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Title: A comprehensive appraisal of laboratory biochemistry tests as major predictors of COVID-19 severity

Context: A relevant portion of COVID-19 patients develop severe disease with negative outcomes. Several biomarkers have been proposed to predict COVID-19 severity, but no definite interpretative criteria have been established to date for stratifying risk. Objective: To evaluate six serum biomarkers (C-reactive protein, lactate dehydrogenase, Ddimer, albumin, ferritin and cardiac troponin T) for predicting COVID-19 severity and to define related cut-offs able to aid clinicians in risk stratification of hospitalized patients. Design: A retrospective study of 427 COVID-19 patients was performed. Patients were divided into groups based on their clinical outcome: non-survivors vs. survivors and patients admitted to intensive care unit vs. others. ROC curves and likelihood ratios were employed to define predictive cut-offs for evaluated markers. Results: Marker concentrations at peak were significantly different between groups for both selected outcomes. At univariate logistic regression analysis, all parameters were significantly associated with higher odds of death and intensive care. At the multivariate analysis, high concentrations of lactate dehydrogenase and low concentrations of albumin in serum remained significantly associated with higher odds of death, while only low lactate dehydrogenase activities remained associated with lower odds of intensive care admission. The best cut-offs for death prediction were >731 U/L for lactate dehydrogenase and ≤18 g/L for albumin, while a lactate dehydrogenase activity <425 U/L was associated with a negative likelihood ratio of 0.10 for intensive treatment. Conclusions: Our study identifies which biochemistry tests represent major predictors of COVID-19 severity and defines the best cut-offs for their use.


INTRODUCTION
At the end of 2019, an outbreak of atypical pneumonia of unknown cause was detected in Wuhan, capital of the province of Hubei, China. 1 The etiological agent of this disease was later identified to be a novel coronavirus, named "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2), phylogenetically similar but distinct from other coronaviruses known to cause disease in humans, such as human severe acute respiratory syndrome and Middle East respiratory syndrome. 2 The disease caused by SARS-CoV-2, named COVID-19, has since spread worldwide warranting the recognition as a pandemic by the World Health Organization on March 11 th , 2020. 3 Most patients infected with SARS-CoV-2 are asymptomatic or present with an uncomplicated mild illness characterized by fever, dry cough, nausea, asthenia, and myalgia. 2 Up to 14% of patients, however, can evolve towards the development of a severe respiratory disease, characterized by radiological findings of interstitial pneumonia and progressively worsening respiratory impairment requiring ventilatory assistance. About 5% of subjects ultimately develop a full-on acute respiratory distress syndrome (ARDS), requiring admittance to an intensive care unit (ICU) to administer invasive mechanical ventilatory support. These patients are also at risk of developing sepsis, septic shock and multiorgan failure. Major risk factors for development of severe disease are old age, male sex, and comorbidities, such as metabolic and cardiovascular disease. 2 Many laboratory test results have been reported significantly altered in patients with severe COVID- 19. In addition to the acute phase proteins, such as C-reactive protein (CRP), ferritin and procalcitonin, studies have reported significant differences in levels of hematological and hemostasis parameters, such as lymphocyte and neutrophil granulocyte count, and Ddimer, and in other biochemistry markers, such as lactate dehydrogenase (LDH), cardiac troponins, serum albumin, aminotransferases, and creatinine. 2,[4][5][6] Most of these parameters are commonly requested in daily clinical practice, however, to the best of our knowledge, no specific interpretative criteria, i.e. cut-offs able to aid in the evaluation of COVID-19 severity, have been reported so far. The aim of this study was to obtain a comprehensive appraisal of the most performing laboratory biochemistry tests in predicting COVID-19 severity in a large group of patients and to define related cut-offs useful for their stratification in terms of prediction of ICU admission and mortality.

Study Population
We performed a retrospective, observational study on adult (≥18 years old) COVID-19 patients admitted between February 21 st and March 31 st , 2020 to the 'Luigi Sacco' academic hospital in Milan, one of the two national reference centers for infectious diseases. Patients were hospitalized in one of the following isolation wards reserved exclusively to the COVID-19 care: one ICU, two infectious disease units, one pulmonology unit, and four low-medium intensity care wards. All patients had clinical and/or radiological findings highly suggestive for COVID-19 at admission and SARS-CoV-2 infection was confirmed by detection of viral RNA on nasopharyngeal material, using a real-time reverse transcription polymerase chain reaction method. The Institutional Review Board approved the study.

