). Inclusion criteria were patients aged from 18 to 85 years. Patients who had previous cranial neurosurgery, neurologic disease, pregnant women and those who were allergic to propofol or unable to provide informed consent were excluded. On the day of surgery, patients who consented to participate in the study were randomly assigned, by a research fellow, to one of two groups using a computer-generated, permuted-block randomization. In the first group, the Decision Support System Group (DSS group), anesthesiologists were able to use the DSS to monitor the occurrence of respiratory or hemodynamic impairments. In this group, anesthesiologists were advised to look at the pop-up menus whenever they occurred and act upon their decision aids. In order to design the DSS in a way that it relates to clinically relevant respiratory and hemodynamic events, our DSS design process respected scientific and clinical guidelines. The design of the pop-up menus, with the possible treatment

123


options, was presented to all members of our department during a dedicated Grand Rounds to modify the system according to standard practice. In the second group, the Control Group, the anesthesiologists followed their standard practice to identify critical events without the help of the DSS. Both groups used the same definition of critical respiratory and hemodynamic events. When these events occurred, predefined clinical interventions were applied in the Control Group (e.g., for respiratory events, increase oxygen flow, perform manual check, thrust jaw, or decrease the propofol dose). In addition, the anesthesiologist in the Control Group verbally communicated critical event detection and types of treatment to a research fellow assigned for this purpose. The duration of time it took to notice the critical events was recorded. In both groups, the DSS automatically recorded all critical events that were not detected by the attending anesthesiologist. The notification popup window for these events disappeared automatically when they were solved. They were also recorded in a database and marked as undetected for later data analysis. Upon arrival in the operating room, standard monitors (i.e., ECG, non-invasive arterial pressure, BIS monitoring, pulse oxymetry, and CO2 monitoring) were applied in both groups using the monitor CASMED 740 (CAS Medical Systems Inc., Branford, CT, USA). All investigators were familiar with this monitor. Anthropometric data were recorded. Respiratory rate, SpO2, HR, MAP and other vital signs were automatically recorded every 5 s. In both groups, patients received spinal analgesia according to standard practice, with propofol sedation offered to maintain a BIS target of 65. Sedation with propofol was delivered manually in the Control Group or automatically in the DSS group. 2.2.1 Clinical outcomes For the purpose of the clinical evaluation of the DSS, safety was defined as a reduction in the number of critical events detected and a reduction in the time it took to detect and, if necessary, treat them. 2.2.2 Statistical analysis Based on preliminary data that had shown 3 ± 2 critical respiratory alarms per hour which (SpO2 \ 92 % and RR\ 8/min) were detected on average at 50 (±40) s without the support of a DSS, we hypothesize that this time could be reduced using a DSS by 50 %, which is considered clinically significant. Considering an alpha error of 0.05 and a power of 0.8, the sample size for clinical evaluation was determined as n = 21 per group. However, 150 patients were enrolled to meet the sample size of another study objective not presented in this article. Parameters between the two groups were compared using a t test for continuous data and the Chi-


45

square test for categorical data. Data are presented as mean ± standard deviation. A P \ 0.05 was considered statistically significant. Statistical tests were performed using XLSTAT (Addinsoft, New York, NY, USA).

3 Results A total of 164 patients undergoing elective, orthopedic, lower-limb surgery were interviewed. Of these, 8 patients were excluded because they did not meet the inclusion criteria. Six patients were excluded prior to randomization for technical problems, leaving 150 for randomization in two equal groups. Patients’ anthropometric data including age, sex, weight and American Society of Anesthesiologists physical status (ASA) were similar between both groups. Patients’ characteristics are shown in Table 1. Patients were exposed to critical events during the surgical procedure for 119.1 ± 37.9 min for the DSS Group and 127.8 ± 40.5 min for the Control Group. Additional critical events, solely noted by the attending anesthesiologist, but not the DSS, did not occur. The details of the

Table 1 Patient characteristics DSS group (n = 75)

Control group (n = 75)

P value

Age (years)

64 ± 27

68 ± 12

NS

Sex ratio (M/F)

35/40

33/42

NS

Weight (kg)

79 ± 16

80 ± 15

NS

ASA (1/2/3)

23/27/25

35/27/13

NS

Data are presented as mean ± standard deviation DSS decision support system, NS not significant, ASA American Society of Anesthesiologists physical status P values \ 0.05 were considered as significant t test for continuous data; Chi-square test for categorical data

occurrences of critical alarms, and the % of false positive alarms are presented in Table 2. The delay to detect and treat critical events was significantly shorter (P \ 0.0001) in the DSS Group at 9.1 ± 3.6 s than in the Control Group (27.5 ± 18.9 s). The numbers of each type of critical event occurring per hour were similar for both groups, except for the incidence of oxygen desaturation which was significantly lower in the DSS group (P = 0.036). Table 3 shows the median propofol dose and propofol change per hour for both groups.

