Predictive Models for Intensive Care Admission and Death of COVID-19

Development and Validation of Predictive Models for Intensive Care Admission and Death of COVID-19 Patients in a Secondary Care Hospital in Belgium.

To build simple and reliable predictive scores for intensive care admissions and deaths in COVID19 patients. These scores adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guidelines.

The outcomes of the study are (i) admission in the Intensive Care Unit admission and (ii) death.

All patients admitted in the Emergency Department with a positive reverse transcription-polymerase chain reaction SARS-COV2 test were included in the study. Routine clinical and laboratory data were collected at their admission and during their stay. Chest X-Rays and CT-Scans were performed and analyzed by a senior radiologist.

Generalized Linear Models using a binomial distribution with a logit link function (R software version X) were used to develop predictive scores for (i) admission to ICU among emergency ward patients; (ii) death among ICU patients. A first panel of Number Models with the highest AIC (BIC) was preselected. Ten-fold cross-validation was then used to estimate the out-of-sample prediction error among these preselected models. The one with the smallest prediction error was in the end singled out .

Study Overview

• Status: Unknown
• Conditions: Pneumonia, Viral; Inflammatory Response; COVID19
• Intervention / Treatment: Other: predict admission of covid-19 patients to ICU and death with routine and quickly avalaible clinical, biological and radiological variables?

• Study Type: Observational
• Enrollment (Actual): 60

Contacts and Locations

Study Locations

• Belgium
  • Brabant Wallon
    • Ottignies, Brabant Wallon, Belgium, 1340
      • Clinique Saint-Pierre

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

all patient with COVID 19 with viral pneumonia

Description

Inclusion Criteria:

• RT-PCR + SARS Cov2 pneumonia

Exclusion Criteria:

• < 18 ans -* GOLD 3 or 4 CPOD

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
covid ICU patients	Other: predict admission of covid-19 patients to ICU and death with routine and quickly avalaible clinical, biological and radiological variables?; patients were questioned about their usual medication (Sartan, angiotensin-converting enzyme inhibitors, nonsteroidal anti-inflammatory drug, immunosuppressive drugs), their health condition (diabetes, hypertension, tobacco use, mental status). The Body mass index was computed. Age, gender, caucasian/african, weight, body mass index, number of days with symptoms before hospitalization, asthenia, pyrexia, dyspnea, chest pain, digestive sign, anosmia, ageusia, confusion, Travel or contact < one month, cigarette consumption ,hypertension, diabetes, mental status, angiotensin-converting-enzyme inhibitors, Sartan, non-steroidal anti-inflammatory drugs, immunosuppressive drugs, SpO2,Thoracic Computerized Tomography : % of lung injury, Thoracic Computerized Tomography : density of lung injury, blood type, white blood cells, neutrophils, lymphocytes, blood platelets, fibrinogen, ferritin, triglycerides, LDH, troponin, CRP. The dates of admission to ICU and death were recorded
covid conventionnal ward	Other: predict admission of covid-19 patients to ICU and death with routine and quickly avalaible clinical, biological and radiological variables?; patients were questioned about their usual medication (Sartan, angiotensin-converting enzyme inhibitors, nonsteroidal anti-inflammatory drug, immunosuppressive drugs), their health condition (diabetes, hypertension, tobacco use, mental status). The Body mass index was computed. Age, gender, caucasian/african, weight, body mass index, number of days with symptoms before hospitalization, asthenia, pyrexia, dyspnea, chest pain, digestive sign, anosmia, ageusia, confusion, Travel or contact < one month, cigarette consumption ,hypertension, diabetes, mental status, angiotensin-converting-enzyme inhibitors, Sartan, non-steroidal anti-inflammatory drugs, immunosuppressive drugs, SpO2,Thoracic Computerized Tomography : % of lung injury, Thoracic Computerized Tomography : density of lung injury, blood type, white blood cells, neutrophils, lymphocytes, blood platelets, fibrinogen, ferritin, triglycerides, LDH, troponin, CRP. The dates of admission to ICU and death were recorded

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
admission to ICU	through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure	Time Frame
death	through study completion, an average of 1 year

Collaborators and Investigators

• Sponsor: Clinique Saint Pierre Ottignies

Publications and helpful links

General Publications

• Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, Schluger NW, Volpi A, Yim JJ, Martin IBK, Anderson DJ, Kong C, Altes T, Bush A, Desai SR, Goldin J, Goo JM, Humbert M, Inoue Y, Kauczor HU, Luo F, Mazzone PJ, Prokop M, Remy-Jardin M, Richeldi L, Schaefer-Prokop CM, Tomiyama N, Wells AU, Leung AN. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society. Chest. 2020 Jul;158(1):106-116. doi: 10.1016/j.chest.2020.04.003. Epub 2020 Apr 7.
• Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020 Apr 7;369:m1328. doi: 10.1136/bmj.m1328. Erratum In: BMJ. 2020 Jun 3;369:m2204.
• Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015 Jan 6;162(1):55-63. doi: 10.7326/M14-0697. Erratum In: Ann Intern Med. 2015 Apr 21;162(8):600.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2020
• Primary Completion (Actual): May 1, 2020
• Study Completion (Anticipated): January 1, 2021

Study Registration Dates

• First Submitted: May 21, 2020
• First Submitted That Met QC Criteria: May 22, 2020
• First Posted (Actual): May 26, 2020

Study Record Updates

• Last Update Posted (Actual): May 26, 2020
• Last Update Submitted That Met QC Criteria: May 22, 2020
• Last Verified: May 1, 2020

More Information

Terms related to this study

• Keywords: COVID19, prediction scores, ICU admission, death, TRIPOD.
• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia; Lung Diseases; COVID-19; Pneumonia, Viral
• Other Study ID Numbers: 0410508057

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No