Imaging-based indices combining disease severity and time from disease onset to predict COVID-19 mortality: A cohort study.

BACKGROUND: COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care.
PURPOSE: The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED).
MATERIALS AND METHODS: All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO2) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort.
RESULTS: Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUCCT = 0.92, AUCCXR = 0.90, AUCCRP = 0.88, AUCsO2 = 0.88). AUCCXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models.
CONCLUSION: Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation.

Introduction

After more than two years since the first case of COVID-19 was detected in December 2019 in China, more than 450 million cases and 6 million deaths have been reported globally up to March 2022 [1]. Although the critical care bed occupancy crisis is expected to subside as immunisation accelerates, because COVID-19 detection and related hospital admissions periodically increase, the pandemic continues to be a challenge.

Much effort has been put into identifying the factors associated with the need for critical care and death, starting from a set of information available at emergency department (ED) admission, including clinical, laboratory, and radiological findings [2, 3].

Among the clinical factors, the most used to predict COVID-19 severity and mortality are older age, pre-existing comorbidities, hypoxia, and laboratory tests indicative of increased inflammatory response, COVID-19-related coagulopathy, and end-organ dysfunction [3-7].

Thoracic imaging with chest radiography (CXR) and computed tomography (CT) is a key tool in the evaluation of the degree of lung involvement. CT is highly sensitive in the diagnosis of COVID-19 pneumonia [8, 9], and CT percentage of lung involvement and the quantitative burden of consolidation are among the most important prognostic factors for short-term prognosis in COVID-19 patients [10-16].

Although CXR is less sensitive than CT in diagnosing COVID-19 pneumonia, the prognostic role of CXR, i.e., its utility in predicting outcomes in patients already diagnosed with COVID-19 pneumonia, is only beginning to be explored. In fact, CXR assessment using either the Brixia score or the Radiographic Assessment of Lung Edema (RALE) score appears to be reliable in predicting the various outcomes of COVID-19 in patients admitted to ED [17-23].

Patients with a rapid worsening of symptoms and a short time between symptom onset and seeking care are more likely to have the worst outcomes [24-26]. However, the combined effect of baseline clinical-laboratory findings and time from symptom onset on COVID-19 patients’ outcomes has never been explored. Similarly, CT or CXR-based scoring systems are mostly based on the combined effects of the extent of pulmonary involvement and respective attenuation patterns (i.e., normal, ground-glass opacities—GGO, and consolidation), but they do not consider the time from symptom onset as an important adjunctive factor in outcome prediction.

The aim of this study was to evaluate the role of indices that combine radiological data at admission and time from disease onset, in predicting mortality of COVID-19 patients admitted to the ED.

Materials and methods

Setting

The province of Reggio Emilia, located in Northern Italy, has a population of 531,751 inhabitants. There are six hospitals in this province, with an emergency department (ED) in the main hospital in the city of Reggio Emilia as well as in four of the five smaller health district hospitals. There is no private ED service. The first case of SARS-COV-2 in the province was diagnosed on 27 February 2020. The cumulative incidence in the first wave of the pandemic reached 0.9% (March–April 2020). During the first pandemic wave in Reggio Emilia, patients presenting to the ED with fever and SpO2>95% were discharged home in case of negative chest X-rays and/or CT or in case of positive chest X-rays and/or CT scan but who were >70 years of age and had no relevant past medical history. Patients >70 years of age and/or with relevant past medical history could be hospitalized even without respiratory failure, while others were admitted to hospital in case of radiological findings of pneumonia combined with respiratory failure. In the case of radiological findings of complicated pneumonia with or without acute respiratory distress syndrome (ARDS), patients started non-invasive ventilation in the ED and were admitted to subintensive/intensive care unit [27].

Study design and selection of participants

This retrospective cohort study included all consecutive patients meeting the following inclusion criteria: patients > 18 years of age; presenting to any one of the provincial EDs between 27 February and 13 March 2020; and with a positive RT-PCR within 10 days from ED admission. Patients who did not undergo both CT scan and CXR at ED presentation were excluded. During the COVID-19 outbreak, the diagnostic protocol for suspected COVID-19 patients presenting to EDs included RT-PCR, blood tests, chest X-rays, and CT in every case of suggestive X-rays or negative X-rays but with highly suggestive clinical features.

Even if the researchers assessed inclusion criteria and conditions at admission retrospectively, all the information registered at the time of ED presentation (e.g., laboratory data or CT disease extension) was not modifiable later, therefore exposures could not be influenced by the occurrence of the outcome.

The main outcome was death occurring in the 30 days following ED visit.

Data of the included patients were partially used in previous studies to assess CT diagnostic accuracy and added prognostic value [8, 16].

Ethical approval

The study was approved by the Ethics Committee Area Vasta Emilia Nord (n.2020/0045199). Given the retrospective nature of the study, the Ethics Committee authorized the use of a patient’s data without his/ her informed consent if all reasonable efforts had been made to contact that patient.

Clinical data

Date of symptom onset, diagnosis, hospitalization, and death were retrieved from the COVID-19 Surveillance Registry, implemented in each Local Health Authority. The surveillance is fed by several sources: the Department of Public Health’s epidemiological investigations, contact tracing, and symptom surveillance for people in self-isolation, laboratory reports, electronic ED and hospital records, and death certificates for hospitalized patients. Data from the COVID-19 Surveillance Registry were linked with the provincial Radiology Information System to search for CT scans performed at the moment of or after the onset of COVID symptoms.

