A CLINICAL DECISION TOOL FOR DIAGNOSING ACUTE HEART FAILURE IN THE UNDIFFERENTIATED DYSPNEIC ED PATIENT
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1 A CLINICAL DECISION TOOL FOR DIAGNOSING ACUTE HEART FAILURE IN THE UNDIFFERENTIATED DYSPNEIC ED PATIENT Brian Steinhart MD, Phillip Levy MD MPH, Hilde Vandenberghe PhD, Gordon Moe MD, Ashley Cohen MSc, Kevin Thorpe M Math, Melissa McGowan MHK,C David Mazer MD
2 Disclosures Ontario Innovation Fund Roche Diagnostics Canada No personal conflict of interest
3
4 History of COPD MI CHF Pulmonary Embolus Pneumonia
5 OPTIONS? UltraSound
6 OPTIONS? Empiric Therapy
7 OPTIONS? Empiric Therapy Biomarkers
8 Biomarkers B-type Natriuretic Peptide BNP; NT-proBNP
9 Biomarkers B-type Natriuretic Peptide BNP; NT-proBNP Diagnosis a)rule Out NPV 1,2-98%
10 Biomarkers B-type Natriuretic Peptide BNP; NT-proBNP Diagnosis a)rule Out NPV 1,2-98% b) Rule In PPV 1,3 - variable (67-93%) - need to age stratify -+LR 3.2 1)Januzzi, J. Am J Card 2005; 2)Felker, G. CMAJ 2006; 3)Worster, A Clin Chem 2005
11 OPTIONS? Empiric Therapy Biomarkers Clinical Decision Tool (CDT)
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14 Clinical Decision Tool AHF
15 Clinical Decision Tool AHF Pretest AHF prob.
16 Clinical Decision Tool AHF Pretest AHF prob. NT-proBNP value
17 Clinical Decision Tool AHF Pretest AHF prob. NT-proBNP value Patient age
18 Derivation &Validation retrospective 1100 patients (IMPROVE CHF,PRIDE RCTs) Pretest AHF prob. NT-proBNP value Patient age Gold standard adj. diagnosis
19 Derivation &Validation retrospective 1100 patients (IMPROVE CHF,PRIDE RCTs) Logistic regression analysis: Prediction Model: Pr(aHF) = 1/1 + exp ( age 5.9 ptprob 2.3lntbnp pt prob x lntbnp)
20 600 patient PRIDE RCT
21 An RCT Utilizing a Prediction Model for Diagnosing Acute Heart Failure in the Emergency Department B. Steinhart, P. Levy et al.
22 Methods 150 patients consecutive enrollment,4 int l sites,11/11-11/13 : ClinicalTrials.gov Identifier NCT Undifferentiated dyspneic patient? AHF EKG/CXR Exclusion criteria: Low Prob (<20%) High Prob(>80%) Dyspnea indeterminate for AHF EXCLUDED Consent/ enroll *CRF, calculate pt prob AHF(21-79%), secondary MD (*CRF) * MD Recording
23 Methods 150 patients consecutive enrollment,4 int l sites,11/11-11/13 : ClinicalTrials.gov Identifier NCT Undifferentiated dyspneic patient? AHF EKG/CXR Exclusion criteria: Low Prob (<20%) High Prob(>80%) Dyspnea indeterminate for AHF EXCLUDED Consent/ enroll secondary MD (*CRF) NT-proBNP/ Model cal n lab *CRF, calculate pt prob AHF(21-79%), * MD Recording
24 Methods Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED NT-proBNP/ Model cal n Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) Random n *CRF, Final EPDX 60 day Follow-up * MD Recording Expert Final Adjudication
25 Objectives Adj. diagnosis agreement with: 1)Pretest AHF probability: 2)EPDx: 3) NT-proBNP 4) SoB-HF model
26 Objectives & Outcome Performance Measures 1)Pretest AHF probability: ROC curve 2)EPDx: Sensitivity, specificity, accuracy 2x2 table comparing accuracy of EP between mngt.arms 3) NT-proBNP Sensitivity, specificity and LRs 4) SoB-HF model ROC curve Cutpoints- Sensitivity, specificity, accuracy, LRs
27 A Prediction Model for Diagnosing Acute Heart Failure in the Emergency Department Results B. Steinhart, P. Levy et al.
