Potential durch Big Data in der Sepsis?

Transcription

1 Potential durch Big Data in der Sepsis? Kongress der DIVI, Hamburg, Prof. Dr. Andreas Schuppert / Jayesh Sudhir Bhat RWTH Aachen University Aachen, Germany 1 A. Schuppert / J. Bhat, DIVI Hamburg,

2 Content Big Data in health care hype & need Big Data the challenges of making sense of data Big Data & Sepsis chance or nightmare? Summary A. Schuppert / J. Bhat, DIVI Hamburg,

3 Real world data in medicine a change of paradigms? 3 A. Schuppert / J. Bhat, DIVI Hamburg,

4 Big Data? Definition is domain and context specific. Hypothesis: Existence of hidden or covert patterns to unravel and gain insights Medical applications Volume & Variety 4 A. Schuppert / J. Bhat, DIVI Hamburg,

5 Multivariate analysis 5 A. Schuppert / J. Bhat, DIVI Hamburg,

6 Projection into higher dimensions -> can be useful too 6 A. Schuppert / J. Bhat, DIVI Hamburg,

7 Projection into higher dimensions -> can be useful too 7 A. Schuppert / J. Bhat, DIVI Hamburg,

8 Projection into higher dimensions -> can be useful too 8 A. Schuppert / J. Bhat, DIVI Hamburg,

9 Content Big Data in health care hype & need Big Data the challenges of making sense of data Big Data & Sepsis chance or nightmare? Summary A. Schuppert / J. Bhat, DIVI Hamburg,

10 Big Data complexity Real life data extreme diversity Integration of heterogeneous data No one2one mapping Data are affected by doctor s experience and protocols. Direct monitoring of all parameters will never be possible: must find & focus on the relevant parameters Life can adapt its organization structures to external stress: relevance can change 10 A. Schuppert / J. Bhat, DIVI Hamburg,

11 All that is not really new the reality today Status today NEWS FOCUS Major Heart Disease Genes Prove Elusive Jennifer Couzin-Frankel > 10 9 > 10 5 $ investments in data publications of promising" candidates for biomarkers: Five years ago, researchers using a new strategy in which they scanned large stretches of the genomes of the sick and the healthy turned up a common gene variant among those with the eye disease macular degeneration. The finding was remarkable: Relatively few people participated in the study, yet those with two copies of the suspect gene variant had 10 times the risk of macular degeneration, a huge increase. Almost immediately, researchers applied GWA to other conditions. But it quickly became clear that macular degeneration was an exception. Most GWA studies needed 10,000 or more volunteers to get a statistically significant result, because the effect of each gene was so small. Since the human genome was sequenced 10 years ago, technology has moved with lightning speed; many now believe that GWA methods, which cover a fraction of the genome, are becoming obsolete. Sequencing costs continue to plunge, and within a few years sequencing entire genomes of hundreds of subjects will be financially feasible. What has the GWA experience taught us? The results from one group of GWA studies, for heart disease, are typical, with a mixed record and an uncertain legacy. > 10 2 biomarkers used in clinics 5 biomarkers approved by FDA 11 A. Schuppert / J. Bhat, DIVI Hamburg,

12 Decision support in Sepsis by single biomarkers (Single parameter) biomarkers are rarely specific & sensitive: example PCT level for inflammation to signal progression towards sepsis. PCT > 10ng/ml: high risk of Sepsis PCT < 10ng/ml: Sepsis is possible Inclusion of additional information in decision making: Patient history, clinical data, microbiology Multiple parameter decision system Specificity: 79%, Sensitivity: 77% 12 Christina Wacker et al, Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis.

