
==== Front
BMC Pulm Med
BMC Pulm Med
BMC Pulmonary Medicine
1471-2466
BioMed Central London

39300448
3281
10.1186/s12890-024-03281-6
Research
Patients with influenza admitted to a tertiary-care hospital in Riyadh between 2018 and 2022: characteristics, outcomes and factors associated with ICU admission and mortality
http://orcid.org/0000-0002-3772-8949
Al-Dorzi Hasan M. aldorzih@yahoo.com

1
Alsafwani Zahra A. 2
Alsalahi Elham 2
Aljulayfi Alaa S. 2
Alshaer Roa 2
Alanazi Salam 2
Aldossari Munira A. 2
Alsahoo Deem A. 2
http://orcid.org/0000-0003-3836-8225
Khan Raymond 1
1 grid.415254.3 0000 0004 1790 7311 College of Medicine, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard - Health Affairs, ICU2, Mail Code 1425, PO Box 22490, Riyadh, 11426 Saudi Arabia
2 https://ror.org/0149jvn88 grid.412149.b 0000 0004 0608 0662 College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
19 9 2024
19 9 2024
2024
24 4641 4 2024
10 9 2024
© The Author(s) 2024
2024
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Background

Influenza is a common cause of hospital admissions globally with regional variations in epidemiology and clinical profile. We evaluated the characteristics and outcomes of patients with influenza admitted to a tertiary-care center in Riyadh, Saudi Arabia.

Methods

This was a retrospective cohort of adult patients admitted with polymerase chain reaction-confirmed influenza to King Abdulaziz Medical City-Riyadh between January 1, 2018, and May 31, 2022. We compared patients who required intensive care unit (ICU) admission to those who did not and performed multivariable logistic regression to assess the predictors of ICU admission and hospital mortality.

Results

During the study period, 675 adult patients were hospitalized with influenza (median age 68.0 years, females 53.8%, hypertension 59.9%, diabetes 55.1%, and chronic respiratory disease 31.1%). Most admissions (83.0%) were in the colder months (October to March) in Riyadh with inter-seasonal cases even in the summertime (June to August). Influenza A was responsible for 79.0% of cases, with H3N2 and H1N1 subtypes commonly circulating in the study period. Respiratory viral coinfection occurred in 12 patients (1.8%) and bacterial coinfection in 42 patients (17.4%). 151 patients (22.4%) required ICU admission, of which 62.3% received vasopressors and 48.0% mechanical ventilation. Risk factors for ICU admission were younger age, hypertension, bilateral lung infiltrates on chest X-ray, and Pneumonia Severity Index. The overall hospital mortality was 7.4% (22.5% for ICU patients, p < 0.0001). Mortality was 45.0% in patients with bacterial coinfection, 30.9% in those requiring vasopressors, and 29.2% in those who received mechanical ventilation. Female sex (odds ratio [OR], 2.096; 95% confidence interval [CI] 1.070, 4.104), ischemic heart disease (OR, 3.053; 95% CI 1.457, 6.394), immunosuppressed state (OR, 7.102; 95% CI 1.803, 27.975), Pneumonia Severity Index (OR, 1.029; 95% CI, 1.017, 1.041), leukocyte count and serum lactate level (OR, 1.394; 95% CI, 1.163, 1.671) were independently associated with hospital mortality.

Conclusions

Influenza followed a seasonal pattern in Saudi Arabia, with H3N2 and H1N1 being the predominant circulating strains during the study period. ICU admission was required for > 20%. Female sex, high Pneumonia Severity Index, ischemic heart disease, and immunosuppressed state were associated with increased mortality.

Keywords

Pneumonia
Influenza
Intensive care
Mechanical ventilation
Outcomes
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

Influenza is an acute disease, caused by influenza A, B, or C viruses, that usually manifests as a spectrum of respiratory illnesses that differ in severity from mild to severe [1]. It occurs as local outbreaks, seasonal epidemics, or even pandemics. In countries with a temperate climate, influenza activity peaks during the winter months, whereas in tropical regions its activity is more variable [2]. Influenza has a significant burden on global health, as it results in approximately 55 million episodes of lower respiratory tract infections yearly, > 9 million hospitalizations, and 3–5 million severe cases requiring admission to the intensive care unit (ICU) [3]. A 2017 estimate by the Global Seasonal Influenza-associated Mortality Collaborator Network found that influenza was responsible for 290,000 to 650,000 deaths annually from respiratory causes alone [4]. Patients with severe influenza infection frequently require admission to the ICU. The rate of ICU admission ranged between 9.1 and 34.5% in four studies [5–8], and varied by the season possibly related to the circulating viral strain [5–7, 9]. Risk factors for severe influenza and hence higher likelihood of ICU admissions include older age, obesity, pregnancy, and chronic medical conditions (such as diabetes, obstructive sleep apnea, and preexisting chronic kidney disease) [3, 10, 11]. Patients who get admitted to ICU frequently require ventilator support and are at risk of developing secondary bacterial and fungal infections [11].

Saudi Arabia is characterized by its predominant desert climate, which may alter the epidemiology of influenza. In a study of severe acute respiratory infections between 2016 and 2018 in 15 Eastern Mediterranean countries, including Saudi Arabia, influenza A was more common than influenza B, and influenza A(H1N1) was the most common circulating subtype (58.5%), followed by influenza A(H3N2) (23.6%) [12]. In a study of 366 patients with influenza pneumonia admitted to a tertiary-care hospital in Riyadh (central region of Saudi Arabia) between 2012 and 2015, 151 (41.2%) had influenza A(non-H1N1), 150 (41.0%) had influenza A(H1N1) and 65 (17.8%) influenza B [13]. Further, 8.8% required ICU admission and mortality rates were 17.7% in influenza A(H1N1), 14.7% in influenza A(non-H1N1) and 8.1% in influenza B [13]. In another study of 1928 patients with influenza admitted to a tertiary-care hospital in Jeddah (Western region of Saudi Arabia) between 2015 and 2019, influenza cases peaked in October [14]. The most common strains of influenza were influenza A(H3N2) (42.0%), influenza B (30.7%), and influenza A(H1N1) (27.3%) [14]. In general, influenza is responsible for approximately 0.8 deaths per 100,000 individuals in Saudi Arabia [3], with an estimated financial impact between 184 and 986 million USD per year in the epidemic phase [15].

Studies on influenza from Saudi Arabia that can inform clinical practice remain scarce. The objectives of this study were to determine the seasonal variation in influenza cases admitted to a tertiary-care center in Riyadh, Saudi Arabia between 2018 and 2022, describe and compare the characteristics and outcomes of patients with influenza who were admitted to the ICU versus the ward, and determine the predictors of ICU admission and mortality.

Methods

Patients and settings

This was a retrospective cohort study of adult (age ≥ 14 years, the cutoff age for admission to adult wards in Saudi Arabia) patients hospitalized with influenza infection in King Abdulaziz Medical City, a 1400-bed tertiary-care hospital in Riyadh, Saudi Arabia, between January 1, 2018, and May 31, 2022. Acutely-ill patients were evaluated by the hospital physicians in clinics or emergency department and hospitalized if needed in accordance with departmental admission policies. The hospital had a rapid response team, consisting of an ICU physician, ICU nurse and a respiratory therapist, that was activated for patients who experienced clinical deterioration in the ward [16]. Patients who needed organ support were admitted to the ICUs under the Intensive Care Department. These ICUs were operated as closed units with 24 h per day, 7 days per week onsite coverage by board-certified intensivists [17]. Patients with Do-Not-Resuscitate orders were not admitted to the ICU as per the hospital policy. Droplet isolation was implemented for patients with influenza until they became asymptomatic. During the study period, pneumonia severity scores were not routinely calculated and management of patients with influenza did not follow a specific protocol.

The diagnosis of influenza infection was established by having symptoms and signs of a respiratory tract infection and the detection of influenza virus by a respiratory real-time polymerase chain reaction assay. Acceptable samples were nasopharyngeal swabs, sputum, endotracheal aspirate, and bronchoalveolar lavage obtained within 48 h of admission. During the study period, Coronavirus disease 2019 (COVID-19) pandemic occurred with the first wave hitting Riyadh in May 2020 [18].

