Temporal Sequence Analysis of Clinical Laboratory Results for Patient Follow-up and Effective Data Display

1
Temporal Sequence Analysis of Clinical Laboratory
Results for Patient Follow-up and Effective Data
Display

• James Harrison, M.D., Ph.D.
• Associate Professor of Pathology
• Faculty-in-residence, Center for Biomedical
  Informatics
• University of Pittsburgh
• jhrsn_at_pitt.edu
• Dept. of Electrical Engineering, Univ. of
  Pittsburgh
• February 25, 2004

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Standard Clinical Data Display
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Standard Clinical Data Display
4
Phenytoin levels
Discharge
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How does this happen?

• Data reduction
• Classification
• Cross-sectional view
• Loss of information

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Outline for Today

• Development and application of a simple temporal
  event monitor (LabScanner)
• Extension of the event monitor lightweight
  general-purpose temporal abstraction system
  (PROTEMPA)
• Applications of temporal abstraction
• Patient identification and monitoring in the
  clinical laboratory
• Clinical data presentation and decision support

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Simple Temporal Pattern Recognition

• Goal Process monitor with simple rule
  maintenance
• Case identification for training, Q/A, clinical
  reporting, consultation
• Clinical lab (TDM) perspective
• Data access limited to text file export from LIS
• No cross-correlation of primary data with other
  results
• Constraints
• Need to recognize short patterns, but data sparse
  and irregular
• Underlying physiological model may be changing
• Pre-analytic errors, time and procedural errors

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A Pragmatic Pattern Detection Strategy

• Define features of short problem sequences from
  inspection and annotation of real data
  (heuristics)
• Model-independent, based on observable sequence
  features only
• Derive a minimal set of sequence templates
  (Discovery Rules) designed to detect problem
  sequences
• Implement Discovery Rules in data-scanning
  software

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Simple Patterns in Patient DataSustained high
and low values
Phenytoin
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Simple Patterns in Patient DataIncreasing and
decreasing trends
Phenytoin
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Simple Patterns in Patient DataVariability and
Frequency
Phenytoin
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General Features of Discovery Rules

• Pattern features
• Value (state), trend, variability, frequency
• -- plus --
• Patient features
• Age, gender, hospital location
• Interval features
• Number of events, max/min overall time span,
  max/min time between neighboring events

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Example Discovery RulesIdentification of Basic
Temporal Sequences
Discovery Rule Categories for Phenytoin (10-20 mg/l) Discovery Rule Categories for Phenytoin (10-20 mg/l) Discovery Rule Categories for Phenytoin (10-20 mg/l) Discovery Rule Categories for Phenytoin (10-20 mg/l)
Values Trends Variability Frequency
3 in up to 5 sequential values gt 20 (lt 28 days) 3 in up to 5 sequential values lt 10 (lt 28 days) Increasing values 3 values with an increase gt 0.7 per day, with a projected value gt 20 within 2 days, and the last value gt 15 (lt 28 days) Decreasing values 3 values with a decrease gt 0.7 per day with a projected value lt 10 within 2 days and the last value lt 15 (lt 28 days) 3 values with an average deviation gt 3.5 (lt 28 days) gt 3 values in 4 days gt 1 value in 8 hours
Target (therapeutic) range. Total pattern
length lt 28 days.
JH Harrison and P Rainey. Am J Clin Pathol
1995103710-717
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule

Example Increasing values?
20
The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule
• Persistence phase If immediately subsequent
  windows also completely satisfy the same rule,
  they are concatenated to form a single pattern
  (rule is greedy).

Example Increasing values?
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The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule
• Persistence phase If immediately subsequent
  windows also completely satisfy the same rule,
  they are concatenated to form a single pattern
  (rule is greedy).

Example Increasing values?
22
The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule
• Persistence phase If immediately subsequent
  windows also completely satisfy the same rule,
  they are concatenated to form a single pattern
  (rule is greedy).
• End last point that satisfies the rule.

Example Increasing values?
23
The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule
• Persistence phase If immediately subsequent
  windows also completely satisfy the same rule,
  they are concatenated to form a single pattern
  (rule is greedy).
• End last point that satisfies the rule.

