Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care

Variability of oxygen requirements in critically ill COVID-19 patients

Background

The global scarcity of medical oxygen has proven to be catastrophic during the surges in COVID-19 cases over the past two years, with the heaviest burden felt in low- and middle-income countries. Despite its criticality, data and analyses of oxygen consumption, even for typical clinical cases, are missing. Consequently, planning oxygen needs, particularly with variable surges in COVID-19 cases, has presented a substantial challenge to policymakers and hospital decision-makers.

Methods

We performed a sub-analysis of the COVID-19 Critical Care Consortium database assessing the oxygen consumption requirements of COVID-19 patients admitted to intensive care units between February 2020 and October 2021. We calculated descriptive statistics for oxygen flow-rates, stratified by oxygen supplementation method, and developed a multi-state model for estimating the frequency, therapy duration, probability of transition, and number of oxygen therapy modes per patient.

Results

Overall, 12 429 patients from 35 countries received oxygen support on at least one day of their hospitalisation. Of the patients with measurable flow rates, 6142 received invasive mechanical ventilation, 838 received high-flow nasal oxygen, and 257 received both modalities. The median flow rate for mechanical ventilation was 3.2 L per minute (interquartile range (IQR) = 2.0-4.9), with a median duration of 12 days (IQR = 6-24), while the median flow rate for high-flow nasal cannula was 40 L per minute (IQR = 15-55), with a median duration of three days (IQR = 2-6).

Conclusions

Oxygen consumption among critical COVID-19 patients varies by mode of delivery (invasive ventilation vs high-flow nasal cannula), across patients, and over treatment duration. Therefore, it is essential that health facilities routinely monitor oxygen utilization to better inform oxygen delivery system design and regular supply planning.

Registration

ClinicalTrials.gov: CTG2021-01 ACTRN12620000421932.

Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment

OBJECTIVE

Reinforcement learning (RL) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to ICU treatment challenges. We set out to systematically review, assess level-of-readiness and meta-analyze the effect of RL on outcomes for critically ill patients.

DATA SOURCES

A systematic search was performed in PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection, Elsevier/SCOPUS and the Institute of Electrical and Electronics Engineers Xplore Digital Library from inception to March 25, 2022, with subsequent citation tracking.

DATA EXTRACTION

Journal articles that used an RL technique in an ICU population and reported on patient health-related outcomes were included for full analysis. Conference papers were included for level-of-readiness assessment only. Descriptive statistics, characteristics of the models, outcome compared with clinician's policy and level-of-readiness were collected. RL-health risk of bias and applicability assessment was performed.

DATA SYNTHESIS

A total of 1,033 articles were screened, of which 18 journal articles and 18 conference papers, were included. Thirty of those were prototyping or modeling articles and six were validation articles. All articles reported RL algorithms to outperform clinical decision-making by ICU professionals, but only in retrospective data. The modeling techniques for the state-space, action-space, reward function, RL model training, and evaluation varied widely. The risk of bias was high in all articles, mainly due to the evaluation procedure.

CONCLUSION

In this first systematic review on the application of RL in intensive care medicine we found no studies that demonstrated improved patient outcomes from RL-based technologies. All studies reported that RL-agent policies outperformed clinician policies, but such assessments were all based on retrospective off-policy evaluation.

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Background

Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.

Methods

Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.

Results

More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.

Discussion

Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.

Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus

BACKGROUND

Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise).

METHODS

The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019. To estimate the efficacy of hypothetical treatment policies, we utilized historical data and reinforcement learning to allow for greater flexibility when constructing new HCV treatment strategies. We tested and compared four new treatment policies: a simple stepwise policy based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), a logistic regression based on APRI, a logistic regression on multiple longitudinal and demographic indicators that were prespecified for clinical significance, and a treatment policy based on a risk model developed for HCV infection.

RESULTS

The risk-based hypothetical treatment policy achieved the lowest overall risk with a score of 0.016 (90% CI 0.016, 0.019) while treating the most high-risk (346.4 ± 1.4) and the fewest low-risk (361.0 ± 20.1) patients. Compared to hypothetical treatment policies that treated approximately the same number of patients (1843.7 vs. 1914.4 patients), the risk-based policy had more untreated time per patient (7968.4 vs. 7742.9 patient visits), signaling cost reduction for the healthcare system.

CONCLUSIONS

Off-policy evaluation strategies are useful to evaluate hypothetical treatment policies without implementation. If a quality risk model is available, risk-based treatment strategies can reduce overall risk and prioritize patients while reducing healthcare system costs.