Data collection

This study was approved by the Institutional Review Board of the Ministry of National Guard – Health Affairs. We collected data on demographics, smoking, influenza vaccination history, comorbid conditions, month of hospitalization, presenting symptoms and signs (including laboratory and radiologic data), Pneumonia Severity Index [19], CURB-65 [20], presence of acute kidney injury on admission (at least stage 1 according to the Kidney Disease : Improving Global Outcomes Clinical Practice Guideline) [21], and management (antimicrobial and antiviral therapy, use of corticosteroids, ICU admission, intubation and mechanical ventilation, and use of vasopressors). Pneumonia severity index and CURB-65 are severity scores that predict the mortality of community-acquired pneumonia and are used to decide on patient management. Pneumonia severity index is based on 20 variables including demographics, comorbidities and clinical variables [19]. Depending on the total score, patients are classified into five classes: class I-III (score ≤ 90, low risk, 30-day mortality < 2.8%), class IV (score 91–130, intermediate risk, 30-day mortality 9.3%) and class V (score > 130, high risk, 30-day mortality 27.0%) [19]. CURB-65, is an acronym of five elements: Confusion, blood Urea nitrogen (> 7 mmol/L), Respiratory rate (≥ 30/min), Blood pressure (systolic < 90 mmHg or diastolic ≤ 60 mmHg) and age (≥ 65 years) [20]. One point is given to each of these elements and an increasing total score correlates with 30-day mortality: score 0–1 (low risk, 30-day mortality < 3.3%), score 2 (intermediate risk, 30-day mortality 9.2%) and score 3–5 (high risk, 30-day mortality ≥ 14.5%) [20]. The primary outcome was all-cause hospital mortality. Secondary outcomes included the occurrence of hospital-acquired bacterial superinfection (secondary infection, defined as respiratory bacterial growth after 48 h of admission), duration of mechanical ventilation, performance of tracheostomy, length of stay in the ICU and hospital, ICU mortality and hospital readmission within 30 days of discharge.

Statistical analysis

As patients with influenza who develop critical illness represent an important subgroup that deserves well characterization, we categorized the study patients into two groups depending on ICU admission. We presented continuous variables as medians with interquartile ranges (IQRs) and categorical variables as frequencies with percentages. For continuous variables, we compared between-group differences using the Student t-test or Mann-Whitney U test, depending on the normality of distribution. For categorical variables, we used the Chi-square test or Fisher’s exact test, as appropriate.

We performed stepwise multivariable logistic regression analysis (backward elimination using the likelihood ratio test) to assess the predictors of admission to the ICU and hospital mortality. The independent variables entered in the two models were baseline demographics, preadmission chronic diseases (diabetes, hypertension, hypothyroidism, heart failure, history of stroke, kidney function, ischemic heart disease, hyperlipidemia, chronic respiratory disease, chronic liver disease, Immunosuppression, malignancy, and transplant), influenza A versus B, Pneumonia Severity Index, baseline laboratory results (white blood cells, lactic acid, international normalized ratio), and presence of bilateral infiltrates on admission chest X-Ray. Continuous variables (such as C-reactive protein and procalcitonin) with high prevalence of missing data (> 20%) were not entered into the model, otherwise missing data for continuous variables were replaced with the median. The results of the model were presented as odds ratios (ORs) with 95% confidence intervals (CIs). We also reported the hospital mortality in selected subgroups of patients. We did not correct for multiple testing and all statistical tests were considered significant at α level less than 0.05. Statistical analysis was performed using SPSS (SPSS Inc., SPSS for Windows, version 16.0. Chicago, IL, SPSS Inc.).

Clinical trial number

This study was not registered in any clinical trial registry and does not have a clinical trial number.

Results

Baseline characteristics and presenting symptoms and signs

The demographics and comorbidities of the study patients are presented in Table 1. There were 675 patients hospitalized with influenza infection. Their median age was 68.0 years (IQR: 50.0, 79.0) and 53.8% were females. Most admissions (560/675, 83.0%) were in the months from October to March (Fig. 1), which are the colder months in Riyadh. There were inter-seasonal influenza cases even in the summertime (June to August). During the COVID-19 pandemic, influenza cases almost disappeared (no influenza cases between May and December 2020). The most common comorbidities were hypertension (59.9%), diabetes (55.1%), and chronic respiratory disease (31.1%). The most common presenting symptoms were fever (66.1%), dyspnea (62.5%), and cough with purulent sputum (58.2%).

Table 1 Characteristics of patients admitted with influenza virus infection (N = 675)

Variable	All Patients N = 675	Ward admission n1 = 524	ICU admission n2 = 151	P-value	
Age (years), median (Q1, Q3)	68.0 (50.0, 79.0)	68.0 (48.0, 79.0)	68.0 (54.0, 78.0)	0.597	
Age 14–49 years*, n (%)	167 (24.7)	139 (26.5)	28 (18.5)	0.045	
Age 50–64 years, n (%)	135 (20.0)	99 (18.9)	36 (23.8)	0.180	
Age ≥ 65 years, n (%)	373 (55.3)	286 (54.6)	87 (57.6)	0.509	
Male sex, n (%)	312 (46.2)	239 (45.6)	73 (48.3)	0.553	
Female sex, n (%)	363 (53.8)	285 (54.4)	78 (51.7)		
BMI (kg/m2), median (Q1, Q3)	28.1 (23.6, 33.7)	28.1 (23.4, 33.7)	28.0 (24.5, 34.0)	0.198	
Smoking, n (%)*					
   Active	44/132 (33.3)	33/97 (34.0)	11/35 (31.4)		
   Previous	24/132 (18.2)	18/97 (18.6)	6/35 (17.1)	0.921	
   None	64/132 (48.5)	46/97 (47.4)	18/35 (51.4)		
Missing data in 543 patients					
Influenza vaccine, n (%)					
Missing data in 510 patients	27/165 (16.4)	17/126 (13.5)	10/39 (25.6)	0.073	
COMORBIDITIES, n (%)	
Diabetes	372 (55.1)	286 (54.6)	86 (57.0)	0.605	
Hypertension	404 (59.9)	298 (56.9)	106 (70.2)	0.003	
Hypothyroidism	73 (10.8)	60 (11.5)	13 (8.6)	0.322	
Heart Failure	139 (20.6)	97 (18.5)	42 (27.8)	0.013	
Cerebrovascular accident	78 (11.6)	62 (11.8)	16 (10.6)	0.676	
Chronic kidney disease	103 (15.3)	75 (14.3)	28 (18.5)	0.206	
Dialysis	22 (3.3)	17 (3.2)	5 (3.3)	1.000	
Ischemic heart disease	98 (14.5)	72 (13.7)	26 (17.2)	0.285	
Hyperlipidemia	177 (26.3)	136 (26)	41 (27.2)	0.778	
Chronic Respiratory disease	210 (31.1)	163 (31.1)	47 (31.1)	0.996	
Liver disease	34 (5.0)	25 (4.8)	9 (6.0)	0.556	
Immunosuppression	27 (4.0)	20 (3.8)	7 (4.6)	0.651	
Malignancy	74 (11.0)	60 (11.5)	14 (9.3)	0.450	
Transplant (solid organ or bone marrow)	30 (4.4)	23 (4.4)	7 (4.6)	0.897	
PRESENTING SYMPTOMS, n (%)	
Fever	446 (66.1)	377 (71.9)	69 (45.7)	< 0.0001	
Respiratory rate > 30/min	88 (13.0)	49 (9.4)	39 (25.8)	< 0.0001	
Systolic < 90 mm Hg	16 (2.4)	8 (1.5)	8 (5.3)	0.007	
Body temperature < 35 or > 39.9 °C	13 (2.0)	9 (1.8)	4 (2.7)	0.503	
Pulse > 125/min	37 (5.6)	22 (4.3)	15 (10.1)	0.006	
Shortness of breath	422 (62.5)	320 (61.1)	102 (67.5)	0.147	
Dry cough	180 (26.7)	144 (27.5)	36 (23.8)	0.373	
Purulent cough	393 (58.2)	316 (60.3)	77 (51.0)	0.041	
Chest pain	66 (9.8)	48 (9.2)	18 (12.0)	0.306	
Myalgia/Arthralgia	38 (5.6)	25 (4.8)	13 (8.6)	0.071	
Fatigue	104 (15.4)	80 (15.3)	24 (15.9)	0.851	
Headache	30 (4.4)	27 (5.2)	3 (2.0)	0.096	
Altered mental status	89 (13.2)	59 (11.3)	30 (19.9)	0.006	
Hemoptysis	20 (3.0)	8 (1.5)	12 (7.9)	< 0.0001	
ILLNESS SEVERITY	
Pneumonia severity index**, median (Q1, Q3)	101 (78, 123)	98 (72, 119)	112 (92, 135)	< 0.0001	
CURB 65^, median (Q1, Q3)	1 (0, 2)	1 (0, 2)	2 (1, 2)	< 0.0001	
Acute kidney injury, n (%)	70 (10.4)	48 (9.2)	22 (14.6)	0.055	
PERTINENT LABORATORY FINDINGS (ADMISSION), median (Q1, Q3)	
White blood cell (109/L)	7.5 (5.4, 10.6)	7.4 (5.3, 10.2)	8.6 (6.0, 12.8)	0.001	
Neutrophil %	74.6 (63.5, 82.1)	74.2 (63.5, 81.6)	75.2 (63.5, 84.1)	0.325	
Lymphocyte %	14.2 (8.8, 23.2)	14.9 (8.9, 23.9)	12.2 (8.1, 22.3)	0.169	
Hematocrit	0.39 (0.34, 0.44)	0.39 (0.34, 0.44)	0.39 (0.33, 0.44)	0.354	
PTT (seconds)	29.8 (27.2, 33.6)	29.6 (27.1, 33.3)	30.8 (27.2, 34.5)	0.113	
INR	1.1 (1.0, 1.3)	1.1 (1.0, 1.3)	1.1 (1.1, 1.3)	0.082	
BUN (mmol/L)	6.6 (4.1, 10.6)	6.2 (3.9, 9.7)	7.8 (4.9, 14.5)	< 0.0001	
Sodium (mmol/L)	135 (135, 138)	135 (132, 137)	135 (131, 139)	0.814	
Creatinine (µmol/L)	86.5 (64.0, 136.0)	82.5 (64.0, 124.8)	101.0 (68.3, 160.3)	0.003	
Lactic acid (mmol/L)	1.7 (1.3, 2.4)	1.7 (1.3, 2.4)	1.8 (1.3, 2.5)	0.345	
pH	7.39 (7.34, 7.43)	7.40 (7.35, 7.43)	7.38 (7.30, 7.44)	0.154	
PaO2(mm Hg)	68 (59, 83)	65 (58, 77)	77 (65, 93)	< 0.0001	
C-reactive protein (mg/L)	53 (19, 95.5)	47 (19, 90.75)	64 (19, 126)	0.274	
Procalcitonin (ng/mL)	0.26 (0.10, 0.86)	0.23 (0.09, 0.73)	0.35 (0.20, 1.60)	0.001	
CHEST X-RAY FINDINGS, n (%)	
Any infiltrates	131 (19.8)	88 (17.2)	43 (28.7)	0.002	
Unilateral lobar infiltrates	47 (7.1)	37 (7.2)	10 (6.7)	0.810	
Unilateral multilobar infiltrates	7 (1.1)	6 (1.2)	1 (0.7)	1.000	
Bilateral infiltrates	77 (11.6)	45 (8.8)	32 (21.3)	< 0.0001	
Unilateral pleural effusion	50 (7.6)	36 (7.0)	14 (9.3)	0.351	
Bilateral Pleural effusion	51 (7.7)	28 (5.5)	23 (15.3)	< 0.0001	
Any Pleural effusion	100 (15.1)	63 (12.3)	37 (24.7)	< 0.0001	
BMI: body mass index, BUN: blood urea nitrogen, CURB-65: Confusion, blood Urea nitrogen, Respiratory rate, Blood pressure and age ≥ 65 years), ICU: intensive care unit, INR: international normalized ratio, PO2: the partial pressure of oxygen in the arterial blood, PTT: partial thromboplastin time, Q1: first quartile, Q3: third quartile, SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

