Measuring calf circumference in frail hospitalized older adults and prediction of in-hospital complications and post-discharge mortality.

Background: Sarcopenia, characterized by muscle mass, strength, and performance decline, significantly impacts outcomes in older adults. This study aims to assess the predictive value of calf circumference (CC), in conjunction with SARC-F and hand grip, concerning in-hospital complications and post-discharge mortality among hospitalized frail older adults. Methods: A cohort of 158 hospitalized patients aged over 65 years underwent Comprehensive Geriatric Assessment and sarcopenia screening, including CC measurement. Multivariable regression analyses, adjusted for confounders, were conducted to assess predictive associations. Results: The study cohort, comprising 53% males with a median age of 86 years, exhibited significant sarcopenia prevalence based on SARC-F (85% indicating sarcopenia), hand grip strength (probable sarcopenia in 77% of males and 72% of females), and CC (sarcopenia in 83%). Multivariate analysis, adjusting for age, sex, Clinical Frailty Scale (CFS), and Mini Nutritional Assessment-Short Form (MNA-SF), demonstrated associations of CC and SARC-F with in-hospital complications, while CC also showed a significant association with reduced risks of in-hospital mortality (OR 0.441, 95% CI 0.257 to 0.754, p = 0.003) and 90-day mortality (OR 0.714, 95% CI 0.516 to 0.988, p = 0.043). Conclusion: This study provides insights into the predictive accuracy of sarcopenia screening tools on mortality in real-world hospitalized older adults with frailty. Notably, CC emerges as a robust predictor of mortality outcomes. Further research is warranted to validate and elucidate the respective contributions of CC and frailty to mortality in vulnerable populations.

Aromatase inhibitor-induced bone loss osteosarcopenia in older patients with breast cancer: effects of the RANK/RANKL system's inhibitor denosumab vs. bisphosphonates.

The raising number of older patients who are diagnosed with breast cancer represents a significant medical and societal challenge. Aromatase inhibitors (AI), which are commonly utilized to treat this condition in these patients have significant adverse events on bone and muscle health. Falling estrogen production leads to an increase in RANKL secretion by osteoblasts with accelerated bone remodeling due to osteoclast activity. Furthermore, estrogen deficiency reduces skeletal muscle strength and mass. The humanized monoclonal antibody, denosumab, neutralizes RANKL, thereby inhibiting osteoclast formation, function and survival and ultimately exerting powerful anti-resorptive effects.. In this study, we report on the efficacy of denosumab in mitigating aromatase inhibitor-induced bone loss (AIBL) and sarcopenia in older women with breast cancer. From January 2022 to January 2023, we enrolled 30 patients (female sex, ≥ 65 years) diagnosed with non-metastatic breast cancer undergoing adjuvant endocrine therapy; patients received, as per clinical practice, primary bone prophylaxis with denosumab (60 mg via subcutaneous injection every 6 months) according to oncologic guidelines. This group was matched with 30 patients with non-metastatic breast cancer, who were treated with biphosphonates (BF) therapy (oral alendronate 70 mg/week). For each patient bone mineral density (BMD) and bone quality in terms of trabecular bone score (TBS) in addition to body composition and Relative Skeletal Muscle Index (RSMI) was assessed by bone densitometry at baseline and after one year of treatment. Significant improvements in TBS at the lumbar spine, RSMI and whole-body composition (arms, legs, and trunk) were observed in the denosumab group compared with the BF group. These findings underscore the role of denosumab as an effective strategy in managing AIBL and osteosarcopenia in older women with breast cancer and undergoing adjuvant endocrine therapy, which is crucial for improving quality of life, preventing functional decline, and optimizing treatment outcomes.

Artificial intelligence and prescription of antibiotic therapy: present and future.

INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED: In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION: Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

COVID-19 throughout pandemic waves and virus variants: a real-life experience in an Italian hospital.

New therapies and vaccines changed the management of COVID-19. The aim of this retrospective study was to describe characteristics, in-hospital mortality and its predictors in patients with moderate/severe COVID-19, considering the 4 different pandemic waves and viral variants' prevalence from February 2020 to January 2022. Among 1135 patients included, 873 (77%) had at least one comorbidity, 177 (16%) were immunocompromised. From waves 1 to 4, patients with severe respiratory failure and ICU admission decreased over time (p < 0.001), like the length of in-hospital stay (p < 0.001). Despite a reduction of in-hospital mortality from 19% to 11%, increased risk of death was related to older age and immunocompromising conditions, especially during the 4th wave (HR = 5.07 and HR = 10.86, p < 0.001 respectively) while remdesivir treatment in the 3rd wave (HR = 0.41, p = 0.010) and positive serology (aHR = 0.66, p = 0.027) were protective for survival. These data support the need for tailoring vaccine campaign for future COVID-19 waves.