Analytical Methods
Patients' data were extracted from the hospital information systems. CRP, LDH, D-dimer, albumin, ferritin, and cardiac troponin T (cTnT) results were collected. As more than one test result was available for each patient, the worst result of the whole hospitalization period was considered for analysis (i.e., the highest result for all evaluated analytes except for albumin, for which the lowest result was selected). CRP, albumin, and LDH were measured on the Alinity platform (Abbott Diagnostics) by using immunoturbidimetry (CRP and albumin) and enzymatic (LDH) assays, respectively. D-dimer was measured on the ACL TOP 750 platform (IL-Werfen) and results expressed in fibrinogen-equivalent units (FEU). Ferritin and cTnT were measured using a chemiluminescent microparticle immunoassay on the Alinity platform and a high-sensitivity electrochemiluminescence immunoassay on a Cobas e601 platform (Roche Diagnostics), respectively. Data about analytical performance of employed methods were previously published. [7][8][9][10][11][12][13] Adult reference intervals (all derived from previously performed ad hoc local studies) are: CRP, up to 10 mg/L; albumin, 35-50 g/L; LDH, 125-220 U/L; D-dimer, up to 500 µg/L FEU (≤50 years old) and up to the 'age years x 10' µg/L FEU (>50 years old); ferritin, 100-250 µg/L; and cTnT, up to 15 ng/L. Conversion factors from conventional units to Système International (SI) units are: CRP, from mg/L to nmol/L multiply by 9.5238; albumin, from g/L to mmol/L multiply by 0.0150; LDH, from U/L to nkat/L multiply by 16.6667; ferritin, from µg/L to nmol/L multiply by 0.0022.

Statistical Analysis
Biomarkers were evaluated according to the following outcomes: 1) death during hospitalization (non-survivors) vs. hospital discharge after clinical recovery (survivors), and 2) hospitalization in ICU vs. hospitalization in non-intensive wards. Demographic, clinical and laboratory characteristics were compared between patients separated in these categories.
Data were reported as percentages for categorical variables and median with interquartile range (IQR) for quantitative variables. Differences between variables in different categories were assessed by applying chi-squared test (categorical) and Mann-Whitney rank-sum test (quantitative).
Optimum biomarker cut-offs both for predicting death and excluding necessity for intensive care were extrapolated from a receiver operating characteristic (ROC) analysis, by maximizing specificity (outcome 1) and sensitivity (outcome 2), respectively. Likelihood ratios (LR) and predictive values (PV) associated with selected cut-offs were then derived.
Univariate logistic regression was used to estimate variables' odds ratios (OR) and their 95% confidence intervals (CI) in relation to the selected outcome. A multivariate logistic regression model was then applied to variables significant at the univariate analysis. Final selection of variables included in the multivariate model was done by applying a stepwise approach. A P value <.05 denoted statistical significance. All analyses were performed using MedCalc software.