Example Increasing values?
24
The Sliding Window Detection Algorithm

• Window size defined in rule
• Does the sequence of values within this window
  completely satisfy a rule?
• Four parts of a positive match
• Initiation phase values that contribute to the
  pattern but do not completely satisfy it by
  themselves
• Trigger and initiation phase together completely
  satisfy the rule
• Persistence phase If immediately subsequent
  windows also completely satisfy the same rule,
  they are concatenated to form a single pattern
  (rule is greedy).
• End last point that satisfies the rule.

Example Increasing values?
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Initial Implementation Data Input
File downloaded from LIS
Flat file import
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Rule Management

• Pattern Types
• Value comparison
• Trend
• Variability
• Frequency

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Patient List and Display
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Incidence of Patterns in Clinical Laboratory Data
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Printed Reports
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LabScanner Downloadhttp//jhh.opi.upmc.edu/labsca
nner/
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Pattern Incidence in Ten Monitored DrugsJune -
September, 2000

• 8311 patients, 35813 TDM values imported
• Limit to patients with three or more values
• 2927 patients, 27512 values scanned
• 9 of values out-of-range high
• 16 out-of-range low
• 1071 patients with patterns (37)
• 673 patients with persistent patterns (23)
• Average persistence 5.9 days, 5.5 specimens
• Average 37 new patterns per day

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Resource Utilization Associated with Patterns
52 Pediatric PTN patients with patterns
(excluding freq.), Jan - Mar 2001 43 Ped.
patients with at least 3 PTN values but no
patterns (same period)
Hospital Days
Hospital Costs
Resource utilization from the day of the first
level to 4 weeks after the last level
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Summary of Simple Temporal Abstraction

• Patterns exist in drug level data that are not
  clearly recognized by clinicians or laboratories
  but may identify patients at risk for increased
  cost of care, suboptimal care, or medical error
• Discovery rules can be developed and applied in
  software that identify patients showing such
  patterns
• We have developed a software tool that can
  analyze data retrospectively or prospectively to
  detect such patterns
• Large datasets can be processed (ca. 10,000
  patients)
• Identifying these patients may be useful for
  education, QA/QC, clinical process improvement,
  and consultative follow-up
• Characteristics of the data patterns, such as
  persistence, may provide new QA indicators
• Tool is limited to single instances of simple
  patterns cannot correlate patterns within or
  across types of events

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Complex Temporal AbstractionsAggregating Simple
Patterns Through Relationships

• The temporal abstraction task has the goal of
  abstracting high level concepts from time-stamped
  data.

Digoxin-Quinidine interaction
High digoxin
Rising digoxin
Rising digoxin
Declining digoxin
On quinidine
On digoxin
time
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PROTEMPAA Temporal Abstraction Engine

• PRoblem-Oriented TEMPoral Analysis
• A symbolic rule-based software framework for
  specifying, detecting, and visualizing temporal
  patterns in time series data
• Supports simple and complex temporal abstraction
• Complex abstractions may include results from
  multiple clinical laboratory tests or other
  events
• Modular, Java-based may be implemented
  stand-alone or in a server environment

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Temporal Pattern RelationshipsSimple
Abstractions Combine to Form Complex Abstractions
Disjoint
Equals
Meets
Overlaps
Coincident
Starts

• Patterns have
• Their own basic characteristics
• Relationships to other patterns

Ends
During
Contains
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Features of PROTEMPA Rules

• Patient features
• Age, gender, location
• Interval features
• Max/min overall span, max/min point-point
  distance
• Optional fixed min/max start and min/max finish
  times
• Simple pattern features
• Value (state), trend, variability, frequency
  (others possible)
• Rule Chain (complex abstraction) features
• Listing of contributing simple patterns and their
  relationships
• Specification of at least one of the following
  defines a relationship
• Max/min time between starts and/or ends
• Max/min times between start1-end2 and/or
  end1-start2
• Represented as 8-tuple
• Negated patterns

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Temporal Constraint NetworksA framework for
storage of rule chain data

