METASTART: A Systematic Review and Meta-Analysis of the Diagnostic Accuracy of the Simple Triage and Rapid Treatment (START) Algorithm for Disaster Triage

INTRODUCTION

The goal of disaster triage at both the prehospital and in-hospital level is to maximize resources and optimize patient outcomes. Of the disaster-specific triage methods developed to guide health care providers, the Simple Triage and Rapid Treatment (START) algorithm has become the most popular system world-wide. Despite its appeal and global application, the accuracy and effectiveness of the START protocol is not well-known.

OBJECTIVES

The purpose of this meta-analysis was two-fold: (1) to estimate overall accuracy, under-triage, and over-triage of the START method when used by providers across a variety of backgrounds; and (2) to obtain specific accuracy for each of the four START categories: red, yellow, green, and black.

METHODS

A systematic review and meta-analysis was conducted that searched Medline (OVID), Embase (OVID), Global Health (OVID), CINAHL (EBSCO), Compendex (Engineering Village), SCOPUS, ProQuest Dissertations and Theses Global, Cochrane Library, and PROSPERO. The results were expanded by hand searching of journals, reference lists, and the grey literature. The search was executed in March 2020. The review considered the participants, interventions, context, and outcome (PICO) framework and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Accuracy outcomes are presented as means with 95% confidence intervals (CI) as calculated using the binomial method. Pooled meta-analyses of accuracy outcomes using fixed and random effects models were calculated and the heterogeneity was assessed using the Q statistic.

RESULTS

Thirty-two studies were included in the review, most of which utilized a non-randomized study design (84%). Proportion of victims correctly triaged using START ranged from 0.27 to 0.99 with an overall triage accuracy of 0.73 (95% CI, 0.67 to 0.78). Proportion of over-triage was 0.14 (95% CI, 0.11 to 0.17) while the proportion of under-triage was 0.10 (95% CI, 0.072 to 0.14). There was significant heterogeneity of the studies for all outcomes (P < .0001).

CONCLUSION

This meta-analysis suggests that START is not accurate enough to serve as a reliable disaster triage tool. Although the accuracy of START may be similar to other models of disaster triage, development of a more accurate triage method should be urgently pursued.

Preparedness for a 'no-notice' mass-casualty incident: a nuclear detonation scenario

PURPOSE

An effective response for a mass-casualty incident requires understanding the relevant basic science and physical impact; detailed preparedness among jurisdictions; and clear, sequential response planning, including formal operational exercises, logistics, interagency, and public-private coordination, rapid activation of resilience, and continual improvement from lessons learned and new knowledge. This ConRad 2021 meeting report describes steps for civilian medical and public health response planning for a nuclear detonation; the utility of this type of planning for broader application; and extension of this planning to the international community.

CONCLUSION

A nuclear detonation requires a response within minutes to what will be a large-scale disaster complicated by radiation, including some elements that are similar to a broad range of incidents. The response could be further complicated if multiple incidents occur simultaneously. Required are detailed planning, preparedness and scripting for an immediate operational response, addressing clinical manifestations of evolving radiation illness, and flexibility to adapt to a rapidly changing situation. This need translates into the use of just-in-time information; effective, credible communication; situational awareness on a global scale; and a template upon which to apply capabilities in a multi-sector response. This effort is greatly facilitated using a 'playbook' approach, the basics of which are presented.

PEEP/ FIO2 ARDSNet Scale Grouping of a Single Ventilator for Two Patients: Modeling Tidal Volume Response

BACKGROUND

The COVID-19 pandemic is creating ventilator shortages in many countries that is sparking a conversation about placing multiple patients on a single ventilator. However, on March 26, 2020, six leading medical organizations released a joint statement warning clinicians that attempting this technique could lead to poor outcomes and high mortality. Nevertheless, hospitals around the United States and abroad are considering this technique out of desperation (eg, New York), but there is little data to guide their approach. The overall objective of this study is to utilize a computational model of mechanically ventilated lungs to assess how patient-specific lung mechanics and ventilator settings impact lung tidal volume (V_T).

METHODS

We developed a lumped-parameter computational model of multiple patients connected to a shared ventilator and validated it against a similar experimental study. We used this model to evaluate how patient-specific lung compliance and resistance would impact V_T under 4 ventilator settings of pressure control level, PEEP, breathing frequency, and inspiratory:expiratory ratio.

RESULTS

Our computational model predicts V_T within 10% of experimental measurements. Using this model to perform a parametric study, we provide proof-of-concept for an algorithm to better match patients in different hypothetical scenarios of a single ventilator shared by > 1 patient.

CONCLUSIONS

Assigning patients to preset ventilators based on their required level of support on the lower PEEP/higher [Formula: see text] scale of the National Institute of Health's National Heart, Lung, and Blood Institute ARDS Clinical Network (ARDSNet), secondary to lung mechanics, could be used to overcome some of the legitimate concerns of placing multiple patients on a single ventilator. We emphasize that our results are currently based on a computational model that has not been validated against any preclinical or clinical data. Therefore, clinicians considering this approach should not look to our study as an exact estimate of predicted patient V_T values.