key: cord-0888610-b3yek957
authors: Kottlors, Jonathan; Große Hokamp, Nils; Fervers, Philipp; Bremm, Johannes; Fichter, Florian; Persigehl, Thorsten; Safarov, Orkhan; Maintz, David; Tritt, Stephanie; Abdullayev, Nuran
title: Early Extrapulmonary Prognostic Features in Chest Computed tomography in COVID-19 Pneumonia: Bone Mineral Density is a Relevant Predictor for the Clinical Outcome - a Multicenter Feasibility Study
date: 2020-12-07
journal: Bone
DOI: 10.1016/j.bone.2020.115790
sha: a4e34d9b1af9df012fbd13e3d663b6cc00d40167
doc_id: 888610
cord_uid: b3yek957

Background Besides throat-nose swab polymerase chain reaction (PCR), unenhanced chest computed tomography (CT) is a recommended diagnostic tool for early detection and quantification of pulmonary changes in COVID-19 pneumonia caused by the novel corona virus. Demographic factors, especially age and comorbidities, are major determinants of the outcome in COVID-19 infection. This study examines the extra pulmonary parameter of bone mineral density (BMD) from an initial chest computed tomography as an associated variable of pre-existing comorbidities like chronic lung disease or demographic factors to determine the later patient’s outcome, in particular whether treatment on an intensive care unit (ICU) was necessary in infected patients. Methods We analyzed 58 PCR-confirmed COVID-19 infections that received an unenhanced CT at admission at one of the included centers. In addition to the extent of pulmonary involvement, we performed a phantomless assessment of bone mineral density of thoracic vertebra 9 - 12. Results In a univariate regression analysis BMD was found to be a significant predictor of the necessity for intensive care unit treatment of COVID-19 patients. In the subgroup requiring intensive care treatment within the follow-up period a significantly lower BMD was found. In a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor. Conclusion Phantomless assessed BMD provides prognostic information on the necessity for ICU treatment in course of COVID-19 pneomonia. We recommend using the measurement of BMD in an initial CT image to facilitate a potentially better prediction of severe patient outcomes within the 22 days after an initial CT scan. Consequently, in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects. Clinical relevance While clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future.

admission at one of the included centers. In addition to the extent of pulmonary involvement, we performed a phantomless assessment of bone mineral density of thoracic vertebra 9 -12.

In a univariate regression analysis BMD was found to be a significant predictor of the necessity for intensive care unit treatment of COVID-19 patients. In the subgroup requiring intensive care treatment within the follow-up period a significantly lower BMD was found.

In a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor.

Phantomless assessed BMD provides prognostic information on the necessity for ICU treatment in course of COVID-19 pneomonia. We recommend using the measurement of BMD in an initial CT image to facilitate a potentially better prediction of severe patient outcomes within the 22 days after an initial CT scan.

Consequently, in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects.

While clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future. caused by the novel coronavirus 2 (SARS-CoV-2). 1,2 Real-time reverse-transcription polymerase chain reaction (RT-PCR) is considered to be the gold standard for the diagnosis of novel coronavirus disease. Additionally, unenhanced chest computed tomography is often performed fast and sensitive diagnostic tool. 3 Besides early detection and assessment of the severity of lung involvement in COVID-19 pneumonia, chest CT also offers the possibility to determine the individual stage of the disease. 4 For this reason, many guidelines recommend use of low dose chest CT for detection and quantification of the pulmonary involvement. 5 While pulmonary CT manifestations may be associated with the progression and prognosis of COVID-19, pulmonary involvement is additionally affected by a large variance regarding the individual stage of the disease as well as other clinical parameters. 6, 2 Demographic factors, especially age, but also comorbidities including cardiovascular disease and chronic obstructive pulmonary disease, are major determinants of patients outcome in COVID-19 infection. 7, 8 As COVID-19 patients are treated in centers for initial care and clinical triage, the full medical history is often not yet fully available. However, undiagnosed or unknown previous diseases comprise an important determinant of the later outcome. 9 Since there are -besides pulmonary involvement -other extrapulmonary parameters captured on chest CT which are associated with pre-existing comorbidities or demographic factors, this aims to assess bone mineral density. Characteristics like the degree of physical activity, body J o u r n a l P r e -p r o o f Journal Pre-proof composition and lifestyle have been shown to have an influence on bone mineral density. 10, 11 Smoking and, chronic lung diseases are proven to affect BMD negatively. [12] [13] [14] Therefore, we hypothesize that bone mineral density is a surrogate variable of many potentially outcomerelevant factors in a COVID-19 infection.

