Cohort 1

Ari Ercole

  • Consultant in Neurointensive Care
  • Cambridge University Hospitals
  • @AriErcole
  • 2019

My original, primary focus for my Topol Fellowship was to characterise the early warning scores that help to identify patients who are deteriorating in the resource-limited ward setting after major elective surgery and to use machine learning to predict the likelihood of these determinants and their impact on length of stay.

Personalising high-risk perioperative care with dynamic models of clinical trajectory

At the beginning of my fellowship...

My intention was to characterize the determinants and trajectory of post-operative complications after major surgery and develop a machine learning algorithm to predict them and their impact on length of stay.

During my fellowship...

A successful computational phenotype was developed and validated so that post-operative complications can be identified at scale without human intervention as basis for the Topol fellowship work (Development and Validation of an Electronic Postoperative Morbidity Score. Stubbs DJ et al (Ercole A). Anesth Analg. 2019;129(4):935-942.)

This was applied to characterise the surgical pathway of chronic subdural patients (Identification of factors associated with morbidity and postoperative length of stay in surgically managed chronic subdural haematoma using electronic health records: a retrospective cohort study. Stubbs DJ, (Ercole A). BMJ Open. 2020;10(6):e037385). This digital project has led to a nationwide project to intervene and improve this pathway.

Methodological work in developing a suitable timeseries machine learning methodology suitable for incorporating unselected variables has been published (Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation. Jacob Deasy, (Ercole A) Sci Rep. 2020;10(1):22129).

Although not part of my fellowship, I have completed (as PI) a multicentre phase II study of a digital intervention for individualized treatment of severe traumatic brain injury (COGITATE study). This will report shortly.

As an intensive care consultant, work has been severely disrupted by COVID-19. I led a group predicting ICU surge capacity. We were able to create a model and make it available within a week using agile techniques (https://www.medrxiv.org/content/10.1101/2020.03.19.20039057v3) and this was used to the inform NHS response locally in March 2020.

I have published a number of other peer-reviewed publications in machine learning methodology.

Lessons learned:

  1. Methodological challenges are visible and obvious targets but non-technical skills are crucial in implementing transformation.
  2. COVID-19 has been a disruptive influence. However, it has brought the benefits of data-driven decision-making to the forefront and removed some barriers to change.
  3. Agile data-driven techniques are not embedded in the NHS. However, they can be implemented and useful in defining national policy.

Links, writing and work that I completed while I was a Topol Fellow.

Deasy,J., Liò, P.,& Ercole, A. (2020). Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation. Scientific Reports, 10(1).  doi:10.1038/s41598-020-79142-z

Åkerlund, C. A. I., Donnelly,J., Zeiler, F. A., Helbok, R., Holst, A., Cabeleira, M., Nelson, … D. W.(n.d.). Impact of duration and magnitude of raised intracranial pressure on outcome after severe traumatic brain injury: A CENTER-TBI high-resolution group study. PLOS ONE, 15(12), e0243427.  doi:10.1371/journal.pone.0243427

van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., . . . Ercole, A. (n.d.). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning.  doi:10.1007/s10994-020-05928-x

Robba, C., Rebora, P.,Banzato, E., Wiegers, E. J. A., Stocchetti, N., Menon, D. K., . . . Zeiler, F. A. (2020). Incidence, Risk Factors, and Effects on Outcome of VentilatorAssociated Pneumonia in Patients With Traumatic Brain Injury. Chest, 158(6), 2292-2303.  doi:10.1016/j.chest.2020.06.064

Rass, V.,Huber, L., Ianosi, B. -A., Kofler, M., Lindner, A., Picetti, E., . . . Zeiler, F. A. (n.d.). The Effect of Temperature Increases on Brain Tissue Oxygen Tension in Patients with Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Substudy. Therapeutic Hypothermia and Temperature Management.  doi:10.1089/ther.2020.0027

Kunzmann, K., Wernisch, L., Richardson, S., Steyerberg, E. W., Lingsma, H., Ercole, A., . . . Wilson, L. (2020). Imputation of Ordinal Outcomes: A Comparison of Approaches in Traumatic Brain Injury. Journal of Neurotrauma.  doi:10.1089/neu.2019.6858

Robba, C., Poole, D., McNett, M., Asehnoune, K., Bösel, J., Bruder, N., . . . Stevens, R. D. (2020). Mechanical ventilation in patients with acute brain injury: recommendations of the European Society of Intensive Care Medicine consensus. Intensive Care Medicine, 46(12), 2397-2410.  doi:10.1007/s00134-020-06283-0

Ercole, A. (2021). Normalising renal tissue oxygen tension with higher inspired oxygen concentration may be falsely reassuring. Comment on Br J Anaesth 2020;125:192–200. British Journal of Anaesthesia, 126(1), e32.  doi:10.1016/j.bja.2020.10.017

Zeiler, F. A., Mathieu, F., Monteiro, M., Glocker, B., Ercole, A., Cabeleira, M., . . . Zeiler, F.A. (n.d.). Systemic Markers of Injury and Injury Response Are Not Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTERTBI) Study. Journal of Neurotrauma.  doi:10.1089/neu.2020.7304

