Exploring Temporal Patterns in Hypertensive Drug Therapy
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1 Exploring Temporal Patterns in Hypertensive Drug Therapy Sophia Wu 1, Margret Bjarnadottir 2, Eberechukwu Onukwugha 3, Catherine Plaisant 4, Sana Malik 5! 1. MSIS, Smith School of Business 2. Assistant Prof, Smith School of Business 3. Assistant Prof, School of Pharmacy 4. Research Scientist, HCIL 5. Ph.D student in Computer Science
2 INTRODUCTION Patients Adherence to Medication Great importance as non-adherence can lead to worsening of conditions and health decline. Medication Possession Ratio (MPR)! Does not adequately capture different adherence patterns of patients, which vary widely.
3 Observe and Summarize Common Patterns in Hypertensive Drug Therapy!
4 DATA DESCRIPTION Pharmacy claims of 493,022 individuals! Angiotension-Converting Enzyme-Inhibitors (Ace) Angiotension II Receptor Blockers (ARB) Calcium Channel Blockers (CCB) 5 Drug Classes Beta blockers (Beta) Diuretics
5 IDEAL DRUG USAGE PATTERN Days ACE
6 IDEAL DRUG USAGE PATTERN Days ACE Beta
7 IDEAL DRUG USAGE PATTERN Days ACE Beta
8 1st PASS What does the whole picture look like?
9 Randomly selected 5000 events (180 individuals)
10 1st PASS 2nd PASS What if we narrow down a little bit?
11
12
13 MATCH WEIR Diur Beta Ace CCB
14 1st PASS 3rd PASS 2nd PASS What is a good pattern?
15 NHLBI (2003), JNC 7 Express
16 NHLBI (2003), JNC 7 Express
17 NHLBI (2003), JNC 7 Express
18 Drug usages&medical records for heart failure patients with ICD Search HF_4289 occurring during the CCB 7 HF_4289 CCB
19 Why are Those Heart Failure Patients Given CCB?
20 Non-dihydropyridines (Good) CCB Dihydropyridines (Bad)
21 Non-dihydropyridines (Good) CCB Dihydropyridines (Bad)
22 1st PASS 3rd PASS 2nd PASS 4th PASS Are there any heart failure patients on bad CCB?
23 HF_any Bad CCB
24 Drug usages&medical records for heart failure patients on Bad CCB Search HF_any occurring during the Bad CCB 28
25 MEDICAL STUDY Non-dihydropyridines (good) CCB Dihydropyridines (bad) Atrial Fibrillation (AF)
26 1st PASS 3rd PASS 5th PASS 2nd PASS 4th PASS Do those heart failure patients on bad CCB have AF?
27 Drug usages&medical records for heart failure patients on Bad CCB Search AF_any not occurring 89
28 37454: Heart failure patients in total 416: Heart failure patients on CCB 89: Heart failure on bad CCB without AF
29 37454: Heart failure patients in total 416: Heart failure patients on CCB 89: Heart failure on bad CCB without AF 0.24% among the heart failure Individuals
30 37454: Heart failure patients in total 416: Heart failure patients on CCB 89: Heart failure on bad CCB without AF 0.24% among the heart failure Individuals 21% among the heart failure on CCB
31 CRITICAL FINDING It is not compliant with medical guideline.
32 CRITICAL FINDING It is not compliant with medical guideline. Doctor s mistake
33 CRITICAL FINDING It is not compliant with medical guideline. Doctor s mistake
34 1st PASS 3rd PASS 5th PASS 2nd PASS 4th PASS 6th PASS What does the pattern look like before and after 1st heart failure inpatient visit?
35 Aggregating HF claims into 5 categories
36 Category Place of Services Code inpatient 21, 51, 56, 61 outpatient all others urgent 20, 23, 41, 42 hospice 34 SNF(skilled nursing facility) 31, 32, 33
37
38 Cleaning multiple records of inpatient visit on the same day Aggregating HF claims into 5 categories
39 Search Add Constraint
40
41 Aligning by the first HF inpatient visit Cleaning multiple records of inpatient visit on the same day Aggregating HF claims into 5 categories
42 Drug usages&medical records for heart failure patients on Bad CCB
43 Patients with AF claims: started by taking good CCB and ACE not long after first heart failure inpatient visit, dropped good CCB but took bad CCB instead. Patients without AF claims: started to take hypertensive drugs after first heart failure inpatient visit dropped bad CCB Patients without AF claims: started to take hypertensive drugs after first heart failure inpatient visit continued to take bad CCB for some periods of time
44 CONCLUSION Patterns are far from ideal!! Doctors may have given wrong prescriptions (use of bad CCB) EventFlow: visualization reveals limitations of Medical Possession Ratio detect common patterns & specific cases
45 FUTURE AVENUES Clustering: discovering underlying patterns of behavior of patients taking medicine, then analyzing the different clusters in EventFlow! Statistic Analysis: logistic regression, including Charlson Comorbidity Index (CCI), to quantify drug impacts on outcomes! EventFlow:! Generating hypothesis (eg. Patient on bad CCB may have higher readmission rate) Supporting the statistic analysis findings!
46 THANK YOU For more information: Website: May 29, 2014 HCIL Symposium
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