Surge Mechanical Ventilation for the COVID-19 Surge and Future Pandemics—Time to Reframe the Strategy
Bibliographic record
Abstract
In the spring of 2020, US regions impacted early by COVID-19, such as northern New Jersey and New York City, exceeded the usual mechanical ventilation capability at numerous hospitals.As COVID-19 spread to other communities with limited immunity, similar challenges to provide surge mechanical ventilation were reported.Hospitals, health care systems, and jurisdictional authorities sought to purchase more mechanical ventilators and quickly realized that the supply chain could not accommodate the perceived need.Numerous creative engineering ideas were proposed to augment the supply of resuscitators or mechanical ventilators.Tremendous public and governmental effort focused on preventing shortages of these potentially life-saving devices and on strategies to ration them should demand exceed supply.To achieve this end, accurate determination of both the quantity and geographic distribution of ventilators at US hospitals was crucial.The last comprehensive assessment of US ventilators was completed more than a decade ago during the H1N1 pandemic.In JAMA Network Open, Tsai et al 1 provide an updated evaluation using questions added to the American Hospital Association's Annual Survey.The reported quantities, although extrapolated estimates from incomplete data and therefore subject to bias, are an important update.Although well-intentioned, the focus on ventilation devices failed to recognize what any critical care clinician knows: mechanical ventilators do not equal mechanical ventilation, and even more importantly, critical care support.The concept of "emergency mass critical care" with specific attention to mass respiratory failure was conceived in the early 2000s after the SARS-1 epidemic and the 2001 US anthrax cases. 2 Subsequent multiple professional expert consensus guidelines endorsed adequate supplies of mechanical ventilators-but importantly, they recognized that stockpiling these devices alone, without comprehensive strategies and tactics to increase adequately trained staff, safe treatment space, and other key equipment, would be insufficient to care for an influx of persons with severe respiratory failure.3,4 Long before the Great Resignation of early 2021, when many workers reconsidered their occupations, critical care professionals (eg, nurses, respiratory therapists, physicians, advanced practice clinicians, pharmacists) were in short supply to meet the baseline US critical care bestpractice staffing needs.Planning for treatment of mass respiratory failure proposed force-multiplier models that ensured expert oversight of all complex care and matched appropriate care functions to additional staff.Despite some excellent work on staff augmentation using tiered staffing models, work on implementation and evaluation of these models of care was limited.Not surprisingly, critical care staffing shortfalls were identified at the outset of the COVID-19 pandemic.Critical care clinicians in the US reported intensive care unit staffing as a critical challenge second only to personal protective equipment shortage.5 Of note, these bedside responders believed ventilator shortages were significant but were not as crucial a shortfall.Stockpiling devices for use during mass respiratory failure has merit, but the strategy must be more than "buy X ventilators."Factors that must be included in a stockpile strategy include the scope of functions for a given device, comfort with competent use and need for additional and repeated training among staff, storage readiness and device transport, integration with local hospital equipment, ownership, and periodic evaluations to ensure the devices remain consistent with evolving practice.The US Strategic National Stockpile had approximately 20 000 devices before the COVID-19 pandemic.The LTV-1200 (Vyaire Medical) was the most recent addition and constituted +
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".