BackgroundCOVID-19 presents diverse clinical manifestations associated with increased mortality, yet a unifying death mechanism remains elusive; here, we suggest such a mechanism that implies a simple way to lower deaths. This work differs from previous studies that use machine learning to identify mortality predictors.MethodsViewing clinical deterioration to a severe stage as a distinct “junction” in disease progression, we collected 173 medical records of COVID-19 patients who deteriorated and divided them into two groups: those who died (nonsurvivors) and those who recovered after deterioration (survivors). We aligned patients’ medical records by clinical deterioration time and statistically compared the two groups using standard blood variables.ResultsSignificant differences between the groups emerged only in the first week after clinical deterioration: nonsurvivors showed a rapid, simultaneous rise in lactate dehydrogenase (p ≤ 0.0001) and D-dimer (p ≤ 0.0001), followed by a decrease in platelet counts in the second week (p ≤ 0.0001). Other variables remained consistent throughout hospitalization. Older patients showed similar but less significant response patterns. Based on these clinical results, we hypothesized that the mechanism of death in COVID-19 involves an abrupt glycolytic surge during deterioration, driven by concurrent hypoxemia and virus-induced mitochondriopathy, resulting in significant disruption of metabolic homeostasis, which leads to imbalanced hemostasis and death.ConclusionOur findings highlight the importance of timing in COVID-19 treatment. Using an available machine learning algorithm to predict imminent deterioration enables prompt, short-term intervention with prophylactic mechanical ventilation and optimal antiglycolytic therapy. Implementing this approach requires further experimental and clinical validation. Identifying metabolism-related genetic or epigenetic anomalies in nonsurvivors will support our hypothesis and aid in classifying the high-risk patients.

Background The assessment of response to therapy in advanced solid cancer diseases is predicated on the Response Evaluation Criteria In Solid Tumours for the evaluation of disease state by the relative changes in lesion size. The underlying assumption is that a larger relative increase in lesion size implicates less efficacious therapy, worse prognosis and shorter survival. Methods We analyzed retrospective data of metastatic colorectal cancer patients from three clinical datasets, stratified into three cohorts by the treatment protocol. We evaluated the first relative and absolute increase in target lesion size for their association with overall survival. Results About fifty four percent of the patient population increased in target lesion size during the first-line treatment. A multivariate analysis showed that patients with larger relative increase in lesion size had slightly longer survival in all three cohorts (\(HR = 0.85, HR=0.95 ,HR = 0.75\) for Cohorts 1–3, respectively); p-values showed no significance. In contrast, patients with larger absolute increase in total lesion size had significantly shorter survival (\(HR = 1.1 \left(p=0.05\right), HR=1.2 \left(p=0.04\right), HR = 1.25(p=0.02)\) for Cohorts 1–3, respectively). We also found a negative correlation between the SLD at nadir and relative increase. Conclusions The different impact of the absolute and the relative increase in SLD at relapse reflects the different growth patterns of small and big tumours. Further validation of our results is required. We believe that the first absolute increase in total lesion size may become a useful metric, upon which a more precise categorization of survival-related disease progression can be based.

Supplementary Materials from Efficacy of Weekly Docetaxel and Bevacizumab in Mesenchymal Chondrosarcoma: A New Theranostic Method Combining Xenografted Biopsies with a Mathematical Model

PDF file - 201K, The contents of the document: 1. Model equations for the vaccine and the maturing DCs 2. Formal description of the algorithm for in-treatment therapy personalization 3. Success-of-validation criteria 4. Model comparison grade and parameters for SOV criterion 5. Selecting the best performing SOV criterion 6. Table S1

Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

Background: Recently, there has been a growing interest in applying immune checkpoint blockers (ICBs), so far used to treat cancer, to patients with bacterial sepsis. We aimed to develop a method for predicting the personal benefit of potential treatments for sepsis, and to apply it to therapy by meropenem, an antibiotic drug, and nivolumab, a programmed cell death-1 (PD-1) pathway inhibitor.Methods: We defined an optimization problem as a concise framework of treatment aims and formulated a fitness function for grading sepsis treatments according to their success in accomplishing the pre-defined aims. We developed a mathematical model for the interactions between the pathogen, the cellular immune system and the drugs, whose simulations under diverse combined meropenem and nivolumab schedules, and calculation of the fitness function for each schedule served to plot the fitness landscapes for each set of treatments and personal patient parameters.Results: Results show that treatment by meropenem and nivolumab has maximum benefit if the interval between the onset of the two drugs does not exceed a dose-dependent threshold, beyond which the benefit drops sharply. However, a second nivolumab application, within 7–10 days after the first, can extinguish a pathogen which the first nivolumab application failed to remove. The utility of increasing nivolumab total dose above 6 mg/kg is contingent on the patient's personal immune attributes, notably, the reinvigoration rate of exhausted CTLs and the overall suppression rates of functional CTLs. A baseline pathogen load, higher than 5,000 CFU/μL, precludes successful nivolumab and meropenem combination therapy, whereas when the initial load is lower than 3,000 CFU/μL, meropenem monotherapy suffices for removing the pathogen.Discussion: Our study shows that early administration of nivolumab, 6 mg/kg, in combination with antibiotics, can alleviate bacterial sepsis in cases where antibiotics alone are insufficient and the initial pathogen load is not too high. The study pinpoints the role of precision medicine in sepsis, suggesting that personalized therapy by ICBs can improve pathogen elimination and dampen immunosuppression. Our results highlight the importance in using reliable markers for classifying patients according to their predicted response and provides a valuable tool in personalizing the drug regimens for patients with sepsis.

We review the evolution, achievements, and limitations of the current paradigm shift in medicine, from the "one-size-fits-all" model to "Precision Medicine." Precision, or personalized, medicine-tailoring the medical treatment to the personal characteristics of each patient-engages advanced statistical methods to evaluate the relationships between static patient profiling (e.g., genomic and proteomic), and a simple clinically motivated output (e.g., yes/no responder). Today, precision medicine technologies that have facilitated groundbreaking advances in oncology, notably in cancer immunotherapy, are approaching the limits of their potential, mainly due to the scarcity of methods for integrating genomic, proteomic and clinical patient information. A different approach to treatment personalization involves methodologies focusing on the dynamic interactions in the patient-disease-drug system, as portrayed in mathematical modeling. Achievements of this scientific approach, in the form of algorithms for predicting personal disease dynamics in individual patients under immunotherapeutic drugs, are reviewed as well. The contribution of the dynamic approaches to precision medicine is limited, at present, due to insufficient applicability and validation. Yet, the time is ripe for amalgamating together these two approaches, for maximizing their joint potential to personalize and improve cancer immunotherapy. We suggest the roadmap toward achieving this goal, technologically, and urge clinicians, pharmacologists, and computational biologists to join forces along the pharmaco-clinical track of this development.

Abstract Background At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment. Methods We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient’s time to progression. We validated the prediction capacity of the algorithm by the Leave-One-Out cross-validation methodology. Results Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen’s κ = 0.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R2 = 0.505). Conclusions Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients’ response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization.

Immune checkpoint inhibitors, such as pembrolizumab, are transforming clinical oncology. Yet, insufficient overall response rate, and accelerated tumor growth rate in some patients, highlight the need for identifying potential responders. To construct a computational model, identifying response predictors, and enabling immunotherapy personalization. The combined dynamics of cellular immunity, pembrolizumab, and the melanoma cancer were modeled by a set of ordinary differential equations. The model relies on a scheme of T memory stem cells, progressively differentiating into effector CD8+ T cells, and additionally includes T cell exhaustion, reinvigoration and senescence. Clinical data of a pembrolizumab-treated patient with advanced melanoma (Patient O') were used for model calibration and simulations. Virtual patient populations, varying in one parameter or more, were generated for retrieving clinical studies. Simulations captured the major features of Patient O's disease, displaying a good fit to her clinical data. A temporary increase in tumor burden, as implied by the clinical data, was obtained only when assuming aberrant self-renewal rates. Variation in effector T cell cytotoxicity was sufficient for simulating dynamics that vary from rapid progression to complete cure, while variation in tumor immunogenicity has a delayed and limited effect on response. Simulations of a-specific clinical trial were in good agreement with the clinical results, demonstrating positive correlations between response to pembrolizumab and the ratio of reinvigoration to baseline tumor load. These results were obtained by assuming inter-patient variation in the toxicity of effector CD8+ T cells, and in their intrinsic division rate, as well as by assuming that the intrinsic division rate of cancer cells is correlated with the baseline tumor burden. In conclusion, hyperprogression can result from lower patient-specific effector cytotoxicity, a temporary increase in tumor load is unlikely to result from real tumor growth, and the ratio of reinvigoration to tumor load can predict personal response to pembrolizumab. Upon further validation, the model can serve for immunotherapy personalization.

