Study design and population
This was a retrospective, single center cohort study in the Maasstad Medical Centre, which is the largest (600-bed) non-academic teaching hospital in the Rotterdam area, the Netherlands. This study was approved by the Hospitals Medical Ethical Committee, application number W20.081.
We conducted a study amongst patients older than 18 years who were admitted with a COVID-19 infection between the 9th of March 2020 and 1th of May 2020 and were admitted to the hospital with severe respiratory insufficiency. Laboratory confirmation of SARS-CoV-2 was done using PCR techniques on a nasopharyngeal swab with a Roche analyzer on admission.
Mechanical ventilation in the Intensive Care Unit was declined in a portion of the admitted patients because of clinical frailty status, performance status or history of disease (e.g. severe cardiopulmonary conditions). These decisions were made in accordance with the patient and his or her relatives and were discussed on a daily basis in a multidisciplinary team together with Critical Care, Internal Medicine, Pulmonology and Palliative Care Specialists. During these meetings, performance scores, comorbidities, cognitive functioning and frailty were used to make a considered decision. When there was unanimous agreement that patients were too frail for mechanical ventilation, HFNC was the only alternative treatment. We started HFNC with a flow of 60 liter per minute with a temperature of 37C and titrated oxygen fraction based on oxygen saturation (spO2 >92%) and respiratory rate (<25 per min).
Data was collected using SQL Server Management Studio version 18.3 from our electronic patient record Chipsoft: Healthcare Information X-change. A first check was performed by the hospital’s data manager (GW) after automatic extraction. A final check on the database was then performed by the two principal investigators (JvS and MvH).
Demographics, Medical history and Drug use
Demographic data and medical history were extracted from the medical record. The following demographic data were extracted. Age, gender, date of admission, days of hospital stay, body mass index (BMI) and survival. Comorbidities that were scored, were hypertension (defined as the use of antihypertensives), diabetes mellitus type 2 (defined as fasting plasma glucose level ≥ 6.1 mmol/L), asthma (bronchodilator use and spirometry with reversibility), chronic obstructive pulmonary disease (COPD Gold classification), smoking (current or former), chronic kidney disease (eGFR <60ml/min/1,73 m2), malignancy (history of malignant neoplasm), occlusive peripheral arterial disease, ischemic heart disease (defined as obstructive coronary artery disease), non-ischemic heart disease, liver disease (radiological or pathological steatosis or cirrhosis) and the total number of comorbidities. Furthermore, out of hospital drug use (immunosuppressive drugs, ACE inhibitors and non-steroidal drugs) and in hospital drugs were scored (cefuroxime and azithromycin).
Signs and clinical parameters
Clinical parameters were extracted from the medical record for every patient on admission, these include pulse rate, blood pressure, temperature, respiratory rate and oxygen saturation. Patients were asked if, and since when they had the following symptoms: cough, dyspnea, weight loss, diarrhea and nausea.
Frailty assessment
For clinical frailty assessment we used the Clinical Frailty Score [10] ranging from 1 (very fit) to 9 (terminally ill). In addition WHO performance status score was extracted from the medical record ranging from 1 (fully active) to 4 (completely disabled) [11].
Laboratory values and Radiological findings
For each patient blood examinations including hemoglobin, leukocyte count with differential count, platelet count, d-dimer, alanine transaminase (ALAT), aspartate transaminase (ASAT), creatine kinase (CK), lactate dehydrogenase (LDH), troponin T and ferritin levels were collected. We choose for these specific laboratory measures as most of them have already proved to be discriminating between mild and severe clinical courses [5]. Chest X-ray was performed on admission in every patient. If there was a high clinical suspicion on pulmonary embolism with elevated d-dimer levels in absence of pulmonary infiltrates on chest x-ray, an additional computed tomography (CT) pulmonary angiogram was performed.
HFNC
We registered days since admission before the start of HFNC. Furthermore, flow (liters per minute), fraction of inspired oxygen (FiO2) and reasons for failure of HFNC therapy were extracted from the medical record. FiO2 increase of ten percent in the first 24 hours were scored separately.
Outcome
The primary end-point of our study was survival on the hospital ward in patients who used HFNC. Furthermore, we checked for determinants associated with mortality during HFNC use.
Definitions
Fever was considered as a tympanic temperature lower than 36 or greater than 38 degrees Celsius. Patients were considered infected if they were proven positive for SARS-CoV-2 using a nasopharyngeal PCR test.
Respiratory failure was defined as persisting hypoxemia despite maximum conventional oxygen administration e.g. saturation (SpO2) lower than 92% despite maximum oxygen administration with a non-rebreathing mask (15 liters per minute). To assess the severity of Acute Respiratory Distress Syndrome (ARDS) SpO2/FiO2 ratios were calculated, as it was impossible to collect PO2/FiO2 ratios for all patients and because SpO2/FiO2 can be considered equally sensitive and specific as compared to PO2/FiO2 ratios [12].
Statistical analysis
Baseline data is presented as median (IQR) or n (%) were appropriate. In order to compare differences between survivors and non-survivors, we used Mann Whitney U test for continuous data and Fisher’s exact test for categorical data. P-values <0.05 were considered to be statistically significant. All statistical analysis were performed using R Studio version 3.6.3. Variables with clinical relevance and in between group differences were used in a univariate logistic regression model to calculate odds ratios in order to assess factors associated with in hospital mortality. No multivariate analysis was performed because of the size of cohort leading to high risk of overfitting the model.