In general, not least because of the increased risk of infection, it is not practical to measure bone mineral density in patients with recent SARS-CoV-2 infection. However, as many guidelines recommend a unenhanced CT examination of the thorax in cases of suspected infection, the information about bone mineral density is generated anyway and must ultimately only be evaluated opportunistically by phantomless assessment of the BMD. 5, 15 Phantomless assessed BMD is -next to dual-energy X-ray -an established and reliable method for the non-invasive determination of bone mineral density (BMD) in vertebrae and other bones. 16, 17 It allows the determination of BMD in contrast agent CTs as well as unenhanced CT, where degenerative changes such as partial calcification or osteophytes are not a relevant interfering factor. 18, 19 We prognosticate that a statement concerning the later outcome, or the degree and necessity of an intensive care treatment can be derived from the bone mineral density measured in an initial chest CT scan.

The methodology used in this study involving human participants was in accordance with the Bone mineral measurements Measurement of vertebral bone density without the use of an external reference phantom was performed using a an earlier described, standardized method using established software (IntelliSpace, Philips, Amsterdam, The Netherlands) . [21] [22] [23] The three most caudally registered vertebrae in the CT scans were primary objects for the bone mineral density measurements. In all patients these were the vertebral bodies TH 11 -L 1. In the case of metastases, fractures or severe degeneration of any of those vertebrae, measurements were extended upwards to vertebrae TH 10 or 9 (n = 3). Sagittal reconstructions were used to angulate the transverse plane parallel to the end plate at each level. In axial reformations of the vertebral bodies an ellipsoid volume of interest (VOI) of 9-mm thickness was placed in the trabecular compartment of the vertebral body. Spectrometric calibration of Hounsfield units to the mineral scale was performed with density measurements in the paravertebral muscles (erector spinae muscle) and the subcutaneous fat tissue (see figure 1 ). The VOIs were placed in the anterior trabecular bone of the vertebral body, avoiding the internal vertebral venous plexus, the surrounding cortical bone, and any focal lytic or sclerotic lesion. 17, 22, 23 To determine interand intrareader reliability, 10 patients were randomly selected from the data set and measured twice by reader 1 (more than one year of experience in CT imaging). Furthermore, another 10 randomly selected patients were measured by a second radiologist (reader 2, more than one year of experience in CT imaging).

Statistical data analysis was performed using R version 3.6.2 on Rstudio version 1.2.5033. 24 Figures were plotted using the ggplot2 package. 25 To predict the categorical variable of intensive care treatment, an uni-and multivariate logistic regression analysis was used. The J o u r n a l P r e -p r o o f Journal Pre-proof glm() function was used in "binomial" specification taking the independent variables of age, gender, and or only BMD into account. To illustrate the accuracy of fit, pseudo-R 2 according to Nagelkerkes / Cragg & Uhlers was calculated using the descr package (.2 > acceptable, .4 > good, .5 > very good) as well as the Akaike-Information-Criterion (AIC; low value indicates a higher informativeness of the model). 26 Moderation and interaction effects between a) age, b) gender and c) BMD were formed using an interaction term from multiplication a) or b) by c). 27 For the group comparisons, a two sample t-test was used after the normal distribution and variance homogeneity was confirmed by the Shapiro-Wilk-and Levene-test. Continuous variables were reported as mean  standard deviation (SD). Intraand interreader reliability was tested using the intraclass correlation coefficient in two-way random-effects model (<.5 poor, >.5 moderate, > .75 good, > .9 excellent) using the irr package. Statistical significance was defined as p  .05. Table 1 .

The phantomless assessed BMD showed a mean bone density of 131.4 mg/ml (± 33.2) in COVID-19 patients. The Z-score and T-score calculations provided by the manufacturer are reported but should be accompanied by a warning that they are not comparable to DXA-based result. For calculations a European reference group in the age between 18-80 years was used within the BMD tool to display the T-and Z-scores. 28 The mean T-score was -1.1 (± 1.1) while the mean the Z-score was -0.4 (± 1).