Carra, G., Güiza, F., Depreitere, B., & Meyfroidt, G. (n.d.). Prediction model for intracranial hypertension demonstrates robust performance during external validation on the CENTER-TBI dataset. Intensive Care Medicine.  doi:10.1007/s00134-020-06247-4

Harrois, A., Anstey,J. R., van der Jagt, M., Taccone, F. S., Udy, A. A., Citerio, G., . . . Bellomo, R. (n.d.). Variability in Serum Sodium Concentration and Prognostic Significance in Severe Traumatic Brain Injury: A Multicenter Observational Study. Neurocritical Care.  doi:10.1007/s12028-020-01118-8

Gravesteijn, B. Y.,Sewalt, C. A., Nieboer, D., Menon, D. K., Maas, A., Lecky, F., . . . Zoerle, T. (2020). Tracheal intubation in traumatic brain injury: a multicentre prospective observational study. British Journal of Anaesthesia, 125(4), 505-517.  doi:10.1016/j.bja.2020.05.067

Price, J., Sandbach, D. D., Ercole, A., Wilson, A., & Barnard, E. B. G. (2020). End-tidal and arterial carbon dioxide gradient in serious traumatic brain injury after prehospital emergency anaesthesia: a retrospective observational study. Emergency Medicine Journal, 37(11), 674-679.  doi:10.1136/emermed-2019-209077

Zeiler, F. A., Ercole, A., Cabeleira, M., Stocchetti, N., Hutchinson, P.J., Smielewski, P.,& Czosnyka, M. (2020). Descriptive analysis of low versus elevated intracranial pressure on cerebral physiology in adult traumatic brain injury: a CENTER-TBI exploratory study. Acta Neurochirurgica, 162(11), 2695-2706. doi:10.1007/s00701-020-04485-5

Gravesteijn, B. Y.,Sewalt, C. A., Stocchetti, N., Citerio, G., Ercole, A., Lingsma, H. F., . . . collaborators, C. -T. (2020). Prehospital Management of Traumatic Brain Injury across Europe: A CENTER-TBI Study. Prehospital Emergency Care, 1-15.  doi:10.1080/10903127.2020.1817210

Zeiler, F. A., Ercole, A., Placek, M. M., Hutchinson, P.J., Stocchetti, N., Czosnyka, M., & Smieleweski, P.(n.d.). Association Between Physiologic Signal Complexity and Outcomes in Moderate and Severe Traumatic Brain Injury: A CENTER-TBI Exploratory Analysis of Multiscale Entropy. Journal of Neurotrauma.  doi:10.1089/neu.2020.7249

Jacob, L., Cogné, M., Tenovuo, O., Røe, C., Andelic, N., Majdan, M., . . . Zoerle, T. (2020). Predictors of Access to Rehabilitation in the Year Following Traumatic Brain Injury: A European Prospective and Multicenter Study. Neurorehabilitation and Neural Repair, 34(9), 814-830.  doi:10.1177/1545968320946038

Andelic, N., Røe, C., Brunborg, C., Zeldovich, M., Løvstad, M., Løke, D., . . . von Steinbuechel, N. (n.d.). Frequency of fatigue and its changes in the first 6 months after traumatic brain injury: results from the CENTER-TBI study. Journal of Neurology.  doi:10.1007/s00415-020-10022-2

Voormolen,D. C., Polinder, S., von Steinbuechel, N., Feng, Y.,Wilson, L., Oppe, M., & Haagsma, J. A. (2020). Health-related quality of life after traumatic brain injury: deriving value sets for the QOLIBRI-OS for Italy, The Netherlands and The United Kingdom. Quality of Life Research, 29(11), 3095-3107.  doi:10.1007/s11136-020-02583-6

Baqui, P.,Bica, I., Marra, V.,Ercole, A., & van der Schaar, M. (2020). Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. The Lancet Global Health, 8(8), e1018-e1026.  doi:10.1016/s2214-109x(20)30285-0

Stubbs, D. J., Davies, B. M., Bashford, T.,Joannides, A. J., Hutchinson, P.J., Menon, D. K., … Burnstein, R. M. (2020). Identification of factors associated with morbidity and postoperative length of stay in surgically managed chronic subdural haematoma using electronic health records: a retrospective cohort study. BMJ Open, 10(6), e037385.  doi:10.1136/bmjopen-2020-037385

Chau, C. Y.C., Mediratta, S., McKie, M. A., Gregson, B., Tulu, S., Ercole, A., . . . Kolias, A. G. (n.d.). Optimal Timing of External Ventricular Drainage after Severe Traumatic Brain Injury: A Systematic Review. Journal of Clinical Medicine, 9(6), 1996.  doi:10.3390/jcm9061996

Qian, Z., Alaa, A. M., van der Schaar, M., & Ercole, A. (2020). Between-centre differences for COVID-19 ICU mortality from early data in England. Intensive Care Medicine, 46 (9), 1779-1780.  doi:10.1007/s00134-020-06150-y

