Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports.

INTRODUCTION: Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies. OBJECTIVES: The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP). METHODS: Data encompassing CONITEC's official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected). RESULTS: The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies' experiences and government as demandant, potentially influencing CONITEC's decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester. CONCLUSIONS: NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.

Genetic and phenotypic profile of Fabry disease in the population of Vale do Paraiba and Eastern São Paulo / Perfil genético e fenotípico da doença de Fabry na população do Vale do Paraíba e Zona Leste de São Paulo

ABSTRACT Introduction: Fabry disease (FD) is an inborn error of metabolism characterized by α-galactosidase A deficiency. The primary objective was to evaluate the genetic and phenotypic profile of Fabry disease in hemodialysis. Methods: Observational cohort study to determine the incidence of genetic variations and phenotypic changes for FD in hemodialysis patients in the Paraiba Valley and Eastern São Paulo. Genetic testing for the GLA gene was performed for men and women over 12 years of age at the hemodialysis clinics between January 2016 and December 2019 as a screening protocol. Results: The cases came from screening exams of the index case among patients with chronic kidney disease, resulting in 17 families and totaling 82 patients under study. The classification of the most prevalent variant was that of uncertain significance (54%), followed by the pathogenic variant (46%). Five patients in two families were described with two types of variants not previously described in the literature, with pathogenic behavior. Comparing the types of variants, the presence of a pathogenic variant was associated with higher levels of lysoGB3, lower values for alpha-GAL activity and higher frequency of symptoms related to FD. Conclusion: We characterized an extensive population of patients with FD variants with rich genetic, clinical and biomarker details. We believe that this study can help to better characterize the Brazilian population with FD and the most frequent types of variants.

RESUMO Introdução: A doença de Fabry (DF) é um erro inato do metabolismo caracterizado pela deficiência da enzima α-galactosidase A. O objetivo primário foi avaliar o perfil genético e fenotípico da doença de Fabry em hemodiálise. Métodos: Estudo de coorte observacional para determinar a incidência de variações genéticas e alterações fenotípicas para DF em pacientes em hemodiálise no Vale do Paraíba e Zona Leste de São Paulo. O teste genético para o gene GLA foi realizado para homens e mulheres em todos os pacientes das clínicas de hemodiálise maiores de 12 anos entre janeiro de 2016 a dezembro de 2019 como protocolo de rastreio. Resultados: Os casos foram provenientes de exames de triagem do caso índice entre pacientes portadores de doença renal crônica, resultando em 17 famílias e totalizando 82 pacientes em estudo. A classificação da variante mais prevalente foi a de significado incerto (54%), seguida da variante patogênica (46%). Foram descritos 5 pacientes em duas famílias com dois tipos de variantes ainda não previamente descritos na literatura com comportamento patogênico. Na comparação entre os tipos de variantes, a presença de variante patogênica foi associada a maiores níveis de lysoGB3, menores valores da atividade da alfa-GAL e maior frequência de sintomas relativos à DF. Conclusão: Caracterizamos uma extensa população de pacientes com variantes para DF com riqueza de detalhes de genética, clínica e de biomarcadores. Acreditamos que este estudo possa auxiliar na melhor caracterização da população brasileira com DF e nos tipos mais frequentes de variantes.

A comprehensive model for assessing and classifying patients with thrombotic microangiopathy: the TMA-INSIGHT score.

BACKGROUND: Thrombotic Microangiopathy (TMA) is a syndrome characterized by the presence of anemia, thrombocytopenia and organ damage and has multiple etiologies. The primary aim is to develop an algorithm to classify TMA (TMA-INSIGHT score). METHODS: This was a single-center retrospective cohort study including hospitalized patients with TMA at a single center. We included all consecutive patients diagnosed with TMA between 2012 and 2021. TMA was defined based on the presence of anemia (hemoglobin level < 10 g/dL) and thrombocytopenia (platelet count < 150,000/µL), signs of hemolysis, and organ damage. We classified patients in eight categories: infections; Malignant Hypertension; Transplant; Malignancy; Pregnancy; Thrombotic Thrombocytopenic Purpura (TTP); Shiga toxin-mediated hemolytic uremic syndrome (STEC-SHU) and Complement Mediated TMA (aHUS). We fitted a model to classify patients using clinical characteristics, biochemical exams, and mean arterial pressure at presentation. RESULTS: We retrospectively retrieved TMA phenotypes using automatic strategies in electronic health records in almost 10 years (n = 2407). Secondary TMA was found in 97.5% of the patients. Primary TMA was found in 2.47% of the patients (TTP and aHUS). The best model was LightGBM with accuracy of 0.979, and multiclass ROC-AUC of 0.966. The predictions had higher accuracy in most TMA classes, although the confidence was lower in aHUS and STEC-HUS cases. CONCLUSION: Secondary conditions were the most common etiologies of TMA. We retrieved comorbidities, associated conditions, and mean arterial pressure to fit a model to predict TMA and define TMA phenotypic characteristics. This is the first multiclass model to predict TMA including primary and secondary conditions.