645 Background: In advanced cancers, predicting disease progression just before its clinical manifestation enables an earlier switch to the next treatment line, preventing deterioration in the patient's state and potentially improving survival. Yet, given the ambiguity of current tumor markers in alerting to progression, physicians are unable to forecast this key event. We developed a diagnostic algorithm for announcing an approaching disease progression in late-stage colorectal cancer (CRC) patients by processing continuous carcinoembryonic antigen (CEA) input. Methods: Longitudinally measured CEA data of advanced CRC patients treated by standard 1st line chemotherapies, collected from 2 clinical trials (projectdatasphere.org), served for algorithm development by machine-learning and training assisted by receiver-operating-characteristic (ROC) analysis and correlation tests. Performance was validated by cross-validation techniques. Results: CEA and response evaluations of 489 CRC patients (median follow-up time: 168 days) were processed by the algorithm, predicting disease progression with 57% sensitivity (100/175 progression events) and 88% specificity (21/175 false positives). Positive and negative predictive values, accuracy and Cohen’s kappa were 64%, 84%, 79% and 0.46, respectively. The algorithm’s predictive power was superior to that of standard statistical analyses of these CEA data (e.g., ROC). Conclusions: Our study offers a new approach to using tumor markers as prognosticators. The algorithm-amplified ability of CEA to predict progression in CRC complements our recent findings in lung cancer, where integration of CEA and 4 other markers provided 66% sensitivity in predicting progression, surpassing the low capacity of each separate marker. Conceivably, future algorithm-integration of multiple markers in CRC may also exceed the limited signal of a single marker. Clinical use of our algorithm, amplifying weak marker signals of imminent progression, should allow physicians to reliably harness tumor markers for improving treatment and potentially extending survival in cancer patients.

Immune checkpoint inhibitors (ICI) are becoming widely used in the treatment of metastatic melanoma. However, the ability to predict the patient's benefit from these therapeutics remains an unmet clinical need. Mathematical models that predict melanoma patients' response to ICI can contribute to better informed clinical decisions. Here, we developed a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, and analyzed the local and global dynamics of the system. Our results show that zero, one, or two steady states of the mathematical system exist in the phase plane, depending on the parameter values of individual patients. Without treatment, the simulated tumors grew uncontrollably. At increased efficacy of the immune system, e.g., due to immunotherapy, two steady states were found, one leading to uncontrollable tumor growth, and the other resulting in tumor size stabilization. Model analysis indicates that a sufficient increase in the activation of CD8+ T cells results in stable disease, whereas a significant reduction in T-cell exhaustion, another process contributing CD8+ T cell activity, temporarily reduces the tumor mass, but fails to control disease progression in the long run. Importantly, the initial tumor burden influences the response to treatment: small tumors respond better to treatment than larger tumors. In conclusion, our model suggests that disease progression and response to ICI depend on the ratio between activation and exhaustion rates of CD8+ T cells. The analysis of the model provides a foundation for the use of computational methods to personalize immunotherapy.