Lindblad, C., Nelson, D. W., Zeiler, F. A., Ercole, A., Ghatan, P.H., von Horn, H., . . . Thelin, E. P.(2020). Influence of Blood–Brain Barrier Integrity on Brain Protein Biomarker Clearance in Severe Traumatic Brain Injury: A Longitudinal Prospective Study. Journal of Neurotrauma, 37(12), 1381-1391.  doi:10.1089/neu.2019.6741

Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans (n.d.). British Journal of Surgery. doi:10.1002/bjs.11746

van Wijk, R. P.J., van Dijck, J. T.J. M., Timmers, M., van Veen,E., Citerio, G., Lingsma, H. F., . . . Zoerle, T. (2020). Informed consent procedures in patients with an acute inability to provide informed consent: Policy and practice in the CENTER-TBI study. Journal of Critical Care, 59, 6-15.  doi:10.1016/j.jcrc.2020.05.004

Ercole, A., Brinck, V.,George, P.,Hicks, R., Huijben, J., Jarrett, M., . . . Wilson, L. (2020). Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD). Journal of Clinical and Translational Science, 4(4), 354-359. doi:10.1017/cts.2020.24

Zeiler, F. A., Cabeleira, M., Hutchinson, P.J., Stocchetti, N., Czosnyka, M., Smielewski, P.,& Ercole, A. (n.d.). Evaluation of the relationship between slow-waves of intracranial pressure, mean arterial pressure and brain tissue oxygen in TBI: a CENTER-TBI exploratory analysis. Journal of Clinical Monitoring and Computing. doi: 10.1007/s10877-020-00527-6

Fleuren, L. M., Thoral, P., Shillan, D., Ercole, A., & Elbers, P.W. G. (2020). Machine learning in intensive care medicine: ready for take-off?. Intensive Care Medicine, 46(7), 1486-1488.  doi:10.1007/s00134-020-06045-y

Timmers, M., van Dijck, J. T.J. M., van Wijk, R. P.J., Legrand, V.,van Veen,E., Maas, A. I. R., . . . Kompanje, E. J. O. (2020). How do 66 European institutional review boards approve one protocol for an international prospective observational study on traumatic brain injury? Experiences from the CENTER-TBI study. BMC Medical Ethics, 21(1).  doi:10.1186/s12910-020-00480-8

Zeiler, F. A., Beqiri, E., Cabeleira, M., Hutchinson, P.J., Stocchetti, N., Menon, D. K., . . . Zeiler, F. A. (2020). Brain Tissue Oxygen and Cerebrovascular Reactivity in Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Exploratory Analysis of Insult Burden. Journal of Neurotrauma, 37(17), 1854-1863.  doi:10.1089/neu.2020.7024

Gravesteijn, B. Y.,Nieboer, D., Ercole, A., Lingsma, H. F., Nelson, D., van Calster, B., . . . Zoerle, T. (2020). Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. Journal of Clinical Epidemiology , 122, 95-107.  doi:10.1016/j.jclinepi.2020.03.005

Zeiler, F. A., Mathieu, F., Monteiro, M., Glocker, B., Ercole, A., Beqiri, E., . . . Younsi, A. (2020). Diffuse Intracranial Injury Patterns Are Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A CENTER-TBI Validation Study. Journal of Neurotrauma, 37(14), 1597-1608.  doi:10.1089/neu.2019.6959

Huijben, J. A., Wiegers, E. J. A., Ercole, A., de Keizer, N. F., Maas, A. I. R., Steyerberg, E. W., . . . van der Jagt, M. (2020). Quality indicators for patients with traumatic brain injury in European intensive care units: a CENTER-TBI study. Critical Care, 24(1). doi:10.1186/s13054-020-2791-0

Deasy,J., Rocheteau, E., Kohler, K., Stubbs, D. J., Barbiero, P.,Liò, P.,& Ercole, A. (n.d.). Forecasting Ultra-early Intensive Care Strain from COVID-19 in England, v1.1.4.  doi:10.1101/2020.03.19.20039057

Huijben, J. A., Wiegers, E. J. A., Lingsma, H. F., Citerio, G., Maas, A. I. R., Menon, D. K., . . . Stocchetti, N. (2020). Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe: a CENTER-TBI analysis. Intensive Care Medicine, 46(5), 995-1004.  doi:10.1007/s00134-020-05965-z

Shen, B., Yin, J., Menon, D., Ercole, A., Carpenter, A., Painter, T., . . . Wang, W.(2020). Development of an HTS magnet for ultra-compact MRI System: Optimization using genetic algorithm (GA) Method. IEEE Transactions on Applied Superconductivity, 30 (4).  doi:10.1109/TASC.2020.2974417

Kohler, K., & Ercole, A. (2020). Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services. BMJ Open, 10(2), e034265.  doi:10.1136/bmjopen-2019-034265

Stubbs, D. J., Grimes, L. A., & Ercole, A. (n.d.). Performance of cardiopulmonary exercise testing for the prediction of post-operative complications in non cardiopulmonary surgery: A systematic review. PLOS ONE, 15(2), e0226480.  doi:10.1371/journal.pone.0226480

Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., . . . Elbers, P.W. G. (2020). Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine, 46(3), 383-400.  doi:10.1007/s00134-019-05872-y