RU2846792C1 - Method for prediction of clinical outcome of acute covid-19 infection with moderate, severe and extremely severe clinical course - Google Patents

Abstract

FIELD: medical science.

SUBSTANCE: invention refers to medicine, namely to clinical and laboratory diagnostics, intensive therapy, and can be used for the outcome prediction in the patients with moderate, severe and extremely severe acute COVID-19 infection. Degree of lung damage according to computed tomography, heart rate, creatinine and HIF-1α levels are determined. Derived data are used to calculate a probability (P) of the lethal outcome by formulas. If P is less than 0.42, a favourable outcome of the disease associated with the patient's recovery is predicted, and if P is greater than 0.42, an unfavourable outcome of the disease associated with the death of the patient is predicted.

EFFECT: method enables detecting a group of patients with a high risk of the unfavourable outcome of the disease, correcting the therapeutic approach and thereby reducing the risk of the lethal outcome by assessing a set of the most significant indicators.

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Description

The invention relates to the field of medicine, namely to clinical and laboratory diagnostics, intensive care, and can be used to predict the outcome of patients with moderate, severe, and extremely severe acute COVID-19 infection.

As of September 2024, more than 776 million confirmed cases of COVID-19 infection and more than 7 million deaths have been registered worldwide [1]. Fatal cases of COVID-19 require special attention, since patient mortality is the most important indicator of the effectiveness of a healthcare organization [2]. The SARS-CoV-2 virus can cause various clinical manifestations - from minor (cough, runny nose, headache, muscle pain, fever) to life-threatening (pneumonitis, acute respiratory failure, acute respiratory distress syndrome, shock, multiple organ failure) [3]. The development of progressive systemic hypoxia is an important pathophysiological feature of COVID-19 infection, especially in patients with severe forms of the disease [4, 5]. Hypoxia-induced factor 1α (HIF-1α) can currently be used to assess the body's adaptive capacity to hypoxic conditions [6]. The body's response to hypoxia can be an increase in heart rate due to increased organ perfusion requirements [7]. One of the most effective methods for primary instrumental diagnosis and assessment of the severity of COVID-19 infection is chest computed tomography [8, 9].

A method for predicting the risk of death in patients with type 2 diabetes mellitus (DM) combined with COVID-19 is currently known [A method for predicting the risk of death in patients with type 2 diabetes mellitus combined with COVID-19 (RU 2764954) was published in 2022]. Patients are tested for levels of urea, creatinine, alanine aminotransferase (ALAT), aspartate aminotransferase (AST), C-reactive protein (CRP), international normalized ratio (INR), D-dimer, lactate dehydrogenase (LDH), leukocytes, and erythrocyte sedimentation rate (ESR). A combination of factors such as urea above 10.15 mmol/L, creatinine above 105.4 μmol/L, ALAT above 20.8 U/L, AST above 36.7 U/L, CRP above 98.2 mg/L, leukocytes above 10.7×10 ⁹ /L, ESR above 33.6 mm/h, INR above 1.02, D-dimer above 295.15 μg/L, LDH above 516.09 U/L predicts a fatal outcome in patients with type 2 diabetes from COVID-19. The disadvantage of the method is that the risk is determined only in patients with concomitant type 2 diabetes, but not with other pathologies.

The closest in essence to the method proposed in this invention is a method for predicting the outcome of viral pneumonia in COVID-19 [Method for Predicting the Outcome of Viral Pneumonia in COVID-19 (RU 2764002) published in 2021]. Oxygenation, total protein, and blood urea levels are determined in patients. Saturation values of less than 77.5 without oxygen support, total protein less than 55.5 g/L, and blood urea greater than or equal to 8.98 mmol/L predict an unfavorable outcome of viral pneumonia in COVID-19. A disadvantage of this method is that the degree of adaptation of the body to hypoxia and the extent of lung tissue damage in patients are not assessed using computed tomography data.

The technical result of the present invention consists in the development of a simple and effective method for predicting the outcome of acute COVID-19 infection with moderate, severe and extremely severe severity.

