Clinical paper| Volume 151, P91-98, June 2020

Optimal in-hospital defibrillator placement



      To determine if mathematical optimization of in-hospital defibrillator placements can reduce in-hospital cardiac arrest-to-defibrillator distance compared to existing defibrillators in a single hospital.


      We identified treated IHCAs and defibrillator placements in St. Michael's Hospital in Toronto, Canada from Jan. 2013 to Jun. 2017 and mapped them to a 3-D computer model of the hospital. An optimization model identified an equal number of optimal defibrillator locations that minimized the average distance between IHCAs and the closest defibrillator using a 10-fold cross-validation approach. The optimized and existing defibrillator locations were compared in terms of average distance to the out-of-sample IHCAs. We repeated the analysis excluding intensive care units (ICUs), operating theatres (OTs), and the emergency department (ED). We also re-solved the model using fewer defibrillators to determine when the average distance matched the performance of existing defibrillators.


      We identified 433 treated IHCAs and 53 defibrillators. Of these, 167 IHCAs and 31 defibrillators were outside of ICUs, OTs, and the ED. Optimal defibrillator placements reduced the average IHCA-to-defibrillator distance from 16.1 m to 2.7 m (relative decrease of 83.0%; P = 0.002) compared to existing defibrillator placements. For non-ICU/OT/ED IHCAs, the average distance was reduced from 24.4 m to 11.9 m (relative decrease of 51.3%; P = 0.002. 8–9 optimized defibrillator locations were sufficient to match the average IHCA-to-defibrillator distance of existing defibrillator placements.


      Optimization-guided placement of in-hospital defibrillators can reduce the distance from an IHCA to the closest defibrillator. Equivalently, optimization can match existing defibrillator performance using far fewer defibrillators.