Clinical covariates included patient characteristics (age, sex) and the presence of comorbidities, calculated separately, as well as the Charlson Comorbidity Index (CCI), which provides an overall measure of an individual patient’s complexity [28]. We categorized the Index in four classes: 0 (absence of relevant comorbidities), 1, 2, and ≥ 3 comorbidities. Information on comorbidities was collected from hospital discharge databases of all hospital admissions occurring in the previous 10 years, before the start of the SARS-CoV-2 pandemic in Italy. Diabetes and cancer diagnoses in people residing in the province of Reggio Emilia were ascertained through linkage with the local Diabetes Registry and the Cancer Registry.

C-reactive protein (CRP) and lactate dehydrogenase (LDH) levels and white blood cell, lymphocyte, neutrophil, and platelet counts, as well as arterial blood gas analysis data, all measured at ER presentation, were collected from the provincial Local Health Authority’s Laboratory Information System database. All the tests were carried out in the Reggio Emilia Hospital Clinical Laboratories with routine automated methods.

In order to have a reference for the added value of radiological imaging, we computed the severity/time indices also for two basic laboratory data usually available at ED presentation, i.e., oxygen saturation level (sO2) as a measure of lung damage, and CRP as a measure of the inflammatory process characterizing COVID-19 pneumonia.

Radiological data

CT scans were performed using one of three scanners (128-slice Somatom Definition Edge, Siemens Healthineers; 64-slice Ingenuity, Philips Healthcare; 16-slice GE Brightspeed, GE Healthcare) without contrast media injection. Scanning parameters were tube voltage 120 KV, automatic tube current modulation, collimation width 0.625 or 1.25 mm, acquisition slice thickness 2.5 mm, and interval 1.25 mm. Images were reconstructed with a high-resolution algorithm at slice thickness 1.0/1.25 mm. The extension of pulmonary lesions estimated by using a visual scoring system, resulting in a percentage of total lung parenchyma which had any pathological changes likely due to COVID-19, was extracted from the verbal and structured CT reports [8].

CXRs were retrospectively reviewed by a single radiologist to collect RALE score [29, 30]. Every CXR was divided into quadrants defined vertically by the vertebral column and horizontally by the first branch of the left main bronchus. An extension score from 0 to 4 (0: none, 1: <25%, 2: 25/50%, 3: 50/75%, 4 >75%) and a density score from 1 to 3 (1: Hazy, 2: Moderate, 3: Dense) were assigned to each quadrant. The RALE score was calculated as the sum of the products of the two scores obtained for each quadrant.

To validate the ability of the CXR-based RALE score to predict death, a validation set from a different province (Modena, Northern Italy) was used: all the consecutive cases presenting at the Policlinico di Modena Hospital ED for suspected pneumonia and with confirmed COVID-19 (i.e., positive for SARS-CoV-2 on RT-PCR) between 24 February and 13 April 2020 for whom a CXR was available. For this set of patients, only age, sex, and time from symptom onset were known.

Severity/Time indices

In order to obtain indices that could incorporate information on disease severity at ED admission and time from disease onset, we combined the CT visual score, the CXR RALE score, CRP levels, and sO2 levels separately with the time elapsed from symptom onset and the measurement of these parameters. The easiest way to do this was to divide the severity measure by the time needed to reach that level of severity. This strategy was used for CRP levels and for CT- and CXR-based extension of parenchymal involvement, while sO2 severity/time index was calculated as the difference in sO2 value to 100 (100-sO2) divided by the time from symptom onset to sO2 measurement.

We set a five-day lag time representing the average amount of time during which the disease had progressed before symptom onset, based on the reported time period between SARS-CoV-2 infection and COVID-19 symptom onset [31].

CXR RALE-based severity/time index was also calculated for the Modena validation cohort.

Statistical analyses

Continuous variables are reported as median and interquartile range, and categorical variables as proportions. Poisson regression models were used to estimate incidence rate ratios (IRR) with 95% confidence intervals (95% CI) for death, unadjusted and adjusted for age and sex.

A multivariable regression model adjusted for age and sex was used to compare models without and with severity/time indices. The comparison of model performances was done by Log likelihood, Akaike’s information criteria (AIC), and P value for Z-test for severity/time index. Area under the ROC curve (AUC) was used to estimate predictive value of different severity/time indices. We used Stata 13.0 SE (Stata Corporation, Texas, TX) software package.

Validation was conducted applying the model parameters estimated in the Reggio Emilia cohort for CXR RALE score and respective severity/time index to a cohort of cases collected in the ED of the Policlinico di Modena hospital. AUC is reported to measure the predictivity of the model in a different setting.

Results

Characteristics of study subjects

After excluding RT-PCR negative patients or those who did not have a complete radiological assessment within 10 days from ED presentation, 308 patients were included (Fig 1).

Fig 1

Flowchart representing patient inclusion.

Of these 308 COVID-19 patients, 55 (17.9%) died (Table 1); the patients who died were older, generally male, and had a higher prevalence of comorbidities, including cancer, ischemic cardiopathy, hypertension, heart failure, and arrhythmias, with an accordingly higher CCI. Median values of CRP were higher, while sO2 was lower in this group. CT- and CXR-RALE-based extension of pulmonary involvement was higher in those who died. Time from symptom onset to chest imaging was shorter in patients who died. CT visual score and CXR RALE score were strongly associated with Spearman’s rho of 0.73 (P < .001) (Fig 2).

Table 1

Patients’ pre-existing condition, and clinical, laboratory, and CT findings at admission.