28 Results Complete data for 197 patients Patient Characteristics and Clinical Findings Demographics Mean age in years (± SD) 64 ± 14 Sex: % female (n) 57% (114) Race: % (n) African American / Black 54% (101) Caucasian / White 36% (68) History, % (n) Heart Failure 53% (102) CAD 27% (51) Atrial Fibrillation 21% (39) COPD 49% (94) CKD 12% (23)
29 Methods Undifferentiated dyspneic patient? AHF EKG/CXR Exclusion criteria: Low Prob (<20%) High Prob(>80%) Dyspnea indeterminate for AHF EXCLUDED NT-proBNP Random n Consent/ enroll *CRF, calculate pt prob AHF(21-79%), secondary MD (*CRF) * MD Recording
30 Methods Undifferentiated dyspneic patient? AHF EKG/CXR Exclusion criteria: Low Prob (<20%) High Prob(>80%) Dyspnea indeterminate for AHF EXCLUDED NT-proBNP Random n Consent/ enroll *CRF, calculate pt prob AHF(21-79%), mean 49% secondary MD (*CRF) * MD Recording
31 Methods Undifferentiated dyspneic patient? AHF EKG/CXR Exclusion criteria: Low Prob (<20%) High Prob(>80%) Dyspnea indeterminate for AHF EXCLUDED NT-proBNP Random n Consent/ enroll *CRF, calculate pt prob AHF(21-79%), mean 49% secondary MD (*CRF) mean 48% * MD Recording
32 ROC for Clinician Pretest Probability Mean value = 49% AUC: 0.76
33 197 patients Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) *CRF, Final EPDX 60 day Follow-up * MD Recording Expert Final Adjudication
34 Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) Final EP DX 75% acc 60 day Follow-up * MD Recording Expert Final Adjudication
35 Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) 74% acc Final EP DX 75% acc 60 day Follow-up * MD Recording Expert Final Adjudication
36 Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) 74% acc 77% acc Final EP DX 75% acc 60 day Follow-up * MD Recording Expert Final Adjudication
37 ROC for Model Result
38 ROC for Model Result AUC: 0.93
39 P = ROC pretest prob ROC Model
40 SoB-HF Model ROC Performance Characteristics single optimal cutpoint Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR (82,96) 88 (82,93) <
41 SoB-HF Model ROC Perfrormance Characteristicsseparate rule in, rule out cutpoints Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR > (39, 61) 97 (92, 99) < (82,96) 88 (82,93) < (93,100) 68(59, 75)
42 SoB-HF Model ROC Perfrormance Characteristicsseparate rule in, rule out cutpoints Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR > (39, 61) 97 (92, 99) % of uncertain cases are redirected < (82,96) 88 (82,93) < (93,100) 68(59, 75)
43 SoB-HF Model ROC Performance Characteristicssignificant LR cutpoints Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR >10 <0.1
44 SoB-HF Model ROC Performance Characteristics Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR > (57, 78) 94 (89, 97) > < (84, 96) 83 (74., 89) <
45 SoB-HF Model ROC Performance Characteristics Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR -LR > (57, 78) 94 (89, 97) > < (84, 96) 83 (74., 89) < % of uncertain cases redirected
46 App
47 Example 1: Indeterminate
48 Example 2: Highly Supportive
49 Example 3: Indeterminate
50 Limitations Comparison based on adjudicated diagnosis
51 Limitations Comparison based on adjudicated diagnosis Kappa.82
52 Limitations Comparison based on adjudicated diagnosis Model requires NT pro BNP
53 Limitations Comparison based on adjudicated diagnosis Model requires NT pro BNP Interobserver pretest AHF variability? similar Means
54 Bland Altman Plot
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58 THE END Brian Steinhart MD
59 Acknowledgments Additional Investigative Team/Sites Donna Clark, St. Boniface Hospital, Winnipeg, CANADA Gerard Devlin, Waikato District Hospital, Hamilton, NEW ZEALAND Phillip Levy, Detroit Receiving Hospital, Detroit, USA Applied Health Research Centre (AHRC) Data Management Centre Kevin Thorpe Ashley Cohen Judi Hall Alice Dang University of Toronto Faculty of Engineering Jonathan Rose Babneet Singh Jiayi Wang Fabian Chan Annie Ngai Adjudicators Howard Leong-Poi St. Michael s Hospital, Toronto, CANADA Andrew Yan St. Michael s Hospital, Toronto, CANADA steinhartb@smh.ca
60 Diagnostic Performance Adjudication No AHF (n) AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR LR EP Final Dx No AHF 92 8 AHF NT pro BNP* No AHF 94 9 AHF (79, 94) 78 (70, 84) Model Optimal Cut Point of 51.5 No AHF AHF (83,96) 88 (82,93) *NT-proBNP cutpoint = combined NT-proBNP cutpoints for age <50, NT-proBNP <450 pg/ml; age 50-75, NT-proBNP >900pg/ml; age >75 NT-proBNP > 1800 pg/ml
61 Best Clinical Cut Points Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR LR > (39, 61) 96.7 (91.8, 98.7) At these > 65 cut points, % 68.4 (57.3, of 77.8) uncertain 94.2 (88.5, 97.2) cases >10 were 0.34 redirected with 92% accuracy < (81.6,96.1) 87.6 (81.8,93.4) < (83.8, 96.3) 82.6 (74.9, 88.4) < (93,100) 67.8 (59, 75.4)
62 CONCLUSION Using patient age, physician pretest probability and NT-proBNP as a continuous variable, we have prospectively validated a novel pragmatic clinical decision tool for diagnosing AHF in the indeterminant dyspneic ED patient. It is generalizable across a range of cases and clinicians. With its implementation as an App, future studies of sensitivity and analysis of the clinical impact are warranted.