Safety of cinacalcet in children and adolescents with chronic kidney disease-mineral bone disorder: systematic review and proportional meta-analysis of case series.

BACKGROUND: Mineral and bone disease in children with chronic kidney disease can cause abnormalities in calcium, phosphorus, parathyroid hormone, and vitamin D and when left untreated can result in impaired growth, bone deformities, fractures, and vascular calcification. Cinacalcet is a calcimimetic widely used as a therapy to reduce parathyroid hormone levels in the adult population, with hypocalcemia among its side effects. The analysis of safety in the pediatric population is questioned due to the scarcity of randomized clinical trials in this group. OBJECTIVE: To assess the onset of symptomatic hypocalcemia or other adverse events (serious or non-serious) with the use of cinacalcet in children and adolescents with mineral and bone disorder in chronic kidney disease. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA: The bibliographic search identified 2699 references from 1927 to August/2023 (57 LILACS, 44 Web of Science, 686 PubMed, 131 Cochrane, 1246 Scopus, 535 Embase). Four references were added from the bibliography of articles found and 12 references from the gray literature (Clinical Trials). Of the 77 studies analyzed in full, 68 were excluded because they did not meet the following criteria: population, types of studies, medication, publication types and 1 article that did not present results (gray literature). PARTICIPANTS AND INTERVENTIONS: There were 149 patients aged 0-18 years old with Chronic Kidney Disease and mineral bone disorder who received cinacalcet. STUDY APPRAISAL AND SYNTHESIS METHODS: Nine eligible studies were examined for study type, size, intervention, and reported outcomes. RESULTS: There was an incidence of 0.2% of fatal adverse events and 16% of serious adverse events (p < 0.01 and I2 = 69%), in addition to 10.7% of hypocalcemia, totaling 45.7% of total adverse events. LIMITATIONS: There was a bias in demographic information and clinical characteristics of patients in about 50% of the studies and the majority of the studies were case series. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: If used in the pediatric population, the calcimimetic cinacalcet should be carefully monitored for serum calcium levels and attention to possible adverse events, especially in children under 50 months. SYSTEMATIC REVIEW REGISTRATION NUMBER (PROSPERO REGISTER): CRD42019132809.

Machine Learning Model to Predict Graft Rejection After Kidney Transplantation.

BACKGROUND: There are few predictive studies about early posttransplant outcomes taking into account baseline and posttransplant variables. The objective of this study was to create a predictive model for 30-day graft rejection using machine learning techniques. METHODS: Retrospective study with 1255 patients undergoing transplant from living and deceased donors at a tertiary health service in Brazil. Recipient, donor, transplantation, and postoperative period data were collected from physical and electronic records. We split the data into derivation (training) and validation (test) datasets. Five supervised machine learning algorithms were developed with this subset of variables in the training set: Simple Logistic Regression, Lasso, Multilayer Perceptron, XGBoost, and Light GBM. RESULTS: There were 147 (12.48%) cases of graft rejection within 30 days of transplantation. The best model was XGBoost (accuracy, 0.839; receiver operating characteristic area under the curve, 0.715; precision, 0.900). The model showed that deceased donor transplantation, glomerulopathy as an underlying disease, and donor's use of vasoactive drugs had more than 20% importance as rejection risk factors. The variables with the greatest predictive values were thymoglobulin induction and delayed graft function. CONCLUSIONS: We fitted a machine learning model to predict 30-day graft rejection after kidney transplantation that reaches a higher accuracy and precision. Machine learning models could contribute to predicting kidney survival using nontraditional approaches.

Development and validation of a simple machine learning tool to predict mortality in leptospirosis.