Variables		All Patients	Deaths
		N (%)	N (%)	P*
		308	55 (17.86)
Age (years), median (IQR)		65.4 (52.8–75.7)	79.7 (72.0–85.0)	<0.001**
Female sex		119 (38.6)	13 (23.6)	0.012
Calendar time	Week 1	36 (11.7)	8 (14.6)	0.005
	Week 2	163 (52.9)	38 (69.1)
	Week 3	109 (35.4)	9 (16.4)
Charlson Comorbidity Index	0	232 (75.3)	26 (47.3)	<0.001
	1	21 (6.8)	6 (10.9)
	2	19 (6.2)	5 (9.1)
	≥3	36 (11.7)	18 (32.7)
Diabetes		42 (13.6)	11 (20.0)	0.129
Cancer		49 (15.9)	14 (25.5)	0.033
Chronic obstructive pulmonary disease		10 (3.3)	7 (12.7)	<0.001
Ischemic cardiopathy		30 (9.7)	12 (21.8)	0.001
Chronic kidney failure		3 (1.0)	2 (3.6)	0.083
Hypertension		55 (17.9)	20 (36.4)	<0.001
Obesity		6 (2.0)	3 (5.5)	0.072
Heart failure		18 (5.8)	12 (21.8)	<0.001
Arrhythmias		23 (7.5)	11 (20.0)	<0.001
Vascular diseases		6 (2.0)	3 (5.5)	0.072
CRP (mg/dl), median (IQR) (missing values = 38)		5.2 (2.1–11.6)	11.4 (4.2–15.9)	0.001
sO2 (%), median (IQR)		94.9 (92.8–96.0)	92.5 (89.6–94.5)	<0.001
Days from symptom onset to CT, median (IQR)		7 (4–8)	5 (2–7)	0.009**
CT disease extension (visual score)	<20%	107 (34.7)	7 (12.7)	<0.001
	20–39%	110 (35.7)	12 (21.8)
	40–59%	58 (18.8)	16 (29.1)
	≥60%	33 (10.7)	20 (36.4)
Days from symptom onset CXR, median (IQR)		6.5 (4–8)	5 (2–7)	0.005**
CXR RALE score, median (IQR)		9 (5–13.5)	15 (11–21)	<0.001

IQR, interquartile range; CRP, C-reactive protein; sO2, oxygen saturation level; CT, computed tomography; CXR, chest X-ray; RALE, Radiographic Assessment of Lung Edema.

*Pearson’s chi-squared test or Fisher exact test and P value for the hypothesis of independence in the two-way table.

** P value nonparametric equality-of-medians test.

Fig 2

Example of RALE scores and respective CT.

Examples of Radiographic Assessment of Lung Edema (RALE) on chest X-rays (CXR) which were divided into quadrants (A, B, C) and correlation with coronal reconstruction of chest computed tomography (CT) performed within 24 hours (D, E, F). Patient 1 (male, 37 y/o) was attributed a RALE score of 7 because of the presence of moderate alveolar opacities in 50/75% of the right inferior quadrant and hazy alveolar opacities in < 25% of the left inferior quadrant (A); chest CT demonstrated the presence of GGO in the right lower lobe and a small area of GGO in the left lower lobe (visual score 10%) (D). Patient 2 (male, 53 y/o) had a RALE score of 19 due to dense alveolar opacities in > 75% of the right lower quadrant, moderate alveolar opacities in 50/75% of the left lower quadrant, and hazy alveolar opacities in < 25% of the left upper quadrant (B); chest CT demonstrated the presence of extensive parenchymal consolidation of the left lower lobe and small areas of GGO in the right lung (visual score 30%) (E). Patient 3 (male, 45 y/o) was attributed a RALE score of 36 because of the involvement of > 75% of every quadrant, with dense opacities in the left lower quadrant and moderate opacities in the remaining three quadrants (C); chest CT demonstrated bilateral consolidations (visual score 60%) (F).

Variables associated with death

Age- and sex-adjusted analyses (Table 2) showed that the variables associated with death were various comorbidities, the strongest associations being for heart failure (IRR 2.68; 95% CI, 1.41–5.10), higher CRP levels (IRR for one mg/dl increase 1.04; 95% CI, 1.01–1.08), lower sO2 levels (IRR for one unit increase 0.97; 95% CI, 0.94–1.00), shorter time from symptom onset to CT or CXR assessment (IRR for one day increase 0.91; 95% CI, 0.83–1.00, and 0.915; 95%CI, 0.84–1.00, respectively), and higher extension of lung involvement assessed both by CT visual score (IRR for one unit increase 1.03; 95% CI, 1.02–1.04) and CXR RALE score (IRR for one unit increase 1.06; 95% CI, 1.03–1.10).

Table 2

Associations of pre-existing conditions and laboratory and radiological findings with death, crude and after adjustment for age and sex.

