Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

Introduction: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources

Building and revolutionising public healthcare: A living ecosystem to link and improve patient health data and outcomes in a Brazilian hospital

Objectives To develop a Brazilian public hospital, Sao Paulo University Medical School Clinics Hospital, HCFMUSP, informational model to link and improve multiple patients' health data, care pathways and outcomes, to build a living real world ecosystem aiming to subsidize policy decision-making, support research and promote patients' engagement and involvement. Methods Policy-relevant linkage data including demography, diagnostics, outpatient and emergency room visits, hospitalizations, intensive care evolution, assisted mechanical ventilation or special equipment’s uses, electronic prescriptions, imaging and clinical laboratory tests results, surgery records, blood components use, and medical and multidisciplinary teams’ evolutions. Telemedicine-based hub developed for patient’s access to his own visits or procedures schedule, comprehensive data and results temporal series, and specific communications channels. Anonymized data sharing for Sao Paulo State Health Secretariat policy decision-making and SP Research Agency multicenter Data Lake for Covid-19 pandemic research. Stratified impact and economic analysis regarding clinical and co-morbid conditions research were published. Results Since March 2020, this informational model example comprises over 10,000 Covid-19 patient’s related data with more than 100,000 events registered. During the first pandemic trimester, upon SP Health Secretariat policy, the HCFMUSP Central Institute’s 900 ward and 300 ICU beds were the SP central reference for severe and critical admissions. In this first evaluation 88.4% had co-morbidities (e.g. 48.1% hypertension, 30.5% diabetes), 51.7% required ICU admission and 28.9% died. Average hospital length of stay was 10.7 days, mean cost per admission was US$12,637.42, and the overall daily cost was US$919.24. Age strata >69 years confirmed COVID-19, ICU, elevated C-reactive protein (inflammation) adjusted by D-dimer levels (thrombosis biomarker), higher mSOFA, mechanical ventilation, dialysis, surgery and comorbidities, remained significantly associated with higher (24%-200%) costs and poorer outcomes. Conclusion The informational model is proving to be beneficial for all stakeholders. Technology-based organized systems increased management accuracy and efficiency, emergency preparedness, facilitates patient’s involvement and participation, promote medical and multi-professionals teams’ knowledge development, and permits to subsidize policy decisions and to improve public health

Timing to Intubation COVID-19 Patients: Can We Put It Off until Tomorrow?

Background: The decision to intubate COVID-19 patients receiving non-invasive respiratory support is challenging, requiring a fine balance between early intubation and risks of invasive mechanical ventilation versus the adverse effects of delaying intubation. This present study analyzes the association between intubation day and mortality in COVID-19 patients. Methods: We performed a unicentric retrospective cohort study considering all COVID-19 patients consecutively admitted between March 2020 and August 2020 requiring invasive mechanical ventilation. The primary outcome was all-cause mortality within 28 days after intubation, and a Cox model was used to evaluate the effect of time from onset of symptoms to intubation in mortality. Results: A total of 592 (20%) patients of 3020 admitted with COVID-19 were intubated during study period, and 310 patients who were intubated deceased 28 days after intubation. Each additional day between the onset of symptoms and intubation was significantly associated with higher in-hospital death (adjusted hazard ratio, 1.018; 95% CI, 1.005–1.03). Conclusion: Among patients infected with SARS-CoV-2 who were intubated and mechanically ventilated, delaying intubation in the course of symptoms may be associated with higher mortality. Trial registration: The study protocol was approved by the local Ethics Committee (opinion number 3.990.817; CAAE: 30417520.0.0000.0068)

Distinct Outcomes in COVID-19 Patients with Positive or Negative RT-PCR Test

Identification of the SARS-CoV-2 virus by RT-PCR from a nasopharyngeal swab sample is a common test for diagnosing COVID-19. However, some patients present clinical, laboratorial, and radiological evidence of COVID-19 infection with negative RT-PCR result(s). Thus, we assessed whether positive results were associated with intubation and mortality. This study was conducted in a Brazilian tertiary hospital from March to August of 2020. All patients had clinical, laboratory, and radiological diagnosis of COVID-19. They were divided into two groups: positive (+) RT-PCR group, with 2292 participants, and negative (−) RT-PCR group, with 706 participants. Patients with negative RT-PCR testing and an alternative most probable diagnosis were excluded from the study. The RT-PCR(+) group presented increased risk of intensive care unit (ICU) admission, mechanical ventilation, length of hospital stay, and 28-day mortality, when compared to the RT-PCR(−) group. A positive SARS-CoV-2 RT-PCR result was independently associated with intubation and 28 day in-hospital mortality. Accordingly, we concluded that patients with a COVID-19 diagnosis based on clinical data, despite a negative RT-PCR test from nasopharyngeal samples, presented more favorable outcomes than patients with positive RT-PCR test(s)

Data_Sheet_1_Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic.PDF

IntroductionThe COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.MethodsAt the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.ResultsOver the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020–2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.DiscussionResearch is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.</p