Considering the CT measurements of bone mineral density within the independent variable, we fit a logistic regression model to predict if the patient would require intensive care within a period of 22 days. In this univariate model, BMD is a significant predictor for the prognosis of intensive care obligation with a pseudo R 2 to the Nagelkerke Index of 0.2 (p < .01), and an AIC of 80.2 (see Table and Figure 2 ). In comparison, a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor achieving a pseudo R 2 to the Nagelkerke Index of .29 (p < .01) and an AIC of 76. None of the interaction terms described above proved to be a significant predictor of the intensive care obligation. 7, 8 It was shown that these, as well as physical activity and body composition impact bone mineral density. [29] [30] [31] Additionally, it has been shown that smoking, chronic lung diseases have a negative effect on bone mineral density. [12] [13] [14] Thus, the present study was concerned with the question whether reduced BMD as a surrogate variable for possible pulmonary comorbidities, lifestyle factors or general physical condition can provide a statement about an increased risk for complications associated with COVID-19 pneumonia. The central issue was to identify cases with severe progressions and to predict whether a patient had to be treated in an ICU within the observation period. Using BMD, it was possible to predict whether a patient would need intensive care treatment in the period following admission. It was shown that in a logistic regression model, BMD acts as a significant predictor for a potential intensive care treatment. Adding age and gender in a multivariate regression model or within the range of interaction terms, BMD as a significant predictor is omitted -primarily due to a high collinearity between age and BMD. Thus, it can be observed that when asking whether intensive care treatment is necessary in the following period, the bone density in this group of patients does not reveal any additional variance compared to patient age alone.

Regarding the graph of logistic regression to predict the risk of intensive care treatment based on bone density, it can be stated that that the risk for an intensive care treatment increases to over 75% at a BMD of under 80 mg/ml, whereas the risk with a bone density measured by CT of over 160 mg/ml is associated with an risk of under 25% for the need of an ICU treatment in the follow-up period (see Figure 2 ).

The strength of the present study lies in the fact that a relatively large number of virologically confirmed COVID-19 cases were evaluated by an initial unenhanced CT of the thorax. With Here, the bone-density was determined in phantomless CT using a validated and established algorithms. 22, 23 The possibility of phantomless determination of the BMD offers the advantage that the bone density can be assessed on the basis of the CT scans made anyway, without the need for further diagnostic procedures. In summary, the methodological approach and the results are consistent with other observational studies and support the hypothesis that multimorbid and elderly people suffer from a more severe and complicated course of COVID-19 infection. 7, 20 The analyses showed that the measurements of BMD provided a good inter-and intrareader reliability and can be considered as robust and well reproducible.

The most relevant limitation of the present study is, the high correlation between age and bone density. It should be noted that a larger sample size would have allowed a focus on exclusively older patients. Thus, it might be possible to determine in a selected group of patients whether an additional decrease in bone density would clarify further variance. In addition, it must be stated that the BMD measurement couldn't be performed at the heights of the lumbar spine used in relevant preliminary studies and had to be performed in the thoracic region. The present study considers only a small, locally targeted patient population within the context of a retrospective design. Furthermore, a fully automated determination of BMD for the evaluation of possible COVID-19 associated complications should be considered.

Future studies should also clarify the extent to which other parameters (e.g. obesity) determined in an initial CT scan show possible interaction effects in order to enable a more precise prognosis of the outcome and possible disease-associated complications. In addition, the extent of pulmonary opacities was not taken into account, which should be considered in the context of larger analyses. Additional studies should verify our hypotheses on the basis of a larger prospective patient collective. In this sense, our pilot study is suggesting that it might be reasonable to add the BMD measurement into AI tools for automatic characterization of This multicentric study emphasizes the prognostic relevance of low BMD as a risk factor for SARS-CoV-2 infected patients. We present a method using an initial unenhanced CT scan to screen opportunistically for patients with low BMD who have an increased risk for the need of intensive care treatment. While there is a high correlation between age and BMD in this patient sample and much of the variance in outcome is explained by this, BMD alone is a significant predictor of whether intensive care treatment is necessary. But in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects.

While clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future. Table 3 : Estimates for the multivariate logistic regression model to predict intensive care obligation. *** = p<.001; **=p<.01; *=p<.05. Table 3 : Intraclass Correlation Coefficient to determine the inter-and intrarater reliability, (<.5 poor, >.5 moderate, > .75 good, > .9 excellent) Figure 1 : Exemplary measurement of the BMD one of three vertebraes in a 36-year-old male patient. No intensive care treatment was necessary for the patient. a) and b) placement of the volumes of interest. c) density-controlled calibration of the reference tissue. Figure 2 : logistic regression model to predict whether intensive care treatment was necessary within the 22 days following LDCT. X-axis: BMD, left Y-axis: bidimensional representation of the need for intensive care treatment. Right-y-axis (blue) Probability (based on logistic regression model) of intensive care treatment being necessary depending on BMD. 

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Conceptualization, Methodology, Writing-Original draft preparation Große Hokamp, Nils: Conceptualization, Bremm, Johannes: Data curation Fichter, Florian: Data curation Safarov

The authors declare that they have no conflict of interest. The present study was conducted without any support from third parties.

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