ORIGINAL RESEARCH

Mortality 30 and 90 days after hospitalisation for COVID-19: prognostic factors on admission to hospital

 

Stephen A. Murphy*, Fatima Khalil, Mohammad Fayaz, Louise Robinson and Natalie M. Cummings

Department of Respiratory Medicine, University Hospital of North Durham, Durham, United Kingdom

Summary points

Abstract

Introduction: Severe acute respiratory syndrome (SARS-CoV-2) infection causes substantial mortality in hospitalised patients. The purpose of this study was to investigate mortality and identify prognostic factors from the data collected on hospital admission.

Methods: This was a retrospective cohort study of patients hospitalised with clinically suspected and/or laboratory confirmed SARS-CoV-2 infection. The primary outcome was mortality 30 and 90 days after admission. Risk factors for death were identified by multivariable logistic regression.

Results: Three hundred and thirty-four patients were included; 93.4% of patients had positive SARS-CoV-2 RT-PCR swab; median (IQR) age 75 (63–84) years, male 54.2%, smoking (≥ 5 CPD/ ≥5 pack years) 37.1%, obesity 24.8% and frailty 42%. Mortality was 30.8% at 30 days after admission and 34% at 90 days after admission. Mortality was greatest in older patients (age >65 years; 36.7 vs. 16.5%; P < 0.001), particularly older males (age >65 years; male 42.5 vs. female 30%; P = 0.046); smoking (41.1 vs. 24.8%; P = 0.003); younger smokers (age ≤ 65 years; 35.3 vs. non-smoker 6.5%; P < 0.001); with chronic kidney disease (CKD: 50 vs. 27.8%; P = 0.003), chronic neurological disease (43.8 vs. 27.2%; P = 0.007), COPD (41.3 vs. 28.8%; P = 0.048), cardiac disease (40.7 vs. 25.1%; P = 0.003), frailty (44.3 vs. 21%; P < 0.001), and with chest x-ray changes of COVID-19 (39 vs. 13.2%; P < 0.001). Mortality was associated with raised C-reactive protein (CRP) and lymphopaenia on admission. Independent predictors of mortality were (adjusted OR; 95% CI): age (1.05; 1.02–1.08), smoking (2.08; 1.19–3.63); CKD (2.32; 1.09–4.92), chronic neurological disease (2.27; 1.17–4.40), frailty (1.92; 1.047–3.53); chest x-ray changes of COVID-19 (4.31; 2.12–8.79) and Log-CRP (3.21; 1.43–7.22); ROC analysis AUC 0.811 (0.765–0.852).

Conclusion: Mortality for patients hospitalised with SARS-CoV-2 infection is high (>30%) with greatest risk in older-age males with chronic disease and frailty. A history of moderate–heavy smoking is a major risk factor, particularly in younger patients (≤ 65 years). Chest x-ray changes of COVID-19 and raised CRP are clinically valuable prognostic indicators.

Keywords: hospitalisation; mortality; prognostic factors; SARS-CoV-2; smoking

 

The clinical characteristics of patients admitted to hospital with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been described extensively [1–5]. Mortality rates have varied from as low as 5% in China to more than 40% in Europe [1, 5, 6]. Some of these reports were published before all patient outcomes were known but disparities are partly attributable to demographic differences, particularly patient age with a 20-year variance between those hospitalised in China and Europe [1, 5, 6].

Increased age is a consistent risk factor for mortality from COVID-19 in all populations but the significance of other demographic variables is less clear; smoking was a risk factor in some studies, seemed protective or had no effect in others and was not included in many [7–10]. The significance associated with obesity varies with a gradient of risk for hospitalisation and critical care related to BMI in the United States and increased mortality in some UK studies but contradictory findings elsewhere [3, 5, 7, 10–13].

Chronic diseases, particularly cardiovascular, chronic lung and kidney disease, are associated with critical illness and mortality in many studies. Hypertension and diabetes mellitus have also been linked with increased risk, particularly in China and the United States [2–7, 14].

In addition to demographic and clinical factors, several biomarkers for disease severity and outcome have been identified; the presence of lymphopaenia and raised CRP are most widely reported [3, 6, 13, 14–18].

The purpose of this study was to investigate the mortality 30 and 90 days after hospitalisation in patients with clinically suspected and laboratory confirmed SARS-CoV-2 during the first wave of the pandemic and to identify prognostic factors from demographic, clinical and laboratory data collected during routine clinical-practice.

Materials and methods

Study design and participants

The study was performed at the University Hospital of North Durham, a 520-bed district general hospital serving about 350,000 residents in the city of Durham and former mining villages of County Durham with a majority (96.6%) indigenous white British population. It was a single-centre, retrospective cohort study of adult patients hospitalised from February 2020 with a discharge diagnosis of COVID-19. Patients admitted for other reasons that developed COVID-19 in hospital were included. Patients were tested for SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction (RT-PCR) on a nasopharyngeal swab. All patients with clinical features of an acute viral illness and a positive RT-PCR test were included in the study. It was anticipated that about 25% of patients with COVID-19 would have a false-negative test (sensitivity 70%) [19]. Patients with a discharge diagnosis of COVID-19 but negative RT-PCR were included if pretest test probability of COVID-19 was considered >90% (post-test probability of disease after single negative test 74%). Follow-up of the cohort was censored 1 September 2020.

