Study design and databases
We performed a retrospective analysis of individuals with SARS throughout the first peak of the COVID-19 pandemic in Manaus, Brazil, from January to June 2020. We analyzed data from two different databases: the EMS database and the Brazilian Health Surveillance System database (SIVEP-Gripe) from Manaus, which consists of data from patients admitted to hospital with SARS or whose death was due to SARS, regardless of hospital admission.
The local EMS (SAMU Manaus) database consists of electronically stored medical records from pre-hospital emergency care. All emergency calls reach a medical coordination center and are entirely recorded according to Brazilian legislation5. Times (patient time of call, time from dispatch to site arrival, time to hospital arrival, etc.) are registered in detail. The data obtained include address information, a first medical interview, radio dispatch order, return call after local assessment by the ambulance team, final medical guidance/prescription, and decision on hospital destination. When necessary, paper-based medical records filled by the ambulance teams are also available, as a complement to the electronic medical records.
The SIVEP-Gripe, which was developed by the Ministry of Health of Brazil, is a system designed for the collection, storage, and analysis of data related to the epidemiological surveillance of influenza and other respiratory viruses. Its main objective is to monitor and analyze flu cases, identifying patterns and trends to enhance the response to acute respiratory diseases. Access to the SIVEP-Gripe database is typically restricted and controlled to ensure the confidentiality of information and compliance with ethical and safety standards.
To access both databases, specific procedures established by health authorities must be followed, such as requesting authorization, meeting ethical and safety requirements, and, in some cases, entering into confidentiality agreements. The availability of access may vary depending on the purpose of the research and the access control policies established by the responsible institutions.
SAMU Database: Occurrence; name; sex; age; date_time; dispatch_time; Action; destination; destination_name; reason; type; nature; neighborhood; arrival_time; dispatch_time; transport; cancel_type; municipal_zone; service_delta; service_month; attended; epi_date. Sivep—Influenza Database: notification_date; symptom_start_date Unit; name; sex; age_in_years; neighborhood_name; fever; cough; sore_throat; dyspnea; respiratory_discomfort; saturation; diarrhea; vomiting; risk factor; puerpera; cardiopathy; other disease; asthma; diabetes; pneumopathy; renal; obesity; other morbidity; vaccine; vaccine dose date; mother vaccine; mother vaccine date; breastfeeding; single dose date; first dose date; second dose date; antiviral; antiviral type; other antiviral; antiviral date; hospitalized; hospitalization date; hospital unit name; ICU; ICU entry date; ICU exit date; ventilatory support; sample; collection date; PCR result; PCR date; final classification; other classification; criterion; evolution; evolution date; closure date; observation; analysis result date; analysis result; COVID vaccine; first COVID dose; second COVID dose; booster dose.
The study was approved by the local IRB Committee, which is named CEP (Comitê de Ética em Pesquisa) da Fundação de Medicina Tropical Dr Heitor Vieira Dourado, with Certificate of Presentation of Ethical Appreciation No. 5 60491122.3.0000.0005, which waived the need for informed consent due to the retrospective nature of the study. All stages of the research were performed following the Declaration of Helsinki.
Study location and participants
All EMS calls occurring during the period of the study in Manaus, Brazil, were analyzed. Trained EMS physicians assess every call and classify the request into different syndromic diagnoses before dispatching emergency vehicles when indicated. All patients whose chief complaints were compatible with SARS in the period of the study were included. SARS was assigned to patients presenting with acute respiratory syndrome (at least two of the following: fever, chills, sore throat, headache, cough, coryza, taste/smell disorder plus at least one of the following: dyspnea, chest pressure, cyanosis, or pulse oximetry < 95% in room air), as defined by the SAMU triage system.
Those in need of medical assistance were transported by the prehospital EMS to a hospital emergency department and were eventually included in the second database, with their in-hospital data. Name, gender, age, home address, and date of system input were used to match patients’ details from the two different databases.
The patient outcome was evaluated in two moments, prehospital and in-hospital. Data obtained in the prehospital setting were age, gender, address, hospital destination, and clinical variables such as blood pressure, body temperature, pulse oximetry (first reading, while breathing room air), heart rate, and respiratory rate. Blood pressure and heart rate values obtained during the initial assessment were used to calculate the shock index (heart rate / systolic blood pressure) and the modified shock index (heart rate / mean arterial pressure5.
We also examined the association between the prehospital clinical variables and the following in-hospital outcomes: length of hospital stay, length of stay in the intensive care unit (ICU), need for mechanical ventilation, and death. Data for these outcomes were extracted from the second database, which included in-hospital care information.
Data processing and statistical analysis
The original SAMU database contained 45,780 records. Following the application of filters, 352 were excluded due to a lack of name, 8251 for missing information, 1546 due to a lack of age, 157 owing to time discrepancies, and 1675 were canceled records, resulting in a filtered SAMU dataset with 33,799 records. The original SIVEP-Gripe dataset comprised 28,112 records. After filtering, 15,718 were removed due to data outside the study period, resulting in a filtered SIVEP dataset with 12,394 records.
The data linkage was performed based on variables that were present in both databases, and the “Levenshtein” method was employed for string comparison. Linked record pairs were obtained by applying a pre-specified minimum threshold, in this case, a 100% match. The time difference between linked records was calculated using the linkage date for EMS and the notification date for SIVEP. Records with missing values were excluded. Those records with a time difference of less than 30 days between the EMS record and SIVEP were retained. After merging both databases, only variables with full completeness were used for outcome analysis.
For data presentation, demographic and clinical data from the EMS database were presented in terms of percentages and means with respective deviations or medians accompanied by their interquartile ranges, depending on data distribution, which was calculated using the Shapiro–Wilk test. For the comparison of proportions, chi-square or Fisher’s exact tests were used. For comparison of means, the T and median tests were performed and, when applicable, the Wilcoxon Mann–Whitney test or the Kruskal Wallis test. The linear relationship between two quantitative variables was calculated with the Pearson correlation coefficient. A multivariate model was carried out to analyze the relationship between the variables in addition to the correlations between clinical data such as mechanical ventilation, admission to the ICU, and those who were discharged/deceased. All analyses were performed using the Stata 16.0 software. The R software (version 4.3.0) was used to merge the EMS and the SIVEP-Gripe databases.
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