key: cord-0775615-8hmnawp4 authors: Ahlstrand, E.; Cajander, S.; Cajander, P.; Ingberg, E.; Lof, E.; Wegener, M.; Liden, M. title: Visual Scoring of Chest CT at Hospital Admission Predicts Hospitalization Time and Intensive Care Admission in Covid-19 date: 2020-11-03 journal: nan DOI: 10.1101/2020.10.30.20222471 sha: 56cdd606fa0ac175e98ab98aa5fe41da13c49327 doc_id: 775615 cord_uid: 8hmnawp4 Background The extent and character of lung involvement on chest computerized tomography (CT) have a prognostic value in covid-19 but there is lack of consensus on how to assess and stage CT features. A scoring system of lung involvement in covid-19, Orebro covid-19 Scale (OCoS) was implemented in clinical routine on April 1 2020 in Orebro Region, Sweden. The OCoS-severity score measures the extent of lung involvement while OCoS-temporal stage characterizes the parenchymal involvement. The objective of the present study was to evaluate the OCoS scores in relation to clinical outcome of covid-19. Methods Population based study including data from all hospitalized patients with covid-19 in Orebro Region during March to July 2020. Chest CT scores at the time of hospital admission and ICU admission were analyzed in relation to hospital and intensive care unit (ICU) length of stay, time to ICU admission and admission to ICU or death. Findings In the 381 included patients, there was a close correlation of the OCoS-severity score on admittance to hospital and the hospital length of stay. The OCoS-severity score on hospital admittance was a strong predictor for both a severe outcome in regards to ICU admittance or death and the time to ICU admittance. On admittance to ICU, both OCoS-severity score and temporal stage were correlated with the ICU length of stay. Interpretation Chest CT visual scoring on admission to hospital predicts the clinical course in covid-19 pneumonia. Funding This work was supported by the Orebro Region, Sweden. The novel coronavirus disease (covid-19) , caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that represents an important threat to human health. Up to September 28, 2020, more than one million people have died from covid-19 in over 160 countries 1 . Although most covid-19 patients present with mild illness, a minority of patients have a severe outcome characterized by pneumonia, respiratory failure and acute respiratory distress syndrome (ARDS) 2 . The strongest independent risk factor for a severe outcome is high age 3 . Other important risk factors include male sex, cardiovascular disease, and diabetes with complications 3,4. Within certain populations up to 30% of infected patients may require hospitalization and increased medical support 5 . Outbreaks of covid-19 are causing a considerable strain on the health system with a shortage of hospital and intensive care units (ICU) beds 6 . To manage the potentially critical burden on the health care system during outbreaks and to triage individual patients, there is a clinical need for robust prognostic models to predict the course of covid-19. Previous research have demonstrated a prognostic role of the extent and character of lung involvement on chest computerized tomography (CT) in covid-19 [7] [8] [9] . These studies have applied an abundance of different methods to measure covid-19 lung involvement and consequently there is no consensus in the literature on how to assess and stage CT features of covid-19. In addition, published predictive models have generally been developed using retrospectively interpreted CT images by expert thoracic radiologists in contrast to clinical routine were chest CTs typically are read by general radiologists. In response to an increased demand for chest CTs in covid-19, a concise scoring system of lung involvement in covid-19, the Örebro covid-19 Scale (ÖCoS) was implemented in clinical routine on April 1 2020 at the Department of Radiology, Örebro Region, Sweden. The intention was to provide a standardized assessment of covid-19 pneumonia. Both the extent of lung involvement, ÖCoSseverity score, and the character of involvement, ÖCoS-temporal stage, are assessed on the scale. The current study is a population-based evaluation of the clinically provided ÖCoS chest CT scores, including all patients hospitalized for covid-19 in the Örebro Region during the first five months, March-July 2020, of the covid-19 outbreak in Sweden. The primary aim was to evaluate chest CT at hospital admission as a predictor of hospital length of stay (LoS) and admission to ICU or mortality. The secondary aim was to evaluate chest CT at ICU admission in relation to ICU LoS. The Swedish Ethical Review Authority approved the study protocol and waived the informed consent requirement for this retrospective study, reference number 2020-02515. The study included all patients ≥18 years admitted as in-patients due to laboratory confirmed covid-19 in one of the three hospitals, one university hospital and two associated hospitals, in Örebro Region, Sweden. Covid-19 patients were identified through the ICD-codes corresponding to either a primary laboratory confirmed diagnosis of covid-19, or a primary diagnosis of covid-19 based on a typical clinical picture in combination with a positive antibody test for covid-19, or a laboratory confirmed secondary diagnosis of covid-19 with a non-etiological pulmonary diagnosis as a primary diagnosis. Data regarding age, sex, hospitalization times, hospitalization routes, ICU admission, death during