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        • Morrison L.J.
        • Neumar R.W.
        • Zimmerman J.L.
        • et al.
        Strategies for improving survival after in-hospital cardiac arrest in the United States: 2013 consensus recommendations: a consensus statement from the American Heart Association.
        Circulation. 2013; 127: 1538-1563
        • Benjamin E.J.
        • Muntner P.
        • Alonso A.
        • et al.
        Heart disease and stroke statistics-2019 update: a report from the American Heart Association.
        Circulation. 2019; 139: e56-e528
        • Andersen L.W.
        • Holmberg M.J.
        • Berg K.M.
        • Donnino M.W.
        • Granfeldt A.
        In-hospital cardiac arrest: a review.
        JAMA. 2019; 321: 1200-1210
        • Holmberg M.J.
        • Ross C.E.
        • Fitzmaurice G.M.
        • et al.
        Annual incidence of adult and pediatric in-hospital cardiac arrest in the United States.
        Circ Cardiovasc Qual Outcomes. 2019; 12: e005580
        • Peberdy M.A.
        • Kaye W.
        • Ornato J.P.
        • et al.
        Cardiopulmonary resuscitation of adults in the hospital: a report of 14720 cardiac arrests from the National Registry of Cardiopulmonary Resuscitation.
        Resuscitation. 2003; 58: 297-308
        • Girotra S.
        • Nallamothu B.K.
        • Spertus J.A.
        • Li Y.
        • Krumholz H.M.
        • Chan P.S.
        Trends in survival after in-hospital cardiac arrest.
        N Engl J Med. 2012; 367: 1912-1920
        • Spearpoint K.G.
        • McLean C.P.
        • Zideman D.A.
        Early defibrillation and the chain of survival in “in-hospital” adult cardiac arrest; minutes count.
        Resuscitation. 2000; 44: 165-169
        • Chan P.S.
        • Krumholz H.M.
        • Nichol G.
        • Nallamothu B.K.
        Delayed time to defibrillation after in-hospital cardiac arrest.
        N Engl J Med. 2008; 358: 9-17
        • Bircher N.G.
        • Chan P.S.
        • Xu Y.
        Delays in cardiopulmonary resuscitation, defibrillation, and epinephrine administration all decrease survival in in-hospital cardiac arrest.
        Anesthesiology. 2019; 130: 414-422
        • Kern K.B.
        • Paraskos J.A.
        31st Bethesda conference, emergency cardiac care. Task force 1: cardiac arrest.
        J Am Coll Cardiol. 2000; 35: 832-846
        • Cummins R.O.
        • Ornato J.P.
        • Thies W.H.
        • et al.
        Improving survival from sudden cardiac arrest: The “chain of survival” concept. A statement for health professionals from the advanced cardiac life support subcommittee and the emergency cardiac care committee, American Heart Association.
        Circulation. 1991; 83: 1832-1847
        • Kronick S.L.
        • Kurz M.C.
        • Lin S.
        • et al.
        Part 4: systems of care and continuous quality improvement.
        Circulation. 2015; 132: S397-S413
        • Chan T.C.Y.
        • Li H.
        • Lebovic G.
        • et al.
        Identifying locations for public access defibrillators using mathematical optimization.
        Circulation. 2013; 127: 1801-1809
        • Sun C.L.F.
        • Demirtas D.
        • Brooks S.C.
        • Morrison L.J.
        • Chan T.C.Y.
        Overcoming spatial and temporal barriers to public access defibrillators via optimization.
        J Am Coll Cardiol. 2016; 68: 836-845
        • Chan T.C.Y.
        • Demirtas D.
        • Kwon R.H.
        Optimizing the deployment of public access defibrillators.
        Manage Sci. 2016;
        • Sun C.L.F.
        • Karlsson L.
        • Torp-Pedersen C.
        • Morrison L.J.
        • Folke F.
        • Chan T.C.Y.
        Spatiotemporal AED optimization is generalizable.
        Resuscitation. 2018; 131: 101-107
        • Sun C.L.F.
        • Karlsson L.
        • Torp-Pedersen C.
        • et al.
        In silico trial of optimized versus actual public defibrillator locations.
        J Am Coll Cardiol. 2019; 74: 1557-1567
        • Dao T.H.D.
        • Zhou Y.
        • Thill J.C.
        • Delmelle E.
        Spatio-temporal location modeling in a 3D indoor environment: the case of AEDs as emergency medical devices.
        Int J Geogr Inf Sci. 2012; 26: 469-494
        • Lee C.T.
        • Lee Y.C.
        • Chen A.Y.
        In-building automated external defibrillator location planning and assessment through building information models.
        Autom Constr. 2019; 1028: 83
        • Hakimi S.L.
        Optimum locations of switching centers and the absolute centers and medians of a graph.
        Oper Res. 1964; 12: 450-459
        • Hakimi S.L.
        Optimum distribution of switching centers in a communication network and some related graph theoretic problems.
        Oper Res. 1965; 13: 462-475
        • ReVelle C.S.
        • Swain R.W.
        Central facilities location.
        Geogr Anal. 1970; 2: 30-42
        • Mandell M.B.
        • Becker L.R.
        A model for locating automatic external defibrillators.
        Socioecon Plann Sci. 1996; 30: 51-66
        • Rauner M.S.
        • Bajmoczy N.
        How many AEDs in which region? An economic decision model for the Austrian red cross.
        Eur J Oper Res. 2003; 150: 3-18
        • Myers D.C.
        • Mohite M.
        Locating automated external defibrillators in a university community.
        J Oper Res Soc. 2009; 60: 869-872
        • Boutilier J.J.
        • Brooks S.C.
        • Janmohamed A.
        • et al.
        Optimizing a drone network to deliver automated external defibrillators.
        Circulation. 2017; 135: 2454-2465
        • Chan T.C.Y.
        • Shen Z.J.M.
        • Siddiq A.
        Robust defibrillator deployment under cardiac arrest location uncertainty via row-and-column generation.
        Oper Res. 2018; 66: 358-379
        • Cummins R.O.
        • Chamberlain D.
        • Hazinski M.F.
        • et al.
        Recommended guidelines for reviewing, reporting, and conducting research on in-hospital resuscitation: the in-hospital “Utstein style”.
        Circulation. 1997; 95: 2213-2239
        • Dijkstra E.W.
        A note on two problems in connexion with graphs.
        Numer Math. 1959; 1: 269-271
        • Sandroni C.
        • Ferro G.
        • Santangelo S.
        • et al.
        In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response.
        Resuscitation. 2004; 62: 291-297
        • Herbers M.D.
        • Heaser J.A.
        Implementing an in situ mock code quality improvement program.
        Am J Crit Care. 2016; 25: 393-399
        • Sondergaard K.B.
        • Hansen S.M.
        • Pallisgaard J.L.
        • et al.
        Out-of-hospital cardiac arrest: probability of bystander defibrillation relative to distance to nearest automated external defibrillator.
        Resuscitation. 2018; 124: 138-144
        • Neves Briard J.
        • De Montigny L.
        • Ross D.
        • De Champlain F.
        • Segal E.
        Is distance to the nearest registered public automated defibrillator associated with the probability of bystander shock for victims of out-of-hospital cardiac arrest?.
        Prehosp Disaster Med. 2018; 33: 153-159