Exploring the Potential of Machine Learning in Optimizing Respiratory Failure Treatment

Machine learning holds promise for optimizing treatment strategies and potentially improving outcomes in respiratory failure but future research and development are necessary to fully realize its potential in clinical practice. | Image Credit: Selvi - stock.adobe.com

Machine learning (ML) holds significant potential for predicting and improving outcomes in acute respiratory failure by leveraging vast amounts of patient data, but its successful integration into clinical practice is contingent upon overcoming challenges related to data quality, system heterogeneity, clinician acceptance, and ensuring health equity, according to a study published in Critical Care.1

The global rise in acute respiratory failure (ARF) has made mechanical ventilation an increasingly critical, but risky, treatment.2 In intensive care units (ICUs), where 35% to 50% of patients require this support, ARF is associated with a high mortality rate of 67.2%. The treatment itself can be dangerous, with lung-related complications from mechanical ventilation contributing to 40% of in-hospital deaths.

Invasive mechanical ventilation is not only expensive, with ICU costs averaging $2300 per day and increasing to over $3900 after 4 days, but it also takes a heavy toll on patients and their families.3 To address this, researchers investigated whether ML could improve the prediction of respiratory failure.1 An expert panel reviewed existing literature and discussed how ML could be applied to better forecast the onset and progression of this condition.

ML and Data Integration in Clinical Settings

Hospitals need to combine data from many sources to manage patient information effectively.1 Integrating large language models (LLMs) could help by incorporating unstructured data like clinical notes, thereby improving predictive capabilities and clinical decision-making.

Both ML and deep learning are being used in modern clinical decision support systems. While ML models have been applied to predict ARF during invasive ventilation, challenges remain with existing deep learning models. Key barriers to implementation include integrating these analytic platforms into electronic health records (EHRs), gaining clinician acceptance, and maintaining the models' accuracy across different patient populations and clinical practices.

ML's Clinical Utility for Respiratory Outcomes

During their discussion on critical respiratory outcomes, panelists reached a consensus that predicting the emergence and progression of respiratory failure represented the most actionable objective. The requirement for invasive mechanical ventilation was also identified as a key outcome of interest. A primary priority was to emphasize the significant impact of having early knowledge of a patient's likelihood of respiratory failure progression. Such advanced warning would provide clinicians with sufficient lead time to collect and interpret targeted diagnostics, thereby enabling potential interventions to avert clinical deterioration.

“There was debate regarding the optimal prediction horizon length, but most agreed that 12 to 24 hours before the onset of respiratory failure would be a useful window to implement preventative strategies,” study authors found.

Effective clinical collaboration and setting appropriate risk thresholds are vital for good decision-making. By only notifying clinicians at significant thresholds, alarm fatigue and over-notification can be avoided. This approach allows for predicting a patient's response to interventions, which can inform both clinical decisions and trial design. The most valuable strategy, panelists agreed, is to predict and track a patient's progression through different levels of respiratory support.

Barriers to the Development and Implementation of ML Models

Deep learning models face challenges at both the patient and system levels. Patient-level difficulties arise from the wide variety of underlying pathologies, types of respiratory failure, and treatment approaches, which complicate model development. For ML models, inconsistencies in the source, format, frequency, and quality of data can compromise accuracy. Furthermore, emergency procedures like intubation are often not accurately recorded or time-stamped in EHRs, making it difficult to properly classify events and ensure model precision.

Substantial heterogeneity across health systems, regional practice patterns, and resource availability complicates the development of generalizable ML models. Successful integration and deployment are contingent upon a safe, efficient, and adequate interface between the EHR and the analytics platform.

Physician hesitancy regarding the deployment of AI models in the ICU environment is a significant barrier. This can be mitigated by promoting transparency and avoiding "black box" models, which can foster greater clinician acceptance and collaboration. Consequently, a robust system for continuous monitoring and iterative improvement of model performance is a necessary component of successful implementation.

Strategies for Successful ML Integration and Validation

Successful ML models must not only perform well initially but also maintain their effectiveness across diverse systems and patient groups. To prepare for full clinical deployment, it's necessary to implement strategies that enhance predictive abilities, detect bugs, and evaluate false positives and negatives. The panel emphasized that a key step in evaluating a model's effectiveness is to compare its performance in a clinical setting against the existing standard of care. To gain wider acceptance, panelists suggested that future trial designs should prioritize prospective, multicenter studies.

Overcoming Bias and Resource Gaps in AI for Health Care

A key concern regarding technological advancements in health care is their potential to disproportionately impact socioeconomically disadvantaged patients who have limited access to supporting resources and infrastructure. Nevertheless, when ML algorithms are appropriately designed, they possess the potential to advance health equity rather than exacerbate existing disparities.

Further considerations from a health equity perspective involve the risk that health systems with fewer resources may be delayed in adopting these new technologies, thereby widening health disparities. Additionally, available data may be inherently biased. While natural language processing and LLMs can assist in analyzing progress notes, implicit bias can also manifest through subtle linguistic patterns. Consequently, it is imperative to address these health disparities during the development of any ML model to ensure equitable performance and mitigate bias.

“Enhancing predictive capabilities through ML could facilitate a more proactive approach to patient care, potentially improving outcomes. However, many challenges must be addressed to achieve meaning­ful integration into clinical care,” study authors concluded.

References

1. Pearce AK, Nemati S, Goligher EC, et al. Can we predict the future of respiratory failure prediction? Crit Care. 2025;29(1). doi:10.1186/s13054-025-05484-7
2. Demem K, Tesfahun E, Nigussie F, Shibabaw AT, Ayenew T, Messelu MA. Time to death and its predictors among adult patients on mechanical ventilation admitted to intensive care units in West Amhara comprehensive specialized hospitals, Ethiopia: a retrospective follow-up study. BMC Anesthesiol. 2024;24(1). doi:10.1186/s12871-024-02495-9
3. Getting patients off the ventilator faster: facilitator guide. Agency for Healthcare Research and Quality. February 2017. Accessed August 8, 2025. https://www.ahrq.gov/hai/tools/mvp/modules/vae/overview-off-ventilator-fac-guide.html