Data collection

Data extracted from the patients’ clinical records included age and gender; smoking history was obtained by comprehensive review of all available hospital records, and smoking status was defined as moderate/heavy smoker (current regular smoker of ≥ 5 cigarettes per day [cpd]; or documented evidence of historic smoking ≥ 5 pack years), non/light-smoker (never smoked, current smoker <5 cpd and/or <5 pack years), unknown (no smoking history in records, unable to quantify); obesity (physician described and/or record of body mass index ≥30 kg/m2); residence (residential/nursing-home/sheltered or warden controlled); professional home carers; chronic disease groups; cardiac disease (atrial fibrillation, ischaemic heart disease, cardiac failure, valvular heart disease); chronic kidney disease (CKD; estimated GFR <30 ml/min); chronic neurological disease (cerebrovascular disease, Parkinson’s disease, other neurodegenerative diseases, multiple sclerosis and any brain injury); chronic lung disease (COPD and non-asthmatic chronic lung conditions); and other chronic conditions.

The primary clinical diagnoses were COVID-19 (community or hospital acquired) if symptoms of lower respiratory infection with chest x-ray changes compatible with COVID-19; lower respiratory tract infection (LRTI with normal/unchanged chest x-ray); and other (no respiratory symptoms and normal/unchanged chest x-ray). The presence of frailty (physician described and/or Rockwood clinical frailty score [CFS] ≥ 6) was recorded [20]; Laboratory tests include lymphocyte count and C-reactive protein (CRP). Chest x-rays were reviewed and categorised as normal/pre-existing abnormality and new-changes consistent with COVID-19.

Ethics

This study was a retrospective review of data obtained for routine clinical care and service evaluation for patients admitted to hospital with COVID-19. The study was approved by the hospital research and innovation department. Formal ethics approval was therefore not considered necessary [21].

Analysis and statistics

The distribution of continuous variables was tested for normality by the Kolmogorov–Smirnov test. Continuous variables with a non-normal distribution are expressed as median and interquartile range (IQR), and categorical variables are expressed as percentages. Variables were compared using non-parametric methods (Mann-Whitney test) for continuous variables and χ2 for categorical data. Cut-off values for lymphocyte count and CRP were obtained from comparison of outcomes between deciles and by probit regression with logarithmic transformation. Collinearity between predictor variables was assessed by Pearson’s phi coefficient. The association(s) between variables on admission and mortality 30-days after admission was evaluated by univariate and multivariable logistic regression. Subgroup analysis of 90-day survival was performed for age range, sex and smoking history by the Kaplan–Meier method with hazard ratios (HRs). Statistical analyses were made using MedCalc® Statistical Software version 19.5.3.

Results

Three hundred and thirty-four patients were included in the study; 312 (93.4%) patients had a positive SARS-CoV-2 RT-PCR swab. A further 22 cases (6.6%) with a negative RT-PCR result were included. Fifty-two (15.6%) cases were due to hospital acquired infection.

Table 1 shows the distribution of demographic variables. The median age was 75 (63–84) years; more than 70% were over 65 years of age and 54.2% were male. A quarter (25.4%) lived in a residential care facility, and 20.6% of those living at home had professional carers. The prevalence of smoking (≥ 5 CPD/ ≥ 5 pack years) was 37.1% (20% in the under 50s and 41.7% for age 76–85 years). Obesity was present in 24.8% overall but more frequent (49.5%) in patients ≤ 65 years.


Table 1. Demographic characteristics of patients
Demographic Total (n = 334) Alive (n = 231) Died (30 days)‡ (n = 103) P
Age (IQR) years 75 (63–84) 72 (61–82) 81 (73–87) <0.0001
Male 181/334 (54.2%) 119/231 (51.5%) 62/103 (60.2%) 0.14
Residential care 85/334 (25.4%) 43/231 (18.6%) 42/103 (40.8%) <0.0001
Home carers 51/248 (20.6%) 35/187 (18.7%) 16/61 (26.2%) 0.2
Smokerƒ 124/334 (37.1%) 73/231 (31.6%) 51/103 (49.5%) 0.001
Smoking unknown 29/334 (8.7%) 18/231 (7.8%) 11/103 (10.6%)
Obesity (total) 83/334 (24.8%) 69/231 (29.8%) 14/103 (13.5%) 0.0015
Obese (> 65 years) 35/237 (14.4%)* 28/150 (18.7%) 7/87 (8%) 0.026
Obese (≤ 65 years) 48/97 (49.5%)* 41/81 (50.6%) 7/16 (43.7%) 0.6
‡Died within 30 days of admission.
*P < 0.001.
ƒCurrent/ex-smoker (≥ 5 pack years or ≥ 5 cpd).

Chronic disease was common with a median of three comorbid condition per patient (Table 2). Cardiac disease and hypertension were most frequent. Chronic neurological disease was present in 22% (67% cerebrovascular disease) and 16.2% of patients had some form of dementia. COPD was present in 19% overall and 25% of those that died but asthma was infrequent (8%). Chronic kidney disease (CKD) affected 13.8% overall and 22.3% of non-survivors.


