RU2845112C1 - Method for prediction of clinical outcome in patients with severe forms of acute covid-19 infection - Google Patents

Abstract

FIELD: medical science.

SUBSTANCE: invention refers to medicine, namely to clinical and laboratory diagnostics, and can be used for the prediction of the clinical outcome in the patients with severe forms of acute Covid-19 infection. A body weight index is measured during admission to hospital and blood is sampled from the patients. Blood is examined for serum ferritin, creatinine, carnitine acetyltransferase level. Probability of lethal outcome is calculated by formula

,

where P is probability of lethal outcome; e is Euler number equal to 2.718; Z is the value obtained in the regression equation Z=-11.4181-0.3227×X₁+0.0192×X₂+0.0608×X₃+0.0896×X₄, where X₁ is body weight index; X₂ is the level of serum ferritin, ng/ml; X₃ is creatinine level, mcmol/l; X₄ is level of carnitine acetyltransferase, ng/ml. If P is less than 0.36, a favourable clinical outcome associated with the patient's recovery is predicted. If P is more than 0.36, the unfavourable outcome of the disease associated with the death of the patient is predicted.

EFFECT: method enables predicting the clinical outcome in the patients with severe forms of acute Covid-19 infection by a simple and effective method ensured by using the routine physical examination findings – body weight index, laboratory diagnostic findings: levels of serum ferritin, creatinine and carnitine acetyltransferase, and can be used in all patients admitted to hospital treatment.

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Description

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

As of October 2024, there have been more than 776 million confirmed cases of COVID-19 infection and more than 7 million deaths worldwide [1]. Fatal outcomes in COVID-19 infection are caused by complications such as the development of bilateral pneumonitis with acute respiratory failure, acute respiratory distress syndrome, and multiple organ failure [2]. Since patient mortality is the most important indicator of the effectiveness of a healthcare organization, predicting the outcome of COVID-19 cases is an important task in intensive care [3]. Patients with COVID-19 infection who are overweight and have concomitant respiratory failure are more likely to require intensive care and have an increased risk of adverse outcomes than patients without obesity [4]. A feature of severe forms of COVID-19 infection is the development of progressive systemic hypoxia, which can lead to increased systemic inflammation, disruption of metabolic processes and energy supply to cells, which ultimately leads to their destruction [5, 6]. Under the influence of hypoxic conditions in the inflammation zone, macrophages can exert a cytotoxic effect on surrounding cells due to excessive production of reactive oxygen species and proinflammatory cytokines (e.g., tumor necrosis factor α), which in turn mediates cell necroptosis with the release of their contents into the extracellular space [7, 8]. It is known that, under physiological conditions, carnitine acetyltransferase (CAT) is located predominantly in the mitochondrial matrix inside cells, so an increase in the activity of mitochondrial enzymes in blood plasma can serve as a marker of cellular destruction associated with both the destruction of mitochondria and the disruption of the integrity of the cell membrane as part of necroptosis processes. An example of such a marker is aspartate aminotransferase (AST) [9]. However, an increase in AST is associated primarily with impaired liver and kidney function, while a relationship with the function of the pulmonary vascular endothelium has been demonstrated for CAT [10]. This relationship makes it relevant to assess the activity of plasma CAT due to the high tropism of SARS-CoV2 for the endothelium of pulmonary vessels [5]. As a result of a meta-analysis, Malik et al. found that elevated creatinine levels were associated with the risk of an adverse outcome in patients with COVID-19 [11]. Determination of serum ferritin levels has been widely used to determine the severity of the disease in patients with a new coronavirus infection [12]. Early detection of the progression of systemic inflammation and biomarkers of cell destruction will allow the physician to make a timely decision on the intensification of anti-inflammatory therapy in patients with COVID-19 infection at the early stages of treatment, which will help improve the prognosis of the disease outcome.

To date, a method for predicting the risk of death in patients with type 2 diabetes mellitus (DM) in combination with COVID-19 is known [A method for predicting the risk of death in patients with type 2 diabetes mellitus in combination with COVID-19 (RU 2764954) was published in 2022]. In patients, the 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) are determined. With a combination of factors such as urea level over 10.15 mmol/l, creatinine over 105.4 μmol/l, ALAT over 20.8 U/l, AST over 36.7 U/l, CRP over 98.2 mg/l, leukocytes over 10.7×10 ⁹ /l, ESR over 33.6 mm/h, INR over 1.02, D-dimer over 295.15 μg/l, LDH over 516.09 U/l, a fatal outcome in patients with type 2 diabetes from COVID-19 is predicted. 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 individual prediction of outcomes of the new coronavirus infection COVID-19 (RU 2 795 141 C1) published in 2023]. In patients, the age, urea level, lactate dehydrogenase, body mass index and the proportion of unsaturated hemoglobin in oxygen are determined. The disadvantage of the method is that the degree of the inflammatory process and cell death are not assessed.

