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Multicenter Study
. 2020 Jun;48(6):790-798.
doi: 10.1097/CCM.0000000000004331.

Morbidity and Mortality in Critically Ill Children. I. Pathophysiologies and Potential Therapeutic Solutions

Murray M Pollack  1 , Russell Banks  2 , Richard Holubkov  2 , Kathleen L Meert  3 ; Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network
Collaborators, Affiliations
Multicenter Study

Morbidity and Mortality in Critically Ill Children. I. Pathophysiologies and Potential Therapeutic Solutions

Murray M Pollack et al. Crit Care Med. 2020 Jun.

Abstract

Objectives: Developing effective therapies to reduce morbidity and mortality requires knowing the responsible pathophysiologies and the therapeutic advances that are likely to be impactful. Our objective was to determine at the individual patient level the important pathophysiological processes and needed therapeutic additions and advances that could prevent or ameliorate morbidities and mortalities.

Design: Structured chart review by pediatric intensivists of PICU children discharged with significant new morbidity or mortality to determine the pathophysiologies responsible for poor outcomes and needed therapeutic advances.

Setting: Multicenter study (eight sites) from the Collaborative Pediatric Critical Care Research Network of general and cardiac PICUs.

Patients: First PICU admission of patients from December 2011 to April 2013.

Interventions: None.

Measurements and main results: Two-hundred ninety-two patients were randomly selected from 681 patients discharged with significant new morbidity or mortality. The median age was 2.4 years, 233 (79.8%) were in medical/surgical ICUs, 59 (20.2%) were in cardiac ICUs. Sixty-five (22.3%) were surgical admissions. The outcomes included 117 deaths and 175 significant new morbidities. The most common pathophysiologies contributing to the poor outcomes were impaired substrate delivery (n = 158, 54.1%) and inflammation (n = 104, 35.6%). There were no strong correlations between the pathophysiologies and no remarkable clusters among them. The most common therapeutic needs involved new drugs (n = 149, 51.0%), cell regeneration (n = 115, 39.4%), and immune and inflammatory modulation (n = 79, 27.1%). As with the pathophysiologies, there was a lack of strong correlations or meaningful clusters in the suggested therapeutic needs.

Conclusions: There was no single dominant pathophysiology or cluster of pathophysiologies responsible for poor pediatric critical care outcomes. Therapeutic needs often involved therapies that are not close to implementation such as cell regeneration, improved organ transplant, improved extracorporeal support and artificial organs, and improved drugs.

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Conflict of interest statement

Copyright form disclosure: Drs. Pollack, Banks, Holubkov, Meert, Berg, Bolton, Dean, Mourani, Newth, and Wessel’s institutions received funding from the National Institutes of Health (NIH). Drs. Pollack, Banks, Holubkov, Meert, Berg, Bolton, Carcillo, Dean, Hall, Mourani, Newth, Priestley, Wessel and Yates received support for article research from the NIH. Drs. Banks and Bolton disclosed government work. Dr. Holubkov received funding from Pfizer (DSMB), Medimmune (DSMB), Physicians Committee for Responsible Medicine (biostatistical consulting), Revance (DSMB), Armaron Bio (DSMB), and DURECT Corporation (biostatistical consulting). Drs. Carcillo, Hall, and Yates institutions received funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Dr. Hall received funding from LaJolla Pharmaceuticals (DSMB service). Dr. Newth received funding from Philips Research North America and Hamilton Medical AG. Dr. Priestley’s institution received funding from Collaborative Pediatric Critical Care Research Network (CPCCRN) of the NICHD. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Clustering of Pathophysiologies and Therapeutic Innovations. In this Figure, the algorithm recursively combines the pathophysiologies into clusters. The clustering process is seen from bottom to top, with the height of each “branch” reflecting relative similarities between clusters using the Euclidean distance. Longer “branches” indicate weaker associations. Abbreviations: Impaired Substrate Delivery = Substrate Del; Electrical Signaling Dysfunction = Elec Sign D; Abnormal Growth / Abnormal Cell Cycle = Abn Growth; Capillary / Vascular Dysfunction = Cap/Vasc D; Immune Dysfunction = Immune D; Coagulation Dysfunction = Coagulation D; Mitochondrial Dysfunction = Mit D. Cell Regeneration = Regeneration; Immune and Inflammatory Modulation = Immune Mod; Extra-corporeal Support and Artificial Organs = ECS & Art Org; Organ Transplant = Transplant; Mechanical Respiratory Support = Mech Resp S; Nutritional Support = Nutrition; Therapeutic Devices = Ther Devices; Monitoring Devices = Monit Devices; Blood and Blood Products = Blood; Renal Replacement and Plasmapheresis = RR & Plasma; Mitochondrial Support = Mit S; Inhaled Respiratory Support = Inhaled Resp S; Suspended Animation = Susp Anim.
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