Predicting risk factors for death in leptospirosis is challenging, and identifying high-risk patients is crucial as it might expedite the start of life-saving supportive care. Admission data of 295 leptospirosis patients were enrolled, and a machine-learning approach was used to fit models in a derivation cohort. The comparison of accuracy metrics was performed with two previous models-SPIRO score and quick SOFA score. A Lasso regression analysis was the selected model, demonstrating the best accuracy to predict mortality in leptospirosis [area under the curve (AUC-ROC) = 0.776]. A score-based prediction was carried out with the coefficients of this model and named LeptoScore. Then, to simplify the predictive tool, a new score was built by attributing points to the predictors with importance values higher than 1. The simplified score, named QuickLepto, has five variables (age > 40 years; lethargy; pulmonary symptom; mean arterial pressure < 80 mmHg and hematocrit < 30%) and good predictive accuracy (AUC-ROC = 0.788). LeptoScore and QuickLepto had better accuracy to predict mortality in patients with leptospirosis when compared to SPIRO score (AUC-ROC = 0.500) and quick SOFA score (AUC-ROC = 0.782). The main result is a new scoring system, the QuickLepto, that is a simple and useful tool to predict death in leptospirosis patients at hospital admission.

Genetic and phenotypic profile of Fabry disease in the population of Vale do Paraiba and Eastern São Paulo.

INTRODUCTION: Fabry disease (FD) is an inborn error of metabolism characterized by α-galactosidase A deficiency. The primary objective was to evaluate the genetic and phenotypic profile of Fabry disease in hemodialysis. METHODS: Observational cohort study to determine the incidence of genetic variations and phenotypic changes for FD in hemodialysis patients in the Paraiba Valley and Eastern São Paulo. Genetic testing for the GLA gene was performed for men and women over 12 years of age at the hemodialysis clinics between January 2016 and December 2019 as a screening protocol. RESULTS: The cases came from screening exams of the index case among patients with chronic kidney disease, resulting in 17 families and totaling 82 patients under study. The classification of the most prevalent variant was that of uncertain significance (54%), followed by the pathogenic variant (46%). Five patients in two families were described with two types of variants not previously described in the literature, with pathogenic behavior. Comparing the types of variants, the presence of a pathogenic variant was associated with higher levels of lysoGB3, lower values for alpha-GAL activity and higher frequency of symptoms related to FD. CONCLUSION: We characterized an extensive population of patients with FD variants with rich genetic, clinical and biomarker details. We believe that this study can help to better characterize the Brazilian population with FD and the most frequent types of variants.

Impact of the COVID-19 Pandemic on Solid Organ Transplant and Rejection Episodes in Brazil's Unified Healthcare System.

BACKGROUND: Brazil has the world's largest public organ transplant program, which was severely affected by the COVID-19 pandemic. The primary aim of the study was to evaluate differences in solid organ transplants and rejection episodes during the COVID-19 pandemic compared to the five years before the pandemic in the country. METHODS: A seven-year database was built by downloading data from the DATASUS server. The pandemic period was defined as March 2020 to December 2021. The pre-pandemic period was from January 2015 to March 2020. RESULTS: During the pandemic, the number of solid organ transplants decreased by 19.3% in 2020 and 22.6% in 2021 compared to 2019. We found a decrease for each evaluated organ, which was more pronounced for lung, pancreas, and kidney transplants. The seasonal plot of rejection data indicated a high rejection rate between 2018 and 2021. There was also an 18% (IRR 1.18 (95% CI 1.01 to 1.37), p = 0.04) increase in the rejection rate during the COVID-19 pandemic. CONCLUSIONS: The total number of organ transplants performed in 2021 represents a setback of six years. Transplant procedures were concentrated in the Southeast region of the country, and a higher proportion of rejections occurred during the pandemic. Together, these findings could have an impact on transplant procedures and outcomes in Brazil.

COVID-19 in Kidney Transplant Recipients With Diabetes Mellitus: A Propensity Score Matching Analysis.

