Predicting, detecting, and responding to clinical deterioration on the wards: Is there room for improvement?

1. Predicting, detecting, and responding to clinical deterioration on the wards: Is there room for improvement? Chris Bonafide, MD, MSCE Division of General Pediatrics bonafide@email.chop.edu CCEB CENTER FOR PEDIATRIC CLINICAL EFFECTIVENESS

2. Case

4. Case • High-risk patient • Worsening vital signs • New oxygen requirement • Worsening labs • Concerned staff • Urgent interventions • Delayed transfer to ICU • Poor outcome

5. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

6. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

7. What is clinical deterioration? Trajectories of Ward Hospitalization D C Clinical Deterioration • Acute worsening of clinical status • On a trajectory toward arrest B C B A A Adapted from: Duncan H, Hutchison J, Parshuram CS. The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. Sep 2006;21(3):271-278.

8. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

9. What are rapid response systems? • Hospital-wide systems designed to prevent cardiac arrest and death in ward patients by: • Facilitating the identification of patients at risk • Deploying an expert team to the bedside of patients exhibiting signs of deterioration • Due to strong support from safety organizations 2005-2010, most US hospitals have some form of rapid response system • CHOP • HUP

10. What are rapid response systems?

11. Rapid response systems: mixed results Mortality rate Cardiac arrest rate better worse better worse Adults No significant reduction Adults 34% reduction Children 38% reduction Children 21% reduction Pooled Pooled Chan PS, Jain R, Nallmothu BK, Berg RA, Sasson C. Rapid Response Teams: A Systematic Review and Meta-analysis. Arch Intern Med. Jan 11 2010;170(1):18-26.

13. Opportunities for rapid response system improvement • IDENTIFY a clinical profile of children at high risk of deterioration, and consider monitoring them more closely • DETECT deterioration more accurately using evidence-based tools • INTEGRATE detection into continuous physiologic monitoring systems • ELIMINATE barriers to calling for urgent assistance

14. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

15. Who deteriorates?

16. CHOP deterioration data Age Hours after admission

18. Development of a predictive score to identify pediatric inpatients at risk of clinical deterioration • Objective: To develop a predictive score for deterioration using non-vital sign risk factors • Intended use: identifying high-risk children who should be intensively monitored • Design: Case-control study • Setting: The Children’s Hospital of Philadelphia • Patients: • Cases (n=141) were children who deteriorated while receiving care on a non-ICU inpatient unit • Controls (n=423) were randomly selected • Exposures: Complex chronic conditions, other patient factors, and laboratory studies in the 72h before deterioration • Outcome: Clinical deterioration, defined as cardiopulmonary arrest, acute respiratory compromise, or urgent ICU transfer • Analysis: Multivariable conditional logistic regression

19. Predictive score

20. Results

21. Conclusions • Identified a group of risk factors that may be useful to assess on admission and periodically during the hospitalization to identify patients who deserve more intensive monitoring for signs of deterioration

22. Next steps • Domain validation and updating of score parameters using patients at the time of admission from the emergency department to predict deterioration in the first 12 hours Hours after admission

23. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

24. Do vital sign abnormalities precede deterioration?

25. Pediatric Early Warning Scores • Combine intermittent vital sign values into a manually-calculated composite score • Monaghan’s Paediatric Early Warning Score • Haines’ Paediatric Early Warning Tool • Parshuram’s Bedside Paediatric Early Warning System Score • Edwards’ Cardiff and Vale Paediatric Early Warning System • Abnormal parameters based on expert opinion • Not adequately validated • Variations of the scores above used widely

26. What is abnormal for hospitalized children? • Age-based reference ranges for HR and RR • not evidence-based • vary widely between sources • Better evidence exists for normal blood pressure in healthy children, but these ranges have not been evaluated in-hospital

27. Development of “expected” vital sign curves • Objective: To develop expected HR, RR, SBP, and DBP curves using data from hospitalized children, to serve as the basis for: • In-hospital reference ranges • Vital sign-based early warning score development • Design: Retrospective cohort study • Setting: Cincinnati Children’s Hospital • Data Source: Manuallydocumented vital signs in EHR • Patients: • Admissions to non-ICU inpatient units in 2008 (n=11,789) • Excluded age >=18, DNR or death during admission, LOS>1 year • Excluded vital sign observations that were physiologically implausible • HR 0-300 = plausible • Analysis: generalized additive models for location scale and shape (GAMLSS) using Box-Cox power exponential distribution

28. Vital sign data: HR n=542,766 obs

29. First set of curves

30. Vital sign data: HR n=542,766 obs 79 high HR values from one patient hospitalized for 56 days 16 low HR values from one patient within a 4-hour window Single observations in patients who survived to discharge and were not DNR

