How Long Before the Clinical Diagnosis of  Heart Failure are HF Signs and Symptoms  First Documented in the Medical Record...
Background • Decompensated HF is the leading cause of hospital   admissions for US Medicare patients. • Therapeutic interv...
Background • A better understanding of the time course of the   earliest clinical manifestations of HF may help   improve ...
Objective            Identify the time course for the       presentation of HF signs and symptoms        in the months to ...
Methods• The dataset used for this project consisted of the full  records for 50,625 patients followed in the primary  car...
Methods – Framingham Criteria              MAJOR SYMPTOMS                                 MINOR SYMPTOMS 1. Paroxysmal Noc...
Results – Patient Characteristics                                           Cases (N=4,644)              Mean Age (years) ...
Results: Average Days Prior to Clinical       Diagnosis of Criterion Documentation                                  # of P...
Results: Percentage of Patients with Select Criterion     in the 4 Years Prior to Clinical Diagnosis                      ...
Limitations• Text and data mining are not perfect in their  ability to recognize and appropriately label  Framingham crite...
Summary• This is the first study we are aware of to  identify the prevalence and timing of onset of  HF signs and symptoms...
Ongoing Work: Matrix Presentation of  Symptom / Sign Co-occurrence
Ongoing Work: Outflow Graph ofRelationships Between Different Criterion
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How Long Before the Clinical Diagnosis of Heart Failure are HF Signs and Symptoms First Documented in the Medical Record STEINHUBL

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How Long Before the Clinical Diagnosis of Heart Failure are HF Signs and Symptoms First Documented in the Medical Record STEINHUBL

  1. 1. How Long Before the Clinical Diagnosis of Heart Failure are HF Signs and Symptoms First Documented in the Medical Record? Steven R. Steinhubl, Jimeng Sun, Roy J. Byrd, Zahra Daar, Rajakrishnan Vijayakrishnan, Shahram Ebadollahi, Walter F. StewartGeisinger Medical Center, Danville, PA and IBM T.J. Watson Research Center, Hawthorne, NYHeal • Teach • Discover • Serve
  2. 2. Background • Decompensated HF is the leading cause of hospital admissions for US Medicare patients. • Therapeutic interventions exist for the HF patient that have been proven to reduce HF admissions by ~33% and decrease mortality by up to ~50%. • The diagnosis of HF in the primary care population is challenging: – Primary care physicians feel uncomfortable making a HF diagnosis1. – False-positive diagnosis in over 30%2, and false negative in over 20% of elderly COPD patients3. – There is marked variability, even among cardiologists, as to what findings are considered diagnostic for HF4. 1BMJ 2003;326(7382):196 2Eur Heart J 1991; 12: 315-321 3BMJ 2005; 26:1887-94 4J Am Coll Cardiol 1986;8:966-70Heal • Teach • Discover • Serve
  3. 3. Background • A better understanding of the time course of the earliest clinical manifestations of HF may help improve the earlier diagnosis of the HF patient. • The limited available data evaluating the onset of HF signs and symptoms have focused only on the days to weeks preceding an acute HF exacerbation.Am J Med. 2003;114:625–630Heal • Teach • Discover • Serve
  4. 4. Objective Identify the time course for the presentation of HF signs and symptoms in the months to years preceding a HF diagnosis through the application of a sophisticated natural language processing protocol to longitudinal EHRs in a large primary care population.Heal • Teach • Discover • Serve
  5. 5. Methods• The dataset used for this project consisted of the full records for 50,625 patients followed in the primary care network of the GHS, consisting of 4,644 cases and 45,981 group-matched controls.• The features used for machine learning include assertions and denials of Framingham HF criteria.• Extracted by text mining tools consisting of a pipeline of dictionary, grammar and text analysis engines developed and deployed within the UIMA system.• In total, there are over 3.3 million clinical notes, with a total of 4,484,666 Framingham criteria mentions; 892,805 of these were affirmed criteria.Heal • Teach • Discover • Serve
  6. 6. Methods – Framingham Criteria MAJOR SYMPTOMS MINOR SYMPTOMS 1. Paroxysmal Nocturnal Dyspnea 1. Bilateral Ankle Edema (PND) or Orthopnea 2. Neck Vein Distension (JVD) 2. Nocturnal Cough 3. Rales 3. Dyspnea on ordinary exertion 4. Radiographic Cardiomegaly 4. Hepatomegaly 5. Acute Pulmonary Edema 5. Pleural effusion 6. A decrease in vital capacity by 1/3 of 6. S3 Gallop the maximal value recorded 7. Increased Central Venous Pressure 7. Tachycardia (>120 BPM) (> 16 cm H2O at RA) 8. Circulation Time of 25 seconds * Not utilized in this analysis as not 9. Hepatojugular Reflux (HJR) documented in routine clinical 10.Weight loss 4.5kg in 5 days in practice. response to treatmentHeal • Teach • Discover • Serve N Engl J Med. 1971;285:1441-1446.
  7. 7. Results – Patient Characteristics Cases (N=4,644) Mean Age (years) 70.9 Female (%) 48.4 EF < 50% (%) 45.3 Hypertension (%) 61.9 Diabetes (%) 31.8 Known CAD (%) 25.5 Valvular Heart Disease (%) 4.2 COPD (%) 18.4 Chronic Kidney Disease (%) 4.4Heal • Teach • Discover • Serve
  8. 8. Results: Average Days Prior to Clinical Diagnosis of Criterion Documentation # of Patients in Whom Criteria Average # of Days were Identified Prior to Clinical Prior to Clinical Diagnosis DiagnosisAnkle Edema 3875 (83%) 238PND 1691 (36%) 210Dyspnea on 3840 (83%) 200ExertionRales 1410 (30%) 133Radiographic 1207 (26%) 83CardiomegalyJVD 549 (12%) 77Night Cough 374 (8%) 53Tachycardia 409 (9%) 45Pleural Effusion 772 (17%) 43Hepatomegaly 246 (5%) 38S3 Gallop 130 (3%) 18Weight Loss 151 (3%) 15Heal • Teach • Discover • Serve
  9. 9. Results: Percentage of Patients with Select Criterion in the 4 Years Prior to Clinical Diagnosis Year 4 Year 3 Year 2 Year 1 70Percent with Criterion Documented 65 60 62.3 Mean 238 Days Mean 50 200 Days 49.5 45.3 40 36.7 34 30 25.2 20 23.2 Mean Mean 17.9 210 Days 17.2 133 Days 10 11.3 10.1 8.4 7.3 6.2 5.2 0 Ankle Edema PND DOE Rales Heal • Teach • Discover • Serve
  10. 10. Limitations• Text and data mining are not perfect in their ability to recognize and appropriately label Framingham criteria. (overall F-score ~0.93)• There is substantial variability in whether and how various clinicians document HF signs and symptoms.Heal • Teach • Discover • Serve
  11. 11. Summary• This is the first study we are aware of to identify the prevalence and timing of onset of HF signs and symptoms in the months to years preceding a clinical HF diagnosis.• In many HF patients the signs and symptoms of HF are first documented well before clinical diagnosis; on average > 6 months.• These results suggest that automated text and data mining of the EHR may play an important role in the earlier identification of the HF patient in the primary care setting.Heal • Teach • Discover • Serve
  12. 12. Ongoing Work: Matrix Presentation of Symptom / Sign Co-occurrence
  13. 13. Ongoing Work: Outflow Graph ofRelationships Between Different Criterion

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