		Death
Variables		Crude		Multivariable
		IRR	95% CI	IRR	95% CI
Age (years)		1.078	1.053–1.103
Sex	Female	1
	Male	2.034	1.092–3.789
Charlson Comorbidity Index	0	1		1
	1	2.549	1.049–6.194	1.231	0.488–3.104
	2	2.348	0.902–6.115	1.259	0.479–3.310
	≥ 3	4.462	2.446–8.137	1.798	0.947–3.412
Diabetes		1.583	0.818–3.066	0.814	0.413–1.607
Chronic obstructive pulmonary disease		4.346	1.966–9.604	1.975	0.878–4.443
Ischemic cardiopathy		2.586	1.364–4.904	1.239	0.642–2.391
Chronic kidney failure		3.836	0.935–15.743	1.549	0.373–6.428
Cancer		1.762	0.961–3.232	1.281	0.698–2.350
Hypertension		2.629	1.517–4.553	1.455	0.833–2.543
Obesity		2.904	0.907–9.298	2.424	0.750–7.841
Heart failure		4.496	2.371–8.526	2.682	1.409–5.104
Arrhythmias		3.098	1.600–5.998	1.537	0.779–3.033
Vascular diseases		2.904	0.907–9.298	2.179	0.680–6.983
Days from symptom onset to CT		0.829	0.754–0.910	0.910	0.830–0.998
CT disease extension (visual score)		1.036	1.024–1.048	1.028	1.016–1.040
Days from symptom onset to CXR		0.835	0.760–0.918	0.915	0.836–1.002
CXR RALE score		1.087	1.058–1.116	1.064	1.032–1.097
CRP		1.065	1.032–1.098	1.041	1.007–1.076
sO2		0.945	0.921–0.970	0.973	0.944–1.004
Severity/time index CT (5-day lag)		1.334	1.240–1.434	1.223	1.128–1.327
Severity/time index CXR (5-day lag)		1.737	1.493–2.022	1.411	1.172–1.698
Severity/time index CRP (5-day lag)		1.944	1.516–2.492	1.419	1.091–1.846
Severity/time index sO2 (5-day lag)		2.101	1.649–2.676	1.423	1.063–1.906

IRR, incidence rate ratio; CT, computed tomography; CXR, chest X-ray; RALE, Radiographic Assessment of Lung Edema; CRP, C-reactive protein; sO2, oxygen saturation level. IRRs of days from symptom onset to CT and to CXR, CT visual score and CXR RALE score, CRP, sO2, and severity/time indices are for one unit increase.

Indices combining severity and time

Severity/time indices were associated with death after adjusting for age and sex, with IRRs varying from 1.22 to 1.42 (Table 2). As depicted in ROC curves (Fig 3), in multivariable logistic models for death at 30 days adjusted for age and sex, severity/time indices showed good discrimination ability, which was higher for radiological than for laboratory measures [AUCCT = 0.92 (95%CI 0.89–0.95), AUCCXR = 0.90 (95%CI 0.86–0.94), AUCCRP = 0.88 (95%CI 0.83–0.93), and AUCsO2 = 0.88 (95%CI 0.84–0.92)].

Fig 3

ROC curves.

Receiver operating characteristic (ROC) curves of each measure of disease severity (red line) and respective severity/time index (blue line) in multivariable logistic models for death at 30 days, adjusted for age and sex. Considered measures of disease severity are computed tomography (CT) visual score of disease extension (panel A), chest X-rays (CXR) Radiographic Assessment of Lung Edema (RALE) score (panel B), CRP levels (panel C), and sO2 levels (panel D). Horizontal axis: 1—Specificity from 0 to 1.00; vertical axis: Sensitivity from 0 to 1.00.

Noticeably, the performance of models including severity/time indices based on the ratio between measures of disease severity and time from symptom onset was slightly better than that of models including the same measures of disease severity not combined with time (Fig 3). For instance, the AUC of the model including CT score only was 0.90 (95% CI 0.87–0.94), increasing to 0.92 (95%CI 0.89–0.95) when substituting CT score with CT-based severity/time index (S1 Table). Moreover, the models including severity/time indices resulted in a minimal information loss when compared to models including the same measures plus time from symptom onset considered separately (S1 Table). Finally, the models including severity/time indices showed a slightly better performance when compared to models including the same measure of disease severity plus the CCI, with the exception of CRP-based models (S1 Table).

Validation cohort

Age- and sex- adjusted models for death at 30 days including CXR-RALE score alone or combined in a severity/time index with a five-day lag were also evaluated in a cohort of 215 consecutive COVID-19 patients who presented to the ED of an adjacent province (Modena). Of these, 48 (22.3%) died (S2 Table). In this cohort, days from symptom onset were similarly associated with death (IRR for one day increase 0.93; 95% CI, .86–1.00), while the association was weaker for RALE score (IRR for one unit increase 1.03; 95% CI, 1.00–1.06) (Table 3). The respective severity/time index in this cohort also showed a higher association (IRR for one unit increase 1.33; 95% CI, 1.01–1.76), and the model including the severity/time index performed slightly better than the model with RALE score (AUCCXR from 0.77 for RALE alone to 0.79 for RALE combined in a severity/time index) (S1 Fig.). However, both these AUC values were noticeably lower than in the Reggio Emilia cohort.

Table 3

Crude and sex- and age-adjusted associations with death in the validation cohort.

		Death
Variables		Crude		Multivariable
		IRR	95% CI	IRR	95% CI
Age (years)		1.057	1.032–1.082
Sex	Female	1
	Male	1.099	0.590–2.049
Days from symptom onset to CXR		0.897	0.832-.968	0.926	0.861–0.997
CXR RALE score		1.029	1.001–1.058	1.025	0.995–1.055
Severity/time index CXR (five-day lag)		1.430	1.101–1. 857	1.334	1.014–1.756

IRR, incidence rate ratio; CXR, chest X-rays; RALE, Radiographic Assessment of Lung Edema; CRP, C-reactive protein; sO2, oxygen saturation level. IRRs of days from symptom onset to CXR, CXR RALE score, and severity/time index are for one unit increase.