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64 Agreement of Model posttest probabilityahf with Final Adjudication diagnosis AUC: 0.93
65 Best Clinical Cut Points Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) +LR LR > (39, 61) 96.7 (91.8, 98.7) > (57.3, 77.8) 94.2 (88.5, 97.2) > < (81.6,96.1) 87.6 (81.8,93.4) < (83.8, 96.3) 82.6 (74.9, 88.4) < (93,100) 67.8 (59, 75.4)
66 Agreement of EP pretest probabilityahf and Model posttest probabilityahf with Final Adjudication Diagnosis Pretest AUC: 0.76 Model AUC: 0.93
67 Validation of a Prediction Model in AHF ROC Curves- Pretest vs Model AUC: 0.76 AUC: 0.93 P =
68 Physician pretest probability combined with NT-proBNP For sites exposed to NT-proBNP (as standard of care) (n=107); EP diagnosis accuracy 75.9% For sites blinded to NT-proBNP value (as standard of care) (n=90), EP diagnosis accuracy 74.8%
69 Example 1: Indeterminate
70 Comparison of Diagnostic Variable Performance Adjudication No AHF AHF (n) (n) EP Final Dx No AHF 92 8 Sensitivit y (95% CI) Specificit y (95% CI) +LR -LR AHF NT-proBNP No AHF 94 9 AHF (79, 94) 78(70, 84) Model Optimal cutpoint 51.5 No AHF AHF (83,96) 88(82,9 3)
71 Agreement of Model posttest probabilityahf with Final Adjudication diagnosis AUC: 0.93
72 Comparison of Diagnostic Variable Performance Model redirects 84% of uncertain cases with 92% accuracy vs 100% of cases with 75% accuracy (with or without NT-proBNP).
73 CONCLUSION Using patient age, physician pretest probability and NT-proBNP as a continuous variable, we have prospectively validated a novel pragmatic clinical decision tool for diagnosing AHF in the indeterminant dyspneic ED patient. It is generalizable across a range of cases and clinicians. With its implementation as an App, future studies of sensitivity and analysis of the clinical impact are warranted.
74 Hierarchy of Evidence for Clinical Prediction Rules Level I: Rules that can be used in a wide variety of settings with confidence that they can change clinician behaviour and improve patient outcomes At least one prospective validation in a different population and one impact analysis, demonstrating change in clinician behaviour with beneficial consequences Level II: Rules that can be used in various settings with confidence in their accuracy Demonstrated accuracy in either one large prospective study including a broad spectrum of patients and clinicians, or validated in several smaller settings who differ from one another consider using with caution and only if patients in the study are similar to those in your clinical settilevel III: Rules that clinicians may ng These rules have been validated in only one narrow prospective sample. Level IV: Rules that need further evaluation before they can be applied clinically These CPRs have been derived but not validated or have only been validated in split samples, large retrospective databases, or by statistical techniques.