32. Addressing documentation error • Used RR as a data integrity check • RR documented simultaneously • RR<HR • RR physiologically plausible (5-120)

33. Addressing Documentation Error

34. Single patient spikes still problematic

35. Ascertainment bias issues • Clustering of extreme values • In a single patient experiencing an acute event over a short time • In a single patient with abnormal baseline values over the course of a long admission • Addressed by: • Randomly selecting one HR from each 6-hour window of each patient’s admission • Randomly selecting up to 10 of these values for each admission

36. Data for curve generation

37. Next steps for curve analysis • Developing second set of curves with data integrity steps in place • Validation using CHOP sample • Will then use the z-scores for these curves to develop early warning score using vital sign data from case-control study

38. Opportunities to integrate detection tools into physiologic monitoring? • Most inpatients are connected to physiologic monitors • Alarm parameters are set manually and adjusted as needed • CHOP monitors generate ~20,000 alarms/day • Nurses are automatically paged with a generic message for each of these alarms • Can we identify and filter out false alarms? • Can physiologic data be combined to generate multi-parameter alarms? • Can alarms be adaptive to recognize important within-subject changes that may not reach pre-set alarm parameters?

39. HUP ICU Smart Alarms Project

40. Evaluates HR, RR, SpO2, Skin Temp continuously • Evaluates BP measured at periodic intervals using a cuff • Compares monitored values to a model of normality generated using neural networking methods applied to a training data set • Variance from data set used to evaluate probability that vital signs are normal • Generates a status index ranging from 0 (no abnormalities) to 10 (severe abnormalities in all variables) • Short-term median filtering for noise removal • Historic filtering for coping with missing parameters http://www.obsmedical.com/products/hospital-patient-monitoring/visensia-central-station

42. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

43. Qualitative evaluation of the mechanisms by whichrapid response systems impact patient safety • Objectives: • To qualitatively determine how the identification and response components of rapid response systems impact nurse decision-making relevant to patient safety • To identify barriers to recognizing and responding to clinical deterioration that exist despite rapid response system implementation • Design: Qualitative study using semi-structured interviews • Setting: CHOP • Subjects: 27 nurses who care for children on non-ICU units • Data Collection and Analysis: • Audio recorded and transcribed interviews • Coded using constant comparative methods • Analyzed using a grounded theory approach

44. Theme: Despite implementation of an open access medical emergency team, some barriers to calling for urgent assistance still exist. • Some nurses doubted their own ability to recognize deterioration. • Some nurses were hesitant to call for help for fear of being viewed as inadequate or unable to handle a difficult situation. • While most nurses reported a collaborative working relationship with physicians, issues of hierarchy were discussed, with nurses reporting that physicians sometimes disagreed with their assessment of the need for urgent assistance. This prevented or delayed some nurses from calling the medical emergency team.

45. Barrier examples • Medical nurse, 2-5 years experience: • I felt very uncomfortable with the patient… I was in there doing blood pressures and I don’t even think I got to write them all down. I was doing them so frequently. She was very sick. I felt resistance from every member of the team. That made me hesitate to speak up. I did speak up several times, but then I stopped. I spoke up so many times saying, “This is not okay. I am extremely concerned.” Multiple times, but I never said, “No, that’s it.” I just didn’t take that last step… • Medical nurse, 5-10 years experience: • We had a child on BiPap who we had tried everything to keep his sats up… and literally nothing was working. At the 6:00 hour both me and the charge nurse were like, to the resident, we said, “We need you to do something. Can we just call the CAT team for a second opinion? Just something, maybe change the CPAP, just something.” We have had issues with this one particular one who insisted that, “He just needs some chest PT.” I insisted that I was doing chest PT for five straight hours now and I was doing it hard. I was doing it good. We just kept meeting resistance…

46. Next steps for qualitative study • Stratify analysis by nursing characteristics • Expansion to physicians to enable direct comparisons with nursing themes

47. Outline • What is clinical deterioration? • What are rapid response systems? • Who deteriorates? • Do vital sign abnormalities precede deterioration? • Once deterioration has been detected, are there barriers to calling for help? • Summary

48. Summary of opportunities for rapid response system improvement • IDENTIFY a clinical profile of children at high risk of deterioration, and consider monitoring them more closely • DETECT deterioration more accurately using evidence-based tools • INTEGRATE detection into continuous physiologic monitoring systems • ELIMINATE barriers to calling for urgent assistance

49. Thank you • Mentors/Collaborators • Ron Keren • John Holmes • Vinay Nadkarni • Russell Localio • Richard Landis • Bob Berg • Kathryn Roberts • Fran Barg • Chris Feudtner • Alex Fiks • Rich Lin • Carrie Daymont • Pat Brady • Research Assistants • Emily Huang • Kathleen McLaughlin • Shelby Drayton • Annie Chung • Duy-An Ho