4 Discussion and conclusion The present investigation demonstrates that the implementation of a DSS, integrating smart alarms with treatment options, is useful to help anesthesiologists to detect more critical events and treat them earlier during surgery. Medical errors and iatrogenic complications are not uncommon. In the USA, their incidence is higher than road traffic accidents or breast cancer [12]. In addition, the societal cost of these events is significant [13]. A recent report indicates that, in Canada only, medical errors are one of the leading causes of death [14]. In an effort to reduce medical errors, DSSs have been implemented in the medical practice. In fact, a recent review analyzing 70 studies indicates that clinical practice was improved in 68 % of cases when a DSS was integrated into clinicians’ work. In anesthesia, reduction of medical errors can be achieved by reducing human errors [15, 16]. The main source of human error in anesthesia is the high number of variables to monitor, up to 100 parameters when the human brain can process no more than 4 or 5 variables at the same time [17]. Thus, a DSS specifically designed to help clinicians monitor vital signs with smart alarms, providing clinical suggestions, has the potential to increase patient safety and reduce human error.

Table 2 Decision support system (DSS) outcomes

Not detected alarms (%) Detection delay (s)

DSS group (n = 75)


P value

0.8 ± 3.4 9.1 ± 3.6

26.5 ± 19.7 27.5 ± 18.9

\0.0001* \0.0001*

Low SpO2 (alarms/h)

0.7 ± 1.0

1.4 ± 2.2

0.036*

Low RR (alarms/h)

3.0 ± 3.0

3.0 ± 3.6

NS

Low MAP (alarms/h)

4.7 ± 6.4

3.5 ± 3.6

NS

Low HR (alarms/h)

0.3 ± 0.7

0.4 ± 0.4

NS

False positive alarms (%)

25

19

NS

Data are presented as mean ± standard deviation NS not significant, RR respiratory rate, MAP mean arterial pressure, HR heart rate * P \0.05 (2-tailed), t test for continuous data

123

46


Table 3 Hypnotic management of propofol DSS group (n = 75)


P value

Median propofol dose (mg)

87.1 ± 38.2

68.7 ± 22.3

0.001*

Dose change/h

22.2 ± 4.6

4.7 ± 2.9

\ 0.0001*

Data are presented as mean ± standard deviation * P \ 0.05 (2-tailed), t test for continuous data

A recent study has indicated that 70 % of computerrelated medical incidents are caused by a delay in initiating a clinical procedure [18]. In order to prevent such delays from happening, the interface of the DSS was designed with accompanying audio and visual alerts. Furthermore, the alerts and decision support could help anesthesiologists to maintain high levels of performance, even after extended periods without sleep: a study of decision aid systems found that providing automated support can help physicians maintain a high level of performance in states of sleep loss [19]. An intuitive interface can reduce errors, lower the time it takes to perform common tasks, and have a measurable improvement in perceived workloads for anesthesiologists [20]. For this reason, the DSS was designed according to intuitive anesthetic interface guidelines [10]. However, the present study has some limitations. It does not determine whether the treatment options offered by the DSS increase patient safety. Further clinical evaluations, where the primary outcome will be to determine the efficacy of the suggestions, could assess the effect of the DSS on patient safety. To address this objective, the study should be specifically designed for non-anesthesiologist staff members (e.g., respiratory technicians or anesthesiologist registered nurses) who are legally allowed to administer propofol. Another limitation is due to the possibility of false negative alarms. A false negative event could be defined as an event determined neither by the research fellow nor the DSS. A third limitation is that the effectiveness of the alerts and interface was not assessed: a usability analysis could be formally conducted in order to evaluate and improve the interface [21]. Finally, the results cannot be generalized to all patients receiving regional anesthesia since the present investigation was performed only in patients under spinal analgesia. In conclusion, we present, to the best of our knowledge, the first DSS detecting critical events, providing clinical suggestions and treatment options. The DSS tested showed that it can help the attending anesthesiologist to detect critical events and treat them more promptly compared to standard practice. Acknowledgments The authors would like to acknowledge The Natural Sciences and Engineering Research Council (NSERC) for their financial support. Conflict of interest of interest.