The advantage of the method is the possibility of application in clinical practice. The method is based on the use of routine data from a functional examination, laboratory and instrumental diagnostics, and includes a method for assessing the body's adaptation to hypoxic conditions. It can be used in all patients admitted for inpatient treatment.

The claimed method is carried out as follows.

1. The heart rate of a patient admitted with a diagnosis of coronavirus infection caused by the SARS-CoV2 virus (COVID-19) is measured.

2. The degree of lung damage is determined based on computed tomography data and expressed as a percentage.

3. The creatinine level is determined by a biochemical blood test.

4. The level of HIF-1α in blood plasma is determined using competitive enzyme immunoassay.

The technical result of the proposed invention has been confirmed by clinical trials: 50 patients (22 men, 28 women) diagnosed with coronavirus infection caused by the SARS-CoV2 virus (COVID-19) were examined. The subjects were 65.5 [58; 70.5] years old. At study enrollment, heart rate was measured, the degree of lung damage was determined as a percentage based on chest computed tomography data, and creatinine and HIF-1α levels were measured upon hospitalization.

Jamovi 2.3 software was used for statistical analysis. A receiver operating characteristic (ROC) analysis was performed, with an ROC curve plotted. Comparison results were considered statistically significant at p < 0.05.

To assess the possibility of predicting the risk of death, the logistic regression method was used.

With a classification threshold of 0.42, the sensitivity of the model is 84.2%, specificity is 86.7%, and accuracy is 85.7%.

McFadden's R-square was 0.395 (R ² = 0.395).

Nigelkerke's R-square was 0.556 (R ² =0.556).

The significance of the model was <0.001 (p<0.001).

Regression equation

Z= -7.5451+0.0784×X ₁ +0.0513×X ₂ +0.0238×X ₃ -2.1274×X ₄ ,

where X ₁ is the heart rate;

X ₂ - the degree of lung damage according to computed tomography of the chest organs in percent;

X ₃ - creatinine level;

X ₄ - HIF-1α level.

Calculating the probability of a fatal outcome

,

where P is the probability of a fatal outcome;

e is Euler's number, equal to 2.718;

Z is the value obtained in the regression equation,

at P less than 0.42, a favorable outcome of the disease associated with the patient's recovery is predicted; at P greater than 0.42, an unfavorable outcome of the disease associated with the patient's death is predicted.

Brief description of the drawings

Fig. 1 shows the ROC curve characterizing the predictive model at a classification threshold of 0.42.

Example 1

Patient 1, a 65-year-old woman, was diagnosed with SARS-CoV2 (COVID-19). Upon admission, her heart rate was 65 beats per minute, CT scan revealed 50% lung damage, creatinine level was 102.18 μmol/L, and HIF-1α level was 1.503 ng/mL.

Let's substitute the values into the regression equation

Z= -7.5451+0.0784×X ₁ +0.0513×X ₂ +0.0238×X ₃ -2.1274×X ₄ ,

Z = -0.6472.

Probability of fatal outcome .

P=0.34364, which is less than 0.42, which corresponds to a favorable outcome of the disease and recovery of the patient.

Positive progress was noted during the treatment, and the patient was discharged. A follow-up visit one month after discharge revealed that rehabilitation was proceeding well.

Example 2.

Patient 2, a 34-year-old male, was diagnosed with SARS-CoV2 (COVID-19). Upon admission, his heart rate was 83 beats per minute, his CT scan revealed 65% lung damage, his creatinine level was 88 μmol/L, and his HIF-1α level was 1.35 ng/mL.

Let's substitute the values into the regression equation:

Z= -7.5451+0.0784×X ₁ +0.0513×X ₂ +0.0238×X ₃ -2.1274×X ₄

Z = 1.52125

Probability of fatal outcome .

P=0.8207, which is greater than 0.42, which corresponds to an unfavorable outcome of the disease and death of the patient.

Upon review of the patient's medical history, he was transferred to the intensive care unit (ICU) over the following 24 hours due to progressively worsening respiratory failure. Six days later, he was transferred to mechanical ventilation, and subsequently died despite receiving all necessary treatment. The calculated prognosis corresponded to the actual outcome.