Background Sepsis-associated immune dysregulation, involving hyper-inflammation and immunosuppression, is common in intensive care patients, often leading to multiple organ dysfunction and death. The aim of this study was to identify the main driving force underlying immunosuppression in sepsis, and to suggest new therapeutic avenues for controlling this immune impairment and alleviating excessive pathogen load. Methods We developed two minimalistic ( skeletal ) mathematical models of pathogen-associated inflammation, which focus on the dynamics of myeloid, lymphocyte, and pathogen numbers in blood. Both models rely on the assumption that the presence of the pathogen causes a bias in hematopoietic stem cell differentiation toward the myeloid developmental line. Also in one of the models, we assumed that continuous exposure to pathogens induces lymphocyte exhaustion. In addition, we also created therapy models, both by antibiotics and by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. Assuming realistic parameter ranges, we simulated the pathogen-associated inflammation models in silico with or without various antibiotic and immunotherapy schedules. Results Computer simulations of the two models show that the assumption of lymphocyte exhaustion is a prerequisite for attaining sepsis-associated immunosuppression, and that the ability of the innate and adaptive immune systems to control infections depends on the pathogen’s replication rate. Simulation results further show that combining antibiotics with immune checkpoint blockers can suffice for defeating even an aggressive pathogen within a relatively short period. This is so as long as the drugs are administered soon after diagnosis. In contrast, when applied as monotherapies, antibiotics or immune checkpoint blockers fall short of eliminating aggressive pathogens in reasonable time. Conclusions Our results suggest that lymphocyte exhaustion crucially drives immunosuppression in sepsis, and that one can efficiently resolve both immunosuppression and pathogenesis by timely coupling of antibiotics with an immune checkpoint blocker, but not by either one of these two treatment modalities alone. Following experimental validation, our model can be adapted to explore the potential of other therapeutic options in this field.

Background: Investigating the association between quantifiable features of tumor burden and overall survival in advanced cancer patients can possibly uncover features whose control would potentially affect survival in these patients. To identify such features, we examined how changes in the tumor dynamics could affect the overall survival (OS) of patients with advanced colorectal cancer (CRC). Methods: Individualized data were obtained from three clinical studies (accessible through projectdatasphere.org) in metastatic CRC patients under 1st and 2nd line standard of care (SOC). In patients with measurable changes in tumor size at the time of Response Evaluation Criteria in Solid Tumors (RECIST), determined progressive disease (PD), the nature of the progression (target, non-target, or new-lesion PD) and the absolute/relative change in sum of longest diameters (SLD) from nadir to progression (ΔSLDP) were evaluated as predictors of post-progression survival (PPS) by univariate/multivariate Cox proportional-hazards models. Other parameters (gender, performance status, liver metastases, age, weight, baseline SLD, etc.) were also assessed. The models were built by stepwise regression, with P = .1 as the entrance criterion. Results: A total of 752 patients in the three studies, which had quantifiable disease and documented target lesion PD, were included in the analysis. In patients under 1st line or 2nd line SOC (n = 240 and n = 512 patients, respectively), ΔSLDP predicted PPS (hazard ratio; HR of 1.42 and 1.37; P < .0001). The median PPS was 6.7 months for 1st line-treated patients with ΔSLDP > 2.5 cm, compared with 13.6 months for ΔSLDP < = 2.5 cm. In 2nd line-treated patients, the median PPS was 5.1 months if ΔSLDP > 2.7 cm, vs. 10.5 months for ΔSLDP < = 2.7 cm. The importance of ΔSLDP persisted in a multivariate model controlling for other prognostic factors, including baseline performance status (ECOG), time to progression, KRAS mutational status, and the existence of a non-target PD or new lesion PD in addition to target lesion PD. The HR for ΔSLDP in the multivariate model was 1.48 (1st line) and 1.36 (2nd line), with a P < .0001. Conclusions: The rise in tumor burden from nadir to progression appears to be a significant feature predicting survival in advanced CRC patients, independent of the treatment line and specific drug they are subject to. This finding complements our past discovery of a ΔSLDP-survival association in metastatic non-small cell lung cancer,1 inferring the cross-indication prognostic importance of the tumor increase at progression for survival of advanced cancer patients. A clinically relevant implication of our study is that detection of impending progression at the optimal time, i.e. when the increase in the patient’s tumor burden is still small, is an important objective which could enable oncologists to effectively influence the survival outcome and extend patient life expectancy (for example by an earlier timely switch to next-line treatment).