The denominator is the number of patients with available data or had the test

*Only 5 patients were younger than 18 years

**For the calculation of Pneumonia Severity Index and CURB-65, missing data were assigned a value of 0

Fig. 1 Number of influenza patients admitted to the ward and intensive care unit during the study period (January 1, 2018, to May 31, 2022)

Admission to the ICU was required for 151 patients (22.4%). These patients had similar median age to those who were admitted to the ward (Table 1). On the other hand, ICU patients were more likely to have respiratory rates > 30/min (25.8% versus 9.4%), systolic BP < 90mmHg (5.3% versus 1.5%), and pulse rate > 125/min (10.1% versus 4.3%), and had higher median CURB-65 scores (2 versus 1), Pneumonia Severity Index (112 versus 98) and procalcitonin levels (0.35 ng/mL versus 0.23 ng/mL).

Microbiology data

Influenza A was responsible for 533 influenza cases (79.0%) in our cohort. H3N2 and H1N1 were the influenza subtypes that commonly circulated in the study period, with H3N2 predominating in 2021 and 2022 (Table 2). Among the 533 influenza A cases, 128 (24.0%) were untyped.

Table 2 Frequency distribution of microbiology data (N = 675)

Variable	All Patients
N = 675	Ward admission
n1 = 524	ICU admission
n2 = 151	P-value	
INFLUENZA SUBTYPE, n (%)	
Influenza A	533 (79.0)	417 (79.6)	116 (76.8)	0.464	
   H1N1	161 (23.9)	110 (21.0)	51 (33.8)	0.001	
   H3N2	244 (36.1)	202 (38.5)	42 (27.8)	0.016	
   Influenza A (unknown subtype)	128 (19.0)	105 (20.0)	23 (15.2)	0.184	
Influenza B (unknown subtype)	142 (21.0)	107 (20.4)	35 (23.2)	0.464	
INFLUENZA SUBTYPE BY STUDY YEAR	
2018 (173 patients)	H3N2 (19.7%), H1N1 (8.1%), influenza A untyped (32.9%), influenza B (39.3%)	< 0.0001	
2019 (340 patients)	H3N2 (39.4%), H1N1 (26.5%), influenza A untyped (18.5%), influenza B (15.6%)		
2020 (73 patients)	H3N2 (6.8%), H1N1 (72.6%), influenza A untyped (4.1%), influenza B (16.4%)		
2021 (71 patients)	H3N2 (80.3%), H1N1 (4.2%), influenza A untyped (5.6%), influenza B (9.9%)		
2022 till May 31 (18 patients)	H3N2 (77.8%), H1N1 (5.6%), influenza A untyped (5.6%), influenza B (11.1%)		
VIRAL COINFECTION*, n (%)	
Any viral coinfection*	12 (1.8)	7 (1.3)	5 (3.3)	0.097	
Adenovirus	3 (0.4)	3 (0.6)	0 (0)		
SARS-CoV-2	2 (0.3)	1 (0.2)	1 (0.7)		
Human rhinovirus	5 (0.6)	2 (0.4)	3 (0.14)		
Parainfluenza-3	1 (0.1)	1 (0.2)	0 (0)		
Parainfluenza-4	1 (0.1)	0 (0)	1 (0.7)		
Respiratory syncytial virus	1 (0.1)	1 (0.2)	0 (0)		
Respiratory culture performed, n (%)	330 (48.9)	228 (43.5)	102 (67.5)	< 0.0001	
   Within 48 h	241/330 (73.0)	184/228 (80.7)	57/102 (55.9)		
   After 48 h	89/330 (27.0)	44/228 (19.3)	45/102 (44.1)	< 0.0001	
Results of respiratory cultures taken within 48 h**, n (%)	
No growth	35/241 (14.5)	17/184 (9.2)	18/57 (31.6)	< 0.0001	
Growth of normal flora	160/241 (66.4)	131/184 (71.2)	29/57 (50.9)	0.005	
Any other bacterial growth	42/241 (17.4)	32/184 (17.4)	10/57 (17.5)	0.979	
Streptococcus pneumoniae	10/241 (4.1)	9/184 (4.9)	1/57 (1.8)	0.459	
Hemophilus influenzae	2/241 (0.8)	1/184 (0.5)	1/57 (1.8)	0.418	
Staphylococcus aures	
   Methicillin sensitive	20/241 (8.3)	17/184 (9.2)	3/57 (5.3)	0.451	
   Methicillin resistant	5/241 (2.1)	2/184 (1.1)	2/57 (3.5)		
Pseudomonas aeruginosa	5/241 (2.1)	2/184 (1.1)	3/57 (5.3)	0.088	
Klebsiella pneumoniae (Carbapenem susceptible)	1/241 (0.4)	1/184 (0.5)	0/57 (0)	1.000	
Enterobacter	1/241 (0.4)	1/184 (0.5)	0/57 (0)	1.000	
Results of respiratory cultures taken after 48 h***, n (%)	
No growth	12/89 (13.5)	0/44 (0)	12/45 (26.7)	< 0.0001	
Growth of normal flora	54/89 (60.7)	37/44 (84.1)	17/45 (37.8)	< 0.0001	
Any other bacterial growth	20/89 (22.5)	6/44 (13.6)	14/45 (31.1)	0.048	
Streptococcus	0/89 (0)	0/44 (0)	0/45 (0)	-	
Hemophilus influenzae	1/89 (1.1)	0/44 (0)	1/45 (2.2)	1.000	
Staphylococcus aures	
   Methicillin sensitive	4/89 (4.5)	2/44 (4.5)	2/45 (4.4)	0.999	
   Methicillin resistant	4/89 (4.5)	2/44 (4.5)	2/45 (4.4)		
Pseudomonas aeruginosa					
   Susceptible	1/89 (1.1)	0/44 (0)	1/45 (2.2)	1.000	
   MDR	2/89 (2.2)	0/44 (0)	2/45 (4.4)	0.494	
Klebsiella pneumoniae	
   Carbapenem susceptible	3/89 (3.4)	0/44 (0)	3/45 (6.7)	0.242	
   Carbapenem resistant	2/89 (2.2)	0/44 (0)	2/45 (4.4)	0.494	
Acinetobacter baumannii	1/89 (1.1)	1/44 (2.3)	0/45 (0)	0.494	
Enterobacter	1/89 (1.1)	0/44 (0)	1/45 (2.2)	1.000	
Stenotrophomonas	2/89 (2.2)	1/44 (2.3)	1/45 (2.2)	1.000	
Other cultures	
Aspergillus species	4/330 (1.2)	2/228 (0.9)	2/102 (2.0)	0.590	
Mycobacterium tuberculosis	2/330 (0.6)	2/228 (0.9)	0 (0)	1.000	
Mycobacterium fortuitum	1/330 (0.3)	1/228 (0.4)	0 (0)	1.000	
ICU: intensive care unit, MDR: multidrug resistant