RESULTS
In the evaluated period, 518 COVID-19 patients were admitted. Of these, 91 patients were excluded from further analysis because they were still hospitalized as of April 13 th , 2020, when we started the collection of data. A total of 427 COVID-19 patients with definite clinical outcomes was therefore included in the final analyses. Of these, 89 (20.8%) patients died during the hospitalization period, while 338 were discharged after clinical recovery. Demographic and medical history data for the studied population are shown in Table 1.
Information about the past medical history could not be retrieved for 18 patients (13 in the ICU group and 5 in the non-ICU group) who deceased suddenly. The most frequent comorbidity was hypertension, present in 134 of the 409 patients with complete data available, followed by cardiovascular disease (85 of the 409 patients, 21%) and diabetes mellitus (56 of the 409 patients, 14%). In non-survivors, age and the frequency of all comorbidities, except for human immunodeficiency virus infection more frequent in survivors, were significantly higher than in surviving patients. On the other hand, no significant differences in age and frequency of comorbidities were found between patients admitted to the ICU and other patients, except for cardiovascular disease, which was more frequent in the non-ICU group ( Table 1).
Values of selected laboratory tests were significantly different between groups for both the examined outcomes ( for the evaluated tests according to the ability to predict the two selected outcomes. For predicting patient death, cTnT displayed the best accuracy, with an area under the ROC curve (AUC) of 0.94 (95% CI: 0.90 to 0.98), followed by LDH, albumin, and CRP (Table 3 14 ).
The best cut-offs maximizing clinical specificity and minimizing false positive test results in predicting patient death are reported in Table 3 (and displayed in Figures 1A-1F and 2A-2F), together with the corresponding positive LR and positive PV. In this regard, the results showed a relevant capability for cTnT >30 ng/L (positive LR: 31.9; 95% CI 4.4 to 228.8) and LDH >731 U/L (positive LR: 19.7; 95% CI 9.1 to 42.7) to foresee death in COVID-19 patients. It should be however noted the wide CI associated to cTnT due to the relatively low number of patients (n=98) who underwent measurements of this biomarker. Given the relevant association found between elevated cTnT and mortality, we checked the death causes of the 35 deceased patients for whom cTnT was measured during hospitalization. For 34 of these patients (97%), the main cause of death was respiratory failure due to pneumonia complications, with no direct evidence of mortal cardiac events. Only one patient, who however had a relatively low peak cTnT measurement of 14 ng/L, died of cardiac arrest after the insurgence of a non-shockable arrythmia unresponsive to manual cardiopulmonary resuscitation.
The best power to predict ICU admission was found for serum albumin, with an AUC of 0.89 (95% CI 0.84-0.94), followed by CRP and LDH (Table 4). Using a cut-off of ≥29 g/L, albumin displayed the best accuracy to exclude the need of ICU admission. Here, in evaluating the test performance, sensitivity was favored to minimize the risk of false negative results, i.e. patients with test results lower (higher for albumin) than cut-off that are actually admitted to ICU.
At univariate analysis, ORs for death during hospitalization were significantly higher for older patients and patients with concentrations of all evaluated tests over the selected cut-offs (under the selected cut-off for albumin) ( Table 5). On the other hand, patient age was not a