Discussion

We used a cohort of consecutive COVID-19 patients who presented to the ED and underwent imaging assessment of COVID-19 pneumonia to test various composite indices combining measures of disease severity with the time from symptom onset. The use of these indices made it possible to better predict mortality than did the same severity measures without incorporating information about the time to reaching a certain level of disease severity. The severity/time indices based on imaging scoring of pneumonia extension were stronger predictors than those based on laboratory tests. The indices describing lung damage (based on chest imaging and sO2) provided more informative value than the presence of comorbidities, which is generally considered one of the main factors driving COVID-19 mortality, along with age [2-7].

The uncovering of factors which may help to identify COVID-19 patients who will face more severe outcomes has been one of the main research goals since the beginning of the pandemic, especially for factors potentially available at ED admission. Among these factors, the most widely used are age, comorbidities, clinical and laboratory signs of lung damage, inflammation, and coagulation disorders [3-7]. The role of chest imaging has been widely evaluated, and even if the majority of available studies focus on CT scan [10-16], CXR-based scores have also been shown to be reliable in predicting COVID-19 outcomes [17-23]. This is particularly important since CT is not routinely recommended by the main international guidelines unless warranted by features of respiratory worsening, especially in resource-constrained environments, where CXR is more readily available [32].

Apart from pre-existing conditions such as age, sex, and comorbidities, other predictive factors are severity measures, which provide a static picture of the disease at ED presentation. By combining these factors with the time from disease onset (time from symptom onset plus lag time), we tried to incorporate information on the evolution of the disease that led the patient to the moment of ED presentation. In order to use these indices as indicators of the velocity of disease progression, we should start from the assumption that the tested severity measures were within normal ranges at disease onset, which is not necessarily true. This limitation acknowledged, the use of these indices combining severity and time remains the only (although imperfect) way to add information on disease progression velocity before ED admission.

The importance of the velocity of disease progression is highlighted both by the worse outcomes of patients whose symptoms become more severe more quickly and thus present to the ED sooner after symptom onset [24-26] and by evidence that, in hospitalized patients with serial assessments, rapidly rising CT disease extension or CRP levels may predict poor outcomes [33-35]. Nonetheless, few attempts have been made to assess the velocity of disease progression before ED presentation. Information regarding the time from symptom onset to seeking medical attention was easy to collect both from the ED and in the home-care setting. This information has a strong association with all negative outcomes, such as death, hospitalization, and ED readmission [24-26]. Also, lung damage evaluated by imaging showed to be strongly associated with negative outcomes; because it is well known that signs of lung damage on imaging last even after the most severe respiratory symptoms have resolved [36], it is intuitive that placing imaging-based severity in the context of the phase of the disease progression is necessary.

In this study, including indices incorporating severity and time in the prediction model for death resulted in a better performance than the model including the measure of disease severity only. Of note, the models with the severity/time indices also slightly outperformed those including the CCI, underlining the important role of the velocity of disease progression, which is at least comparable to the much more acknowledged role of comorbidities. This was not true for CRP, however, probably because, unlike lung damage, CRP level does not increase steadily. Thus, a single measure obtained at ED admission does not permit building a reliable severity/time index.

This study has some limitations. The study collected cases retrospectively, including only those that had both CT and CXR. This condition was necessary to fulfil the study aim, but obviously introduced a selection bias since some milder cases who were not referred to CT were not included in the cohort. It is worth noting that during the study period, as the diagnostic workup for suspected COVID-19 pneumonia included CT, the vast majority of patients presenting at the ED with symptoms highly suggestive of COVID-19 underwent it [30]. In the validation cohort, all patients who underwent CXR were included, resulting in a higher prevalence of milder cases, which may explain the lower predictive value of the models in this cohort. Furthermore, as other relevant severity biomarkers, in particular D-dimer levels, were not routinely assessed at the ED during the study period, it was not possible to build another severity/time index based on the third important pathway of disease progression (i.e., coagulopathy, along with lung damage and inflammation). Finally, the impact of severity/time indices on intermediate outcomes such as hospitalization and mechanical ventilation has not been evaluated. However, while the association of indices with intermediate outcomes is plausible, it would have only led to a further increase in indices association with death.

Conclusion

In conclusion, our study confirms that one of the most powerful prognostic factors for COVID-19 is the time from symptom onset to seeking medical assistance. This information is readily available and gives added value to the interpretation of other imaging and laboratory findings at ED presentation. Thus, our findings should encourage clinicians who evaluate a COVID-19 patient at admission to critically interpret the patient’s current disease severity also in light of the time from symptom onset: the shorter the time the worse the prognosis.

Models for death comparing CT-, CXR-, CRP-, and sO2- based measures of disease severity and severity/time indices.

(DOCX)

Click here for additional data file.

Characteristics associated with death in the validation cohort.

(DOCX)

Click here for additional data file.

Validation cohort ROC curves.

Receiver Operating Characteristic (ROC) curves of CXR RALE score (red line) and respective severity/time index (blue line) in multivariable logistic models for death at 30 days adjusted for age and sex. AUC values were: AUCCXR-RALE = 0.77 (95% CI, 0.71–0.84) and AUCCXR-RALE severity/time index = 0.79 (95% CI, 0.73–0.85).

(DOCX)

Click here for additional data file.

7 Mar 2022

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-21-29669 Reviewer comment

I would like to commend the authors for the time and dedication towards this manuscript. Technically, this is a good work however, I have some issues with the study design which need to be addressed.

1. Aim of the study versus what was done

Aim / Purpose of the study was stated as: “The study aim was to evaluate indices of the velocity of disease progression to predict mortality of COVID-19 patients admitted to the emergency department (ED).”

Your title and various sections in your manuscript referred to “velocity of disease progression”.