and after hospitalization, laboratory tests for covid-19, and radiology reports were extracted from the information system and radiological information system of the Örebro Region. Data from March 1 to August 31, 2020 were extracted, but only patients admitted to hospital before July 4 were included to enable at least 60 days observation time in the extracted data. Patients arriving from hospitals external from the Örebro Region were excluded. Figure 1 describes the inclusion process in detail. The structured ÖCoS chest CT report was introduced on April 1, 2020. The scale consists of the disease severity score (ÖCoS-severity score) and temporal stage (ÖCoS-temporal stage) on discrete scales, Figure 2 . The ÖCoS-severity score is a visual assessment of the global lung involvement on a six-point scale (0%, <10%, 10-25%, 25-50%, 50-75%, >75%) whereas the ÖCoS-temporal score is a five-point ordinal scale assessing the parenchymal characteristics based on the transition from normal parenchyma, via ground glass opacities (GGO) to consolidations as described in early reports of the covid-19 disease evolution 10 . Radiologists were instructed to provide only one selection for temporal stage and one selection for severity score for each examination. Scores were provided similarly regardless of whether covid-19 was confirmed or not confirmed at the time of reading. Figure 3 gives examples of ÖCoS scores. In the study, the ÖCoS scores were extracted from the clinical radiology reports. Approximately 30 different radiologists and residents provided clinical scores that were extracted for the study. In cases where no clinical ÖCoS scores were provided, mostly because of nighttime overseas teleradiological reading and CT preformed before April 1, a separate ÖCoS reading for the study was performed by a radiology resident (MW) blinded to all clinical information. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint The CT at hospital admission (CTadm) was defined as the chest CT closest in time to hospital admission, with no longer than two days difference. The CT at ICU transfer (CTICU) was defined as the chest CT closest in time to ICU transfer, with no longer than two days difference. For detection of SARS-CoV-2 RNA, nasopharyngeal swab specimens were analyzed by different methods during the study period. The vast majority of samples were analyzed by an in-house realtime reverse-transcription polymerase chain reaction (RT-PCR) targeting the E gene (with an RdRp gene assay as confirmation) adapted from the protocol recommended by WHO, or the RdRp gene assay alone. For antibody testing, the Diasorin (Saluggia, Italy) Liaison XL test for SARS-CoV-2 IgG was used, in combination with Euroimmun (Lübeck, Germany) SARS-CoV-2 IgG ELISA for confirmation in weakly positive samples to increase specificity. To summarize the effect of covid-19 on hospitalization time in the presence of the competing event of death, we used the composite measure hospital free days 60 days post-admission (HFD60). For each patient, the total number of HFD60, including readmissions, during the 60 days following the first admission to hospital with covid-19 was computed. The hospital length of stay (LoS) was defined as 60-HFD60. This outcome equals the hospitalization time within 60 days in non-deceased patients whereas deceased patients and patients with a hospitalization time over 60 days will have a hospital LoS of 60. The combined risk for ICU admission or death within 60 days was used as outcome measure in a multivariate logistic regression. The intervals in days between CTadm and ICU admission were derived for all patients admitted to an ICU. For patients admitted to an ICU, the 60-day ICU free time (IFD60) following the day of ICU transfer was computed. The ICU LoS used in the analysis was 60-IFD60, which corresponds to the total ICU-time within 60 days in non-deceased patients. Matlab R2020a (The MathWorks Inc., Natick, MA) was used for statistics. Multivariate linear regression with 60-HFD60 as dependent variable was performed to identify the predictors for LoS. Age was treated as a continuous variable whereas ÖCoS temporal stage, ÖCoS severity score and sex were treated as categorical variables. A reduced model was developed, where the temporal stage A, B and C were grouped, forming the temporal stages: N (No lung involvement), ABC (GGO extent greater than or equal consolidation extent), and D (Predominantly is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint consolidations), Figure 2 . Only linear terms with no interactions were included in the models. Twenty-fold cross validation was performed to assess overfitting on the reduced linear regression model with LoS as dependent variable. For the analysis of time to ICU admission, Spearman correlation coefficient was computed for CTadm ÖCoS-severity score, CTadm ÖCoS-temporal stage and age, and Wilcoxon signed rank test was used to assess dependency of patient sex. For the analysis of ICU length of stay, Spearman correlation coefficient was used to assess dependency of CTICU ÖCoS-severity score, CTICU ÖCoS-temporal stage and age, and Wilcoxon signed rank test was used to assess dependency of patient sex. This work was supported by the Örebro Region, Sweden. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint In 375 out of 381 included patients, SARS-CoV-2 RNA was confirmed by RT-PCR and the additional six patients were included on basis on a typical clinical picture supported by a positive covid-19 antibody test. Patient characteristics and outcomes are described in Table 1 . Clinically provided ÖCoS scores were available in 309 out of 381 CTadm, and in 53 out of 67 CTICU. During the retrospective inclusion period, in-patient covid-19 patients were treated with a standard of care in line with international recommendations of that time 11, 12 , including oxygen support and low-molecular weight heparins. The hospital length of stay (LoS) in relation to CTadm ÖCoS-severity scores are shown for different age groups in Figure 4 . In patients ≤70 years old there was a close correlation between the ÖCoS severity score and the LoS, while the ÖCoS-severity score was less clearly correlated to the LoS in older patients. The multivariate regression analysis identified patient sex, age and ÖCoS-severity score as statistically significant predictor variables for LoS, Table 2 . Since temporal stage A, B and C demonstrated similar coefficients in the multivariate analysis, a reduced model was developed with temporal stage A, B and C grouped. In the reduced model, the temporal stage was a statistically significant predictor variable, and there was a consistent reduction in hospital LoS for temporal stage D (predominantly consolidations) compared to earlier stages A-C (demonstrating more GGO) and the first stage N (no lung involvement), Table 2 . The root mean square errors (RMSE) of the full model and the reduced model were similar, 17.2 days, indicating little loss of information in the reduction of predictors. Twenty-fold cross validation of the reduced model linear regression showed a comparable RMSE, 17.6 days, indicating only minor overfitting in the model. The coefficients in the linear regression provides an interpretation of the impact of each variable in terms of LoS days: the LoS increases with four days per ten years age difference and with four days in males compared to females. A higher ÖCoS severity score is associated with longer LoS: Compared to ÖCoS 0-1 (<10% extent), LoS in patients with CTadm demonstrating ÖCoS 2 (10-25%) increased one day, ÖCoS 3 (25-50%) eleven days, ÖCoS 4 (50-75%) 20 days, and ÖCoS 5 (>75%) 30 days. A more advanced ÖCoS-temporal stage, suggesting a later phase of covid-19 pneumonia at hospital admission, was associated with a shorter LoS. Compared to ÖCoS N (no lung involvement), LoS in patients with CTadm demonstrating ÖCoS A-C (GGO extent up to equal consolidation extent) decreased seven days and ÖCoS D (predominantly consolidations) decreased ten days. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint ICU admission and mortality rate Figure 5 shows the combined ICU admission and death rate in relation to ÖCoS-severity score at hospital admission. In the multivariate logistic regression analysis, patient age was dichotomized as over or under 70 years. The analysis identified the ÖCoS-severity score at hospital admission, p<0.001, patient sex, p=0.006 and age, p=0.002 as significant predictor variables for the combined outcome of ICU admission and mortality, Table 3 . The interval between the CTadm and ICU transfer was inversely related to the CTadm ÖCoS-severity score, p=0.002, and CTadm ÖCoS-temporal stage p=0.051, but was not significantly associated with age, p=0. 15 . There was no significant difference between male and female patients, p=0.39. The interval between CTadm and ICU admission was longer for lower ÖCoS-severity scores and earlier ÖCoS-temporal stages, Figure 6 . The relation of ÖCoS scores on CT at the time of ICU transfer (CTICU) and ICU outcomes are shown in Figure 7 . The ICU LoS was positively correlated to CTICU ÖCoS-severity score, p<0.001, and inversely correlated to CTICU ÖCoS-temporal stage, p=0.044. The ICU LoS was correlated to patient age p<0.001, but not to patient sex p=0.33. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint Covid-19 is an ongoing pandemic causing hospital crowding and shortage of ICU beds during outbreaks. The disease has a variable prognosis and established validated scores such as CURB-65 have a low overall performance 13, 14 . Instead, we demonstrate that clinically provided chest CT visual scores at hospital admission can robustly predict the clinical course of covid-19 and that chest CT at ICU admission can predict ICU time, especially in patients up to 70 years old. The two aspects of the ÖCoS visual score -the temporal development of the CT pattern from GGO to consolidations, and severity of lung involvement -are closely correlated to the patient outcomes in the present study. In particular, we demonstrate that the ÖCoS-severity score, a visual estimation of the extent of lung involvement, at hospital admission is a strong predictor of uneventful outcome in terms of death or ICU admission and that it is an important predictor for hospital LoS. The weaker association of these parameters in elderly patients, >70 years, may be due to frequent comorbidities in the elderly group creating a more complex relationship. Although different chest CT findings have been described in covid-19, the typical features are GGO and consolidations 15 . Three findings in the current study highlight that the transition from GGO to consolidation on the ÖCoS-temporal stage reflects the clinical course in the acute phase of covid-19, and often coincides with a deterioration of respiratory symptoms: 1) The inverse relationship of ÖCoS-temporal stage and time to ICU transfer, Figure 6 , 2) the inverse