Research to support innovative decision tools for ... · Data harmonization key for improving...
Transcript of Research to support innovative decision tools for ... · Data harmonization key for improving...
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Research to support innovative decision tools for healthcare providers in
Personalized Medicine
Enrico Capobianco
Center for Computational Science, School of Medicine, University of Miami
Miami, FL-USA
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Outline
Introductory Remarks
Emerging Directions
Recent Studies
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Q1:
How to define
complexity
in the medical decisional processes?
Introductory Remarks
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. Theoretical aspects
Distributional laws and associations are generally unknown. • data complexity structural features • computational complexity algorithmic features Linkages between the two complexities are generally unclear.
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.
Empirical aspects • Focus on context to orient the choice of methods • Start from essential data characteristics.
How?
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Complex systems characterized by interactions between components
Properties emerging from them are hardly predictable
Equilibrium/non-equilibrium
Adaptability
Robustness
Resilience
Self-organization
Non-linear dynamics
Criticality
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Need to determine modes of failure
Complex systems ...
may not produce the desired results
may fail without warning
may generate cascading phenomena
may end into catastrophes
Next-Generation inference methods are needed
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Q2:
Find appropriate definitions
for these properties
within the domain of health
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Emerging Directions
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Quantitative & Medical Thinking Integration
Collect, Assemble, Learn = Diagnosis
Select, Prioritize = Therapy
Analyze, Predict = Prevention
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Translational Medicine
Superior precision expected from data-intensive individualized medicine
accelerating scientific discovery and innovation in diagnosis, therapy, disease mgt
Scientific method adapted to the increased diversity of data
revealing more on disease onset and progression
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Ability to assess human health enabled by multiple sources
NGS, physiological monitoring, environmental capture, imaging analysis, behavioural tracking.
Ability to model observed/measured environment
advancing IT, AI, genome editing, tissue engineering, cellular reprogramming, precision treatment, pharmacogenomics.
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Patient-focused health data brings augmented complexity due to inherent heterogeneity
Data harmonization key for improving diagnosis, therapy assessment and prevention
.
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Role of big data and integrated modeling toward:
• to reclassify diseases
• to define high-resolution patients
• to build dynamic knowledge repositories
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Common Pitfalls
• p-values: owerpowered studies detecting too small effects as significant?
• Data: not conforming to model assumptions
• Biases: what mitigation of effects is possible?
• Confounders: what correction methods?
• Validations: need independent datasets
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Empowering efficient computing
Data re-use strategies
EHR Systems
• Missing data and Inconsistencies
• Covariations / dependencies
• Similarities / dissimilarities
• Models cross-verification
• Validation of significant results
• Errors Quantification
• Confidence measures
• Loss / gain functions
Diagnostic (comorbidity progression)
Therapeutic (response and toxicity)
Prognostic (time to recurrence, death)
Preventive (prediction of occurrence, risk
profile)
Generating patient outcomes from large sets of features
Open problems
Limiting Factors
Multiple Dimensions
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data far from research scopes
Limitations of EHR observational data to address clinical questions
I
diversified aspects
conceived as support to clinical care
variety
1
2
3
different record types .
multiple testing penalties
4
5
6
Ad hoc protocols for sharing digital information interoperability
claims reflect reimbursement, diagnosis codes
required by providers to perform services not build medical histories.
populations, geographic, demographic, disease severity, geo-differentiation and ethnic balance
longitudinal lengths
of studies,
temporal quality
structured and non
replication needed for reproducibility of results
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Q3:
Find examples of problems and limitation
referred to EHR systems
within your local environment
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Clinical Decision Support Systems - CDSS
are next-generation inference tools
a) to put *value of information* theory into practice
• analytical support to decisions by assessing the value of acquiring additional evidence from research
• benefit/gain quantification about improvement of population health vs the cost of implementation
b) aimed to develop efficient high-performance algorithms
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CDSS methodological challenges a. ensure accurate estimations
for risk assessment, diagnosis, therapeutic intervention and prognosis
b. optimize data predictive learning models c. select evidence supporting validation for prognostic scopes
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CDSS ethical challenges a. guide clinical decision making at reduced risk of bias
b. assess algorithm’s influence on probabilistically-determined complex/critical conditions
c. prioritize secondary findings in the context of treatment
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Data access
Lower barriers
Knowledge transfer to point-of-
care
Patient engagement
Patient care
conditions, symptoms, tests…. many data types Patient-reported outcome measures
Snapshots of patient’s health status temporal trajectory
heterogeneous knowledge base (genetic, omic, epigenomic, etc.)