The denominator is the number of patients with available data or had the test

*One patient had a coinfection with two viruses

**Two patients had coinfection with two bacteria

***One patient had coinfection with two bacteria

Respiratory viral coinfection occurred in 12 patients (1.8%), two of whom had coinfection with SARS-CoV-2. Respiratory culture was performed on 330 patients, mostly (241 patients) within 48 h of hospitalization. Bacterial growth/coinfection was observed in 42 patients (17.4%). The most common bacteria were Staphylococcus aureus (25 patients; MSSA 20 and MRSA 5) and Streptococcus pneumonia (4.1%). Pseudomonas aeruginosa was the most frequent gram-negative organism, isolated in 5/241 patients (2.1%). The prevalence of bacterial coinfection was similar in ICU and non-ICU patients with no significant difference in the type of bacteria. Compared with patients whose respiratory culture did not grow bacteria within 48 h of admission, patients with bacterial coinfection had similar median values of admission white blood cells (7.9 versus 7.6 × 109/L, p = 0.319), neutrophils (79.0% versus 75.0%, p = 0.092), lymphocytes (12.0% versus 14.0%, p = 0.097), C-reactive protein (59 versus 60 mg/L, p = 0.923), procalcitonin (0.28 versus 0.18 ng/L, p = 0.203) and Pneumonia Severity Index (108 versus 99, p = 0.276). Among the 89 patients who had respiratory culture after 48 h of admission, 20 (22.5%) had bacterial growth (bacterial superinfection).

Management of patients

Table 3 summarizes the antiviral and antimicrobials treatment of influenza cases. Oseltamivir was given to 636/675 patients (94.2%). The median duration was 5 days. Antibacterial therapy was initiated on hospital admission in 93.8% of the patients and was more commonly used in those admitted to ICU (98.0% versus 92.6%, p = 0.014). Antipseudomonal beta-lactams were more commonly used in ICU patients (64.2% versus 44.1%, p < 0.0001). Methicillin-resistant Staphylococcus aureus coverage with vancomycin or linezolid was used in 28.4% of patients (54.7% for ICU patients versus 20.4% for the ward patients, p < 0.0001). Systemic corticosteroids were commonly used, especially for ICU patients (72.2% versus 45.4%, p < 0.0001). Of the patients admitted to ICU, 48.0% required intubation and mechanical ventilation, and 62.3% had shock requiring vasopressors.

Table 3 Management of patients with influenza virus infection

Variable	All Patients
N = 675	Ward admission
n1 = 524	ICU admission
n2 = 151	P-value	
Antimicrobial therapy, n (%)	
Oseltamivir	636 (94.2)	489 (93.3)	147 (97.4)	0.061	
Oseltamivir daily dose (mg), median (Q1, Q3)	75 (30, 150)	75 (30, 150)	75 (30, 150)	0.018	
Oseltamivir duration (days), median (Q1, Q3)	5 (3, 6)	5 (3, 5)	6 (5, 10)	<0.0001	
Any antibacterial	633 (93.8)	485 (92.6)	148 (98.0)	0.014	
Azithromycin	384/633 (60.7)	296/485 (61.0)	88/148 (59.5)	0.732	
Doxycycline	20/633 (3.2)	19/485 (3.9)	1/148 (0.7)	0.058	
Ceftriaxone	220/633 (34.8)	202/485 (41.6)	18/148 (12.2)	<0.0001	
Fluoroquinolones	6/633 (0.9)	4/485 (0.8)	2/148 (1.4)	0.628	
Antipseudomonal beta lactams (pipercillin/tazobactam or meropenem)	309/633 (48.8)	214/485 (44.1)	95/148 (64.2)	< 0.0001	
Vancomycin/linezolid	180/633 (28.4)	99/485 (20.4)	81/148 (54.7)	<0.0001	
Other treatments	
Use of corticosteroids, n (%)	347 (51.4)	238 (45.4)	109 (72.2)	<0.0001	
Mechanical Ventilation, n (%)	72 (39.3)	0 (0)	72 (48.0)	<0.0001	
Vasopressors, n (%)	94 (13.9)	0 (0)	94 (62.3)	<0.0001	
ICU: intensive care unit, Q1: first quartile, Q3: third quartile

Outcomes of patients

Table 4 describes the outcomes of the study patients. The median length of hospital stay was 4 days and was significantly longer for ICU patients (median of 15 days versus 3 days, p < 0.0001). For the patients intubated, the median duration of mechanical ventilation was 5 days, and 11.9% were tracheotomized.

Table 4 Outcomes of patients with influenza virus infection

Variable	All Patients
N = 675	Ward admission
n1 = 524	ICU admission
n2 = 151	P-value	
MV duration (days), median (Q1, Q3)	5 (11, 25)	-	5 (2, 13)	-	
LOS in ICU (days), median (Q1, Q3)	11 (5, 25)	-	11 (5, 25)	-	
LOS in hospital (days), median (Q1, Q3)	4 (2, 10)	3 (2, 6)	15 (7, 32)	<0.0001	
Tracheostomy, n (%)	18 (2.7)	-	18 (11.9)	-	
ICU mortality, n (%)	29 (4.3)	-	29 (19.2)	-	
Hospital mortality, n (%)	50 (7.4)	16 (3.1)	34 (22.5)	<0.0001	
Hospital readmission within 30 days, n%	74 (11.8)	62 (12.2)	12 (10.3)	0.556	
ICU: intensive care unit, LOS: length of stay, MV: mechanical ventilation, Q1: first quartile, Q3: third quartile

The overall hospital mortality of the cohort was 7.4%. The mortality rate for ICU patients was 22.5% compared with 3.1% for patients who were not admitted to the ICU (p < 0.0001). The 16 patients who died without ICU admission were old (median 77.5 years [IQR: 66.5, 86.0] versus 67.0 years [IQR: 47.3, 79.0] for the 508 patients who were not admitted to the ICU and survived; p = 0.074). Figure 2 describes the hospital mortality in selected subgroups of patients. Patients who had H1N1 influenza had a mortality of 11.2% compared with 7.8% for those who had H3N2 influenza (p = 0.246). Elderly patients (≥ 65 years) had a mortality of 9.9% (versus 4.3% for younger patients, p = 0.006). Patients who received systemic corticosteroids had a mortality of 9.8% (versus 4.9% for patients who did not receive steroids, p = 0.015). Patients with bacterial superinfection had high mortality (45.0%), while those who required vasopressors for shock or mechanical ventilation for acute respiratory failure had a mortality of 30.9% and 29.2% respectively.

Fig. 2 Hospital mortality in subgroups of patients

Predictors of admission to the ICU and hospital mortality

For the multivariable logistic regression analysis for ICU admission, the overall model was statistically significant when compared to the null model, (chi-square = 89.4, p < 0.0001). The risk factors for ICU admission were age (OR per year increment, 0.945; 95% CI 0.928, 0.961), hypertension (OR, 2.092; 95% CI, 1.254, 3.489), bilateral lung infiltrates on chest X-Ray (OR, 2.749; 95% CI, 1.611, 4.691) and Pneumonia Severity Index (OR, 1.041; 95% CI, 1.030, 1.052) (Table 5). History of malignancy and previous stroke were associated with lower risk of ICU admission (Table 5).

