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Software to evaluate clinical, epidemiological data and chest computed tomography to predict which patients with COVID-19 will develop a severe form of the disease.

Machine Learning model to predict the prognosis and severity by computed tomography and clinical-epidemiological correlation in COVID-19 patients.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
REBEC
Registry ID
RBR-7dsxsv
Enrollment
Unknown
Registered
2020-08-04
Start date
2020-03-02
Completion date
Unknown
Last updated
2025-10-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Coronavirus infection, unspecified. Shock, unspecified. Septicaemia, unspecified. Adult respiratory distress syndrome. Acute renal failure

Interventions

A group of 500 hospitalized patients suspected of having COVID-19 will be monitored for clinical, laboratory and imaging data (chest tomography) throughout their hospitalization, until discharge or de
Other
R76.9

Sponsors

Diagnósticos da América Sociedade Anônima (DASA)
Lead Sponsor
Hospital Alemão Oswaldo Cruz
Collaborator

Eligibility

Inclusion criteria

Inclusion criteria: Signs and symptoms of acute respiratory syndrome. Positive epidemiological history for COVID-19, which may include recent contact (last 14 days) with a confirmed or suspected case, recent trip (last 14 days) to a high-incidence location, or presentation of symptoms after the start of the community transmission phase of SARS-CoV-2 (after 20/03/2020) when the date of hospitalization. Have performed, when symptomatic, a chest computed tomography.

Exclusion criteria

Exclusion criteria: Presence of neoplastic (primary or metastatic) lung lesions, manifested as nodules, masses, consolidations, septal thickening (lymphatic carcinomatosis) or pleural thickening. Chest computed tomography with the presence of movement, acquisition or reconstruction artifacts that make it impossible to apply the segmentation algorithms. Computed tomography exams with low quality pulmonary segmentation, or cut slice thickness greater than 3.0 mm.

Design outcomes

Primary

MeasureTime frame
1) Time to hospital discharge (length of stay, LOS), defined as the period (in days) between the date of admission and the date of discharge (or death).;2) Length of stay in the ICU (ICU LOS), defined as the period (in days) elapsed between admission and discharge (or death) from the ICU. ;3) Orotracheal intubation due to acute respiratory failure. ;4) Development of Acute Respiratory Discomfort Syndrome: defined as Acute respiratory failure with acute bilateral opacities on radiographys or CT not fully attributable to pleural effusions, pulmonary congestion, atelectasis or nodules, within one week after installation the triggering injury and, when signs suggestive of edema are present, may not be completely attributable to cardiac dysfunction or fluid overload.

Secondary

MeasureTime frame
1) Sepsis: defined as the systemic response to an infectious disease (probable or confirmed), whether caused by bacteria, viruses, fungi or protozoa (old definition), or an unregulated response to infection leading to organ dysfunction (current definition). ;2) Hypotension or cardiocirculatory dysfunction requiring the prescription of vasopressors or inotropes. ;3) Coagulopathy. ;4) Acute Myocardial Infarction. ;5) Acute Renal Insufficiency: Increase in serum creatinine greater than or equal to 0.3 mg / dl (26.5 lmol / l), in 48h or increase in serum creatinine greater than 1.5 from baseline levels that may have occurred in previous seven days and a 6-hour urine volume less than 0.5 ml / kg / h. ;6) Death.

Countries

Brazil

Contacts

Public ContactFelipe Kitamura

Dasa

kitamura.felipe@gmail.com+5511979630032

Outcome results

None listed

Source: REBEC (via WHO ICTRP)