LAUSR

COVID-19 is an evolutionarily unprecedented natural experiment testing human immunological fitness and the potential to recover from a virus-activated ‘cytokine storm syndrome’ [1], a manifestation of viral sepsis [2]. Elderly persons with comorbidities such as diabetes mellitus, renal failure, chronic obstructive pulmonary disease, heart conditions, and obesity are at higher risk of severe COVID-19 disease than the general population [3–14], likely because of weaker immune responses, called immune-senescence. This immunological pattern is characterized by decreased phagocyte and lymphocyte function, antigen presentation, cellular replication response to cytokine stimuli, persistent T cell exhaustion, constant low-level inflammation with elevated baseline levels of cytokines such as interleukin-1 (IL-1), IL-6 and tumor-necrosis factor alpha (TNF-a) and prolonged inflammatory states after infections, allowing viral infections to easily evade eradication by the immune system and develop into serious systemic infections [15]. Cytokine-storm-induced pro-inflammatory coagulopathies presumably also play a role in younger persons without preexisting conditions who develop severe COVID-19 phenotypes including strokes [16]. In addition to causing pneumonia [3,17], sepsis and septic shock [3,18], COVID-19 has been reported to cause injury and dysfunction in virtually every organ system, including the cardiovascular [19–22], neurological [7,16,23,24], renal [25,26], hepatic [11,27,28], gastro-intestinal [18,29], clotting [16] and immune [18] systems. Shortand long-term organ dysfunction and survival outcome prediction is important to stratify COVID-19 positive patients for disease severity, treatment escalation and resource allocation. Clinical tools alone, such as respiratory, cardiovascular and other organ function scores, may not be informative enough to precisely assess the prognosis of this novel disease. As of 31 March 2020, a systematic review identified 10 clinical models of predictive outcomes and concluded that these models were ‘poorly reported and at high risk of bias, raising concerns that their prediction could be unreliable when applied in daily practice’ [30]. There is a need for a comprehensive precision risk stratification and prediction that is independently validated, guided by complete shortand long-term outcome event observation including organ dysfunction and death [31,32]. Several biomarkers have been suggested, including higher leukocyte counts, levels of C-reactive protein, procalcitonin, creatinine kinase, myohemoglobin, high-sensitivity troponin I, N-terminal pro-B-type natriuretic peptide, aspartate aminotransferase, creatinine [20], higher white blood cell and neutrophil counts, lymphopenia, higher neutrophil-to-lymphocyte ratio as well as lower percentages of monocytes, eosinophils, basophils [11], and viral load of SARS-CoV-2. In 76 patients studied Jan 21 to 4 February 2020 in China, mean viral load of severe cases was 60 times higher than that of mild cases. While 90% of mild cases tested negative by day 10, all severe cases tested positive beyond day 10 [33]. We hypothesize that shortand long-term outcomes such as organ dysfunction and death in COVID-19 patients are related to the potential to cope with immunological stressors such as novel infections and that this coping potential is a resultant of the combined impact of the primary culprit (i.e. SARS-CoV-2 infection), secondary organ dysfunction, comorbidities, frailty, disabilities as well as chronological age, jointly termed functional recovery potential [34], governed by the balance between innate and adaptive immunity networks [35]. and reflected in peripheral blood mononuclear cell (PBMC) biology. We have previously developed peripheral blood mononuclear cell (PBMC) -based molecular outcome prediction algorithms for other forms of organ failure [34,36–41]. During the development of the test for advanced heart failure survival prediction, age, respiratory rate, white blood cell count and diastolic blood pressure were identified as clinical predictor candidates for algorithm development. In the transcriptome discovery phase of the test development, 12 genes that represented the shared set between 28 genes predicting early functional recovery and 105 genes correlating with 1-year survival (AGRN, SAP25, ANKRD22, BATF2, FRMD6, HEXA-AS1, DNM1P46, NAPSA, KIR2DL4, RHBDD3, TIMP3, and BCORP1) were selected [36]. Based on these 4 clinical parameters and 12 differentially expressed genes, a predictive algorithm was constructed and independently validated in a larger, independent mixed advanced heart failure cohort. In this independent validation study, prediction of 1-year survival using 4 clinical parameters alone achieved an AUC = 0.69. Adding the 12