Bibliography:

1. World Health Organization 2023 data.who.int, WHO Coronavirus (COVID-19) dashboard > Cases [Dashboard]. https://data.who.int/dashboards/covid19/cases.

2. Fomin V.V., Royuk V.V., Reshetnikov V.A., Volkova O.S., Korshever N.G., Kozlov V.V. Analysis of in-hospital mortality in patients with a new coronavirus infection (COVID-19) at the Sechenov University Clinical Center // Pavlov Russian Medical and Biological Bulletin. 2023. - Vol. 31. - No. 3. - Pp. 381-389. doi: 10.17816/PAVLOVJ569334.

3. Prompetchara E., Ketloy C., Palaga T. Immune responses in COVID-19 and potential vaccines: Lessons learned from SARS and MERS epidemic. Asian Pac J Allergy Immunol. 2020;38(1):1-9. doi:10.12932/AP-200220-0772.

4. Serebrovska Z.O., Chong E.Y., Serebrovska T.V., et al. Hypoxia, HIF-1α, and COVID-19: from pathogenic factors to potential therapeutic targets //Acta pharmacologica Sinica. 2020; 41(12): 1539-1546. doi:10.1038/s41401-020-00554-8.

5. Lyubavin A. V., Kotlyarov S. N. Features of the course of acute coronary syndrome in patients with a new coronavirus infection COVID-19 // Science of the Young (Eruditio Juvenium). 2022. Vol. 10, No. 1. P. 101-112. https://doi.org/10.23888/HMJ2022101101-112 .

6. Kalinin R.E., Suchkov I.A., Raitsev S.N., Zvyagina V.I., Belskikh E.S. The role of hypoxia-inducible factor 1α in adaptation to hypoxia in the pathogenesis of the new coronavirus disease 2019 // Pavlov Russian Medical and Biological Bulletin. 2024. Vol. 32, No. 1. Pp. 133-144. DOI: https://doi.org/10.17816/PAVLOVJ165536.

7. Faulhaber M., Gatterer H., Haider T., Linser T., Netzer N., Burtscher M. Heart rate and blood pressure responses during hypoxic cycles of a 3-week intermittent hypoxia breathing program in patients at risk for or with mild COPD. Int J Chron Obstruct Pulmon Dis. 2015;10:339-45. doi: 10.2147/COPD.S75749.

8. Kudryavtsev Yu.S., Beregov M.M., Berdalin A.B. et al. Comparison of the main scales for assessing the severity of lung damage in COVID-19 based on computed tomography data and assessment of their prognostic value. Bulletin of Roentgenology and Radiology. 2021; 102(5): 296-303. https://doi.org/10.20862/0042-4676-2021-102-5-296-303.

9. Temporary guidelines: Prevention, diagnosis, and treatment of novel coronavirus infection (COVID-19), version 10 dated 08.02.2021. from: https://static-0.minzdrav.gov.ru/system/attachments/attaches/000/054/662/original/Времные_МР_COVID-19_%28v.10%29.PDF.

Claims (11)

A method for predicting the outcome of acute COVID-19 infection with moderate, severe and extremely severe severity, including determining the degree of lung tissue damage in a patient during hospitalization based on chest computed tomography data in percentage, characterized in that the heart rate, creatinine level and HIF-1α are additionally determined, and using the obtained data, the probability (P) of a fatal outcome is determined using the formula , where P is the probability of a fatal outcome; e is Euler's number, equal to 2.718; Z is the value obtained in the regression equation: Z= -7.5451+0.0784×X ₁ +0.0513×X ₂ +0.0238×X ₃ -2.1274×X ₄ , where X ₁ is the heart rate; X ₂ – the degree of lung damage according to computed tomography of the chest organs in percent; X ₃ – creatinine level, μmol/l; X ₄ – HIF-1α level; at P less than 0.42, a favorable outcome of the disease associated with the patient’s recovery is predicted; at P greater than 0.42, an unfavorable outcome of the disease associated with the patient’s death is predicted.