Table 5 Results of the multivariable stepwise logistic regression analysis for the predictors of admission to the Intensive Care Unit (ICU) and hospital mortality. The significant variables (p < 0.10) are presented

Variable	Odds ratio	95% confidence interval	P-value	
Admission to the ICU	
Age per year increment	0.945	0.928, 0.961	< 0.0001	
Hypertension	2.092	1.254, 3.489	0.005	
Malignancy	0.296	0.143, 0.611	0.001	
Previous stroke	0.557	0.285, 1.089	0.098	
Bilateral infiltrates on chest X-Ray	2.749	1.611, 4.691	< 0.0001	
Pneumonia Severity Index per unit increment	1.041	1.030, 1.052	< 0.0001	
Hospital mortality	
Female versus male sex	2.096	1.070, 4.104	0.031	
Ischemic heart disease	3.053	1.457, 6.394	0.003	
Immunosuppression	7.102	1.803, 27.975	0.005	
Hyperlipidemia	0.371	0.157, 0.877	0.024	
Common respiratory Disease (asthma, chronic obstructive lung disease)	0.383	0.170, 0.865	0.021	
Chronic liver disease	0.170	0.029, 1.005	0.051	
Acute kidney injury on admission versus normal function	2.146	0.974, 4.730	0.058	
Pneumonia severity index per unit increment	1.029	1.017, 1.041	< 0.0001	
Baseline lactic acid per mmol increment	1.394	1.163, 1.671	< 0.0001	
Variables entered in both models: Age, sex, body mass index, influenza A versus B, diabetes, hypertension, hypothyroidism, heart failure, history of stroke, kidney function (normal, acute kidney injury, chronic kidney disease), ischemic heart disease, hyperlipidemia, chronic respiratory disease (i.e., asthma, chronic obstructive pulmonary disease), chronic liver disease, immunosuppression, malignancy, transplant, Pneumonia Severity Index, baseline laboratory results (white blood cells, lactic acid, international normalized ratio), and presence of bilateral infiltrates on chest X-Ray

For the multivariable logistic regression analysis for hospital mortality, the overall model was statistically significant when compared to the null model, (chi-square = 75.9, p < 0.0001). The following variables were independently associated with increased hospital mortality: female sex (OR, 2.096; 95% CI 1.070, 4.104), ischemic heart disease (OR, 3.053; 95% CI 1.457, 6.394), immunosuppressed state (OR, 7.102; 95% CI 1.803, 27.975), Pneumonia Severity Index (OR per unit increment, 1.029; 95% CI, 1.017, 1.041), higher leukocyte count and serum lactate level (OR, 1.394; 95% CI, 1.163, 1.671) (Table 5). However, chronic respiratory disease (OR, 0.383; 95% CI, 0.170, 0.865), and hyperlipidemia (OR, 0.371; 95% CI 0.157, 0.877) were associated with a lower risk of mortality.

Discussion

In this study of patients with influenza admitted to a tertiary-care hospital in Riyadh between 2018 and 2022, there was a seasonal pattern of admissions with a dramatic decrease in influenza cases during the COVID-19 pandemic; H1N1 and H3N2 were the predominant influenza viruses; bacterial coinfection was common (17.4%); ICU admission was needed in 22.4% of patients; and the associated mortality was significant at 7.4% for all patients and 22.5% for ICU patients.

Influenza is primarily caused by influenza types A and B viruses. In this study, Influenza A was responsible for 533 out of the 675 influenza cases (79%). H3N2 infections accounted for 244 cases (36.1%), H1N1 for 161 (23.9%), while 128 cases (24.0%) were untyped. H3N2 and H1N1 were the influenza subtypes circulating in Saudi Arabia, the region, and the world during the study period as observed in other studies [12–14, 22]. In the northern hemisphere, seasonal influenza occurs between November and April, peaking in December to February, whereas in the southern hemisphere, it occurs between May and September, peaking in July to August [23]. Our data confirm this seasonal distribution of influenza, with our peaks being in October to December. Although, the desert climate of Riyadh, Saudi Arabia may alter influenza epidemiology, our infection pattern mirrored that of the northern hemisphere, also with inter-seasonal circulation [23]. We found a notable decrease in influenza cases during the COVID-19 pandemic with no influenza cases admitted between May and December 2020, which spanned the first wave of COVID-19 outbreak in Riyadh. This decline in influenza infection during the COVID-19 pandemic was observed globally, with a decline in influenza cases relative to pre-pandemic levels by 50–99% in some countries [24–27]. The decreased influenza circulation during the COVID-19 pandemic can be attributed to the implementation of non-pharmaceutical interventions (facemask usage, stay home policy, social distancing, school closure, discouraging patients with mild flu symptoms to come to hospitals, and travel restrictions) for COVID-19 control [27–29]. Also the viral interference hypothesis, where one respiratory virus impedes the infection with another virus through the stimulation of antiviral defenses and releasing interferon, may have contributed to the low number of influenza cases during the COVID-19 pandemic [30, 31].

In the current study, bacterial coinfection was common (17.4%). A multi-center study of 683 ICU patients with severe H1N1 influenza revealed that 207 patients (30.3%) had bacterial coinfection [32]. Another study found that 114 patients out of 507 adult and pediatric patients (22.5%) had bacterial coinfection [33]. Similar to our findings, Staphylococcus aureus was the most common coinfection in influenza in other studies [32–34]. Whereas only 5/25 (20.0%) of the isolated strains in our study were MRSA, the other studies had higher MRSA rates ranging between 42.8 and 61.7% [32–34]. Streptococcus pneumoniae, Hemophilus influenzae, and Pseudomonas aeruginosa are other common causes of bacterial coinfection in patients with influenza [32–34]. Antibiotics should be part of the empirical antimicrobial regimen in patients with severe influenza. Whether markers of bacterial infection, such as neutrophilia and procalcitonin, have clinical utility for antibiotic stewardship in patients with influenza is unknown and needs further study. In the current study, viral coinfection was rare (12/675 patients,1.8%). Another study observed a higher rate viral coinfection of 4.5% (23/507 patients with severe influenza) [33].

We found that 22.4% of the study patients were admitted to the ICU. ICU admission rates vary between studies. In a Canadian study, 55 of 607 patients with influenza (9.1%) required ICU admission [5]. A Dutch study observed an ICU admission rate of 22.6% (45/199 patients) [11], whereas a Spanish study reported an admission rate of 34.5% (595/1726 patients) [8]. We found that younger age was associated with ICU admission on multivariable logistic regression analysis. This was observed in other studies [8, 11]. Older patients may have had less severe disease and may have chosen to withhold life support leading to less ICU admission. Pneumonia Severity Index was also associated with ICU admission. Other studies suggested that pneumonia severity scores had limited clinical utility in predicting ICU admission [5].

In our study, the mortality of patients hospitalized with influenza was 7.4% (3.1% for non-ICU and 22.5% for ICU patients). Similar mortality rates were observed in another study in a tertiary-care hospital in Riyadh (448 patients with influenza, 6.9% mortality) [13], and in a Dutch study (199 patients with influenza, 9.0% mortality) [11]. In our study, the influenza subtype was not associated with mortality. Based on the available evidence, a systematic review concluded that the influenza subtype might not be a major determinant of disease severity [35]. Patients who needed ICU admission had higher mortality (22.5%). The International data from the Canadian Nosocomial Infection Surveillance Program showed an influenza-related mortality of 17.8% in ICU patients [7]. Data from 33 US ICUs revealed that 20.9% of 507 patients with influenza died [36]. In an analysis of 13,368 influenza cases admitted to the ICUs in the European Union between 2009 and 2017, the mortality rate was 21.0% [37]. Other studies reported other mortality rates likely related to the study population [38]. In our study, mortality was linked to multiple variables that included female sex, immunosuppressed state, Pneumonia Severity Index, and ischemic heart disease. Studies on the association between sex and influenza outcome had conflicting results. A study from Japan found that females aged 30–70 years had higher influenza-related morbidity compared to males during the 2009 H1N1 pandemic [39]. In European studies, male versus female sex was associated with increased mortality in ICU patients with influenza between 2009 and 2017 (OR, 1.12; 95% CI, 1.02, 1.23) [37], and female sex, but not pregnancy, was protective against acute respiratory distress syndrome secondary to influenza pneumonia during the 2009 H1N1 pandemic [40]. Studies from China showed that sex was not associated with influenza hospitalization and mortality in adults [41, 42]. Animal studies have demonstrated that females have greater respiratory inflammatory responses and more severe outcome from influenza infection than males [43, 44]. Sex hormones may partly explain these differences [44, 45]. Gender-related sociocultural and behavioral differences (such as cigarette smoking, exercise, and seeking of healthcare) may also contribute to differences in influenza infection rate, disease course and severity between males and females [44].