DISCUSSION
Months after the initial spread of SARS-CoV-2-related disease in China, it is now evident from published studies that, together with age and other risk factors such as comorbidities, alterations of different laboratory markers can be useful to assess disease severity and risk of evolution towards critical stages. 15 However, available studies only reported purely descriptive analyses of the studied populations and no clear interpretative criteria for commonly requested biochemistry parameters were defined for use in COVID-19 patients to predict negative outcomes with a defined probability. 2,[4][5][6][16][17][18][19][20] In our study, we depicted this probability by deriving LR and PV associated with selected cut-offs. Positive LR expresses the quotient between the probability that a value of the test overlapping the indicated cut-off is associated with the defined outcome and the probability that it does not associate with such outcome. Negative LR, on the other hand, expresses the quotient between the probability that a value of the test lower (higher in the case of albumin) than the indicated cut-off is associated with a negative outcome and the probability that it does not associate with such outcome. 14 Positive and negative PV are two essential calculations that provide insight into the accuracy of positive or negative test results within the population tested. These values are based on the test sensitivity and specificity, but also incorporate and are dependent on the prevalence of selected outcomes in the studied population. In our study, positive PV reported in Table 3, last column, indicate the number of deceased COVID-19 patients that a test accurately identifies out of the total number of dead patients within our population. On the other hand, negative PV listed in Table 4 define the accurate detection of cases that did not require intensive treatment. Our cut-off values were specifically selected to have a high specificity, i.e. rule-in ability, in detecting patients at risk of in-hospital death and a high sensitivity, i.e. rule-out ability, in detecting patients not at risk of ICU admission.
To our knowledge, this study is the largest case-series of COVID-19 patients in Italy so far and one of the largest worldwide. In terms of population description, our findings are similar to those from other studies, mainly carried out on Chinese population. 2 (Table 1), and the insurgence of cardiac complications directly related to the viral infection or to the compromised pulmonary function. 25,26 In terms of death prediction, the only test with an AUC above 0.90, the limit indicating high global accuracy, 27 was cTnT. COVID-19 patients with a peak cTnT value >30 ng/L (corresponding to two times the upper reference limit selected at the 99 th percentile of the reference population 28 ) had a chance of dying more than 30 times higher than other patients. On the other hand, the cTnT value for predicting ICU admission was relatively poor. This is probably due to the fact that COVID-19 patients are generally admitted to the ICU following the development of respiratory impairment and ARDS, while the development of cardiac complications caused by SARS-CoV-2 infection, such as myocarditis, usually does not require intensive care treatment. Previous studies have shown that cardiac troponin I concentrations exceeding the 99th percentile upper reference limit can be observed in 8-12% of COVID-19 patients. 25 Only one study has previously measured cTnT, detecting elevated concentrations, defined as above the 99th percentile upper reference limit, in 27.8% of evaluated patients and showing that myocardial injury, as detected by a cTnT increase, is significantly associated with fatal outcome of COVID-19. 26 Unfortunately, the assay used in the study was not specified and a fixed cut-point for marker application not stated, so results were not directly replicable in other settings. Our data confirm that COVID-19 patients displaying myocardial injury, reveled by elevated cTnT concentrations, are at high risk of death and enlarge the previous information by indicating the best biomarker cutoff associated with this outcome. Due to the relatively low number of patients tested, cTnT reached only borderline significance when a multivariable model was applied. The best fitting variables for death prediction at multivariate logistic regression were patient age, LDH, and albumin concentrations. Markedly altered levels of these two laboratory parameters, reflecting a general impairment of the patient's health status and organ functions, independently predicted death during hospitalization.
The tests that had the higher power for excluding need of intensive care were serum albumin, CRP, and LDH, with an AUC of 0.88-0.89. Patients for which these analytes did not show marked variations during the whole hospitalization period had a low probability of requiring admission to ICU. This is not surprising as these markers reflect a combination of heightened inflammatory state and organ tissue damage and/or disfunction that could lead to worsening of clinical conditions and require intensive treatment. The possible role of LDH as the most powerful clinical predictor of outcome worsening in COVID-19 patients is indicated by the fact that this test is the only biomarker that remains significantly associated with both selected outcomes at the multivariate logistic regression analysis.
One of the strengths of our study is that all evaluated biomarkers, except for D-dimer for which harmonization initiatives are still ongoing, 10,11 were determined using methodologies which harmonization has been verified and validated. Ferraro et al previously stressed how the issues of measurement standardization and harmonization represent an absolute priority for optimizing health care. 29 Only the use of assays providing harmonized results will allow the use of common reference intervals and decision limits, enabling the universal application of results of clinical studies undertaken in different locations or times and permitting their unambiguous interpretation. Accordingly, all the selected cut-offs reported in this study can be directly applied in other situations providing that the related institutions also use assays that produce harmonized results. In regards to this, it is worth mentioning that, in this study, serum albumin was measured with an immunoturbidimetric assay, which is fully specific for the protein measurement contrary to colorimetric methods, such as those based on protein dye-binding, e.g. the bromocresol green methods, which are in use in the majority of clinical institutions worldwide. 30 This explains why albumin concentrations reported in our study appear to be lower than other data reported in literature for COVID -19 patients. 18,20 On the other hand, we are aware that the programs about harmonization of Ddimer assays are ongoing and that higher order reference materials are still not available. However, preliminary studies comparing different D-dimer assays seem to support a certain grade of comparability between results. 10,11 It should also be noted that another confounding issue for D-dimer test is represented by the lack of homogeneity in reporting units of measurement. In this study, results were reported as µg/L FEU, which relate the mass of D-dimer to the mass of fibrinogen, as previously recommended. 31 Reporting values using alternative units could result in erroneous classification of normal and elevated results.
The major limitation of our study is represented by its retrospective nature. However, as the results were obtained on a large population of over 450 COVID-19 patients, it is safe to say that they are statistically robust and may represent a significant aid in decision making for prioritized treatment and more aggressive strategies in this still poorly known disease.
Another potential confounder is represented by the possible inability of admitting to the ICU all the patients who would have required intensive care due to ICU capacity constraints.
However, due to an effective territorial organization, no major obstacles to ICU admission when needed were experienced during the study period in our institution.

CONCLUSIONS
Performing risk stratification in COVID-19 patients based solely on clinical features is often difficult as signs and symptoms are usually lacking specificity. From the results of this study, it appears that some laboratory biochemistry parameters may represent an invaluable aid in identifying patients with low risk of disease progression and consequent need of ICU admission, and, at the opposite, patients with higher risk of mortality. The interpretative criteria for laboratory tests defined in this study were specifically selected to obtain accurate rule-out of patients who did not need intensive care treatment and rule-in of patients at higher risk of death. These two sets of test cut-offs should be optimally used in combination to perform an accurate evaluation of this serious disease.