The effectiveness of using artificial intelligence in clinical medicine

Objective: to investigate the effectiveness (based on accuracy, sensitivity, and specificity) of using artificial intelligence (AI) in clinical medicine.Material and methods. The study was conducted based on a search and analysis of scientific publications presented in the PubMed/MEDLINE, Scopus, Web of Science, Embase, eLibrary, and CyberLeninka databases from 2009 to 2023, including various types and approaches to training AI, as well as various areas of its application in clinical practice. Sequential analysis of articles in a random sample enabled to select 30 publications: 4 were devoted to the use of AI in endocrinology, 3 – in dermatovenerology, 1 – in cardiology, 1 – in radiology, 1 – in gastroenterology, 5 – in neurology, 5 – in hematology, 4 – in nephrology, 4 – in orthopedics and rheumatology, 2 – in oncology.Results. AI demonstrated sufficient effectiveness: accuracy ranged from 49% to 99%, sensitivity from 42% to 100%, and specificity from 48% to 100% in areas such as cardiology, endocrinology, gastroenterology, dermatovenereology, and radiology. In some cases, AI was more effective than clinical diagnostics by medical specialists, such as in detecting melanoma and diagnosing atrial fibrillation.Conclusion. AI shows high diagnostic efficiency, increases accuracy and speeds up diagnostic search, which makes wider use of AI in clinical medicine promising

Non-invasive respiratory support in patients with severe community-acquired pneumonia

Acute respiratory failure (ARF) is the leading cause of death in hospitalized patients with severe forms of COVID-19. At the beginning of COVID-19 pandemic the starting respiratory protocol suggested early use of intubation and artificial lung ventilation (ALV) in patients with severe pneumonia complicated by ARF. However, after the analysis of the published studies it was noted that the pathophysiology of the development of ARF in COVID-19 had features that determine the atypical clinical pattern – “silent hypoxemia”. This leads to the late onset of respiratory support (RS) and, as a result, to the lower effectiveness of non-invasive RS methods. This article discusses the creation of an algorithm for the early use of various non-invasive RS methods in patients with COVID-19 complicated by ARF, that will decrease the frequency of hospitalization to the Intensive care units, tracheal intubation and ALV, reduce the duration of treatment and improve prognosis

Cystatin C: diagnostic and prognostic value in acute kidney injury

Acute kidney injury (AKI) is a life-threatening condition that occupies one of the leading places in the structure of mortality in intensive care units. AKI markers common in clinical practice are characterized by a number of disadvantages: serum creatinine – late response to damage to the kidney tubules, an increase in damage to more than 50% of nephrons; urine volume – limited diagnostic value and overdiagnosis of AKI in dehydration, the impossibility of assessing on the basis of a single measurement, as well as the need for regular and frequent dynamic monitoring. The review considers the diagnostic and prognostic possibilities of cystatin C (CysC) in AKI. The results of 55 researches were analyzed. The influence of a number of physiological conditions and non-renal diseases on blood serum and urinary CysC levels were shown. These indicators proved to be highly sensitive and specific biomarkers for AKI diagnosis and prognosis, allowing the specialists to verify renal dysfunction at an early stage of development, ahead of structural changes, and thereby to timely correct treatment, including withdrawal of nephrotoxic drugs and initiation of nephroprotection therapy

Modern biomarkers of acute kidney injury

The results of published studies of modern biomarkers used in the diagnosis of acute kidney injury (AKI) were summarized. The search was carried out in the PubMed/MEDLINE, Scopus, eLibrary databases. AKI occurs in 10–15% of all inpatients and 50% of intensive care patients, and affects economic aspects of treatment and rehabilitation. The literature review allowed to draw conclusions about the significant advantage of new AKI biomarkers (cystatin C, neutrophil gelatinase-associated lipocalin, β2-microglobulin, kidney injury molecule-1, fatty acid binding protein) over the conventional glomerular filtration rate, serum creatinine and urinary volume. Serum creatinine increases only in cases when 50–60% of nephrons are damaged, urinary volume has limitations such as the overdiagnosis of AKI in dehydrated patients, the inability to assess based on a single measurement, and the need for regular and frequent follow-up. Modern biomarkers make it possible to verify renal dysfunction in advance, at the subclinical level. This allows to make a correction in the therapy of the underlying disease and initiate nephroprotection to prevent the development of AKI and the further development of multiple organ failure, which may be more effective than the treatment of already developed AKI

Cystatin C: factors affecting diagnostic and prognostic value in acute kidney injury

The level of serum and urinary cystatin C (CysC) can be modulated by some factors (weight, gender, age, ethnicity, smoking), diseases (sepsis, cardiovascular disease, diabetes mellitus, metabolic syndrome, obesity, hypo- and hyperthyroidism) as well as administration of glucocorticosteroids, but all of them do not affect its prognostic and diagnostic value in acute kidney injury (AKI). The CysC concentration can predict adverse outcomes, such as in-hospital and out-hospital mortality, chronicity of renal dysfunction, the demand and duration of renal replacement therapy (RRT). The sCysC is an independent predictor of RRT completion in critically ill AKI patients

Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine

Objective: to explore the potential and challenges of artificial intelligence (AI) in clinical medicine and healthcare, and to determine the prospects for its implementation to improve diagnosis, treatment, and medical data management.Material and methods. A literature review on the main terms and concepts of AI, its classification by application area, technologies, and methodologies was carried out. The learning methods such as supervised, unsupervised, and reinforcement learning were considered, as well as examples of AI application in various areas of medicine, including disease diagnosis and personalized medicine.Results. AI shows significant potential in improving diagnosis, optimizing treatment processes, and managing healthcare resources. Main application areas are related to medical image analysis, developing individualized treatment plans, and healthcare management. However, using AI faces challenges such as data availability and bias, fragmentation of systems, and complexity of algorithm interpretation.Conclusion. Despite the existing challenges, the implementation of AI in medicine has great prospects, including improved diagnostic accuracy, reduced task completion time, and development of personalized medicine. It is important to consider the ethical aspects and the demand for further study of AI application in medicine to achieve the best results

Prognostic Value of Cystatin C as a Predictor of Adverse Outcome in Severe Pneumonia Associated with COVID-19

Objective. To assess the cystatin C (CysC) prognostic value for probability of death in patients with severe and extremely severe pneumonia associated with COVID-19.Material and methods. A single-center prospective study included 72 patients with severe and extremely severe pneumonia associated with COVID-19 undergoing treatment in the ICU of multifunctional medical center from September 2020 to October 2021. Recovered survivors (N=55) were analyzed as a Group 1, nonsurvivors (N=17) were considered as a Group 2.Results. The serum (s-CysC) and urine (u-CysC) CysC concentrations were significantly lower in Group 1 patients vs Group 2, averaging 1.31 mg/l vs 1.695 mg/l (P=0.013550), and 0.25 mg/l vs 0.94 mg/l (P=0.026308), respectively. Significant differences were also revealed in the subgroups differed by age (P=0.0094), platelet count (P=0.001), serum fibrinogen concentration (P=0.016), as well as CURB (P=0.02334), CRB-65 (P=0.032564), and SOFA (P=0.042042) scores. Therefore, s-CysC and u-CysC were statistically significant predictors of death in patients with pneumonia associated with severe and extremely severe COVID-19: 16.273 (95% CI: 2.503–105,814), P=0.003 and 1.281 (95% CI: 1.011–1.622), P=0.040, respectively. Urine and serum CysC were established as predictors of death in pneumonia associated with severe and extremely severe COVID-19, where u-CysC was defined as highly informative (ROC AUC 0.938 (95% CI: 0.867–1.000; P=0.000), with 90% sensitivity and specificity), and s-CysC — as informative (ROC AUC 0.863 (95%CI: 0.738–0.988; P=0.000) with 80% sensitivity and 72% specificity) predictive markers.Conclusion. Levels of S-CysC and u-CysC are of high prognostic significance and may contribute to identifying patients at a high risk of unfavorable outcome (death) due to pneumonia associated with severe and extremely severe COVID-19. Both S-CysC and u-CysC concentrations increasing up to 1.44 mg/l and 0.86 mg/l, respectively, were associated with high probability of death

The Predictive Value of Cystatin C for AKI in Patients with COVID-19

Objective. To evaluate a potential of cystatin C blood concentration to predict acute kidney injury (AKI) in patients with severe and extremely severe pneumonia associated with a COVID-19.Materials and methods. An observational prospective study of 117 patients with severe and extremely severe pneumonia associated with a COVID-19 in an ICU setting was conducted in 2020-2022 (site: multi-functional Medical Center, 1586 Military Clinical Hospital of the Ministry of Defense of Russia, Moscow Region, Russia). Routine laboratory tests and instrumental examinations were performed according to generally accepted protocols. Cystatin C concentrations in blood (s-CysC) and urine (u-CysC) were measured by immunoturbidimetric method.Results. AKI was diagnosed in 21 (17.9%) patients, kidney dysfunction without AKI was found in 22 (18.8%) patients with severe and extremely severe pneumonia associated with COVID-19. s-CysC and u-CysC levels in the group of patients with AKI were statistically significantly higher compared to the levels in the group of patients without AKI. The levels of s-CysC obtained within Day 1 — T (-1), and Day 2 — T (-2) prior to AKI onset turned out to be the independent factors for AKI development in patients with severe and extremely severe pneumonia associated with COVID-19: OR 5.37, Wald chisquare 5.534 (CI: 1.324; 21.788); P=0.019 and OR 3.225, Wald chi-square 4.121 (CI: 1.041; 9.989); P=0.042, respectively. s-CysC T (-2) value is informative, and s- CysC T (-1) is a highly informative predictor of AKI development in severe and extremely severe pneumonia associated with COVID-19: ROC AUC 0.853 (95% CI, 0.74-0.966), P<0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.67 mg/L, and ROC AUC 0.905 (95% CI, 0.837-0.973), P<0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.69 mg/l, respectively. Serum CysC levels started increasing 3 days prior to AKI onset, outpacing the increase of SCr levels. The u-CysC levels were not predictive of AKI development. Impaired renal function probability was increasing with patients' age (P<0.0001).Conclusions. Serum CysC seems to be a statistically significant predictor of AKI. s-CysC levels started increasing 3 days prior to AKI onset, surpassing the increase of SCr levels in patients with severe and extremely severe pneumonia associated with COVID-19. Urine CysC did not achieve statistical significance as a predictor for AKI, although u-CysC concentrations were significantly higher on days 3, 2, 1 prior to AKI onset and on the day of AKI onset in the group of patients with AKI