Influenza is well known to worsen the outcomes of patients with ischemic heart disease [46]. In immunosuppressed patients, influenza has a more severe disease course and is associated with increased mortality [47]. This worse outcome may be related to influenza-induced apoptosis of T cells, leading to lymphocytopenia and delayed viral clearance [47]. Patients with chronic respiratory diseases had a lower mortality risk in the current study. A similar pattern of better outcome has been seen in other studies [11]. These patients may present earlier, may be treated more appropriately or may have other exposures that may improve their outcomes. In this study, mortality was further increased in patients requiring vasopressors (31.0%), and mechanical ventilation (29.2%) and in those with bacterial superinfection (45.0%). In a recent meta-analysis, bacterial coinfection/ superinfection in influenza was associated with a 3.4-fold increase in mortality [48]. Clinicians should focus on the prevention and early recognition of hospital-acquired pneumonia to improve outcomes.

This study provided valuable data on a large number of patients with influenza admitted to both ward and ICU between 2018 and 2022, a period that spanned the COVID-19 pandemic. However, it has limitations. Due to the study nature, we did not evaluate the hospitalization rate of patients who had influenza. The retrospective design can result in missing information leading to information bias. For example, we did not collect data about pregnancy status, statin therapy, and Do-Not-Resuscitate orders. Hence, the observed associations may be related to these and other unmeasured confounding factors. Acute kidney injury was measured at the lowest grade in this study, which might account for its non-significant association with hospital mortality in our results. As our study was conducted in a single tertiary-care hospital in Riyadh and lacked comparisons with other hospitals, our findings might not be generalizable to other settings.

Conclusions

We found that influenza follows a seasonal pattern in Saudi Arabia, with H3N2 being the predominant circulating strain between 2018 and 2022. Additionally, viral pneumonia caused by influenza is associated with high mortality. Female gender, high Pneumonia Severity Index, and immunosuppressed state were predictors of increased mortality.

Acknowledgements

None.

Author contributions

HMD contributed to the study concept, design, data interpretation, and drafting the manuscript, performed statistical analysis and had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. ZAA, EA, ASA, RA, and SA contributed to data collection, data interpretation and drafting the manuscript. MAA and DAA contributed to data collection and data interpretation. RK contributed to the study concept, design, data interpretation and drafting the manuscript. All authors critically reviewed the manuscript and approved the final manuscript.

Funding

The study did not receive any direct or indirect financial contribution or support from any organization/donor for completion of this research.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to institutional policies but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of the Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia (NRC23R/171/03); as the study was a retrospective with no direct contact with patients, informed consent was waived. This study was conducted in accordance with the guidelines of the Declaration of Helsinki (2000) and Good Clinical Practice E6 (R2).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

