УДК 616.345-002-07:616-003.829.1-056.7-008.89-053.2

Посттрансплантаційний лімфопроліферативний розлад (Posttransplant lymphoproliferative disorder, PTLD) — це лімфоїдна і/або плазмоцитарна проліферація, що виникає внаслідок імуносупресивної терапії (ІСТ), яку призначають пацієнтам для запобігання відторгнення трансплантованого органа, в умовах трансплантації солідних органів або алогенних гемопоетичних клітин. Тригером розвитку PTLD може бути активна реплікація Епштейна–Барра вірусу (Epstein–Barr virus, EBV), а також інші фактори ризику. Цей розлад становить спектр клінічних захворювань, від доброякісного захворювання, подібного до мононуклеозу, до фульмінантної лімфоми. Раннє розпізнавання PTLD важливе при трансплантації органів у пацієнтів, оскільки має тенденцію до швидкого прогресування. Знайомство з клінічними особливостями PTLD і підвищена пильність є важливими для встановлення діагнозу

INTRODUCTION AND OBJECTIVE:

Access to renal transplantation in children with severe chronic kidney disease can be endangered in dangerous sociopolitical environments. Despite such challenges, we established the very first adult and pediatric renal transplantation program in Ukraine in 2021 during an ongoing war and the COVID-19 pandemic, which caused significant delays and difficulties, including disrupted supply chains and shortages of critical medical supplies and equipment and availability and access to transplant resources and personnel. Here, we describe our experience with establishing and conducting a pediatric renal transplantation program during wartime and a pandemic in Ukraine.

METHODS:

We conducted a retrospective cohort study of 20 pediatric patients who underwent renal transplantation between January 2021 and September 2023 at two large-volume pediatric care centers in Lviv. Due to Ukrainian laws, donations could not be taken from soldiers and military personnel or civilians who suffered due to hostilities. We managed immunosuppressive medications and antibiotic prophylaxis or treatment post-transplant, and nearly all patients were on dialysis prior to transplantation.

RESULTS:

Our program constituted 23% (189/821) of all transplants performed in Ukraine in the last three years, and we have expanded our efforts to Western Ukraine. The majority of our patients did not undergo native nephrectomy, and most patients were on dialysis prior to transplantation. Average age at the time of transplant was 12.6 +4.5 (years), and average length of time on dialysis was 18 months. Overall, 30-day graft survival was 95%. Two patients experienced acute rejection that was successfully managed medically, while one had graft thrombosis requiring nephrectomy on the day of surgery.

CONCLUSIONS:

Despite the challenges of establishing a renal transplantation program during wartime and the impact of COVID-19, we have successfully started a pediatric renal transplantation program in Ukraine with a 95% 30-day graft survival rate. Our efforts have constituted 23% of all transplants performed in Ukraine in the last three years, and we have expanded our program to Western Ukraine. Our experience highlights the importance of access to necessary care in challenging environments and the need for continued support and collaboration in these settings.

The patient suffered from a mild form of COVID-19 and was treated on an outpatient basis. According to the family doctor’s prescription, she took Azithromycin 500 mg a day per os for 6 days, and then Ceftriaxone 1.0 g twice a day i.m. for another 6 days. Diarrhea appeared on the 10th day of treatment up to 10-15 times a day, a month later - blood admixtures in the stool appeared. The result was negative. Data from colonoscopy and histological examination of the intestinal mucosa and the clinical picture showed nonspecific ulcerative colitis, moderately severe. The patient started treatment with Salofalk first at a dose of 2 mg and then 4 mg per day. Due to the insufficient clinical effect, the patient was additionally prescribed Budenofalk in a daily dose of 9 mg with a positive clinical effect

The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, which will help identify critical personal characteristics based on conditioned space analysis. The condition space is given in cube form as a reflection of the functional relationship of the general parameters to the studied object. The dataset consists of 51 instances, and ten parameters are processed using different clustering and regression models. Days in hospital is the target variable. A condition space cube is formed based on clustering analysis and features selection. In this manner, a hierarchical predictor based on clustering and an ensemble of weak regressors is built. The quality of the developed hierarchical predictor for Root Mean Squared Error metric is 1.47 times better than the best weak predictor (perceptron with 12 units in a single hidden layer).

Introduction:
Studies on age differences of arterial trauma (AT) carry significant methodological differences in terms of selection of the most appropriate age classification.

Aim:
This study aims to verify the most optimal age classification when comparing clinical patterns of the civil AT.

Material and methods:
222 AT patients were identified from the Lviv Clinical Regional Hospital. In each case the following clinical patterns were identified: patient age, etiology, mechanism, AT type, topography, diagnostics mode, treatment type. Patients were distributed using six age classifications (Erikson 1950, UN 1989, Quinn 1994, Craig 2000, WHO physical activity recommendations 2010, by decades of life). Generalized linear models (GLMs) were created, with age distributions as predictors and clinical patterns as dependent factors. Akaike information criterion (AIK) was used to compare the quality of statistical sets.

Results and discussion:
Six GLMs were obtained, in each age of them age classifications were compared using the AIK. Rating list of age classifications was developed (starting with the most appropriate and ending with the least appropriate): E. Erikson (1950) → V. Quinn (1994) → G. Craig (2000) → UN (1989) → Decades → WHO (2010).

Conclusions:
Human development classifications may be preferable in assessing the age differences of AT in patients of wide range.