Kidney transplant recipients present higher rates of pre-existing comorbidities, in particular diabetes mellitus (DM), hypertension, and cardiac disease. We aimed to verify the main risk factors related to DM that contribute to COVID-19 progression and mortality in a kidney transplant setting. From March to August 2020, we evaluated 300 kidney transplant recipients affected by COVID-19. We used propensity score matching (PSM) to estimate the impact of DM on COVID-19. After matching, all baseline characteristics were well balanced between those with and without DM (n = 100 in each group). Case fatality rate, the requirement of invasive mechanical ventilation (IMV), and acute kidney injury (AKI) were associated with previous fasting blood glucose, and C-reactive protein (CRP), and lactate dehydrogenase (LDH) levels on admission. These findings were similar in kidney transplant patients with and without DM. Glycemia on admission and estimated glomerular filtration rate (eGFR) either on admission or basal correlated to the need of IMV and development of AKI, respectively. Poor glycaemic control, eGFR, markers of inflammation (CRP) and tissue damage (LDH) were indicative of COVID-19 burden in kidney transplant recipients and may be useful tools for risk-stratifying this population, independently of the DM status, during the pandemic.

A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic.

A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO2) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.

Association between urinary sodium excretion and hard outcomes in non-dialysis chronic kidney disease patients.

BACKGROUND: Restriction of sodium intake is routinely recommended for patients with chronic kidney disease (CKD). Whether or not sodium intake is associated with the progression of CKD and mortality remains uncertain. We evaluated the association between urinary sodium excretion (as a surrogate for sodium intake) with the occurrence of renal failure and mortality in patients with non-dialytic CKD. METHODS: We conducted a retrospective study of patients followed at a CKD clinic care hospital from October 2006 to March 2017. Adult patients with non-dialytic CKD were included. Using a time-to-event analysis, we examined the association of urinary sodium excretion as a categorical variable (categorized as quintiles: 1st quintile: 0.54-2.51 g; 2nd quintile: 2.52-3.11 g, 3rd quintile: 3.12-3.97 g, 4th quintile: 3.98-5.24 g and 5th quintile: 5.26-13.80 g) and the outcomes of interest. The primary outcome was defined as progression to end-stage renal disease requiring any type of renal replacement therapy. The secondary outcome was mortality. RESULTS: Two hundred five patients were included in the study (mean follow up of 2.6 years) with a mean eGFR of 26 (19-41) ml/min/1.73m2. 37 patients (18%) required renal replacement therapy and 52 (25,3%) died. There was association between urinary sodium excretion and need for renal replacement therapy (adjusted HR 0.245; 95%CI 0.660-0.912). There was no association between urinary sodium excretion and mortality in adjusted models. CONCLUSION: Moderate sodium intake was associated with a lower risk of renal failure.

Baseline characteristics and evolution of Brazilian patients with atypical hemolytic uremic syndrome: first report of the Brazilian aHUS Registry.

Background: Atypical hemolytic uremic syndrome (aHUS) is an ultra-rare disease. Therefore, studies involving large samples are scarce, making registries powerful tools to evaluate cases. We present herein the first analysis of the Brazilian aHUS Registry (BRaHUS). Methods: Analysis of clinical, laboratory, genetic and treatment data from patients inserted in the BRaHUS, from 2017 to 2020, as an initiative of the Rare Diseases Committee of the Brazilian Society of Nephrology. Results: The cohort consisted of 75 patients (40 adults and 35 pediatric). There was a predominance of women (56%), median age at diagnosis of 20.7 years and a positive family history in 8% of cases. Renal involvement was observed in all cases and 37% had low C3 levels. In the <2 years of age group, males were predominant. Children presented lower levels of hemoglobin (P = .01) and platelets (P = .003), and higher levels of lactate dehydrogenase (LDH) (P = .004) than adults. Genetic analysis performed in 44% of patients revealed pathogenic variants in 66.6% of them, mainly in CFH and the CFHR1-3 deletion. Plasmapheresis was performed more often in adults (P = .005) and 97.3% of patients were treated with eculizumab and its earlier administration was associated with dialysis-free after 3 months (P = .08). Conclusions: The cohort of BRaHUS was predominantly composed of female young adults, with renal involvement in all cases. Pediatric patients had lower hemoglobin and platelet levels and higher LDH levels than adults, and the most common genetic variants were identified in CFH and the CFHR1-3 deletion with no preference of age, a peculiar pattern of Brazilian patients.

Comparison of 30-day case-fatality rate between dialysis and transplant Covid-19 patients: a propensity score matched cohort study.