123

The authors declare that they have no conflict

References 1. Ho¨hener D, Blumenthal S, Borgeat A. Sedation and regional anaesthesia in the adult patient. Br J Anaesth 2008;100(1):8–16. 2. Miller RD. Miller’s anesthesia, 7 edn, vol 1. Churchill Livingstone: Elsevier; 2009. 3. Rodgers A, Walker N, Schug S, McKee A, Kehlet H, Van Zundert A, Sage D, Futter M, Saville G, Clark T, et al. Reduction of postoperative mortality and morbidity with epidural or spinal anaesthesia: results from overview of randomised trials. BMJ 2000;321(7275):1493. 4. Todd MM, Brown DL. Regional anesthesia and postoperative pain management: long-term benefits from a short-term intervention. Anesthesiology 1999;91(1):1–2. 5. Smith I, Monk TG, White PF, Ding Y. Propofol infusion during regional anesthesia sedative, amnestic, and anxiolytic properties. Anesth Analg 1994;79(2):313–19. 6. Biebuyck JF, Smith I, White PF, Nathanson M, Gouldson R. Propofol: an update on its clinical use. Anesthesiology 1994; 81(4):1005–43. 7. Kurtz S, Mowat F, Ong K, Chan N, Lau E, Halpern M. Prevalence of primary and revision total hip and knee arthroplasty in the united states from 1990 through 2002. J Bone Joint Surg 2005;87(7):1487–97. 8. de Graaf PM, van den Eijkel GC, Vullings HJ, de Mol BA. A decision-driven design of a decision support system in anesthesia. Artif Intell Med 1997;11(2):141–53. 9. Hemmerling TM. Decision support systems in anesthesia, emergency medicine and intensive care medicine. In Jao, CS, editor. Efficient decision support systems—practice and challenges in biomedical related domain. InTech: Rijeka, Croatia; 2011. p. 239–62. 10. Charabati S, Bracco D, Mathieu PA, Hemmerling TM. Comparison of four different display designs of a novel anaesthetic monitoring system, the integrated monitor of anaesthesia (IMA). Br J Anaesth 2009;103(5):670–7. 11. Schecke TH, Rau G, Popp HJ, Kasmacher H, Kalff G, Zimmermann HJ. A knowledge-based approach to intelligent alarms in anesthesia. Eng Med Biol Mag IEEE. 1991;10(4):38–44. 12. Kohn LT, Corrigan J, Donaldson MS. To err is human: building a safer health system, volume 6. Washington, D.C.: National Academies Press; 2000. 13. Vincent C, Neale G, Woloshynowych M. Adverse events in british hospitals: preliminary retrospective record review. BMJ 2001;322(7285):517–9. 14. Baker GR, Norton PG, Flintoft V, Blais R, Brown A, Cox J, Etchells E, Ghali WA, He´bert P, Majumdar SR, et al. The canadian adverse events study: the incidence of adverse events among hospital patients in Canada. Can Med Assoc J 2004; 170(11):1678–86. 15. Reason J. Human error: models and management. BMJ 2000; 320(7237):768–70. 16. Helmreich RL On error management: lessons from aviation. Br Med J. 2000;320(7237):781. 17. Halford GS, Baker R, McCredden JE, Bain JD. How many variables can humans process?. Psychol Sci. 2005;16(1):70–6.

J Clin Monit Comput (2014) 28:41–47 18. Magrabi F, Ong MS, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc. 2010;17(6):663–70. 19. Reichenbach J, Onnasch L, Manzey D. Human performance consequences of automated decision aids in states of sleep loss. Hum Fact J Hum Fact Ergon Soc. 2011;53(6):717–28. 20. Syroid N, Liu D, Albert R, March JA, Egan TD, Pace NL, Johnson KB, Dowdle MR, Pulsipher D, Westenskow DR.

47 Graphical user interface simplifies infusion pump programming and enhances the ability to detect pump-related faults. Anesth Analg. 2012;115(5):1087–97. 21. Wanderer JP, Rao AV, Rothwell SH, Ehrenfeld JM. Comparing two anesthesia information management system user interfaces: a usability evaluation. Can J Anesth. 2012;59(11):1023–31.

123