Drowning in Data A day on the ICU Dr Dan Harvey Consultant - - PowerPoint PPT Presentation

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Drowning in Data A day on the ICU Dr Dan Harvey Consultant - - PowerPoint PPT Presentation

Jan 04, 2024 422 likes •726 views

Drowning in Data A day on the ICU Dr Dan Harvey Consultant Critical Care, Nottingham University Hospitals Consultant Critical Care, Nottingham University Hospitals Hon. Associate Prof. University of Nottingham FICM Professional

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Drowning in Data – A day on the ICU

Dr Dan Harvey Consultant Critical Care, Nottingham University Hospitals

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Consultant Critical Care, Nottingham University Hospitals

  • Hon. Associate Prof.

University of Nottingham FICM Professional Standards NIHR Critical Care Specialty Lead - East Midlands

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Objectives

  • What does the data look like ?
  • Why is it difficult for us to analyse it ?
  • The Result….
  • The Opportunity
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10 20 30 40 50 60 70 80 90 100 01-Nov 03-Nov 05-Nov 07-Nov 09-Nov 11-Nov 13-Nov 15-Nov 17-Nov 19-Nov 21-Nov 23-Nov 25-Nov 27-Nov 29-Nov 01-Dec 03-Dec 05-Dec 07-Dec 09-Dec 11-Dec 13-Dec 15-Dec 17-Dec 19-Dec 21-Dec 23-Dec 25-Dec 27-Dec 29-Dec 31-Dec 02-Jan

Patients whose observations meet criteria for High Risk Red Sepsis (NICE, 2016), who have an infection* (blue), of which have an EWS of 3 or more (red). Data from 1.11.17-3.1.18.

Matched condition Yes Of which EWS >2

*Based on FreeText analysis of eHandover

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5 10 15 20 25 30 35 40 45

Adult emergency admission patients with initial EWS of 4 or more on arrival at hospital

NUH | Observations for arrivals between 1 November 2017 and 3 January 2018 Source: Nervecentre and Medway PAS

Patients with EWS>=4

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1 Occ 8 Occ 15 Occ 2 Occ 9 Occ 16 Occ 3 Occ 10 Occ 17 Occ 4 Occ 11 Occ 18 Occ 5 Occ 12 Occ 19 Occ 6 Occ 13 Occ 20 Occ 7 Occ 14 Occ 21 Occ (Recovery + 2, ED Resus + 1, Theatres + 1)

Resource

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  • Mr. A

Hepatectomy – 3 hours Cancer

  • Mr. B

Colectomy – 3 hours Cancer

  • Mrs. C

Laryngectomy & Flap – 8 hours Cancer

  • Mr. D

Spinal Fusion Cancelled x 1

  • Mrs. E

Aortic Aneurysm Repair

Planned Workload

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Mortality among Patients Admitted to Strained Intensive Care

  • Units. Gabler et al. Am J Respir Crit Care Med. 2013 Oct 1; 188(7):

800–806.

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  • Present data rather than analyses it
  • Fails to recognise relationships
  • Fails to use models for prediction
  • …assumes mental models & patterns

are correct

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10 20 30 40 50 60 70

Could we predict this ?

Blood Sugar Insulin Rate NG Feed

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2 4 6 8 10 12 14

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Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU Curtis E Kennedy & James P Turley Theoretical Biology and Medical Modelling20118:40

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Rx

  • Harm
  • Benefit

Control •Harm

  • Benefit
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Mechanical Support Cardiovascular Respiratory Pharmacology Metabolism Cellular Function

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Precision digital monitors Model Recommend Action Effect Machine Learning Personalised Model

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