A Business & Clinical Case for Continuous Surveillance Session 209, February 14, 2019 Leah Baron, MD, Anesthesiologist John Zaleski, PhD, CAP, CPHIMS, Chief Analytics Officer, Bernoulli Health 1
Conflict of Interest Leah Baron, MD Principal investigator and clinical lead of study. 2
Conflict of Interest John Zaleski, PhD Employed by Bernoulli Health developer and manufacturer of software & hardware for data capture employed in the study. 3
Agenda The Challenge Capnography for Continuous Monitoring The Pilot Alarm Fatigue IRB Study Findings Summary 4
Learning Objectives Identify the perioperative care challenges from technical, clinical, and workflow perspectives associated with monitoring and identifying patients either at-risk for or experiencing respiratory depression Analyze the use of continuous monitoring to detect potential signs of respiratory depression while differentiating non-actionable from actionable alarm signals, and understand the impact on clinical alarm fatigue Assess the creation and use of multivariate alarm signals using real-time data to generate more clinically actionable notifications to clinical staff Investigate the strategies, technologies and workflow processes required for continuous patient monitoring and remote patient surveillance in a general care floor setting Identify opportunities and challenges associated with scaling continuous patient monitoring and surveillance across the enterprise 5
Questions Please complete online session evaluation Leah Baron, MD Anesthesiologist 609-670-6000 www.linkedin.com/in/leah-baron-14b5b818 John Zaleski, PhD, CAP, CPHIMS Chief Analytics Officer, Bernoulli Health 484-319-7345 www.linkedin.com/in/johnzaleski 6
General Acknowledgement What follows and the studies conducted could only have been performed with the contributions of multiple individuals and departments within the health system. The names are many, and roles include nursing and nurses aids, telemetry technicians, respiratory therapy, physicians, information technology, biomedical/clinical engineering, & vendors 7
The Challenge Improving patient safety for post-operative, at-risk patients 8
The Challenge Improving post-operative patient safety Caring for increasingly complex, co-morbid patients Bariatrics, joint replacements, etc. Co-morbidities: apnea, COPD, diabetes, heart failure, etc. Improving clinical oversight of post-operative quality of care Reduce incidents of Opioid-Induced Respiratory Depression (OIRD) on general care floor Seeking ways to improve clinical oversight of at-risk patients Continuous surveillance of key parameters Notification paradigm to clinical stakeholders 9
Background Pain management in the post-operative general care environment 10
Recent Literature Clinical deterioration on the general hospital wards is common... all too often results in patients progressing to cardiopulmonary arrest, which carries significant morbidity and mortality Surgical patients may be prone to cardiopulmonary arrest due to their underlying diseases (especially conditions such as obstructive sleep apnea and cardiac disease) This is a major concern for anesthesia professionals who help determine if the patient is safe to go to an unmonitored floor or should request a monitored bed which may be a limited resource Source: B.D. Winters: Early Warning Systems: Found Dead in Bed Should be a Never Event https://www.apsf.org/article/early-warning-systems-found-dead-in-bed-should-be-a-never-event/ 11
Recent Literature the frequency of vital sign acquisition on the wards may be insufficient to allow detection of clinical deterioration Electronic health records (EHRs) do little to improve this situation as their performance is dependent on the intermittently and sometimes inaccurately collected data points Surveillance monitoring may be a better way to collect and act on clinical data for a patient who is deteriorating on a general ward vital-sign data collection should be continuous, since use of intermittently collected data may miss early signs of deterioration. Source: B.D. Winters: Early Warning Systems: Found Dead in Bed Should be a Never Event https://www.apsf.org/article/early-warning-systems-found-dead-in-bed-should-be-a-never-event/ 12