CI Confidence intervals

COVID-19 Coronavirus disease 2019

ICU Intensive care unit

IQR Interquartile range

OR Odds ratio

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Sellers SA Hagan RS Hayden FG Fischer WA The hidden burden of influenza: a review of the extra-pulmonary complications of influenza infection Influenza Other Respir Viruses 2017 11 5 372 93 10.1111/irv.12470 28745014
Sellers SA, Hagan RS, Hayden FG, Fischer WA. The hidden burden of influenza: a review of the extra-pulmonary complications of influenza infection. Influenza Other Respir Viruses. 2017;11(5):372–93.28745014
2. Tamerius JD Shaman J Alonso WJ Bloom-Feshbach K Uejio CK Comrie A Environmental predictors of seasonal influenza epidemics across temperate and tropical climates PLoS Pathog 2013 9 3 e1003194 10.1371/journal.ppat.1003194 23505366
Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, et al. Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog. 2013;9(3):e1003194.23505366
3. Troeger CE Blacker BF Khalil IA Zimsen SR Albertson SB Abate D Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the global burden of Disease Study 2017 Lancet Respiratory Med 2019 7 1 69 89 10.1016/S2213-2600(18)30496-X
Troeger CE, Blacker BF, Khalil IA, Zimsen SR, Albertson SB, Abate D, et al. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the global burden of Disease Study 2017. Lancet Respiratory Med. 2019;7(1):69–89.
4. Iuliano AD Roguski KM Chang HH Muscatello DJ Palekar R Tempia S Estimates of global seasonal influenza-associated respiratory mortality: a modelling study Lancet 2018 391 10127 1285 300 10.1016/S0140-6736(17)33293-2 29248255
Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet. 2018;391(10127):1285–300.29248255
5. Muller MP McGeer AJ Hassan K Marshall J Christian M Network TIBD Evaluation of pneumonia severity and acute physiology scores to predict ICU admission and mortality in patients hospitalized for influenza PLoS ONE 2010 5 3 e9563 10.1371/journal.pone.0009563 20221431
Muller MP, McGeer AJ, Hassan K, Marshall J, Christian M, Network TIBD. Evaluation of pneumonia severity and acute physiology scores to predict ICU admission and mortality in patients hospitalized for influenza. PLoS ONE. 2010;5(3):e9563.20221431
6. Campbell A Rodin R Kropp R Mao Y Hong Z Vachon J Risk of severe outcomes among patients admitted to hospital with pandemic (H1N1) influenza CMAJ 2010 182 4 349 55 10.1503/cmaj.091823 20159893
Campbell A, Rodin R, Kropp R, Mao Y, Hong Z, Vachon J, et al. Risk of severe outcomes among patients admitted to hospital with pandemic (H1N1) influenza. CMAJ. 2010;182(4):349–55.20159893
7. Taylor G Abdesselam K Pelude L Fernandes R Mitchell R McGeer A Epidemiological features of influenza in Canadian adult intensive care unit patients Epidemiol Infect 2016 144 4 741 50 10.1017/S0950268815002113 26384310
Taylor G, Abdesselam K, Pelude L, Fernandes R, Mitchell R, McGeer A, et al. Epidemiological features of influenza in Canadian adult intensive care unit patients. Epidemiol Infect. 2016;144(4):741–50.26384310
8. Martínez A Soldevila N Romero-Tamarit A Torner N Godoy P Rius C Risk factors associated with severe outcomes in adult hospitalized patients according to influenza type and subtype PLoS ONE 2019 14 1 e0210353 10.1371/journal.pone.0210353 30633778
Martínez A, Soldevila N, Romero-Tamarit A, Torner N, Godoy P, Rius C, et al. Risk factors associated with severe outcomes in adult hospitalized patients according to influenza type and subtype. PLoS ONE. 2019;14(1):e0210353.30633778
9. Catania J Que LG Govert JA Hollingsworth JW Wolfe CR High intensive care unit admission rate for 2013–2014 influenza is associated with a low rate of vaccination Am J Respir Crit Care Med 2014 189 4 485 7 10.1164/rccm.201401-0066LE 24512430
Catania J, Que LG, Govert JA, Hollingsworth JW, Wolfe CR. High intensive care unit admission rate for 2013–2014 influenza is associated with a low rate of vaccination. Am J Respir Crit Care Med. 2014;189(4):485–7.24512430
10. Fezeu L Julia C Henegar A Bitu J Hu FB Grobbee DE Obesity is associated with higher risk of intensive care unit admission and death in influenza A (H1N1) patients: a systematic review and meta-analysis Obes Rev 2011 12 8 653 9 10.1111/j.1467-789X.2011.00864.x 21457180
Fezeu L, Julia C, Henegar A, Bitu J, Hu FB, Grobbee DE, et al. Obesity is associated with higher risk of intensive care unit admission and death in influenza A (H1N1) patients: a systematic review and meta-analysis. Obes Rev. 2011;12(8):653–9.21457180
11. Beumer M Koch R Van Beuningen D OudeLashof A Van de Veerdonk F Kolwijck E Influenza virus and factors that are associated with ICU admission, pulmonary co-infections and ICU mortality J Crit Care 2019 50 59 65 10.1016/j.jcrc.2018.11.013 30481669
Beumer M, Koch R, Van Beuningen D, OudeLashof A, Van de Veerdonk F, Kolwijck E, et al. Influenza virus and factors that are associated with ICU admission, pulmonary co-infections and ICU mortality. J Crit Care. 2019;50:59–65.30481669
12. Elhakim M Rasooly MH Fahim M Ali SS Haddad N Cherkaoui I Epidemiology of severe cases of influenza and other acute respiratory infections in the Eastern Mediterranean Region, July 2016 to June 2018 J Infect Public Health 2020 13 3 423 9 10.1016/j.jiph.2019.06.009 31281105
Elhakim M, Rasooly MH, Fahim M, Ali SS, Haddad N, Cherkaoui I, et al. Epidemiology of severe cases of influenza and other acute respiratory infections in the Eastern Mediterranean Region, July 2016 to June 2018. J Infect Public Health. 2020;13(3):423–9.31281105
13. Al-Baadani AM Elzein FE Alhemyadi SA Khan OA Albenmousa AH Idrees MM Characteristics and outcome of viral pneumonia caused by influenza and Middle East respiratory syndrome-coronavirus infections: a 4-year experience from a tertiary care center Annals Thorac Med 2019 14 3 179 10.4103/atm.ATM_179_18
Al-Baadani AM, Elzein FE, Alhemyadi SA, Khan OA, Albenmousa AH, Idrees MM. Characteristics and outcome of viral pneumonia caused by influenza and Middle East respiratory syndrome-coronavirus infections: a 4-year experience from a tertiary care center. Annals Thorac Med. 2019;14(3):179.
14. Althaqafi A Farahat F Alsaedi A Alshamrani M Alsaeed MS AlhajHussein B Molecular detection of influenza A and B viruses in four consecutive influenza seasons 2015–16 to 2018–19 in a tertiary center in Western Saudi Arabia J Epidemiol Global Health 2021 11 2 208 10.2991/jegh.k.210427.001
Althaqafi A, Farahat F, Alsaedi A, Alshamrani M, Alsaeed MS, AlhajHussein B, et al. Molecular detection of influenza A and B viruses in four consecutive influenza seasons 2015–16 to 2018–19 in a tertiary center in Western Saudi Arabia. J Epidemiol Global Health. 2021;11(2):208.
15. Assiri AM Alsubaie FSF Amer SA Almuteri NAM Ojeil R Dhopte PR The economic burden of viral severe acute respiratory infections in the Kingdom of Saudi Arabia: a nationwide cost-of-illness study IJID Reg 2024 10 80 6 10.1016/j.ijregi.2023.11.016 38173861
Assiri AM, Alsubaie FSF, Amer SA, Almuteri NAM, Ojeil R, Dhopte PR, et al. The economic burden of viral severe acute respiratory infections in the Kingdom of Saudi Arabia: a nationwide cost-of-illness study. IJID Reg. 2024;10:80–6.38173861
16. Al-Qahtani S Al-Dorzi HM Tamim HM Hussain S Fong L Taher S Impact of an intensivist-led multidisciplinary extended rapid response team on hospital-wide cardiopulmonary arrests and mortality Crit Care Med 2013 41 2 506 17 10.1097/CCM.0b013e318271440b 23263618
Al-Qahtani S, Al-Dorzi HM, Tamim HM, Hussain S, Fong L, Taher S, et al. Impact of an intensivist-led multidisciplinary extended rapid response team on hospital-wide cardiopulmonary arrests and mortality. Crit Care Med. 2013;41(2):506–17.23263618
17. Arabi Y Alshimemeri A Taher S Weekend and weeknight admissions have the same outcome of weekday admissions to an intensive care unit with onsite intensivist coverage Crit Care Med 2006 34 3 605 11 10.1097/01.CCM.0000203947.60552.DD 16521254
Arabi Y, Alshimemeri A, Taher S. Weekend and weeknight admissions have the same outcome of weekday admissions to an intensive care unit with onsite intensivist coverage. Crit Care Med. 2006;34(3):605–11.16521254
18. Al-Dorzi HM Aldawood AS Almatrood A Burrows V Naidu B Alchin JD Managing critical care during COVID-19 pandemic: the experience of an ICU of a tertiary care hospital J Infect Public Health 2021 14 11 1635 41 10.1016/j.jiph.2021.09.018 34627058
Al-Dorzi HM, Aldawood AS, Almatrood A, Burrows V, Naidu B, Alchin JD, et al. Managing critical care during COVID-19 pandemic: the experience of an ICU of a tertiary care hospital. J Infect Public Health. 2021;14(11):1635–41.34627058
19. Fine M Auble T Yealy D Hanusa B Weissfeld L Singer D A prediction rule to identify low-risk patients with community-acquired pneumonia N Engl J Med 1997 336 4 243 50 10.1056/NEJM199701233360402 8995086
Fine M, Auble T, Yealy D, Hanusa B, Weissfeld L, Singer D, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336(4):243–50.8995086
20. Lim W Van der Eerden M Laing R Boersma W Karalus N Town G Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study Thorax 2003 58 5 377 82 10.1136/thorax.58.5.377 12728155
Lim W, Van der Eerden M, Laing R, Boersma W, Karalus N, Town G, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377–82.12728155
21. Kellum JA Lameire N Aspelin P Barsoum RS Burdmann EA Goldstein SL Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury Kidney Int Supplements 2012 2 1 1 138
Kellum JA, Lameire N, Aspelin P, Barsoum RS, Burdmann EA, Goldstein SL, et al. Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Supplements. 2012;2(1):1–138.
22. Cobbin JC, Alfelali M, Barasheed O, Taylor J, Dwyer DE, Kok J, et al. Multiple sources of genetic diversity of influenza A viruses during the Hajj. J Virol. 2017;91(11). https://journals.asm.org/doi/10.1128/jvi.00096-17.