BACKGROUND AND OBJECTIVES: Patients undergoing kidney replacement therapies (KRTs) have a poor prognosis after Covid-19 infection. Few studies have compared the outcomes of such patients in the different KRT modalities. This study aimed to analyze the 30-day Covid-19-associated case-fatality rate of dialysis and kidney transplant patients. METHODS: Retrospective cohort study analyzing data from patients with confirmed Covid-19 between Mar/20 and Jan/21 included in two multicenter studies, the Brazilian Covid-19 Dialysis Study (Dialysis group, n = 703) and the Covid-19-KT Brazilian Study (Transplant group, n = 1907). To assess the risk factors for death, adjusted Cox hazards models were used. A sensitivity analysis was performed using a propensity score analysis to match the groups (n = 587 patients in each group). RESULTS: A higher percentage of transplant patients required hospitalization (68 vs. 51%, p < 0.001), intensive care (37 vs. 30%, p = 0.023), and invasive mechanical ventilation (28 vs. 22%, p = 0.035). Multivariate analysis of the before-matching sample showed that subjects in the transplant group were at a lower death risk at baseline (HR 0.380.560.85). However, they showed higher risk over time (HR 1.031.061.09). Kaplan-Meier analysis after propensity score matching confirmed the inferior 30-day cumulative survival in the transplant recipients (83 vs. 78%, p = 0.0014). CONCLUSION: Both transplant and dialysis patients have high 30-day case-fatality rates after a Covid-19 diagnosis. Despite lower death risk at baseline, transplant patients have an increased death risk of 6% per day than dialysis patients.

Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach.

Acute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score). This was a cross-sectional multicentre prospective cohort study in the Latin America AKI COVID-19 Registry. A total of 870 COVID-19 patients with AKI defined according to the KDIGO were included between 1 May 2020 and 31 December 2020. We evaluated four categories of predictor variables that were available at the time of the diagnosis of AKI: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using tenfold cross-validation and validated the accuracy using the area under the receiver operating characteristic curve (AUC-ROC). The coefficients of the best model (Elastic Net) were used to build the predictive AKI-COV score. The AKI-COV score had an AUC-ROC of 0.823 (95% CI 0.761-0.885) in the validation cohort. The use of the AKI-COV score may assist healthcare workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.

Thrombotic microangiopathy after kidney transplantation: Analysis of the Brazilian Atypical Hemolytic Uremic Syndrome cohort.

BACKGROUND: Atypical Hemolytic Uremic Syndrome (aHUS) is an ultra-rare disease that potentially leads to kidney graft failure due to ongoing Thrombotic Microangiopathy (TMA). The aim was evaluating the frequency of TMA after kidney transplantation in patients with aHUS in a Brazilian cohort stratified by the use of the specific complement-inhibitor eculizumab. METHODS: This was a multicenter retrospective cohort study including kidney transplant patients diagnosed with aHUS. We collected data from 118 transplant centers in Brazil concerning aHUS transplanted patients between 01/01/2007 and 12/31/2019. Patients were stratified into three groups: no use of eculizumab (No Eculizumab Group), use of eculizumab for treatment of after transplantation TMA (Therapeutic Group), and use of eculizumab for prophylaxis of aHUS recurrence (Prophylactic Group). RESULTS: Thirty-eight patients with aHUS who received kidney transplantation were enrolled in the study. Patients' mean age was 30 years (24-40), and the majority of participants was women (63% of cases). In the No Eculizumab Group (n = 11), there was a 91% graft loss due to the TMA. The hazard ratio of TMA graft loss was 0.07 [0.01-0.55], p = 0.012 in the eculizumab Prophylactic Group and 0.04 [0.00-0.28], p = 0.002 in the eculizumab Therapeutic Group. CONCLUSION: The TMA graft loss in the absence of a specific complement-inhibitor was higher among the Brazilian cohort of kidney transplant patients. This finding reinforces the need of eculizumab use for treatment of aHUS kidney transplant patients. Cost optimization analysis and the early access to C5 inhibitors are suggested, especially in low-medium income countries.

Impact of Time-Zero Biopsy on the Outcome of Transplanted Kidneys.