The term “velocity of disease progression” conferred the notion that, there were at least teo measurements of same severity measure within a time interval and then the velocity of progression computed from the change in severity measure over the time.

However, in your described methods, the time interval was measured from time of onset of symptoms to time of presentation at the emergency department, when the various tests were carried out. This measured interval directly measures, time from onset of symptoms to time of presentation at the emergency department which is influenced by various determinants including several factors contributing to delays in decision to seek care, delays in arriving at the facility and delays in the hospital before the measured tests were carried out. Please check this and revise you’re the aim and title of the study appropriately.

2. Velocity indexes

Also linked with the above comment, is a concern about the computation of the velocity index as described in lines 141- 147. “In order to obtain indices that could indicate the velocity of disease progression before the first ED admission, we combined the CT visual score, the CXR RALE score, CRP levels, and sO2 levels separately with the time elapsed from symptom onset and the measurement of these parameters. The easiest way to obtain a velocity was to divide the severity measure by the time needed to reach that level of severity. This strategy was used for CRP levels and for CT- and CXR-based extension of parenchymal involvement, while sO2 velocity index was calculated as the difference in sO2 value to 100 (100-sO2) divided by the time from symptom onset to sO2 measurement.”

This approach assumes that the baseline estimate for each severity measure was normal range and same value for all study participants, so that “dividing the severity measure by the time needed to reach that level of severity” would give a composite measure that is comparable among all study participants. However, based on past medical history of each study participants, the severity measure ie. sO2, CXR RALE, CT visual score, CRP at baseline which was not measured and practically difficult to determine based on the current study methodology, may be abnormal. This approach at estimating velocity index did not account for these variations in estimate at baseline. Please critically consider this.

3. Interventions carried out

There was no mention of the interventions at the emergency department which might have prevented outcome of death or delayed it beyond the 30days mark. I feel this is an important omission.

4. Retrospective study or prospective study?

Study design and selection of participants section, line 86 stated; “This prospective cohort study included all consecutive patients aged > 18 years who presented to…..”

And in same section, line 90 -91 stated that “Given 91 the retrospective nature of the study, the Ethics Committee authorized the use of a patient’s data….”

Please revise and be consistent

5. Conclusion of the study

Conclusion in abstract “Indices describing the velocity of COVID-19 progression, especially those based on imaging, better predicted mortality than the same severity measures not incorporating the time needed to reach a certain level of severity.

Conclusion as stated in the last paragraph of the discussion stated “In conclusion, our study confirms that one of the most powerful prognostic factors for COVID-19 is the time from symptom onset to seeking medical assistance. This information is readily available and gives added value to the interpretation of other imaging and laboratory findings at ED presentation.”

The conclusion in the abstract need to be revised to conform with what was stated in the last paragraph of the discussion

6. Minor concerns

a) Introduction, lines 48-49, “…more than 140 million cases and 3 million deaths have been reported globally up to May 2021 [1]” I will suggest a more current statistic.

b) Discussion section, lines 263-267 stated that “The role of chest imaging has been widely evaluated, and even if the majority of available studies focus on CT scan [10-16], CXR-based scores have also been shown to be reliable in predicting COVID-19 outcomes [17-23]. This is particularly important since CT is not routinely recommended by the main international guidelines unless warranted by features of respiratory worsening, especially in resource-constrained environments, where CXR is more readily available [31]”

This is true and the implication on your method of selection such that only patients with CT scan and Chest X-ray were included suggested that this study primarily admitted patients with features of worsening respiratory features. This limitation needs to be acknowledged.

Thank you

Reviewer #2: Appreciating your work, please find hereunder some comments and suggestions:

1. Can the authors explicitly define their inclusion and exclusion criteria? Or provide an explanation as to why they forwent on an exclusion criteria?

2. Can the authors provide their ethical statement in a separate segment?

3. "Given the retrospective nature of the study, the Ethics Committee authorized the use of a patient’s data without his/ her informed consent if all reasonable efforts had been made to contact that patient." Can the authors provide a number or a citation that states/further explains this clause?

4. Can the authors write their conclusion in a separate segment and add some statements that highlight the implication of their findings to clinical and global/public health practice?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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Submitted filename: reviewer comment PONE-D-21-29668 velocity index.docx

Click here for additional data file.

29 Apr 2022

PONE-D-21-29669 Reviewer comment

Reviewer #1: I would like to commend the authors for the time and dedication towards this manuscript. Technically, this is a good work however, I have some issues with the study design which need to be addressed.

Re: We thank the Reviewer for the positive appraisal and for having underlined some important issues, allowing us to improve our work.

1. Aim of the study versus what was done

Aim / Purpose of the study was stated as: “The study aim was to evaluate indices of the velocity of disease progression to predict mortality of COVID-19 patients admitted to the emergency department (ED).”

Your title and various sections in your manuscript referred to “velocity of disease progression”.

The term “velocity of disease progression” conferred the notion that, there were at least teo measurements of same severity measure within a time interval and then the velocity of progression computed from the change in severity measure over the time.

However, in your described methods, the time interval was measured from time of onset of symptoms to time of presentation at the emergency department, when the various tests were carried out. This measured interval directly measures, time from onset of symptoms to time of presentation at the emergency department which is influenced by various determinants including several factors contributing to delays in decision to seek care, delays in arriving at the facility and delays in the hospital before the measured tests were carried out. Please check this and revise you’re the aim and title of the study appropriately.