e21114 Background: Extending survival in advanced non-small cell lung cancer (NSCLC) is of great importance. Finding survival predictors and assessing the potential for improving patient outcomes through their modification can assist these efforts. Here we tested tumor burden changes at disease progression as survival predictors in NSCLC. Methods: Data were taken from 2 clinical trials in advanced NSCLC patients under 1st line CarboTaxol (projectdatasphere.org). Magnitude and rate of changes in tumor size (sum of longest diameters; SLD) close to progression, and other patient parameters, were evaluated as predictors of post-progression survival (PPS) using univariate/multivariate Cox proportional hazards models. The latter were built by stepwise regression, p = 0.2 being the entrance criterion. Results: In both trials (n = 381 patients with progressive disease (PD) on target lesions), the SLD rise from nadir to progression (dSLD) was significantly correlated with PPS (Table). Patients with lower-than-median dSLD showed longer survival, with a 4.9 and 9.6 month gain in PPS over large-dSLD patients. The impact of dSLD persisted in a multivariate model that included also baseline performance status, gender, appearance of new lesions, and time to progression as significant factors; the additive contribution of dSLD to the multivariate model (Table) was verified by model comparison. Conclusions: Tumor burden increase near first progression was found to be a unique survival predictor for advanced NSCLC. Continuous control of SLD features in NSCLC patients may thus assist in predicting outcomes and suggesting appropriate therapy. We have used these features to develop a new predictive tool that alerts to imminent progression and suggests a treatment switch to limit tumor growth and extend survival. Clinical Study -Identifier Target lesion PD patients (% of all PD patients) Median PPS (months) Median dSLD (cm) Median PPS (months) dSLD Hazard ratio [95% CI], p value > median dSLD < median dSLD Univariate model Multivariate model CA031 -NCT00540514 159 (44%) 7.8 2 4.8 9.7 1.46 [1.22, 1.74], 4.37x10-5 1.26 [1.04, 1.53], 0.01 IPASS -NCT00322452 222 (49%) 12.4 1.5 9.4 19 1.21 [1.12, 1.32], 5.45x10-6 1.18 [1.08, 1.29], 0.0004

Background: In advanced non-small cell lung cancer (NSCLC), most patients deteriorate rapidly and die within 1 year of diagnosis. Forecasting disease progression just prior to its clinical manifestation would allow an earlier switch to the next treatment line, thus preventing major deterioration in the patient's stature and potentially improving response to therapy. However, present serum tumor biomarkers, e.g. carcinoembryonic antigen (CEA), lack the power to signal progression. We developed PrediCare, an innovative diagnostic for continuous monitoring and alerting to forthcoming progression in late-stage NSCLC. Methods: PrediCare was constructed by machine-learning modeling, and designed to process patient data throughout treatment. Data of late-stage NSCLC patients under first-line standard-of-care therapies, collected in a retrospective observational trial (NCT02577627), served for algorithm training and testing. The algorithm's predictive ability was evaluated using diverse features of 1–3 longitudinally measured serum tumor markers (CEA, CA125, CA15.3), as pre-selected by receiver-operating-characteristic analysis. Performance was evaluated by cross-validation. Results: A total of 167 NSCLC patients were assessed, the median follow-up time being 101 days. The CEA/CA125/CA15.3 combination showed statistically significant prediction ability, while the use of only 1–2 markers had lower performance. Combining the 3 markers, PrediCare accurately predicted 87/165 of the progression events (52.6% sensitivity), with 15/165 false positives (91.1% specificity). Positive predictive value, negative predictive value, accuracy and Cohen's kappa were 68%, 85%, 81% and 0.47, respectively. Conclusions: PrediCare is a new individualized medicine software tool, predicting imminent disease progression in advanced NSCLC. This improves treatment planning, and potentially increases survival. Our technology uses standard tumor biomarkers, but integrates three of them in a unique way that offers superiority over their current interpretation in the clinic. Testing of PrediCare under a larger biomarker panel is underway. Legal entity responsible for the study: Optimata Ltd. Funding: Optimata Ltd. Disclosure: Y. Kogan, M. Kleiman, S. Shannon, M. Elishmereni, E. Taub, Z. Agur: Employee of Optimata Ltd., which sponsored the study. R. Brenner, R. Berger: Member of the advisory board of Optimata Ltd., which sponsored the study. All other authors have declared no conflicts of interest.

e21190Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer death in America. Predicting disease progression just prior to its clinical manifestation would allow an earlier ...