• Nodes are start and end points of intervals
• Constraints define spans and relationships of
  intervals
• Constraint values are maintained in matrices
• Efficient algorithms are available to test
  network consistency

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PROTEMPA Processing Sequence

• Locate simple pattern intervals (sliding window)
• Aggregate intervals into complex abstractions via
  constraint network consistency checking
• Continue aggregation until no new rule chains are
  identified

Simple Abstraction Modules (Math or stat
functions across sequences)
Value
Complex Abstraction Module (Temporal Constraint
Networks)
Trend
Raw Data
Identified Intervals
Report Patterns
Rule Chain Matching
Output
Variability
Frequency
Others
Re-analysis
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PROTEMPA Framework
Dataset
Input
Scanner Engine
Temporal Pattern Detector
Output
Pattern Matches
Rules Database
Input
Encode as
Visualize matches as
Patterns of Interest
Selection Criteria
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PROTEMPA Stand-alone Interface
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Application PatientPatterns

• Designed to allow the clinical laboratory to
  specify temporal patterns for monitoring and
  follow-up
• The PROTEMPA framework is implemented as a Java
  servlet running in a JBoss/MySQL environment
• LIS transactions transferred and scanned every
  six hours
• Rule chains identify situations appropriate for
  follow-up
• Laboratory personnel log on via the web to view
  found patterns

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PatientPatterns Interface
Prototype implementation Pattern detection
engine runs as a servlet accesses laboratory
data in MySQL DB via JDBC user interaction
through the Web.
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Rule Entry and Editing
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Interval Relationship Specification
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Prototype Web Display
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Application Adaptive Clinical Displays

• Lab data displays are usually tabular and static
• Sequence, form and proximity of displayed data
  affects decision-making
• Graphical data display is best for decisions
  requiring quantitative comparisons or sequence
  assessment under time pressure
• Clinically significant situations may be
  recognizable by processing data at display time
• Automated aggregation and optimal display of
  data based on the content of the data may improve
  clinical decision-making (a form of decision
  support)
• Project funded by NLM October, 2003

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Adaptive Display Evaluation Protocol

• Set of validated clinical cases for diagnostic
  training
• Extend with one week hospital course including
  diagnostic cues
• Physician subjects review and write orders under
  time pressure using alternative clinical displays
• Cognitive analysis think-aloud protocol,
  videotape
• Transcriptions/tapes coded for searching vs.
  integration/evaluation activities
• Evaluate orders for completeness and accuracy

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Alternative Displays for Investigation

• Control Cerners new web display, flowsheet
  component
• Tabular text, static
• Organized by laboratory
• Test Semigraphical display
• PROTEMPA processing on query
• Display organization prioritized by rule chains
  based on data content (decision cluster)
• Default display format for each test (graphics or
  text) controlled by rule chains based on data
  content
• Both offer similar data access after default
  display

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Adaptive Display Hypotheses

• The adaptive display will provide aggregated,
  rapidly-interpretable data for decision-making (a
  decision cluster)
• Data viewing and decision-making will occur
  primarily in the adaptive portion of the display
• Cognitive load will be decreased (fewer steps)
  and the ratio of searching/integrative steps will
  decrease
• Orders will be more complete and accurate
• Diagnostic cues will be recognized more frequently

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Summary

• We have developed a flexible software tool
• Analyzes medical events retrospectively or
  prospectively
• Detects simple temporal patterns alone or in
  arbitrary combinations
• May be implemented as a standalone tool or a
  server
• Supports a hypothesis-testing form of data
  mining
• Likely to be useful for clinical laboratory and
  other QA
• Possibly helpful for identifying suboptimal
  therapy or medical errors, either in retrospect
  or as a process monitor
• Possibly useful in data display and decision
  support
• Possibly useful for annotating temporal
  relationships in data sets to allow improved
  clustering/machine learning

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Acknowledgements

• Pete Rainey, MD, PhD
• Thira Choojitarom, MD
• Juan Lertora, MD, PhD
• Andrew Post, MD
• Valerie Monaco, PhD, MS