Re: We thank the Reviewer for this comment. We agree on the confounding factors (delays in decision to seek care/in arriving at hospital/in performing tests) which can influence the time between symptom onset and disease severity measure. However, even if it is an imperfect indicator, time between symptom onset and emergency department admission has been shown to be one of the most important predictive factors of poor outcome (see references 24-26). On the other hand, we usually use different laboratory and imaging severity measures as prognostic/predictive factors even if they represent a static picture of the patient at the time of admission (so, even more imperfect). We thought that the only possible way to include both pieces of information (time and severity) in a single parameter was to compute these indices, to have an indication on the speed at which the disease is worsening. However, we agree with the Reviewer that since we do not have two timepoints for the measurement of disease severity measures, speaking of “velocity” is incorrect: we changed the definition “velocity index” to “severity/time index”. We used the severity measure instead of a variation between two timepoints starting from the assumption that each severity measure was normal before disease onset. We agree that this assumption must be explicit when we discuss the opportunity to use these indices as measures of disease progression velocity. Nevertheless, when a patient arrives at the ED, we only have this information to estimate prognosis.

We changed the title and the aim into “Imaging-based Indices Combining Disease Severity and Time from Disease Onset to Predict COVID-19 Mortality: A Cohort Study” and “The aim of this study was to evaluate the role of indices that combine radiological data at admission and time from disease onset, in predicting mortality of COVID-19 patients admitted to the ED.” respectively. Throughout the text we changed “velocity indices” to “severity/time indices”.

We have changed a paragraph in the discussion in order to add the aforementioned assumption:

“Apart from pre-existing conditions such as age, sex, and comorbidities, other predictive factors are severity measures, which provide a static picture of the disease at ED presentation. By combining these factors with the time from disease onset (time from symptom onset plus lag time), we tried to incorporate information on the evolution of the disease that led the patient to the moment of ED presentation. In order to use these indices as indicators of the velocity of disease progression, we should start from the assumption that the tested severity measures were within normal ranges at disease onset, which is not necessarily true. This limitation acknowledged, the use of these indices combining severity and time remains the only (although imperfect) way to add information on the velocity of disease progression before ED admission.”

2. Velocity indexes

Also linked with the above comment, is a concern about the computation of the velocity index as described in lines 141- 147. “In order to obtain indices that could indicate the velocity of disease progression before the first ED admission, we combined the CT visual score, the CXR RALE score, CRP levels, and sO2 levels separately with the time elapsed from symptom onset and the measurement of these parameters. The easiest way to obtain a velocity was to divide the severity measure by the time needed to reach that level of severity. This strategy was used for CRP levels and for CT- and CXR-based extension of parenchymal involvement, while sO2 velocity index was calculated as the difference in sO2 value to 100 (100-sO2) divided by the time from symptom onset to sO2 measurement.”

This approach assumes that the baseline estimate for each severity measure was normal range and same value for all study participants, so that “dividing the severity measure by the time needed to reach that level of severity” would give a composite measure that is comparable among all study participants. However, based on past medical history of each study participants, the severity measure ie. sO2, CXR RALE, CT visual score, CRP at baseline which was not measured and practically difficult to determine based on the current study methodology, may be abnormal. This approach at estimating velocity index did not account for these variations in estimate at baseline. Please critically consider this.

Re: We thank the Reviewer for underlining this point. We have added a paragraph on this in the discussion (see the answer above).

3. Interventions carried out

There was no mention of the interventions at the emergency department which might have prevented outcome of death or delayed it beyond the 30days mark. I feel this is an important omission.

Re: We should clarify that the indices were not used as criteria to decide whether to hospitalize or not COVID-19 patients, in fact these indices have been calculated retrospectively (we have now clarified the study design, see following answer). Still, indices are obviously associated with disease severity measures that were used for patient stratification. All severe COVID-19 patients were treated in the same way during the first pandemic wave (hospitalization and oxygen therapy, and if necessary mechanical ventilation, tocilizumab and/or steroids, which were only administered to hospitalized patients at the time). It is plausible that indices are associated with intermediate outcomes such as hospitalization, but should this be the case, this bias should go in a conservative direction with more intensive therapies given to the cases with worst prognostic indices, thus therapies could partially improve the observed prognosis. In the methods section we have added a short paragraph on the therapeutic protocol during the first pandemic wave in Reggio Emilia, while in the limitation section we have added a paragraph explaining that the lack of evaluation of the impact of severity/time indices on intermediate outcomes such as hospitalization may have resulted in an under- but not over-estimation of associations of indices with death.

Methods: “During the first pandemic wave in Reggio Emilia, patients presenting to the ED with fever and SpO2>95% were discharged home in case of negative chest X-rays and/or CT or in case of positive chest X-rays and/or CT scan but who were >70 years of age and had no relevant past medical history. Patients >70 years of age and/or with relevant past medical history could be hospitalized even without respiratory failure, while others were admitted to hospital in case of radiological findings of pneumonia combined with respiratory failure. In the case of radiological findings of complicated pneumonia with or without acute respiratory distress syndrome (ARDS), patients started non-invasive ventilation in the ED and were admitted to subintensive/intensive care unit.” We added a reference for this point.

Limitations: “Finally, the impact of severity/time indices on intermediate outcomes such as hospitalization and mechanical ventilation has not been evaluated. However, while the association of indices with intermediate outcomes is plausible, it would have only led to a further increase in indices association with death.”

4. Retrospective study or prospective study?

Study design and selection of participants section, line 86 stated; “This prospective cohort study included all consecutive patients aged > 18 years who presented to…..”

And in same section, line 90 -91 stated that “Given 91 the retrospective nature of the study, the Ethics Committee authorized the use of a patient’s data….”