Table 2. Prevalence of chronic disease
Comorbidity Total (n = 334) Alive (n = 231) Died‡ (n = 103) P
No. chronic conditions (median [IQR]) 3 (2–3) 2 (1–3) 3 (2–4) 0.009
Cardiac disease 123/334 (36.8%) 73/231 (31.6%) 59/103 (48.5%) 0.003
Hypertension 99/334 (29.6%) 67/231 (29.1%) 32/103 (30%) 0.7
Diabetes 85/334 (25.4%) 58/231 (25%) 27/103 (26.2%) 0.8
Chronic neurological disease§ 73/334 (22%) 41/231 (17.8%) 32/103 (31.1%) 0.006
Chronic lung disease* 71/334 (21.3%) 44/231 (19%) 27/103 (26.2%) 0.14
COPD 63/334 (19%) 37/231 (16%) 26/103 (25%) 0.046
Cerebrovascular disease 49/333 (14%) 28/230 (12.2%) 21/103 (20.4%) 0.051
Chronic kidney disease 46/334 (13.8%) 23/231 (10%) 23/103 (22.3%) 0.0025
Dementia 54/334 (16.2%) 33/231 (14.3%) 21/103 (20%) 0.16
Malignancy 34/334 (10.2%) 26/231 (11.3%) 8/103 (7.8%) 0.33
Asthma 28/334 (8.4%) 25/231 (10.8%) 3/103 (2.9%) 0.016
Alcohol excess 14/334 (2.2%) 11/231 (4.8%) 3/103 (2.9%) 0.43
‡Died within 30 days of admission.
*COPD (chronic obstructive pulmonary disease) and other non-asthma chronic lung diseases.
§includes cerebrovascular disease.

Clinical and laboratory variables are shown in Table 3. Frailty was common and more frequent in those with hospital acquired SARS-CoV-2 infection (57.7 vs. 39%; P = 0.012). Frail patients were older (median [IQR]: 83 [76–88] vs. 69 [59–78] years; P < 0.001) and had more chronic conditions (median [IQR]: 3 [2–4] vs. 2 [1–3]; P < 0.001). Most (84.6%) patients had pneumonia or a LRTI.


Table 3. Clinical characteristics and laboratory variables on admission
Clinical feature Total (n = 334) Alive (n = 231) Died* (n = 103) P
Frailtyƒ 140 (42%) 78 (33.8%) 62 (60.2%) < 0.0001
CXR** changes of COVID-19 228 (68.2%) 139 (60.4%) 89 (86.4%) < 0.0001
CXR changes (male) 138/181 (76.2%) 81/119 (68.1%) 57/62 (91.9%) <0.001
CXR changes (female) 90/153 (58.8%) 58/112 (51.8%) 32/41 (78%) 0.003
COVID-19-CAP§ 208 (62.3%) 130 (56.3%) 78 (75.7%) 0.015
COVID-19-HAP§ 19 (5.7%) 8 (3.5%) 11 (10.7%) 0.001
LRTI§ 55 (16.5%) 50 (21.6%) 5 (4.9%) 0.002
No acute respiratory disease 52 (15.6%) 43 (18.6%) 9 (8.7%) 0.018
Biomarkers
Lymphocyte count (IQR) x 109/l (admission) 0.9 (0.6–1.2) 0.9 (0.6–1.3) 0.8 (0.6–1.05) 0.011
Lymphopaenia (< 1.0 x 109/l; admission) 186/328 (56.7 %) 123/228(53.9 %) 63/100 (63 %) 0.12
Lymphopaenia (Male)† 112/177 (63.3 %) 70/117 (59.8%) 42/60 (70%) 0.18
Lymphopaenia (Female)† 74/151 (49 %) 53/111 (47.7%) 21/40 (52.5%) 0.6
Lymphocytes ≤ 0.8 x 109/l (admission) 157/328 (47.9 %) 99/228 (43.4 %) 58/100 (58 %) 0.015
Lymphocytes (IQR) x 109/l (lowest value) 0.7 (0.5–1.0) 0.75 (0.5–1.1) 0.6 (0.4–0.8) 0.0015
CRP (IQR) mg/l (admission) 97 (50–172) 87 (41.2–152) 124 (74–194) 0.0012
CRP (IQR) mg/l (highest value) 149 (79–245) 133 (63–228) 198 (105–280) <0.001
CRP ≤ 50 mg/l (admission) 81/331 (24.8%) 65/228 (28.5%) 16/103 (15.5%) 0.011
CRP ≤ 50 mg/l (Male)‡ 35/178 (19.7%) 25/116 (21.6%) 10/62 (16.1%) 0.38
CRP ≤ 50 mg/l (female)‡ 47/152 (30.9%) 41/111 (36.9%) 6/41 (14.6%) 0.008
CRP > 100 mg/l (admission) 161/331 (48.6%) 98/231 (42.4%) 63/103 (61.1%) 0.002
CRP > 100 mg/l (Male)$ 95/178 (53.4%) 56/116 (48.3%) 39/62 (62.9%) 0.06
CRP > 100 mg/l (Female)$ 67/153 (43.8%) 42/112 (37.5%) 25/41 (61%) 0.009
* Died within 30 days of admission; ƒ Physician described frailty or CFS ≥ 6; **CXR chest x-ray; §CAP community acquired pneumonia, §HAP hospital-acquired pneumonia, §LRTI lower respiratory tract infection. Male v Female p<0.001; †p=0.009; ‡p=0.018; $p=0.08.

The distributions of lymphocyte count and CRP values were wide (Fig. 1). More than half (56.7 %) of patients were lymphopaenic which was more pronounced in non-survivors and worsened after admission in 52.9% of patients, particularly those that died (63.6 vs. 48.2%; P = 0.01).

Fig 1
Fig. 1. Distribution of lymphocyte count and C-reactive protein levels. Lymphocyte × 109 cells/l (L1, lymphocyte count on admission; L2, minimum lymphocyte count). Error bars median and IQR; CRP, C-reactive protein mg/l (CRP1, level on admission; CRP2, highest level).