Carbon Dioxide Narcosis Up to 14% of patients using opioids suffer from respiratory depression. ¹ 50% of CODE BLUE events involve patients who recieve opioid analgesia ² Respiratory failure caused by acute hypercapnia can occur in a matter of minutes. ³ ¹Maddox, R. R., & Williams, C. K. (2012). Clinical experience with capnography monitoring for PCA patients. Anesthesia Patient Safety Foundation Newsletter, 1-7. ²Overdyk, F. J. (2010). Postoperative opioids remain a serious patient safety threat. Anesthesiology, 113(1), 259-260. ³Kaynar, A. M., & Pinsky, M. R. (2012). Respiratory Failure. Retrieved from http://emedicine.medscape.com/article/167981-overview 13
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APSF Newsletter, February 2018 Source: APSF Newsletter, February 2018 15
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Source: APSF Highlights 12 Perioperative Patient Safety Priorities For 2018 https://www.apsf.org/articl e/apsf-highlights-12- perioperative-patientsafety-priorities-for-2018/ 17
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Capnography for Continuous Monitoring (Virtua Project) Ventilation monitoring using capnography to detect immediate changes in respiratory issues 20
Patient Assessment for Patients Receiving Narcotics Sedation Level Use modified POSS Monitor Respiration Rate q ½ hour x 2 on initiation of therapy q 1 hour until stable as indicated by achievement of comfort q 4 hours once stable count for a full minute assess rhythm and depth of chest excursion assess for pauses in breathing pattern Oxygenation: Pulse oximetry Do not rely on pulse oximetry alone because pulse oximetry can suggest adequate oxygenation in patients who are actively experiencing respiratory depression, especially when supplemental oxygen is being used. Ventilation: Capnography Measures carbon dioxide in the exhaled breath Source: Preparing to Use Capnography: Pre-Capnography Class Nursing Education September, 2013 21
Difference Between Capnography & Pulse Oximetry Source: Preparing to Use Capnography: Pre-Capnography Class Nursing Education September, 2013 22
Source: Preparing to Use Capnography: Pre- Capnography Class Nursing Education September, 2013 23
Initial Rollout 2013 Employ capnography to detect opioid-related respiratory insufficiency in post-operative patients, using remote telemetry monitoring 24
Pilot Overview Description 2 hospital sites Memorial and Marlton 4NE, 4N 2 weeks- Mon- Fri (controlled environment) Patient association in the PACU, via bar-coding Transferred patients to floor Remote monitoring to tele room for ~ 24 hours Disassociation in Central Processing 25
Initial Pilot Rollout Target population for capnography: 60 YOA Significant: COPD Heart Failure Cardiomyopathy Emphysema O 2 dependence OSA presence Concomitant use of CNS depressant meds (benzodiazepines) Persistent hypoxemia Patients requiring escalating doses of PCA, bolus or basal Patients with known increased sensitivity to narcotics Patients with history of chronic high doses of narcotics Prolonged anesthesia times > 5 hours 26
STOP-BANG CRITERIA 27
Vital Signs Limit Thresholds Monitoring Alarm Signal Limit Settings Urgent Caution ETCO2 High 50 45 ETCO2 Low 29 35 FiCO2 High 8 2 RR High 24 21 RR Low 6 8-10 No Breath Sec 30 SpO2 High 100 100 SpO2 Low 91 95 PR High 110 100 PR Low 54 60 SAT Sec 25 IPI Low 3 5 28
Capnostream 20 Capnography Monitor Sampling Line Capnogram: Wave form Capnometer: Numeric measurement of End-tidal CO₂ IPI-Integrated Pulmonary Index: a single number that describes the patient s respiratory status awrr: Airway Respiratory Rate Oxygen Saturation Heart Rate Source: Preparing to Use Capnography: Pre-Capnography Class Nursing Education September, 2013 29
Tele-Tech Role: Workflow Decrease variation Assessment and impact 30
Pop-Up Notifications: Bed-Board View 31
Urgent Alert URGENT ALERT with SOUND 32
CO2 Pump Paused Pump Paused 33
Cautionary Alert CAUTION ALERT 34
Alarm Print For Tele Workflow 35
Data Continuously Measured CO2-EX (mmhg) 80 70 60 50 40 30 20 10 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 100 98 96 94 SPO2 (%) 92 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Time (minutes) 36
