In previous studies, both our group and others have demonstrated a strong association between SARS-CoV-2-specific T cell responses and favorable outcomes [13, 33, 34]. Furthermore, in a recent study, we showed that incorporating SARS-CoV-2-specific T cells (CoV-2-STs) generated from recovered donors, into the standard care regimen—including remdesivir and dexamethasone—, markedly improved recovery and survival rates in patients with severe, COVID-19, caused by the delta variant, compared to the SoC alone. This method of adoptive immunotherapy led to a rapid increase of circulating CoV-2-STs in vivo and contributed into the overall rebuilding of the immune system. Expanding on these findings, we here, utilized machine learning for a post-hoc analysis, to identify pivotal timepoints, characteristics, and biomarkers that are decisive for the outcome in severe COVID-19 cases under both treatment approaches. Our analysis pinpointed Day-5 post-enrollment as a critical juncture at which, distinct immune responses to CoV-2-ST administration compared to SoC become apparent, especially in the kinetics of T lymphocytes, circulating CoV-2-specific T cells, cytotoxic T lymphocytes, and natural killer (NK) cells. This early distinction indicates a superior immune modulation by CoV-2-STs, impacting both innate and adaptive immunity, and corresponds with our clinical findings of faster recovery from lymphopenia, maintenance of innate immunity homeostasis and enhancement of SARS-CoV-2-specific immunity in patients treated with CoV-2-STs in addition to SoC [27].
Furthermore, by employing Linear Discriminant Analysis (LDA), we determined key predictors of patient outcomes by Day 60, underlining the role of SARS-CoV-2-specific immunity in managing severe COVID-19 cases. Our LDA models, tailored for forecasting responses to CoV-2-STs + SoC or SoC-alone treatment, facilitated the development of a computational tool aimed at guiding therapeutic decisions. This tool, by considering initial patient characteristics and biomarker levels, seeks to identify individuals among those with severe COVID-19, who may not respond to SoC favorably thus becoming prime candidates for alternative interventions, such as adoptive immunotherapy with CoV-2-STs. By simulating over 1000 potential disease progression profiles and using our models to compute survival probabilities, we present a new method for choosing treatment strategies that optimize patient outcomes. Testing this tool in hypothetical scenarios, we have further validated its potential for practical application, showcasing its adaptability to treatment modifications and its predictive accuracy across diverse patient responses, thereby highlighting its value in personalized medicine for severe COVID-19.
Our study benefits from the inclusion of a consistent sample of delta variant, high-risk individuals, mirroring real world scenarios. Additionally, the availability of longitudinal, clinical and laboratory data prospectively collected throughout our previous phase I/II clinical trial, adds strength to our analysis, despite its post-hoc nature. Leveraging this data, our prognostic approach enabled us to make early and precise estimates of mortality upon admission. Although reported ML models have provided promising prognostic implications, they are still hampered by certain limitations, most commonly the lack of capability or predicting the outcome at, or early after, admission. Our rapid risk assessment tool addresses this gap, empowering health care professionals to identify patients who are highly susceptible to experiencing unfavorable outcomes under routine treatment alone. This allows for immediate and individualized interventions, such as improved care protocols and monitoring practices, and the consideration of administering CoV-2-STs. Notably, our research is pioneering in integrating circulating CoV-2-STs into a machine learning-based predictive tool, enhancing the precision and applicability of our prognostic model.
Our study also has certain limitations, that need to be acknowledged. First, the patient sample size was limited, as the original clinical trial was not specifically tailored to accommodate a machine learning analysis whereas the retrospective nature of our research precluded prospective evaluation in patient cohorts. Second, since the initial trial only included severe cases, our tool's evaluation was confined to severe COVID-19 context, limiting its broader applicability. Furthermore, vaccination status was not included as a variable in our computational tool, due to the low vaccination rates among study patients (17.2% of the CoV-2-ST group and 27.6% of the SoC group). Since significant protection was offered by vaccination against severe illness, the majority of the eligible patients with severe COVID-19 were unvaccinated. To prevent potential bias stemming from underrepresented vaccinated patients, we chose not to include vaccination status in the tool.
While directly useful, the ML models depend heavily on the quality and breadth of the data used. If the data are not representative of the broader population or if key variables are missing, the predictions may not be accurate or generalizable. Additionally, treatment outcomes can be influenced by multifactorial and dynamic patient conditions not fully captured in the model. The ML COVID-19 studies mentioned in the introduction utilize a variety of data types (e.g., imaging, clinical parameters) and are often designed to process and analyze complex datasets quickly, providing critical insights in fast-moving clinical scenarios like a pandemic [19–22, 35]. Their use of advanced algorithms can detect patterns not readily apparent to humans, which is a significant advantage in diagnostic and prognostic contexts. While our treatment comparison model provides specific actionable data for clinical decisions between two treatments, the broader ML studies offer insights into disease characterization, risk stratification, and initial management strategies. A comprehensive approach in clinical settings might involve integrating insights from both types of models — using ML-driven diagnostics and risk assessments to initially guide treatment choices and then applying the treatment comparison model to select the optimal therapy based on clinical outcome predictions. In essence, while the referenced ML works provide broad diagnostic and prognostic tools useful in managing COVID-19, our model comparing treatments between CoV-2-STs and SoC offers targeted, actionable insights specifically designed to optimize therapeutic outcomes. Both have distinct roles in clinical decision-making, demonstrating the diverse applications of ML in healthcare.
In conclusion, we provide a user-friendly computational tool which integrates clinical, biochemical and immunological features, facilitating timely and accurate risk-stratification of severe COVID-19 patients. This will enable clinicians to make informed clinical decisions upon admission, predicting either recovery or clinical deterioration and promptly intervening when necessary. Independent, continuous validation of our computational tool in prospective settings and larger clinical trials will confirm its predictive accuracy, effectiveness and usefulness in guiding therapeutic decisions.