The admission CRP was raised in 97.6% of patients and increased further in 62.4% (survivors 60 vs. 67%) with good correlation between admission and peak CRP values (r 2 = 0.61; P < 0.001). Admission and peak CRP were higher in non-survivors but there was no difference in the incremental change in CRP. The chest x-ray was abnormal with changes compatible with COVID-19 in more than two-thirds of cases (died 86.4 vs. 60.4%; P < 0.001)

There was no gender difference for age, smoking or comorbidity, and frailty but males were more likely to be lymphopaenic (63.3% v 49% P =0.009) had higher CRP levels (median (IQR) 107.5 (62–184) v 83.5 (45–167) mg/l P = 0.03) and chest x-ray changes of COVID-19 (76.2 vs. 58.8%; P < 0.001).

Thirty-five patients (10.5%) were admitted to the intensive care unit (ICU) with marked disparity by age (≤65 years 29.9 vs. 2.5%; P < 0.001). Obese patients were more likely to be admitted to ICU (25 vs. 5.4%; P < 0.001), particularly patients ≤65 years (41.7 vs. 18.4%; P = 0.012).

Mortality was 30.8% at 30 days after hospitalisation increasing to 34% at 90 days after hospitalisation. Mortality increased with age, and there was a trend to poorer survival in males overall with significantly worse survival in older men (Tables 4 and 5; Fig. 2). Mortality was higher amongst patients admitted from a residential care/supported living facility. Smoking was associated with a poorer survival, particularly in younger patients (Fig. 2). Obesity was associated with lower mortality in older patients. The risk of death was greatest with CKD, chronic neurological disease, COPD, cardiac disease and cumulative comorbidity.


Table 4. Univariate analysis of demographic characteristics and 30-day mortality
Variable Mortality % OR (95% CI) P
Present Absent
Age (years) 1.05 (1.03–1.07) <0.001
Age > 65 years 87/237 (36.7%) 16/97 (16.5%) 2.94 (1.61–5.55) <0.001
Sex = Male 62/181 (34.6%) 41/153 (27.2%) 1.42 (0.88–2.27) 0.144
Sex = Male (>65 years) 54/127 (42.5%) 33/110 (30%) 1.7 (1.0073–2.95) 0.047
Obesity (total) 14/83 (16.8%) 89/251 (35.5%) 0.37 (0.19–0.69) <0.001
Obese (> 65 years) 7/35 (20%) 80/202 (39.6%) 0.38 (0.16–0.91) 0.03
Obese (≤ 65 years) 7/48 (14.6%) 9/49 (18.4%) 0.76 (0.26–2.23) 0.6
Smoker* 51/124 (41.1%) 52/210 (24.8%) 2.12 (1.32–3.41) 0.003
Smoking unknown§ 11/29 (38.3%) 41/181 (22.7%) 2.08 (0.91–4.77) 0.08
Care home 42/85 (49.5%) 61/249 (24.4%) 3.0 (1.8–5.0) <0.001
Home carers 16/51 (31.4%) 45/197 (22.8%) 1.54 (0.78–3.04) 0.2
*Current/ex-smoker (≥ 5 CPD/ ≥ 5 pack years) versus non/light-smokers/unknown status.
§compared with non-smokers.


Table 5. Univariate analysis of comorbidity/clinical variables and 30-day mortality
Variable Mortality % OR (95% CI) p
Present Absent
Comorbidity† 1.30 (1.07–1.58) 0.008
Cardiac disease 50/123 (40.7%) 53/211 (25.1%) 2.04 (1.27–3.28) 0.003
Chronic neurological 32/73 (43.8%) 71/261 (27.2%) 2.09 (1.22–3.57) 0.007
CKD‡ 23/46 (50%) 80/288 (27.8%) 2.6 (1.38–4.9) 0.003
COPD 26/63 (41.3%) 77/271 (28.8%) 1.77 (1.004–3.12) 0.048
Chronic lung 27/71 (38%) 76/263 (29.4%) 1.51 (0.87–2.61) 0.14
Diabetes 27/85 (31.8%) 76/249 (30.5%) 1.06 (0.62–1.80) 0.8
Hypertension 32/99 (32.3%) 71/235 (30.2%) 1.10 (0.66–1.82) 0.70
Malignancy 8/34 (23.5%) 94/298 (31.8%) 0.66 (0.29–1.53) 0.34
Dementia 21/54 (38.9%) 83/281 (29.5%) 1.51 (0.83–2.77) 0.17
Asthma 3/28 (10.7%) 100/306 (32.9%) 0.24 (0.072–0.83) 0.02
CXR** changes of COVID-19 89/228 (39%) 14/106 (13.2%) 4.21 (2.56–7.84) <0.001
Frailty 62/140 (44.3%) 41/194 (21%) 2.96 (1.84–4.8) <0.001
Hospital-acquired SARS-CoV-2 21/52 (40.4%) 82/282 (29.1) 1.65 (0.89–3.04) 0.10
CRP* mg/l 1.004 (1.001–1.006) 0.005
Log-C-reactive protein (CRP) (admission) 3.07 (1.62–5.82) <0.001
Log-CRP (peak) 3.64 (1.75–7.56) <0.001
*CRP ≤ 50 mg/l 16/81 (19.8%) 87/250 (34.5%) 0.46 (0.25–0.84) 0.012
*CRP > 100 mg/l 63/161 (39.1%) 40/170 (23.5%) 2.08 (1.29–3.36) 0.0024
Lymphocytes × 109/l * 0.85 (0.6–1.2) 0.36
*Lymph ≤ 0.8 × 109/l 58/157 (36.9%) 42/171 (24.6%) 1.79 (1.12–2.89) 0.015
L2 < L1$ 63/173 (36.4%) 36/154 (23.4%) 1.87 (1.15–3.04) 0.01
†OR for each additional condition; ‡CKD chronic kidney disease, COPD chronic obstructive pulmonary disease; §CAP Community acquired pneumonia, HAP hospital-acquired pneumonia, **CXR chest x-ray, *Admission value, $ L1 Lymphocyte count on admission, L2 minimum lymphocyte count.