Data Continuously Measured RR (breath/min) 30 25 20 15 10 5 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 etco2 (mmhg) 80 70 60 50 40 30 20 10 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Time (minutes) 37
Significant Alarm Signals Signal threshold breaches for individual measurements became overwhelming RR (breath/min) 30 25 20 15 10 5 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 etco2 (mmhg) 80 70 60 50 40 30 20 10 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Time (minutes) 38
Some Improvement: Sustained Signal Delays 70 CO2-EX etco2 (mmhg) Raw data may have aberrations leading to false alarms these would cause notifications to be communicated when thresholds are exceeded. Running 30-sec average establishes trend over 5 measurements (each measurement 6 seconds apart) so that when a threshold is exceeded, is occurring on basis of multiple values more indicative of a real trend CO2-EX etco2 30 (mmhg) Sec Running 60 30-s running Ave ave 50 40 30 20 10 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 39 Time (minutes)
Pilot 2013 Findings Communication of monitoring alarms directly to telemetry technicians impractical Overwhelming quantities of non-actionable notifications Signals interleaved with artifact made identifying truly actionable events next to impossible Aside: No industry-standard alarm recommendations for adoption at the time Decision: explore the field of combinatorial machine alarms to help reduce non-actionable alarm load Tightly-controlled environment, with institutional oversight With assistance from research nurses to add a layer of safety to our patients 40
Alarm Fatigue Non-actionable notifications that have no clinically-relevant cause 41
ECRI Institute. Top 10 Health Technology Hazards Report for 2014. https://www.ecri.org/press/pages/2014-top-10-health- Technology-Hazards-Report.aspx. Published November 4, 2014. 10 February, 2018. Default machine-issued alarms are not very skillful in identifying adverse events. 350 cardiac alarms / patient / day (ICU) False Alarm Rate as high as 99%! 42
MIMIC II Database (Beth Israel Deaconess) 962 ICU patient sample; SpO2 alarm rate threshold simulated by applying range from 84%, 86%, 88%, 90%. SpO2 alarm rates vary by patient, time; Affected by alarm settings; Personalized settings may help reduce alarm fatigue. 43
IRB Study 2015 Evaluate the use of smart rules in the environment on a smaller cohort of patients & evaluate outcomes 44
Study Parameters Conducted with IRB oversight Limit to a specific hospital general medical surgical unit Obtain patient consent prior to surgery Identify patients particularly at-risk for respiratory depression 45
Study System Architecture 46
Alarm Notifications via VoIP Phone Capnostream Monitor Wireless Serial Bridge Sources: Supe et al. 2017; photos by author 47
Alarm Signals Based on Threshold Breaches 50 45 40 35 etco2 (mmhg) 30 25 20 15 threshold 10 5 0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Time (minutes) Non-consecutive etco2 measurements which exceed threshold 48
Limit Threshold Alarm Annunciations Were Significant Alarm Condition Frequency of Occurrence ALR-DISC-SPO2 8527 ALR-LO-BAT 8301 ALR-NO-BREATH 7065 ALR-LO-IPI 5153 ALR-LO-CO2EX 3065 ALR-FL-DISC-CO2 3051 ALR-PR-NF 1925 ALR-HI-RR 2173 ALR-CO2-PUMP-OFF 1768 ALR-LO-PR 1637 ALR-OFF-SPO2 1318 ALR-LO-RR 1116 ALR-LO-SPO2 873 ALR-CO2-CHK-CAL 331 FL-BLOCK 308 ALR-STBY-CO2 264 ALR-CO2-CHK-FLW 52 CO2-MLFNC 45 SPO2-MLFNC 37 ALR-HI-PR 9 ALR-HI-CO2EX 3 ALR-HI-SPO2 0 ALR-STBY-SPO2 0 Total 47021 49
Alarm Signals Based on Sustained Behavior (non-self-correcting) 50 45 40 35 etco2 (mmhg) 30 25 20 15 threshold 10 5 0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Time (minutes) Consecutive parameter measurements which exceed threshold 50
Combinatorial Parameter Alarm Annunciations Proved to be Most Effective 51
270 255 240 225 210 195 180 165 150 135 120 105 90 75 60 45 30 15 0 Example: Pulse versus time PR (beats/minute) 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 0 time after arrival from operating room (minutes) Urgent Caution Caution Urgent 52
270 255 240 225 210 195 180 165 150 135 120 105 90 75 60 45 30 15 0 Example: Oxygen Saturation v. Time SpO2 (%) 100 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 84 83 82 81 80 Caution Urgent time after arrival from operating room (minutes) 53