13 Risk assessment by scoring systems Multiple parameter scoring: SOFA Organ dysfunction status, predicting mortality S (Scores) 13 Source: Wikipedia

14 The Big Data promises the challenge of multiple scoring for alarm systems incidence rate 5%, 1 score rate of false negatives rate of false positives Single biomarker scores: Rarely satisfactory - high false alarm rate - high rate of missed incidence noise / signal ratio incidence rate 5%, 10 scores, max.score alarm rate of false negatives rate of false positives Multiple biomarker scores: - Alarm set if 1 out of 10 scores positive noise / signal ratio Rarely satisfactory - high false alarm rate - very low rate of missed incidence

15 The Big Data promises the challenge of multiple scoring for alarm systems incidence rate 5%, 10 scores, 80% score alarm rate of false negatives rate of false positives noise / signal ratio Multiple biomarker scores: - Alarm set if 8 out of 10 scores positive Rarely satisfactory - very low false alarm rate - very high rate of missed incidence incidence rate 5%, 10 scores, mean score alarm rate of false negatives rate of false positives Most used: e.g. SOFA score noise / signal ratio Multiple biomarker scores: - Alarm set if 5 out of 10 scores positive Most satisfactory(?) - Acceptable (?) false alarm rate - Acceptable (?) rate of missed incidence

16 Content Big Data in health care hype & need Big Data the challenges of making sense of data Big Data & Sepsis chance or nightmare? Summary A. Schuppert / J. Bhat, DIVI Hamburg,

17 17 Sepsis & Big Data motivation

18 Why is Sepsis a candidate for Big Data in health care? Patients especially vulnerable to infection in ICU Very good data availability in ICU: Patients in ICU are continuously monitored by standardized multi-parametric monitoring systems with high sampling rates Lab parameters of patients in ICU are tightly monitored (Short term) patient history available Large retrospective patient cohorts are available externally. Uniklinikum Aachen:» > patient - encounters in the PDMS.» diagnosis at admission shows high degree of complexity 18 A. Schuppert / J. Bhat, DIVI Hamburg,

19 Multivariate model to predict time to septic shock AUC: 0.83 Sensitivity: 0.85 Specificity:

20 normalized data Sepsis & Big Data the challenges Example: can we predict onset of severe sepsis from data? 150 PATIENT NO 3. PATIENT NO 3. Breathing rate Heart rate Temperature O2 saturation 10 5 Breathing rate Heart rate Temperature O2 saturation time until onset of severe sepsis time until onset of severe sepsis 20 A. Schuppert / J. Bhat, DIVI Hamburg, Are these events biomarkers for onset of severe sepsis in all patients?

21 normalized data normalized data Sepsis & Big Data the challenges 10 5 PATIENT NO 3. PATIENT NO 4. Breathing rate Heart rate Temperature O2 saturation 10 5 Breathing rate Heart rate Temperature O2 saturation time until onset of severe sepsis time until onset of severe sepsis UNFORTUNATELY NO! 21 A. Schuppert / J. Bhat, DIVI Hamburg,

22 JRC-COMBINE + UKA Interdisciplinary Team JRC-COMBINE Prof. Andreas Schuppert Dr. Satya Samal Post-doc Pejman Farhadi PhD Student Jayesh Bhat PhD Student Uniklinikum Aachen 22 Dr.med Robert Deisz Intensive Care Univ-Prof. Dr. med. Gernot Marx Anaesthesiology

23 Current ongoing work Find common trends in time series in a patient cohort Cluster trends into patient sub-groups For a new patient at admission/emergency room, predict which sub-group based on lab reports, patient history etc. Prediction 23 Admission Disease Onset

24 24 Current ongoing work

25 25 Current ongoing work

26 Content Big Data in health care hype & need Big Data the challenges of making sense of data Big Data & Sepsis chance or nightmare? Summary A. Schuppert / J. Bhat, DIVI Hamburg,

27 Summary There is great potential in Big Data and especially to assist the physician to diagnose Sepsis earlier Challenging to extract valuable information from Big Data into actionable knowledge Initial findings suggest multiple routes to sepsis progression Patients can be stratified at admission with routine interventions 27 A. Schuppert / J. Bhat, DIVI Hamburg,