Fig 2
Fig. 2. Kaplan–Meier analysis of 90-day survival by age, sex and smoking. (a) Age range; Hazard ratio (HR 95% CI); (b) Sex (total); (c) Sex for age > 65 years; (d) Smoking-total; (e) Smoking for age ≤ 65 years.

Mortality rate was doubled by frailty and trebled with chest x-ray changes of COVID-19. Lymphopaenia and raised CRP were both associated with increased mortality. Mortality was higher if lymphocyte count fell after admission (36.4 vs. 23.4%; P = 0.01). A CRP level >100 mg/l almost doubled the mortality risk compared to the CRP level <50 mg/l. Males were more likely to have a peak CRP > 100 mg/l (72.1 vs. 61.2%; P = 0.036), and females were more likely to have CRP ≤ 50 mg/l (30.9 vs. 19.7%; P = 0.018). Logarithm of CRP (admission or peak value) had a highly significant association with mortality.

Variables were included in a multivariable logistic regression analysis if P < 0.15 on univariate analysis. The final models are shown in Table 6. Sex, COPD, cardiac disease, cumulative comorbidity and lymphopaenia were not independent predictors of mortality due to multicollinearity with other predictor variables; Pearson’s phi coefficients for sex (chest x-ray changes 0.186, lymphopaenia 0.129 and log CRP 0.125); COPD (smoking 0.61); cardiac disease (age 0.349 and frailty 0.23); cumulative comorbidity (age 0.249, cardiac disease 0.459, CKD 0.30 and frailty 0.218).


Table 6. Multivariable logistic regression of 30-day mortality
Variable Model 1 Model 2
OR 95% CI P OR 95% CI P
Age (years) 1.05 1.02–1.079 0.0005 1.05 1.02–1.08 0.0002
Smoker (>5 pack years) 2.59 1.26–5.31 0.009 2.08 1.19–3.63 0.009
Chronic neurological disease 2.33 1.20–4.53 0.0125 2.27 1.17–4.40 0.014
Cardiac disease 1.56 0.87–2.81 0.130
COPD* 0.71 0.30–1.65 0.429
Chronic kidney disease 2.24 1.05–4.80 0.036 2.32 1.09–4.92 0.028
Frailty 1.82 0.98–3.36 0.055 1.92 1.047–3.53 0.014
CXR** changes of COVID-19 4.34 2.12–8.87 0.0001 4.31 2.12–8.79 0.0001
Log-CRP on admission 3.42 1.50–7.78 0.003 3.21 1.43–7.22 0.0046
Nagelkerke R2 0.3509 0.3415
AUC (95% CI) 0.814 (0.768–0.855) 0.811 (0.765–0.852)
Correctly classified 75.76% 76.67%
*COPD chronic obstructive pulmonary disease; **CXR chest x-ray.

Discussion

This study describes the outcomes of the first cohort of patients admitted to the University Hospital of North Durham with SARS-CoV-2 infection. The 30-day mortality (30.8%) rate was comparable to other UK and European studies but higher than in the United States and China where hospitalised patients were younger [1–6, 12, 13, 18]. The risk of death increased incrementally with age as observed elsewhere [3–5]. There was a male predominance as reported by others, and survival was significantly worse in older men but sex was not an independent predictor of mortality due to collinearity with variables associated with disease severity (chest x-ray changes, lymphopaenia and CRP), but there was no gender difference for other risk factors. Speculation as to why men may be more susceptible to SARS-CoV-2 embraces biological (immune system, ACE 2 receptor expression) and behavioural differences including attitude towards the COVID-19 pandemic [22].

We found a strong association of mortality with smoking not universally observed by others. Smoking prevalence reflects historical UK smoking rates; 70% in men and 50% in women in the 1970s [23]. Differences between studies is likely due to variation in completeness of smoking record; many had smoking rates much less than the general population others omitted data on smoking [5, 7]. Our search of available records identified past smoking often absent from contemporary clinical notes, or patients were misclassified (as smokers or vice-versa). Furthermore, as cumulative tobacco consumption outweighs current smoking habit, grouping light smokers with non-smokers seems apposite. In a large UK study, current smoking was not a mortality risk after adjusting for comorbidity but past smoking was (OR 1.19; 1.14–1.24) [10].

Meta-analyses confirm an association between disease severity/inpatient mortality and past-smoking history rather than current smoking [24, 25]. As in this study, Mesas et al. found smoking-related risk to be greater in younger patients (≤ 60 y OR 2.78; 1.43–5.39 v 1.2; 1.04–1.41). In older patients, smoking as an independent risk factor is offset due to clustering of smoking-related diseases with increased age [25]. The greater risk from COVID-19 in smokers is not unexpected; smoking increases severity of respiratory infections [26]. Many former smokers may have unrecognised smoking-related lung damage increasing susceptibility to respiratory infection [27].