23. Li Y Reeves RM Wang X Bassat Q Brooks WA Cohen C Global patterns in monthly activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus: a systematic analysis Lancet Global Health 2019 7 8 e1031 45 10.1016/S2214-109X(19)30264-5 31303294
Li Y, Reeves RM, Wang X, Bassat Q, Brooks WA, Cohen C, et al. Global patterns in monthly activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus: a systematic analysis. Lancet Global Health. 2019;7(8):e1031–45.31303294
24. Olsen SJ. Decreased influenza activity during the covid-19 pandemic—United States, Australia, Chile, and South Africa, 2020. MMWR Morbidity and mortality weekly report. 2020;69.
25. Bonacina F Boëlle P-Y Colizza V Lopez O Thomas M Poletto C Global patterns and drivers of influenza decline during the COVID-19 pandemic Int J Infect Dis 2023 128 132 9 10.1016/j.ijid.2022.12.042 36608787
Bonacina F, Boëlle P-Y, Colizza V, Lopez O, Thomas M, Poletto C. Global patterns and drivers of influenza decline during the COVID-19 pandemic. Int J Infect Dis. 2023;128:132–9.36608787
26. Lampros A Talla C Diarra M Tall B Sagne S Diallo MK Shifting patterns of influenza circulation during the COVID-19 pandemic, Senegal Emerg Infect Dis 2023 29 9 1808 10.3201/eid2909.230307 37610149
Lampros A, Talla C, Diarra M, Tall B, Sagne S, Diallo MK, et al. Shifting patterns of influenza circulation during the COVID-19 pandemic, Senegal. Emerg Infect Dis. 2023;29(9):1808.37610149
27. Kim M-C Kweon OJ Lim YK Choi S-H Chung J-W Lee M-K Impact of social distancing on the spread of common respiratory viruses during the coronavirus disease outbreak PLoS ONE 2021 16 6 e0252963 10.1371/journal.pone.0252963 34125839
Kim M-C, Kweon OJ, Lim YK, Choi S-H, Chung J-W, Lee M-K. Impact of social distancing on the spread of common respiratory viruses during the coronavirus disease outbreak. PLoS ONE. 2021;16(6):e0252963.34125839
28. Cauchemez S Valleron A-J Boelle P-Y Flahault A Ferguson NM Estimating the impact of school closure on influenza transmission from Sentinel data Nature 2008 452 7188 750 4 10.1038/nature06732 18401408
Cauchemez S, Valleron A-J, Boelle P-Y, Flahault A, Ferguson NM. Estimating the impact of school closure on influenza transmission from Sentinel data. Nature. 2008;452(7188):750–4.18401408
29. Rizvi RF Craig KJT Hekmat R Reyes F South B Rosario B Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: a rapid evidence-based review and meta-analysis SAGE Open Med 2021 9 20503121211022973 10.1177/20503121211022973 34164126
Rizvi RF, Craig KJT, Hekmat R, Reyes F, South B, Rosario B, et al. Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: a rapid evidence-based review and meta-analysis. SAGE Open Med. 2021;9:20503121211022973.34164126
30. Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza a virus: a clinical data analysis and experimental infection study Lancet Microbe 2020 1 6 e254 62 10.1016/S2666-5247(20)30114-2 33103132
Wu A, Mihaylova VT, Landry ML, Foxman EF. Interference between rhinovirus and influenza a virus: a clinical data analysis and experimental infection study. Lancet Microbe. 2020;1(6):e254–62.33103132
31. Schultz-Cherry S. Viral interference: the case of influenza viruses. Oxford University Press; 2015. pp. 1690–1.
32. Rice TW Rubinson L Uyeki TM Vaughn FL John BB Miller RR III Critical illness from 2009 pandemic influenza a virus and bacterial coinfection in the United States Crit Care Med 2012 40 5 1487 98 10.1097/CCM.0b013e3182416f23 22511131
Rice TW, Rubinson L, Uyeki TM, Vaughn FL, John BB, Miller RR III, et al. Critical illness from 2009 pandemic influenza a virus and bacterial coinfection in the United States. Crit Care Med. 2012;40(5):1487–98.22511131
33. Shah NS Greenberg JA McNulty MC Gregg KS Riddell IVJ Mangino JE Bacterial and viral co-infections complicating severe influenza: incidence and impact among 507 US patients, 2013–14 J Clin Virol 2016 80 12 9 10.1016/j.jcv.2016.04.008 27130980
Shah NS, Greenberg JA, McNulty MC, Gregg KS, Riddell IVJ, Mangino JE, et al. Bacterial and viral co-infections complicating severe influenza: incidence and impact among 507 US patients, 2013–14. J Clin Virol. 2016;80:12–9.27130980
34. Bartley PS Deshpande A Yu P-C Klompas M Haessler SD Imrey PB Bacterial coinfection in influenza pneumonia: Rates, pathogens, and outcomes Infect Control Hosp Epidemiol 2022 43 2 212 7 10.1017/ice.2021.96 33890558
Bartley PS, Deshpande A, Yu P-C, Klompas M, Haessler SD, Imrey PB, et al. Bacterial coinfection in influenza pneumonia: Rates, pathogens, and outcomes. Infect Control Hosp Epidemiol. 2022;43(2):212–7.33890558
35. Caini S Kroneman M Wiegers T El Guerche-Séblain C Paget J Clinical characteristics and severity of influenza infections by virus type, subtype, and lineage: a systematic literature review Influenza Other Respir Viruses 2018 12 6 780 92 10.1111/irv.12575 29858537
Caini S, Kroneman M, Wiegers T, El Guerche-Séblain C, Paget J. Clinical characteristics and severity of influenza infections by virus type, subtype, and lineage: a systematic literature review. Influenza Other Respir Viruses. 2018;12(6):780–92.29858537
36. Shah NS Greenberg JA McNulty MC Gregg KS Riddell J Mangino JE Severe influenza in 33 US hospitals, 2013–2014: complications and risk factors for death in 507 patients Infect Control Hosp Epidemiol 2015 36 11 1251 60 10.1017/ice.2015.170 26224364
Shah NS, Greenberg JA, McNulty MC, Gregg KS, Riddell J, Mangino JE, et al. Severe influenza in 33 US hospitals, 2013–2014: complications and risk factors for death in 507 patients. Infect Control Hosp Epidemiol. 2015;36(11):1251–60.26224364
37. Adlhoch C, Gomes Dias J, Bonmarin I, Hubert B, Larrauri A, Oliva Domínguez JA, et al. editors. Determinants of fatal outcome in patients admitted to intensive care units with influenza, European Union 2009–2017. Oxford University Press US; 2019. Open forum infectious diseases.
38. Ni Y-N Chen G Sun J Liang B-M Liang Z-A The effect of corticosteroids on mortality of patients with influenza pneumonia: a systematic review and meta-analysis Crit Care 2019 23 1 9 10.1186/s13054-019-2395-8 30606235
Ni Y-N, Chen G, Sun J, Liang B-M, Liang Z-A. The effect of corticosteroids on mortality of patients with influenza pneumonia: a systematic review and meta-analysis. Crit Care. 2019;23:1–9.30606235
39. Eshima N Tokumaru O Hara S Bacal K Korematsu S Tabata M Sex-and age-related differences in morbidity rates of 2009 pandemic influenza A H1N1 virus of swine origin in Japan PLoS ONE 2011 6 4 e19409 10.1371/journal.pone.0019409 21559366
Eshima N, Tokumaru O, Hara S, Bacal K, Korematsu S, Tabata M, et al. Sex-and age-related differences in morbidity rates of 2009 pandemic influenza A H1N1 virus of swine origin in Japan. PLoS ONE. 2011;6(4):e19409.21559366
40. Bonmarin I Belchior E Bergounioux J Brun-Buisson C Mégarbane B Chappert JL Intensive care unit surveillance of influenza infection in France: the 2009/10 pandemic and the three subsequent seasons Eurosurveillance 2015 20 46 30066 10.2807/1560-7917.ES.2015.20.46.30066
Bonmarin I, Belchior E, Bergounioux J, Brun-Buisson C, Mégarbane B, Chappert JL, et al. Intensive care unit surveillance of influenza infection in France: the 2009/10 pandemic and the three subsequent seasons. Eurosurveillance. 2015;20(46):30066.
41. Jin S Li J Cai R Wang X Gu Z Yu H Age-and sex-specific excess mortality associated with influenza in Shanghai, China, 2010–2015 Int J Infect Dis 2020 98 382 9 10.1016/j.ijid.2020.07.012 32663600
Jin S, Li J, Cai R, Wang X, Gu Z, Yu H, et al. Age-and sex-specific excess mortality associated with influenza in Shanghai, China, 2010–2015. Int J Infect Dis. 2020;98:382–9.32663600
42. Wang X-L Yang L Chan K-H Chan K-P Cao P-H Lau EH-Y Age and sex differences in rates of influenza-associated hospitalizations in Hong Kong Am J Epidemiol 2015 182 4 335 44 10.1093/aje/kwv068 26219977
Wang X-L, Yang L, Chan K-H, Chan K-P, Cao P-H, Lau EH-Y, et al. Age and sex differences in rates of influenza-associated hospitalizations in Hong Kong. Am J Epidemiol. 2015;182(4):335–44.26219977
43. Hoffmann J Otte A Thiele S Lotter H Shu Y Gabriel G Sex differences in H7N9 influenza a virus pathogenesis Vaccine 2015 33 49 6949 54 10.1016/j.vaccine.2015.08.044 26319064
Hoffmann J, Otte A, Thiele S, Lotter H, Shu Y, Gabriel G. Sex differences in H7N9 influenza a virus pathogenesis. Vaccine. 2015;33(49):6949–54.26319064
44. Silveyra P Fuentes N Rodriguez Bauza DE Sex and gender differences in lung disease. Lung inflammation in Health and Disease 2021 Volume II Springer 227 58
Silveyra P, Fuentes N, Rodriguez Bauza DE. Sex and gender differences in lung disease. Lung inflammation in Health and Disease. Volume II: Springer; 2021. pp. 227–58.
45. Klein SL Hodgson A Robinson DP Mechanisms of sex disparities in influenza pathogenesis J Leukoc Biol 2012 92 1 67 73 10.1189/jlb.0811427 22131346
Klein SL, Hodgson A, Robinson DP. Mechanisms of sex disparities in influenza pathogenesis. J Leukoc Biol. 2012;92(1):67–73.22131346
46. Chaves SS, Nealon J, Burkart KG, Modin D, Biering-Sørensen T, Ortiz JR et al. Global, regional and national estimates of influenza-attributable ischemic heart disease mortality. EClinicalMedicine. 2023:55.
47. Chen L Han X Li Y Zhang C Xing X The severity and risk factors for mortality in immunocompromised adult patients hospitalized with influenza-related pneumonia Ann Clin Microbiol Antimicrob 2021 20 1 55 10.1186/s12941-021-00462-7 34429126
Chen L, Han X, Li Y, Zhang C, Xing X. The severity and risk factors for mortality in immunocompromised adult patients hospitalized with influenza-related pneumonia. Ann Clin Microbiol Antimicrob. 2021;20(1):55.34429126
48. Arranz-Herrero J, Presa J, Rius-Rocabert S, Utrero-Rico A, Arranz-Arija JÁ, Lalueza A et al. Determinants of poor clinical outcome in patients with influenza pneumonia: a systematic review and meta-analysis. Int J Infect Dis. 2023.
