adverseeventcluster analysisfor syndromic’ surveillance’€¦ · background’ •...
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Adverse event cluster analysis for syndromic surveillance
G.N. Norén, J. Fransson, K. Juhlin, R. Chandler, I.R. Edwards
Background
• Syndromic surveillance is used in disease outbreak detec4on to iden4fy illness clusters early, before diagnoses are confirmed and reported.
• In contrast, tradi4onal methodology for signal detec4on in pharmacovigilance relies on dispropor4onality using a drug and an individual adverse event term
Ques4ons:
• Is there a be?er way to summarize data than to look at each adverse reac4on separately?
• Can we iden4fy natural groups of reports with similar pa?erns of adverse reac4ons?
Two big challenges
1. Many possible ways to code the same adverse reac4on Myocardial infarc4on, acute myocardial infarc4on, cardiac failure acute, cardiac failure, acute coronary syndrome, chest pain + shortness of breath, ...
2. Seemingly diverse symptoms may relate to the same underlying condi4on or pathophysiology High fever, swea4ng, unstable blood pressure, stupor, muscular rigidity, autonomic dysfunc4on = Neurolep(c malignant syndrome
Methodology
• Assump4on of mixture model
• Expecta4on-‐maximiza4on algorithm was used to op4mize the alloca4on of reports
• Assurance of robustness
• Consensus clustering algorithm using single linkage
Mixture model assumpIon
Assume reports are generated by a mixture model � Marginal probability for each report class � Each report class has associated set of probabili4es for each
adverse reac4on to occur
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Mixture model assumpIon
Mixture model assumpIon
Rand
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Mixture model assumpIon
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ExpectaIon-‐maximizaIon algorithm
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ExpectaIon-‐maximizaIon algorithm
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ExpectaIon-‐maximizaIon algorithm
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ExpectaIon-‐maximizaIon algorithm
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Stop when resulIng model is ’good enough’
Robustness
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Run algorithm mulIple Imes to create mulIple models
The algorithm was run 100 Imes
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Consensus clustering using single linkage
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Orange arrows indicate that reports co-‐occur in 100% of the models
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Consensus clustering using single linkage
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The output is 5 clusters in the consensus model when having threshold 100%
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SeUng the threshold to 66% yields the following results
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The output is 2 larger clusters in the consensus model when having threshold 66%
M1 M2
M3 Consensus model
Consensus clustering
864 reports on average 3.9 ADR terms Somnolence 35% Confusion 27% Gait abnormal 19% Speech disorder 16% Fall 14% Ataxia 12% Stupor 9% Saliva increased 8% Extrapyramidal disorder 7% Asthenia 7% Tremor 7% Cerebrovascular disorder 6% Amnesia 6% Coma 6%
87 reports on average 3.3 ADR terms Fall 47% Fracture 26% Transient ischaemic attack 25% Cerebrovascular disorder 23% Cerebral infarction 17% Aphasia 13% Hemiparesis 10% Gait abnormal 8% Paralysis facial 7% Hypotension postural 7% Pneumonia 6%
684 reports on average 4 ADR terms Somnolence 40% Confusion 32% Gait abnormal 21% Speech disorder 17% Ataxia 14% Stupor 11% Fall 11% Saliva increased 9% Extrapyramidal disorder 8% Asthenia 8% Tremor 8% Amnesia 7% Coma 6%
460 reports on average 3.8 ADR terms Somnolence 43% Confusion 38% Gait abnormal 19% Speech disorder 15% Ataxia 13% Stupor 12% Fall 11% Asthenia 8% Amnesia 8% Hypotension 7% Coma 6% Dehydration 6% Extrapyramidal disorder 6%
69 reports on average 3.2 ADR terms Fall 49% Fracture 28% Transient ischaemic attack 26% Cerebrovascular disorder 26% Cerebral infarction 19% Aphasia 13% Gait abnormal 9% Paralysis facial 7% Hemiparesis 7% Pneumonia 6% Fracture pathological 6%
39 reports on average 4.2 ADR terms Saliva increased 46% Gait abnormal 44% Ataxia 33% Somnolence 31% Speech disorder 28% Tremor 23% Extrapyramidal disorder 18% Confusion 18% Hypertonia 15% Fall 15% Apathy 13% Asthenia 13% Hypokinesa 10%
80% 90% 100%
Results
Risperidone • 16 323 ICSRs with two or more co-‐reported adverse event terms (WHO-‐ART
terminology)
• 92% of the ISCRs were sorted into one of 35 clusters (90% hierarchy in consensus clustering)
• Largest cluster included 1 883 reports with an average of 3.2 ADR terms
• Smallest clusters included 5 reports with 2, 2.6 and 5.4 ADR terms
Results
Three largest clusters:
Cluster 1 Cluster 2 Cluster 3
1883 reports 1799 reports 1407 reports
Agita4on 21% Aggressive reac4on 18% Condi4on aggravated 16% Psychosis 16% Hallucina4on 15% Anxiety 15% Insomnia 13% Depression 12% Medicine ineffec4ve 11% Hyperkinesia 9% Suicide idea4on 8% Nervousness 6% Manic reac4on 6% Delusion 6% Paranoid reac4on 6% Personality disorder 5% Schizophrenic reac4on 5%
Extrapyramidal disorder 33% Dystonia 20% Hyperkinesia 18% Hypertonia 18% Dyskinesia 18% Tremor 17% Dyskinesia tardive 14% Saliva increased 11% Speech disorder 8% Muscle contrac4ons involuntary 7% Dysphagia 7% Gait abnormal 6%
Hyperprolac4nemia 76% Lacta4on nonpuerperal 46% Amenorrhea 38% Gynecomas4a 7% Weight increase 7% Menstrual disorder 7% Breast pain 6%
Results
Other clusters of interest:
Cluster 6 Cluster 16 Cluster 28
784 reports 114 reports 8 reports Neurolep4c malignant
syndrome 54% Crea4nine phosphokinase increased 49% Fever 41% Hypertonia 27% Tachycardia 16% Confusion 10% Hypertension 9% Extrapyramidal disorder 9% Rhabdomyolysis 8% Agita4on 8% Swea4ng increased 8% Leukocytosis 7% Tremor 7% Somnolence 6%
Impotence 53% Ejacula4on disorder 34% Libido decreased 29% Priapism 28% Ejacula4on failure 13% Pain 8% Penis disorder 8% Sexual func4on abnormal 6% Anorgasmia 6%
Papilloedema 88% Hypertension intracranial 88% Headache 50% Diplopia 25% Vision abnormal 25% Eye pain 13% Oedema periorbital 13% Re4nal haemorrhage 13% Photophobia 13% Conjunc4vi4s 13% Personality disorder 13% Vomi4ng 13% Manic reac4on 13% Eyelid skin disorder 13% Op4c atrophy 13%
IdenIfied use cases
• Iden4fy clusters of reports with similar profiles – detec4on of syndromes which may not have diagnos4c labels
• Iden4fy reports similar to a number of index cases (case series building)
• Explore differences in coding due to geographic, prac4ce or guideline differences
• Other?
Conclusions
• Pa?ern recogni4on can be used to iden4fy clusters of clinically similar reports
• There are poten4ally several iden4fied use cases for such an algorithm
• More extensive analyses of spontaneous reports such as clustering techniques can likely be?er inform decisions in pharmacovigilance.
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