We observed a higher frequency of obesity in younger patients (≤ 65 years) than in the general population (49.5 vs. 35%) but less for older patients (>65 years 14.4 vs. 32%). This contrasts with an obesity rate of only 10.5% in the ISARIC study that is unrepresentative of community prevalence [5, 28]. Our findings concur with reports that obesity increases the risk of hospitalisation and of ICCU admission [3, 13, 29]. But we did not see increased mortality with obesity. Similarly, the study by Petrilli had a high prevalence of obesity (35.3%) but no mortality risk [3], whereas the findings of two Italian studies differed. In the Giacomelli study, the obesity rate was only 16.3% but associated with higher mortality (aHR 3.04, 1.42−6.49), whereas Ciceria reported more obesity (22.9%) but no association with mortality (HR for BMI 0.96; 0.90–1.02) [12, 13]. Obesity appeared protective in older patients in our study; this resembles the effect of obesity in severe COPD and community-acquired pneumonia [30–32]. The cohort effect of chronic disease in older patients is likely to be a factor. This concurs with the meta-analysis by Mesas that suggests obesity is a prominent prognostic factor only in patients with fewer comorbidities [25].

Most patients had multiple chronic conditions; the frequency of hypertension mirrors community prevalence but diabetes was more common (25.4% all ages vs. UK prevalence of 10% for age <65 years and 15% for age >65 years) [33]. This corresponds with many studies in which diabetes is a risk factor for hospital admission but as reported by others neither diabetes or hypertension was associated with increased inpatient mortality [5, 6]. Cumulative comorbidity count, cardiac disease and COPD were not independently predictive of mortality due to collinearity with age, smoking and frailty. Chronic kidney disease was particularly high risk as observed in many previous reports [3, 5–7, 13]. Chronic neurological disease was twice as common in this study (22%) as in the ISARIC study (11%) but equivalent to other studies. Variance between studies is due to differences in conditions included but there is consensus that chronic neurological disease is an independent predictor of hospital mortality [5, 34].

For simplification, we limited the clinical variables to the primary diagnosis, presence of frailty and chest x-ray changes. The clinical frailty scale (CFS) is included in the hospital admission document but the score was often not recorded; patients were commonly described as being frail, and frailty was often a reason for ceiling of care or do not attempt resuscitation decisions. Physician-described frailty was accepted as synonymous with moderate frailty (CFS score of ≥ 6). Frailty was common and associated with increased age and greater comorbidity. The finding that frailty was more frequent with hospital-acquired SARS-CoV-2 infection has been observed previously [35]. As in other studies, frailty was associated with worse survival independent of age [36–38]. In the recently published multicentre COMET study of patients hospitalised with COVID-19, frailty (CFS 6–9) was less frequent than in the present study (20 vs. 42%) but more than half of the patients were excluded due to no CFS score [38]. Hospital mortality was higher for frail patients of all ages (36.9% v 10.3%; OR adjusted for age and comorbidity 2·71; 2·04–3.60) but excluded patients were older with more comorbidities suggesting that our inclusion of physician-described frailty in lieu of a CFS score maybe more representative [38]. Frailty is a readily identifiable clinical finding that transcends diagnoses [20, 39] and given its association with high mortality is a key determinant of the level of care for patients hospitalised with SARS-CoV-2 infection [36–41].

This study shows that evidence of pneumonitis is the most important prognostic factor in patients hospitalised with SARS-Cov-2 infection. The absence of respiratory symptoms and/or chest x-ray changes of COVID-19 is evidently predictive of survival. Others have shown that radiographic evidence and extent of pneumonitis are of prognostic importance [1,4,12,13,16, 42, 43]. Some of these are based upon CT scans only [1, 4], and many larger studies did not include radiological findings as a variable [3, 5, 18, 44].

Multiple putative biomarkers of disease severity in COVID-19 have been identified. We included only lymphocyte count and CRP as they are measured routinely in all acute hospital admissions. Lymphopaenia on admission and/or a fall in lymphocyte count is a risk factor for mortality but we did not find it sufficiently discriminating or significant on multivariable regression. Many studies have shown the prognostic value of CRP on hospital admission with COVID-19 [3, 6, 13, 17, 18]. Both CRP on admission and its logarithm remained significant on multivariable analysis but model fit was superior with the latter. A threshold CRP ≤ 50 mg/l may identify patients with a good prognosis, whilst CRP > 100 mg/l is associated with almost doubling of mortality risk. Petrilli reported increased mortality proportional to CRP level with HRs >3.5 for all abnormal CRP levels [3]. Similarly, Giacomelli described higher mortality related to incremental rise in CRP (aHR 1.17, 95% CI 1.02−1.35 per 50 mg/L) [13].

In summary, this study confirms the high mortality rate for patients hospitalised with SARS-CoV-2 infection with greatest risk in older-age males with chronic disease and frailty. A history of moderate-heavy smoking is a major risk factor, particularly in patients aged 65 years and younger. Chest x-ray changes of COVID-19 and raised CRP are clinically valuable prognostic indicators.

Limitations of this study include the following: due to its retrospective design, several clinical variables and biomarkers were not available in many patients and therefore not included. Frailty was based upon subjective physician assessment rather than the CFS in many cases and we did not report the scores that were available. Furthermore, as a single centre study and lack of ethnic diversity of our patient population, the generalisability of our findings may be limited.