Risk scenarios for patients
Algorithmically cross-reference patient characteristics
covering first consultation, diagnosis, follow up, hospitalization etc.
test new patient-specific hypotheses
drive effective prevention and/or more timely targeted therapies
include connected data from disparate but integrable sources and digitalized environments
CDSS linked to EHR
exploit patient health records & novel clinical trials development
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Q4:
What literature reports about
outcome improvements
obtained through CDSS?
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Recent studies/examples
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Predictive value of inflammatory markers for cancer diagnosis in primary care: a prospective study using electronic health records
Watson et al, BJC 2019
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* Lack of strong association between raised CPR and risk of future cancer to make it clinically useful. * When present, bladder and kidney, lymphoma and myeloma, show very low predictive values.
Background This study examined the diagnostic utility of inflammatory markers or IM • C-reactive protein, CRP • erythrocyte sedimentation rate, ESR • plasma viscosity, PV
for cancer diagnosis in primary care.
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• Clinical Practice Research Datalink (CPRD) source
UK prospective cohort study of primary care patients using EHR
Cancer Registry linkage (110245 patients)
• 160000 patients with one inflammatory marker (CRP, ESR,PV) selected at random from CPRD vs 40000 patients without.
• Linked data form English Cancer Registry (110245 patients)
• Outcome: 1- and 2-year cancer incidence. Primary care patients with raised IM Primary care patients with normal IM Untested controls
Methods
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• Sensitivities for cancer were
• 46.1% for CRP,
• 43.6% ESR • 49.7% for PV
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Results Cancer risk * with higher inflammatory marker levels, with older age and in men; * further rise when a repeat test is abnormal (but falls if it normalizes). Conclusion: cancer should be considered in patients with raised IM.
Strong point: large sample size
Weak point: lack of explanation of the markers (heterogeneity, confounders)
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Deloitte: insights on digital R&D
Transforming the future of clinical development
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Digital endpoints
ehr
cdss
ml
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Can symptoms be objectively measured?
What change of measurement reflects treatment benefit?
If endpoint can be captured, best device can be selected, verified, validated
observational study first, then trial .
Stratifications can speed up approval of drug candidates intended for patient subsets
Clinical trial endpoints (morbidity, mortality) require timeframes difficult to evaluate
Digital endpoints
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Digital BM are objective quantifiable physiological and behavioral data collected and measured by means of digital devices.
• Collection of data may occur on frequent and even continuous basis.
• Real time feedback for clinicians.
Digital Biomarkers
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Comparison and development of machine learning tools in the prediction of chronic kidney disease progression Jing Xiao et al. J Transl Med 2019
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Background
• Urinary protein quantification critical for assessing severity of chronic kidney disease (CKD).
• The current procedure for determining CKD severity is completed by evaluating 24-h urinary protein (inconvenient during follow-up).
Objective
Quickly predict CKD severity using more easily available demographic and blood biochemical features during follow-up
The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.
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Methods
• Collected clinical and blood biochemical results from 551 patients with proteinuria.
• 13 blood tests + 5 demographic features used as non-urinary clinical variables to predict the 24-h urinary protein outcome response.
• 9 predictive models - logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor.
• Evaluated AU-ROC, sensitivity (recall), specificity, accuracy, log-loss, precision of each model.
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Results
• Effect sizes of variables analyzed and ranked.
• Linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively.
• Blood tests could be applied as non-urinary predictors during outpatient follow-up.
• Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity.
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Identification of type 2 diabetes subgroups through topological analysis of patient similarity (Li et al, Sci Transl Med, 2015). diabetic population (11k individual EHR)
A) Patient-patient network for topology patterns Nodes = groups of patients with significant similarity of clinical features. Edges indicate shared patients. Red color = high enrichment for T2D Blue color = low enrichment for T2D B) Patient-patient network for topology patterns on 2551 T2D patients. Red color = enrichment for females Blue color = enrichment for males.
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3 patient subgroups found from topological networks.
Diabetic complications (nephropathy and retinopathy)
Co-occurrence of cancer and cardiovascular diseases
Association with neurological diseases, allergies, infections.
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Thanks!