BACKGROUND: A low supply of donated organs led to the expansion of criteria for kidney transplantation (KT), and the impact on late glomerular function rates (eGFR) is still uncertain. This study aimed to correlate the histologic findings at time-zero biopsy (TzB) with the final eGFR, to identify criteria that could help achieve a more thorough preimplantation evaluation of the organ. METHODS: Records from 395 adult deceased KTs were reviewed. TzBs were analyzed considering histologic criteria by compartment (vascular, interstitial, tubular, and inflammatory) and correlated with the eGFR after 1 year. RESULTS: Among donors, 56.9% were men (mean age 39 years), with the main causes of death being brain trauma (44.2%) and stroke (46.0%). Histologic analysis of TzB revealed 6.0% of glomerulosclerosis; 18.8% presenting vascular alterations; interstitial fibrosis in 54.6%; tubular changes in 76.9%, and nonspecific inflammatory infiltrate in 2.3%. Linear regression analysis showed that the main histologic findings that had impact in the eGFR were interstitial fibrosis (P = .000), followed by tubular alterations (P = .036) and glomerulosclerosis (P = .008). CONCLUSIONS: Histologic variables like interstitial fibrosis and tubular alterations show the most significant negative correlation with final eGFR. The effect of glomerulosclerosis may not be as important as formerly suggested in the literature.

Nutritional Parameters in Early and Late Kidney Transplantation.

BACKGROUND: Kidney transplantation (KT) has the advantage of providing a better quality of life and freedom for the patient. However, nutritional changes can occur, with clinical repercussions. The aim of the study was to compare the nutritional status in the initial and late post-KT period. METHODS: A cross-sectional study was conducted involving 169 outpatients post-KT. Clinical, demographic, biochemical, food intake, handgrip strength (HGS), and anthropometric data were collected from medical records for the first nutritional care after KT. Statistical tests were performed to compare the groups according to the time of KT: early (≤1 year) and late (>1 year). The level of significance adopted was 5%. RESULTS: The median age of the patients was 46 years (range, 38-57), 50.3% were men, and it was observed that 66.9% underwent KT with a deceased donor. There was a higher prevalence of diabetes mellitus (42.6% vs 23.5%; P = .011), and higher body mass index (28.80 ± 7.26 vs 26.51 ± 6.62 kg/m2; P = .046), arm muscle circumference (25.84 ± 4.63 vs 24.09 ± 3.36 cm; P = .019), and HGS (26.97 ± 10.70 vs 20.21 ± 10.83 kg; P = .010) in patients with late KT. Linear regression analysis showed that at each log of time, there was an increase of 1.90 kg in HGS (P = .045) and 0.48 cm (P = .036) in mid-arm muscle circumference. CONCLUSION: The present study demonstrated that late kidney transplantation was associated with higher values of body mass index, mid-arm muscle circumference, and HGS.

A machine learning prediction model for waiting time to kidney transplant.

BACKGROUND: Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. METHODS: A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. RESULTS: Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). CONCLUSION: The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.

Feature-tracking cardiac magnetic resonance left ventricular global longitudinal strain improves 6 months after kidney transplantation associated with reverse remodeling, not myocardial tissue characteristics.

To determine whether left ventricular (LV) global longitudinal strain (GLS) measured by feature-tracking (FT) cardiac magnetic resonance (CMR) improves after kidney transplantation (KT) and to analyze associations between LV GLS, reverse remodeling and myocardial tissue characteristics. This is a prospective single-center cohort study of kidney transplant recipients who underwent two CMR examinations in a 3T scanner, including cines, tagging, T1 and T2 mapping. The baseline exam was done up to 10 days after transplantation and the follow-up after 6 months. Age and sex-matched healthy controls were also studied for comparison. A total of 44 patients [mean age 50 ± 11 years-old, 27 (61.4%) male] completed the two CMR exams. LV GLS improved from - 13.4% ± 3.0 at baseline to - 15.2% ± 2.7 at follow-up (p < 0.001), but remained impaired when compared with controls (- 17.7% ± 1.5, p = 0.007). We observed significant correlation between improvement in LV GLS with reductions of left ventricular mass index (r = 0.356, p = 0.018). Improvement in LV GLS paralleled improvements in LV stroke volume index (r = - 0.429, p = 0.004), ejection fraction (r = - 0.408, p = 0.006), global circumferential strain (r = 0.420, p = 0.004) and global radial strain (r = - 0.530, p = 0.002). There were no significant correlations between LV GLS, native T1 or T2 measurements (p > 0.05). In this study, we demonstrated that LV GLS measured by FT-CMR improves 6 months after KT in association with reverse remodeling, but not native T1 or T2 measurements.