 

Citation: Journal of Global Medicine 2021, 1: 13 - http://dx.doi.org/10.51496/jogm.v1.13

Copyright: © 2021 Murphy et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Competing interests and funding: The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

*Stephen A. Murphy
Department of Respiratory Medicine, University Hospital of North Durham, North Rd, Durham DH1 5TW, UK. Email: Stephen.murphy13@nhs.net

Received: 09 January 2021; Revised: 26 February 2021; Accepted: 26 February 2021; Published: 09 April 2021

 

References

  1. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 2020; 323(11): 1061–9. doi: 10.1001/jama.2020.1585
  2. Fang H, Liu Q, Xi M, Xiong D, He J, Luo P, et al. Impact of comorbidities on clinical prognosis in 1280 patients with different types of COVID-19. J Investig Med 2020; 69(1): 75–85. doi: 10.1136/jim-2020-001555
  3. Petrilli CM, Jones SA, Yang J, Harish R, O’Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369: m1966. doi: 10.1136/bmj.m1966
  4. Chen F, Sun W, Sun S, Li Z, Wang Z, Yu L. Clinical characteristics and risk factors for mortality among inpatients with COVID-19 in Wuhan, China. Clin Transl Med 2020; 10: e40. doi: 10.1002/ctm2.40
  5. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: prospective observational cohort study. BMJ 2020; 369: m1985. doi: 10.1136/bmj.m1985
  6. Vena A, di Biagio A, Malgorzata Mikulska M, Taramasso L, de Maria A, Ball L, et al. Clinical characteristics, management and in-hospital mortality of patients with coronavirus disease 2019 in Genoa, Italy. Clin Microbiol Infect 2020; 26(11): 1537–44. doi: 10.1016/j.cmi.2020.07.049
  7. Guan W, Liang W, Zhao Y, Liang H, Chen Z, Li Y, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J 2020; 55: 2000547. doi: 10.1183/13993003.00547-2020
  8. Farsalinos K, Konstantinos F, Barbouni A, Niaura R. Systematic review of the prevalence of current smoking among hospitalized COVID19 patients in China: could nicotine be a therapeutic option? Intern Emerg Med 2020; 15: 845–52. doi: 10.1007/s11739-020-02355-7
  9. Clift AK, Coupland CAC, Keogh RH, Diaz-Ordaz K, Williamson E, Harrison EM, et al. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ 2020; 371: m3731. doi: 10.1136/bmj.m3731
  10. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020; 584: 7821. doi: 10.1038/s41586-020-2521-4
  11. Kalligeros M, Shehadeh F, Mylona EK, Benitez G, Beckwith CG, Chan PA, et al. Association of obesity with disease severity among patients with coronavirus disease 2019. Obesity 2020; 28(7): 1200–4. doi: 10.1002/oby.22859
  12. Ciceria F, Castagnaa A, Rovere-Querinia P, de Cobellia F, Ruggerib A, Gallib L, et al. Early predictors of clinical outcomes of COVID-19 outbreak in Milan, Italy. Clin Immunol 2020; 217: 108509. doi: 10.1016/j.clim.2020.108509
  13. Giacomelli A, Ridolfo AL, Milazzo L, Oreni L, Bernacchia D, Siano M, et al. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: a prospective cohort study. Pharmacol Res 2020; 158: 104931. doi: 10.1016/j.phrs.2020.104931
  14. Bhargava A, Fukushima EA, Levine M, Zhao W, Tanveer F, Szpunar SM, et al. Predictors for severe COVID-19 infection. Clin Infect Dis 2020; 71(8): 1962–8. doi: 10.1093/cid/ciaa674
  15. Huang I, Pranata R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis. J Intensive Care 2020; 8: 36. doi: 10.1186/s40560-020-00453-4
  16. Gude F, Riveiro V, RodríguezNúñez N, Ricoy J, LadoBaleato Ó, Lourido T, et al. Development and validation of a clinical score to estimate progression to severe or critical state in COVID19 pneumonia hospitalized patients. Sci Rep 2020; 10: 19794. doi: 10.1038/s41598-020-75651-z
  17. Sharifpour M, Rangaraju S, Liu M, Alabyad D, Nahab FB, Creel-Bulos CM, et al. C-reactive protein as a prognostic indicator in hospitalized patients with COVID-19. PLoS One 2020; 15(11): 0242400. doi: 10.1371/journal.pone.0242400
  18. Knight SR, Ho A, Pius R, Buchan I, Carson G, Drake TM, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: development and validation of the 4C mortality score. BMJ 2020; 371: m3777. https://www.bmj.com/content/370/bmj.m3339
  19. Watson J, Whiting PF, Brush JE. Interpreting a covid-19 test result. BMJ 2020; 369: m1808. doi: 10.1136/bmj.m1808
  20. Rockwood K. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J [Internet] 2005 [cited 2021 Mar 18]; 173(5). Available from: https://www.cmaj.ca/content/173/5/489
  21. Health Research Authority. Is my study research? [Internet]. [cited 2021 Mar 13]. Available from: http://www.hra-decisiontools.org.uk/research/docs/DefiningResearchTable_Oct2017-1.pdf
  22. Bwire GM. Why men are more vulnerable to Covid-19 than women? Compr Clin Med 2020; 2: 874–6. doi: 10.1007/s42399-020-00341-w
  23. Peto R, Darby S, Deo H, Silcocks P, Whitley E, Doll R. Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies. BMJ 2000; 321(7257): 323. doi: 10.1136/bmj.321.7257.323
  24. Reddy RK, Charles WN, Sklavounos A, Dutt A, Seed PT, Khajuri A. The effect of smoking on COVID-19 severity: a systematic review and meta-analysis. J Med Virol [Internet] 2020 [cited 2021 Jan 7]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436545/