Background Despite considerable investigational efforts, no method to overcome the pathogenesis caused by loss of function (LoF) mutations in tumor suppressor genes has been successfully translated to the clinic. The most frequent LoF mutation in human cancers is Adenomatous polyposis coli (APC), causing aberrant activation of the Wnt pathway. In nearly all colon cancer tumors, the APC protein is truncated, but still retains partial binding abilities. Objective & methods Here, we tested the hypothesis that extracellular inhibitors of the Wnt pathway, although acting upstream of the APC mutation, can restore normal levels of pathway activity in colon cancer cells. To this end, we developed and simulated a mathematical model for the Wnt pathway in different APC mutants, with or without the effects of the extracellular inhibitors, Secreted Frizzled-Related Protein1 (sFRP1) and Dickhopf1 (Dkk1). We compared our model predictions to experimental data in the literature. Results Our model accurately predicts T-cell factor (TCF) activity in mutant cells that vary in APC mutation. Model simulations suggest that both sFRP1 and DKK1 can reduce TCF activity in APC1638N/1572T and Apcmin/min mutants, but restoration of normal activity levels is possible only in the former. When applied in combination, synergism between the two inhibitors can reduce their effective doses to one-fourth of the doses required under single inhibitor application. Overall, re-establishment of normal Wnt pathway activity is predicted for every APC mutant in whom TCF activity is increased by up to 11 fold. Conclusions Our work suggests that extracellular inhibitors can effectively restore normal Wnt pathway activity in APC-truncated cancer cells, even though these LoF mutations occur downstream of the inhibitory action. The insufficient activity of the truncated APC can be quantitatively balanced by the upstream intervention. This new concept of upstream intervention to control the effects of downstream mutations may be considered also for other partial LoF mutations in other signaling pathways.

Our current understanding of the connections between systemic responses to injury and their biochemical underpinnings is not well developed. It is based on the relationships between basic biological knowledge and clinical manifestations, which are mostly theoretical and based on statistical correlations. For instance, although the basic biology of tumour necrosis factor alpha (TNFα) in inflammation is well known, we still don’t know how TNFα-antagonists cause their beneficial clinical effects, why only some patients respond, or why the effect sometimes wanes over time1. There are several published trials trying to connect biochemical analytes with clinical responses2, yet they suffer from significant drawbacks and have provided few novel insights. We have yet to convincingly bridge the molecular and cellular responses to injury with their clinical effects on a systemic level3. We have started a trial on bedside tracheostomies in order to further our knowledge on the connection between the biochemical processes and clinical presentations and outcomes. Tracheostomy represents a standard injury performed in relatively stable patients (therefore increasing the signal-to-noise ratio), and it provides an amenable platform for a meticulous study of the molecular and cellular response to injury. Although bedside tracheostomy is a common procedure and its outcomes have been well-studied in different populations, its systemic physiobiochemical effects haven’t been studied4. The second aspect of our research is based on the application of a systems biology approach5. This approach combines the measurement of a large number of mediators representing different physiological pathways at several time points, integrating clinical measures and outcomes in the analysis; and, finally, the use of both statistical and bio-mathematical computational approaches to produce explanatory and predictive models. In this study, detailed clinical and laboratory data will be collected from 14 days before and up to 28 days after tracheostomy. A large panel of 21 cytokines, chemokines and biomarkers will be measured from blood samples taken at five time points around the time of tracheostomy in a 24-hour period. Based on the observed standard deviations from our preliminary results, assuming 80% power and an α error=0.05, 40 subjects are required to detect a twofold difference between different time points, which is expected according to the published literature. We have institutional permission (ethics approval number: 0220-14HMO) and an Correspondence