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85 Patient Characteristics and Clinical Findings Vital Signs % O2 Saturation (mean ± SD) 94.70±7.31 Respiratory rate (breaths/min) 21±4 Systolic Blood Pressure (mmhg) 148.4±29.7 Diastolic Blood Pressure (mmhg) 82.2±17.6 Resting Pulse (BPM) 91.2±19.7 Clinical Signs Diaphoretic (%; n) 4% (8) Jugular Venous Distention 19% (36) Hepato Jugular Reflux 11% (20) Crackles 42% (80) Wheeze 37% (69) Cardiac Murmurs 13% (25) S3 Gallop 5% (9) New/Increased leg edema 50% (93)
86 Patient Characteristics and Clinical Findings ECG findings New Ischemia (%; n) 4% (7) Normal Sinus Rhythm 68% (125) Atrial Fibrillation 14% (25) Other significant abnormality 46% (83) Chest X-ray Results Cardiomegaly 60% (111) Redistribution vasculature 45% ( 83) Pleural Effusion 19% ( 35) Alveolar Edema 16% ( 30) Hyperinflation 23% ( 42) New Infiltrate 10% ( 18)
87 NT-proBNP Performance Characteristics as a Binary Test Adjudication No AHF (n) AHF (n) Sensitivit y (95% CI) Specificit y (95% CI) +LR -LR NT-ProBNP Cutpoint* No AHF (79, 94) 78 (70, 84) AHF 27 68
88 SoB-HF Model ROC Performance Characteristics Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) > (39, 61) 96.7 (91.8, 98.7) > (57.3, 77.8) < (81.6,96.1) 94.2 (88.5, 97.2) 87.6 (81.8,93.4) +LR -LR > < (83.8, 82.6 (74.9, ) 88.4) < (93,100) 67.8 (59, 75.4)
89 Bland-Altman Plot
90 ESTIMATION OF CLINICAL PROBABILITY OF HEART FAILURE after hx,phx,cxr interpretation Indicate the clinical certainty that the symptoms are due to CHF: 8.0a Do you think there is low probability for AHF(0 20%), ie you will not treat for AHF but pursue/treat other causes of SOB? Yes No 8.0b Do you think there is high probability for AHF(80 100%) ie you will treat for AHF and not pursue/treat other causes of SOB? Yes No IF YOU ANSWER YES TO EITHER OF ABOVE, THE PATIENT SHOULD NOT BE ENROLLED! 8.1Otherwise state the clinical certainty (21 79%) that the symptoms are due to CHF 4 _8 _% 8.2 Number of years MD work experience 14 yrs
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92 Methods. Undifferentiated dyspneic ED patients with an EP assessed initial indeterminate (21-79%) pretest probability for AHF were enrolled prospectively across 4 sites. At time of ED disposition, EP recorded EPDx as AHF or noahf. Receiver-operator characteristic (ROC) curves were constructed and area under the curve (AUC) calculated to illustrate both pretest AHF and SoB-HF posttest value agreement with gold standard adjudicated diagnosis by two blinded cardiology experts. For model agreement, optimal cut-points using sensitivity, specificity and likelihood ratios (LR) were calculated.
93 Undifferentiated dyspneic patient CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pt probahf%, NT-proBNP req RN secondary MD (*CRF) In-lab randomization, NT-proBNP, Model post test prob result BLINDED EXPOSED Management * CRF Disposition (Time, diagnosis) * MD Recording 60 day f/u Adjudication
94 Components of a CDT-AHF Patient age (yrs) NT-proBNP value(pg/ml) Clinician gestalt after assessment/cxr interpretation ( % prob AHF)
95 Validation 600 patient PRIDE RCT
96 Comparison of Diagnostic Variable Performance Adjudication No AHF AHF (n) (n) EP Final Dx No AHF 92 8 Sensitivit y (95% CI) Specificit y (95% CI) +LR -LR AHF NT-proBNP No AHF 94 9 AHF (79, 94) 78(70, 84) Model Optimal cutpoint 51.5 No AHF AHF (83,96) 88(82,9 3) Model Optimal LR Cutpoints >67, AHF (57, 78) 94(89,9 7) >10
97 SoB-HF Model ROC Performance Characteristics Model Outcome (%) AHF (n) No AHF (n) Sensitivity (95% CI) Specificity (95% CI) > (39, 61) 96.7 (91.8, 98.7) > (57.3, 77.8) < (81.6,96.1) 94.2 (88.5, 97.2) 87.6 (81.8,93.4) +LR -LR > < (83.8, 82.6 (74.9, ) 88.4) < (93,100) 67.8 (59, 75.4)
98 Undifferentiated dyspneic patient? AHF CXR Exclusion criteria Low/High pt prob AHF EXCLUDED Dyspnea indeterminate for AHF Consent/ enroll *CRF, calculate pretest prob AHF%, secondary MD (*CRF) *CRF, Final EP DX 60 day Follow-up * MD Recording Expert Final Adjudication
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