Re: We thank the Reviewer and we understand that the classification is misleading. We have removed the term prospective, as the cohort definition was retrospective. However, we have added a clarification on the fact that information that has been registered at the time of ED presentation (e.g., laboratory data or CT disease extension) could not have been modified at the time of retrospective data collection: “Even if the researchers assessed inclusion criteria and conditions at admission retrospectively, all the information registered at the time of ED presentation (e.g. laboratory data or CT disease extension) was not modifiable later, therefore exposures could not be influenced by the occurrence of the outcome.”

5. Conclusion of the study

Conclusion in abstract “Indices describing the velocity of COVID-19 progression, especially those based on imaging, better predicted mortality than the same severity measures not incorporating the time needed to reach a certain level of severity.

Conclusion as stated in the last paragraph of the discussion stated “In conclusion, our study confirms that one of the most powerful prognostic factors for COVID-19 is the time from symptom onset to seeking medical assistance. This information is readily available and gives added value to the interpretation of other imaging and laboratory findings at ED presentation.”

The conclusion in the abstract need to be revised to conform with what was stated in the last paragraph of the discussion

Re: We thank the Reviewer for the suggestion. The conclusion in the abstract has been changed to “Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation.”

6. Minor concerns

a) Introduction, lines 48-49, “…more than 140 million cases and 3 million deaths have been reported globally up to May 2021 [1]” I will suggest a more current statistic.

Re: Data have been updated to March 2022.

b) Discussion section, lines 263-267 stated that “The role of chest imaging has been widely evaluated, and even if the majority of available studies focus on CT scan [10-16], CXR-based scores have also been shown to be reliable in predicting COVID-19 outcomes [17-23]. This is particularly important since CT is not routinely recommended by the main international guidelines unless warranted by features of respiratory worsening, especially in resource-constrained environments, where CXR is more readily available [31]”

This is true and the implication on your method of selection such that only patients with CT scan and Chest X-ray were included suggested that this study primarily admitted patients with features of worsening respiratory features. This limitation needs to be acknowledged.

Re: We thank the Reviewer for the comment. In the limitation section, we have already stated: “The study collected cases retrospectively, including only those that had both CT and CXR. This condition was necessary to fulfil the study aim, but obviously introduced a selection bias since some milder cases who were not referred to CT were not included in the cohort. It is worth noting that during the study period, as the diagnostic workup for suspected COVID-19 pneumonia included CT, the vast majority of patients presenting at the ED with symptoms highly suggestive of COVID-19 underwent it [30].” In order to better explain the point, we have added a sentence in the study design section, explaining that during the first COVID-19 wave in Reggio Emilia (which was months before the publications of guidelines that discouraged the routinary use of CT), CT was performed by almost all patients presenting to the ED for suspected COVID-19: “During the COVID-19 outbreak, the diagnostic protocol for suspected COVID-19 patients presenting to EDs included RT-PCR, blood tests, chest X-rays, and CT in every case of suggestive X-rays or negative X-rays but with highly suggestive clinical features.”

Reviewer #2: Appreciating your work, please find hereunder some comments and suggestions:

1. Can the authors explicitly define their inclusion and exclusion criteria? Or provide an explanation as to why they forwent on an exclusion criteria?

Re: We thank the Reviewer for the comment. We rephrased the sentence at the beginning of the study design as follows: “This retrospective cohort study included all consecutive patients meeting the following inclusion criteria: patients > 18 years of age; presenting to any one of the provincial EDs between 27 February and 13 March 2020; and with a positive RT-PCR within 10 days from ED admission. Patients who did not undergo both CT scan and CXR at ED presentation were excluded.

2. Can the authors provide their ethical statement in a separate segment?

Re: A segment named “Ethical approval” has now been added.

3. "Given the retrospective nature of the study, the Ethics Committee authorized the use of a patient’s data without his/ her informed consent if all reasonable efforts had been made to contact that patient." Can the authors provide a number or a citation that states/further explains this clause?

Re: This sentence is the translation of what is reported by the Ethics Committee in its authorization to conduct the study, according to Italian law in terms of privacy (GDPR n. 679/2016, D.Lgs. 196/2003, modified by D.Lgs. 101/2018 and Measure by the Personal Data Protection Guarantor n. 146 date 05/06/2019)

4. Can the authors write their conclusion in a separate segment and add some statements that highlight the implication of their findings to clinical and global/public health practice?

Re: We thank the Reviewer for the suggestion. A segment named “Conclusion” has been created, and a sentence on implications has been added: “Thus, our findings should encourage clinicians who evaluate a COVID-19 patient at admission to critically interpret the patient’s current disease severity also in light of the time fro

Submitted filename: response to reviewer comments.docx

Click here for additional data file.

6 Jun 2022

Imaging-based Indices Combining Disease Severity and Time from Disease Onset to Predict COVID-19 Mortality: A Cohort Study

PONE-D-21-29668R1

Dear Dr. Besutti,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Antonino Salvatore Rubino, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The Reviewers positively accepted Your comments

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review this manuscript. The authors have taken the necessary steps to fully address all my concerns

Reviewer #2: All questions and comments have been addressed. No further feedback or suggestions. I thank the authors for making the time to integrate these comments into their manuscript to better the scientific content.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

9 Jun 2022

PONE-D-21-29668R1

Imaging-based Indices Combining Disease Severity and Time from Disease Onset to Predict COVID-19 Mortality: A Cohort Study

Dear Dr. Besutti:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Antonino Salvatore Rubino

Academic Editor

PLOS ONE