The technical result of the present invention consists in developing a simple and effective method for predicting the outcome of patients with severe forms of acute COVID-19 infection.

The advantage of the method is the possibility of application in clinical practice; the method is based on the use of routine physical examination data, laboratory diagnostic data and can be used in all patients admitted for inpatient treatment.

The claimed method is carried out as follows.

1. The body mass index of a patient admitted with a diagnosis of coronavirus infection caused by the SARS-CoV2 virus (COVID-19) is determined;

2. The level of ferritin in the blood serum is determined;

3. The level of creatinine in the blood serum is determined;

4. The level of carnitine acetyltransferase in blood plasma is determined using the competitive enzyme immunoassay method.

The technical result of the proposed invention has been confirmed by testing in clinical practice: 50 patients (30 men, 20 women) diagnosed with coronavirus infection caused by the SARS-CoV2 virus (COVID-19) were examined. The age of the subjects was 64 [51; 71] years. At the stage of inclusion in the study, the body mass index is determined, the level of serum ferritin, serum creatinine and the level of carnitine acetyltransferase in the blood plasma are determined upon hospitalization.

The statistical analysis was performed using the Jamovi 2.3 program. ROC analysis was performed with the construction of the ROC curve. The comparison results were considered statistically significant at p<0.05. Thus, the ROC curve shown in Fig. 1 characterizes the prognostic model at a classification threshold of 0.36.

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

With a classification threshold of 0.36, the sensitivity of the model is 92.9%, specificity is 87.5%, and accuracy is 89.5%.

Nigelkerke's R-square was 0.612 (R ² =0.612).

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

Regression equation

Z=-11.4181-0.3227×X ₁ +0.0192×X ₂ +0.0608×X ₃ +0.0896×X ₄

where X ₁ is the body mass index;

X ₂ - serum ferritin level;

X ₃ - creatinine level;

X ₄ - level of carnitine acetyltransferase.

Calculation of the probability of a fatal outcome

where P is the probability value 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 0.36, a favorable outcome of the disease associated with the patient's recovery is predicted; at P greater than 0.36, an unfavorable outcome of the disease associated with the patient's death is predicted.

Example 1

Patient 1, female, 60 years old, diagnosis: coronavirus infection caused by the SARS-CoV2 virus (COVID-19). On hospitalization, body mass index was 30.47 kg/ ^m2 , serum ferritin level was 538 ng/ml, creatinine level was 84 μmol/l, carnitine acetyltransferase level was 56.24 ng/ml.

Let's substitute the values into the regression equation:

Z=-11.4181-0.3227×X ₁ +0.0192×X ₂ +0.0608×X ₃ +0.0896×X ₄

Z = -0.774865

Probability of fatal outcome:

P=0.315445, which is less than 0.36, which corresponds to a favorable outcome of the disease and recovery of the patient.

Positive dynamics were noted against the background of the treatment, the patient was discharged. During observation a month after discharge, it was noted that rehabilitation was proceeding successfully.

Example 2

Patient 2, male, 69 years old, diagnosis: coronavirus infection caused by the SARS-CoV2 virus (COVID-19). On hospitalization, the patient had a body mass index of 26.01 kg/ ^m2 , serum ferritin level of 239 ng/ml, creatinine level of 160.59 μmol/l, carnitine acetyltransferase level of 73.18 ng/ml.

Let's substitute the values into the regression equation:

Z=-11.4181-0.3227×X ₁ +0.0192×X ₂ +0.0608×X ₃ +0.0896×X ₄

Z = 1.098073

Probability of fatal outcome:

P=0.74988, which is greater than 0.36, which corresponds to an unfavorable outcome of the disease and death of the patient.

When analyzing the medical history on the ninth day of hospitalization, the patient was transferred to the intensive care unit due to progressive increase in respiratory failure. The patient was on non-invasive mechanical ventilation, subsequently died 18 days later, despite all the therapy. The calculated prognosis corresponded to the actual outcome of the disease.

Bibliography:

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

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Claims (11)

A method for predicting the outcome of the disease in patients with severe forms of acute COVID-19 infection, including determining the body mass index during hospitalization, taking blood from patients, characterized in that the level of serum ferritin, creatinine level, carnitine acetyltransferase level are determined in the blood, and the probability of a fatal outcome is calculated using the formula , where P is the probability value of a fatal outcome; e is Euler's number, equal to 2.718; Z is the value obtained in the regression equation: Z= -11.4181-0.3227×X ₁ +0.0192×X ₂ +0.0608×X ₃ +0.0896×X ₄ , where X ₁ is the body mass index; X ₂ – serum ferritin level, ng/ml; X ₃ – creatinine level, µmol/l; X ₄ – level of carnitine acetyltransferase, ng/ml, and at P less than 0.36, a favorable outcome of the disease associated with the patient's recovery is predicted, at P greater than 0.36, an unfavorable outcome of the disease associated with the patient's death is predicted.