Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32726
Evaluation of the Analytic for Hemodynamic Instability as A Prediction Tool for Early Identification of Patient Deterioration

Authors: Bryce Benson, Sooin Lee, Ashwin Belle


Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: Clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 319


[1] J. Van Den Bos, K. Rustagi, T. Gray, M. Halford, E. Ziemkiewicz, and J. Shreve, “The $17.1 billion problem: the annual cost of measurable medical errors,” Health Affairs, vol. 30, no. 4, pp. 596–603, 2011.
[2] R. Schwendimann, C. Blatter, S. Dhaini, M. Simon, and D. Ausserhofer, “The occurrence, types, consequences and preventability of in-hospital adverse events–a scoping review,” BMC health services research, vol. 18, no. 1, pp. 1–13, 2018.
[3] J. Kause, G. Smith, D. Prytherch, M. Parr, A. Flabouris, and K. Hillman, “A comparison of Antecedents to Cardiac Arrests, Deaths and EMergency Intensive care Admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study,” Resuscitation, vol. 62, no. 3, pp. 275–282, Sep. 2004, doi: 10.1016/j.resuscitation.2004.05.016.
[4] M. J. Johnston et al., “A systematic review to identify the factors that affect failure to rescue and escalation of care in surgery,” Surgery, vol. 157, no. 4, pp. 752–763, Apr. 2015, doi: 10.1016/j.surg.2014.10.017.
[5] F. Bonanno, “Clinical pathology of the shock syndromes,” Journal of Emergencies, Trauma, and Shock, vol. 4, no. 2, p. 233, 2011, doi: 10.4103/0974-2700.82211.
[6] J. K. Rhim et al., “Prediction of prolonged hemodynamic instability during carotid angioplasty and stenting,” Neurointervention, vol. 11, no. 2, p. 120, 2016.
[7] H. Brown, J. Terrence, P. Vasquez, D. W. Bates, and E. Zimlichman, “Continuous Monitoring in an Inpatient Medical-Surgical Unit: A Controlled Clinical Trial,” The American Journal of Medicine, vol. 127, no. 3, pp. 226–232, Mar. 2014, doi: 10.1016/j.amjmed.2013.12.004.
[8] W. L. Chua, S. Mackey, E. K. C. Ng, and S. Y. Liaw, “Front line nurses’ experiences with deteriorating ward patients: a qualitative study,” International nursing review, vol. 60, no. 4, pp. 501–509, 2013.
[9] W. Mok, W. Wang, S. Cooper, E. N. K. Ang, and S. Y. Liaw, “Attitudes towards vital signs monitoring in the detection of clinical deterioration: scale development and survey of ward nurses,” International Journal for Quality in Health Care, vol. 27, no. 3, pp. 207–213, 2015.
[10] J. Hogan, “Why don’t nurses monitor the respiratory rates of patients?,” British Journal of nursing, vol. 15, no. 9, pp. 489–492, 2006.
[11] L. Clifton, D. A. Clifton, M. A. Pimentel, P. J. Watkinson, and L. Tarassenko, “Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors,” IEEE journal of biomedical and health informatics, vol. 18, no. 3, pp. 722–730, 2013.
[12] O. C. Redfern, P. Griffiths, A. Maruotti, A. R. Saucedo, and G. B. Smith, “The association between nurse staffing levels and the timeliness of vital signs monitoring: a retrospective observational study in the UK,” BMJ open, vol. 9, no. 9, p. e032157, 2019.
[13] A. Goldfain, B. Smith, S. Arabandi, M. Brochhausen, and W. R. Hogan, “Vital sign ontology,” 2011.
[14] S. Grant and K. Crimmons, “Limitations of track and trigger systems and the National Early Warning Score. Part 2: sensitivity versus specificity,” British Journal of Nursing, vol. 27, no. 12, pp. 705–710, 2018.
[15] C. L. Downey, W. Tahir, R. Randell, J. M. Brown, and D. G. Jayne, “Strengths and limitations of early warning scores: a systematic review and narrative synthesis,” International Journal of Nursing Studies, vol. 76, pp. 106–119, 2017, doi:
[16] E. Koch, S. Lovett, T. Nghiem, R. A. Riggs, and M. A. Rech, “Shock index in the emergency department: utility and limitations,” Open Access Emergency Medicine: OAEM, vol. 11, p. 179, 2019.
[17] A. Belle, B. Benson, M. Salamango, F. Islim, R. Daniels, and K. Ward, “A Continuous Real-Time Analytic for Predicting Instability in Acute Care Rapid Response Team Activations,” International Journal of Medical and Health Sciences, vol. 14, no. 11, pp. 380–387, 2020.
[18] D. L. Reich et al., “Predictors of hypotension after induction of general anesthesia,” Anesthesia & Analgesia, vol. 101, no. 3, pp. 622–628, 2005.
[19] S. T. Morozowich and H. Ramakrishna, “Pharmacologic agents for acute hemodynamic instability: recent advances in the management of perioperative shock- a systematic review,” Ann Card Anaesth, vol. 18, no. 4, pp. 543–554, Dec. 2015, doi: 10.4103/0971-9784.166464.
[20] R. M. Schein, N. Hazday, M. Pena, B. H. Ruben, and C. L. Sprung, “Clinical antecedents to in-hospital cardiopulmonary arrest,” Chest, vol. 98, no. 6, pp. 1388–1392, Dec. 1990, doi: 10.1378/chest.98.6.1388.
[21] U. S. Bhalala et al., “Antecedent bradycardia and in-hospital cardiopulmonary arrest mortality in telemetry-monitored patients outside the ICU,” Resuscitation, vol. 83, no. 9, pp. 1106–1110, Sep. 2012, doi: 10.1016/j.resuscitation.2012.03.026.
[22] L. W. Andersen et al., “The prevalence and significance of abnormal vital signs prior to in-hospital cardiac arrest,” Resuscitation, vol. 98, pp. 112–117, Jan. 2016, doi: 10.1016/j.resuscitation.2015.08.016.
[23] C. W. Seymour et al., “Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),” JAMA, vol. 315, no. 8, pp. 762–774, Feb. 2016, doi: 10.1001/jama.2016.0288.