Example: Respirations & etco2 v. Time 54
0.4500 0.4000 0.3500 0.3000 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 0.1800 0.1600 0.1400 0.1200 0.1000 0.0800 0.0600 0.0400 0.0200 0.0000 SPO2 Density Function 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100 SpO2 (%) RR Density Function 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Respiratory Rate (breaths/minute) 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 0.3000 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 PR Density Function 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Pulse Rate (beats/minute) etco2 Density Function 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 4255 44 etco2 (mmhg)
Use of Measurements to Create Combination Alarm Signals: etco2 Hypocapnia Alarm Limit (CO2-EX < 15 mmhg) Instantaneous & sustained occurrences of CO2-EX < 15 mmhg 56
Use of Measurements to Create Combination Alarm Signals: Resp Bradypnea Alarm Limit (f R < 6 rpm) Instantaneous & sustained occurrences of f R < 6 rpm 57
Use of Measurements to Create Combination Alarm Signals: SpO2 Hypoxia Alarm Limit (SpO2 < 90%) Instantaneous & sustained occurrences of SpO2 < 90% 58
Single Combination Alarm f R rpm Possible instances of central or obstructive sleep apnea: etco2 = 0 & f R = 0, with valid measurements (i.e., no technical alarm thrown & cannula properly in place on patient) etco2 mm Hg Reference: J. Garah, O.E. Advi, I. Rosen, R. Shaoul, The value of Integrated Pulmonary Index (IPI) monitoring during endoscopies in children J Clin Monit Comput (2015) 29:773-778 13 individual alarms translate into 2 combined sustained alarms 59
Alarm Threshold Limits Considered Alarm level changes or policies to reduce alarms must be validated for clinical efficacy & safety 60
Correlations Among Machine-Reported Alarm Signals Low expired carbon dioxide (<15 mmhg) Low respirations (< 6 per minute) Low hemoglobin oxygen saturation (< 90%) Low etco2 Low etco2 1 Low Respirations Low Respirations 0.987 1 Low SpO2 Low SpO2 0.892 0.885 1 61
Combination Alarms per Study Combination of low respirations, oxygen saturation & end-tidal carbon dioxide were chiefly correlated with patients who required active interventions: truly apneic patients 62
3-Parameter Sensitivity & Specificity (Small Patient Sample) Limited data based on not identifying patients with condition due to cuffs off patient. No patient who truly required intervention was not correctly identified Measure Value Sensitivity 0.800 Specificity* 0.750 PPV 0.364 NPV* 0.955 Sensitivity = 100 TP TP+FN Specificity = 100 Sensitivity: probability or fraction indicating that condition is present among those having the condition. TN TN+FP Specificity: probability or fraction indicating a negative result in those not having the condition. Positive Predictive Value (PPV): What is the probability that a patient with a positive result truly has condition? Negative Predictive Value (NPV): What is the probability that a patient with a negative result truly does not have the condition? TP PPV = 100 TP + FP TN NPV = 100 TN + FN 63
The 6 Rights of OIRD Surveillance 1. Patients receiving parenteral opioids should be monitored continuously regardless of location in the hospital. 2. Minimal set of continuous data should include pulse, oxygen saturation, respirations and expiratory carbon dioxide. 3. Monitoring should be a team effort involving floor nursing, respiratory therapy and physicians, and all should be trained in the workflows and technologies associated with continuous ventilation monitoring. 4. Notifications for clinical alarms related to hypoventilation concurrent with hypoxia should be directed to nursing AND respiratory therapy. 5. Clinical or biomedical engineering should be part of the notification chain for machine-specific alarms and errors that indicate possibility of false readings. 6. When continuous surveillance is used, smart alarms using multiple parameters that identify hypoventilation together with declining oxygenation provide the best indicator of impending respiratory distress. 64
A Business & Clinical Case for Continuous Surveillance Thank you! jzaleski@bernoullihealth.com 65