  25. Mesas AE, Cavero-Redondo I, lvarez-Bueno C, Sarria-Cabrera MA, Maffei de Andrade S, Sequı-Dominguez I, et al. Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and metaanalysis exploring differences by age, sex and health conditions. PLoS One 2020; 15(11): e0241742. doi: 10.1371/journal.pone.0241742
  26. Arcavi L, Benowitz NL. Cigarette smoking and infection. Arch Intern Med 2004 [cited 2021 Jan 9]; 164: 2206–16. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/217624
  27. Ho T, Cusack RP, Chaudhary N, Satia I, Kurmi OP. Under- and over-diagnosis of COPD: a global perspective. Breathe 2019; 15(1): 24–35. doi: 10.1183/20734735.0346-2018
  28. National Statistics publication. Health survey for England 2017. Adult and child overweight and obesity [Internet] 2019 [cited 2021 Jan 9]. Available from: https://files.digital.nhs.uk/4D/F4FF7D/HSE17-report-summary-v2.pdf
  29. Cai Q, Chen F, Wang T, Luo F, Liu X, Wu Q, et al. Obesity and COVID-19 severity in a designated hospital in Shenzhen, China. Diabetes Care [Internet] 2020 [cited 2021 Mar 19]; 43(7): 1392–8. Available from: https://care.diabetesjournals.org/content/diacare/43/7/1392.full.pdf
  30. Lainscak M, von Haehling S, Doehner W, Sarc I, Jeric T, Ziherl K, et al. Body mass index and prognosis in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease. J Cachexia Sarcopenia Muscle 2011; 2: 81–6. doi: 10.1007/s13539-011-0023-9
  31. Yamauchi Y, Hasegawa W, Yasunaga H, Sunohara M, Jo T, Matsui H, et al. Paradoxical association between body mass index and in-hospital mortality in elderly patients with chronic obstructive pulmonary disease in Japan. Int J Chronic Obstructive Pulm Dis 2014; 9(1): 1337–46. doi: 10.2147/COPD.S75175
  32. Singanayagam A, Singanayagam A, Chalmers JD. Obesity is associated with improved survival in community-acquired pneumonia. Eur Respir J [Internet] 2013 [cited 2021 Jan 7]; 42: 180–7. Available from: https://erj.ersjournals.com/content/42/1/180.long
  33. National Statistics publication. Health survey for England 2017 summary of key findings [Internet] 2019 [cited 2021 Mar 2]. Available from: https://files.digital.nhs.uk/4D/F4FF7D/HSE17-report-summary-v2.pdf
  34. Romagnolo A, Balestrino R, Imbalzano G, Ciccone G, Riccardini F, Artusi CA, et al. Neurological comorbidity and severity of COVID19. J Neurol 2020; 268: 762–9. doi: 10.1007/s00415-020-10123-y
  35. Brill S, Jarvis H, Ozcan E, Burns TLP, Warraich RA, Amani LJ, et al. COVID-19: a retrospective cohort study with focus on the over-80s and hospital-onset disease. BMC Med [Internet] 2020 [cited 2021 Jan 7]; 18: 194. Available from: https://bmcmedicine.biomedcentral.com/track/pdf/10.1186/s12916-020-01665-z.pdf
  36. Chinnadurai R, Ogedengbe O, Agarwal P, Money-Coomes S, Abdurrahman AZ, Mohammed S, et al. Older age and frailty are the chief predictors of mortality in COVID-19 patients admitted to an acute medical unit in a secondary care setting – a cohort study. BMC Geriatr 2020; 20: 1. doi: 10.1186/s12877-020-01803-5
  37. Moledina SM, Maini AA, Gargan A, Harland W, Jenney H, Phillips G, et al. Clinical characteristics and predictors of mortality in patients with COVID-19 infection outside intensive care. Int J Gen Med [Internet] 2020 [cited 2021 Jan 7]; 13(13): 1157–65. Available from: https://www.dovepress.com/clinical-characteristics-and-predictors-of-mortality-in-patients-with-peer-reviewed-article-IJGM
  38. Sablerolles RSG, Lafeber M, van Kempen JAL, van de Loo BPA, Boersma E, Rietdijk WJR, et al. Association between clinical frailty scale score and hospital mortality in adult patients with COVID-19 (COMET): an international, multicentre, retrospective, observational cohort study. The Lancet Healthy Longevity [Internet] 2021 [cited 2021 Feb 25]. Available from: https://www.thelancet.com/action/showPdf?pii=S2666-7568%2821%2900006-4
  39. Xue Q-L. The frailty syndrome: definition and natural history. Clin Geriatr Med 2011; 27(1): 17–26. doi: 10.1016/j.cger.2010.08.009
  40. Carter B, Collins JTF, Barlow-Pay F, Rickard F, Bruce E, Verduri A, et al. Nosocomial COVID-19 infection: examining the risk of mortality. The COPE-nosocomial study (COVID in Older PEople). J Hosp Infec 2020; 106(2): 376–84. doi: 10.1016/j.jhin.2020.07.013
  41. Heras E, Garibaldi P, Boix M, Valero O, Castillo J, Curbelo Y, et al. COVID-19 mortality risk factors in older people in a long-term care center. Eur Geriatr Med 2020. doi: 10.1007/s41999-020-00432-w
  42. Borghesi A, Maroldi R. COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiologia Medica [Internet] 2020 [cited 2021 Mar 18]; 125(5): 509–13. Available from: https://link.springer.com/content/pdf/10.1007/s11547-020-01200-3.pdf
  43. Borghesi A, Zigliani A, Golemi S, Carapella N, Maculotti P, Farina D, et al. Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: a study of 302 patients from Italy. Int J Infect Dis 2020; 96: 291–3. doi: 10.1016/j.ijid.2020.05.021
  44. Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med 2020; 180(8): 1081–9. doi: 10.1001/jamainternmed.2020.2033