relationship of ÖCoStemporal stage at CTICU and ICU LoS, Figure 7 and 3) the lower number of hospital LoS days in late ÖCoS-temporal stages on CTadm, Table 2 . To put the current study in context, we performed a systematic literature search, Supplementary material. In summary, we found several reports on protocols of visual quantitative analysis of CT evaluated lung involvement demonstrating a correlation to the clinical severity of covid-19 9,16 . In addition, several semi-automatized 8,13,17 and computerized 7,18,19 quantitative measures of covid-19 lung involvement on CT have been described to be associated with outcomes related to a severe course of covid-19. However, to the best of our knowledge, up to date only one smaller study, published as a letter to the editor, reported real-life data on the predictive role of CT visual scoring in clinical routine 20 . In this study, we report that the predictive role of chest CT can be reproduced in a non-selected population-based context with CT evaluations made by several reviewers as part of clinical routine. Since almost 80% of the study cohort underwent chest CT on admission there was probably only a limited degree of selection of patients referred for CT. We used a concise visual scoring system as a predictive model for outcome of hospitalized covid-19 patients. A strength is that the model apart from patient age and sex rely solely on CT findings, excluding clinical and laboratory data. The results indicate that triage with chest CT on admittance to hospital is a valuable tool for covid-19 patients, provided that a consistent scoring system is applied. The simplicity of the chest CT ÖCoS scoring enables a straightforward implementation in clinical practice, supported by its rapid acceptance among reading radiologists and referring clinicians in the Örebro Region, Sweden. Moreover, a strength of this study is that we could, in contrast to other studies, provide outcomedata up 60 days post-admission including mortality after hospital discharge. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020. 10.30.20222471 doi: medRxiv preprint The study has several limitations. Consistent with the inclusion criteria, the results only apply to inpatients. The use of clinically provided scores by multiple readers is a limitation, but also a strength in the study. Visual scoring is subjective and prone to interobserver variation, which reduces the precision of the provided scores. On the other hand, the scores used in the study are a reasonable estimate of the precision in a clinical scenario. Since the ÖCoS scores were provided in clinical routine, the reviewers were not formally blinded, but the main study outcomes of HFD60 and ICU admittance were naturally unknown to reviewers by the time of chest CT evaluation. Furthermore, we only included laboratory confirmed cases of covid-19, and consequently a few covid-19 cases are likely to have been excluded from the analysis 21 . In conclusion, concise visual scoring of chest CT at hospital admission and at ICU transfer in clinical routine predicts the clinical outcome of covid-19, especially in patients <70 years. In situations where adjuvant treatments and hospital beds are limited, we believe that scoring of chest CT is informative and a valuable tool for clinical decision making. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020. 10 Note: GGO -ground-glass opacities. HFD60 -60-day hospital free days. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. Note: GGO -ground-glass opacities. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint A systemic literature search was performed on September 24th 2020 and 860 articles were retrieved from the Medline database via Pubmed using the search string (covid OR sars-cov-2) AND (CT OR "computed tomography" OR CAT OR "computer assisted tomography" OR "computerized tomography" OR "computerized tomography") AND (prognos* OR predict* OR outcome). All titles were scanned for potential relevance and among the selected articles, abstracts were reviewed. Ultimately, the search resulted in 54 articles that were considered to address the question of interest, namely how chest CT images are associated with disease severity or outcome in patients with covid-19. A selection of relevant articles are referred to in the main text. . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 3, 2020. ; https://doi.org/10.1101/2020.10.30.20222471 doi: medRxiv preprint An interactive web-based dashboard to track COVID-19 in real time Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Factors associated with COVID-19-related death using OpenSAFELY Epidemiology and clinical features of COVID-19: A review of current literature Rapid Emergence of SARS-CoV-2 in the Greater New York Metropolitan Area: Geolocation, Demographics, Positivity Rates, and Hospitalization for 46,793 Persons Tested by Northwell Health Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia Multi-Center Study of Temporal Changes and Prognostic Value of a CT Visual Severity Score in Hospitalized Patients with COVID-19 Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) Surviving Sepsis Campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019 (COVID-19) Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients with COVID-19 Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study COVID-19): A Systematic Review of Imaging Findings in 919 Patients The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method