Use of Cefiderocol in Adult Patients: Descriptive Analysis from a Prospective, Multicenter, Cohort Study.

INTRODUCTION: Cefiderocol is a siderophore cephalosporin showing activity against various carbapenem-resistant Gram-negative bacteria (CR-GNB). No data currently exist about real-world use of cefiderocol in terms of types of therapy (e.g., empirical or targeted, monotherapy or combined regimens), indications, and patient characteristics. METHODS: In this multicenter, prospective study, we aimed at describing the use of cefiderocol in terms of types of therapy, indications, and patient characteristics. RESULTS: Cefiderocol was administered as empirical and targeted therapy in 27.5% (55/200) and 72.5% (145/200) of cases, respectively. Overall, it was administered as monotherapy in 101/200 cases (50.5%) and as part of a combined regimen for CR-GNB infections in the remaining 99/200 cases (49.5%). In multivariable analysis, previous isolation of carbapenem-resistant Acinetobacter baumannii odds ratio (OR) 2.56, with 95% confidence interval (95% CI) 1.01-6.46, p = 0.047] and previous hematopoietic stem cell transplantation (OR 8.73, 95% CI 1.05-72.54, p = 0.045) were associated with administration of cefiderocol as part of a combined regimen, whereas chronic kidney disease was associated with cefiderocol monotherapy (OR 0.38 for combined regimen, 95% CI 0.16-0.91, p = 0.029). Cumulative 30-day mortality was 19.8%, 45.0%, 20.7%, and 22.7% in patients receiving targeted cefiderocol for infections by Enterobacterales, A. baumannii, Pseudomonas aeruginosa, and any metallo-ß-lactamase producers, respectively. CONCLUSIONS: Cefiderocol is mainly used for targeted treatment, although empirical therapies account for more than 25% of prescriptions, thus requiring dedicated standardization and guidance. The almost equal distribution of cefiderocol monotherapy and cefiderocol-based combination therapies underlines the need for further study to ascertain possible differences in efficacy between the two approaches.

Prediction of candidemia with machine learning techniques: state of the art.

In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia. However, further research is needed in terms of sample size, data quality, recognition of biases and interpretation of model outputs by clinicians to better understand if and how these techniques could be safely adopted in daily clinical practice.

Candida is a type of fungus that can cause fatal infections. To confirm the presence of the infection, doctors may search for the fungus in the blood. Here, we discuss if computer systems can help to identify infection more easily and more rapidly.

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of artificial intelligence and ML indicates the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed health care personnel. Improving our knowledge about how complex ML models work could help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.

BACKGROUND: Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48-72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. METHODS: In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. RESULTS: Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum ß-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. CONCLUSIONS: Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.

Pneumocystis jirovecii pneumonia in intensive care units: a multicenter study by ESGCIP and EFISG.

BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an opportunistic, life-threatening disease commonly affecting immunocompromised patients. The distribution of predisposing diseases or conditions in critically ill patients admitted to intensive care unit (ICU) and subjected to diagnostic work-up for PJP has seldom been explored. MATERIALS AND METHODS: The primary objective of the study was to describe the characteristics of ICU patients subjected to diagnostic workup for PJP. The secondary objectives were: (i) to assess demographic and clinical variables associated with PJP; (ii) to assess the performance of Pneumocystis PCR on respiratory specimens and serum BDG for the diagnosis of PJP; (iii) to describe 30-day and 90-day mortality in the study population. RESULTS: Overall, 600 patients were included in the study, of whom 115 had presumptive/proven PJP (19.2%). Only 8.8% of ICU patients subjected to diagnostic workup for PJP had HIV infection, whereas hematological malignancy, solid tumor, inflammatory diseases, and solid organ transplants were present in 23.2%, 16.2%, 15.5%, and 10.0% of tested patients, respectively. In multivariable analysis, AIDS (odds ratio [OR] 3.31; 95% confidence interval [CI] 1.13-9.64, p = 0.029), non-Hodgkin lymphoma (OR 3.71; 95% CI 1.23-11.18, p = 0.020), vasculitis (OR 5.95; 95% CI 1.07-33.22, p = 0.042), metastatic solid tumor (OR 4.31; 95% CI 1.76-10.53, p = 0.001), and bilateral ground glass on CT scan (OR 2.19; 95% CI 1.01-4.78, p = 0.048) were associated with PJP, whereas an inverse association was observed for increasing lymphocyte cell count (OR 0.64; 95% CI 0.42-1.00, p = 0.049). For the diagnosis of PJP, higher positive predictive value (PPV) was observed when both respiratory Pneumocystis PCR and serum BDG were positive compared to individual assay positivity (72% for the combination vs. 63% for PCR and 39% for BDG). Cumulative 30-day mortality and 90-day mortality in patients with presumptive/proven PJP were 52% and 67%, respectively. CONCLUSION: PJP in critically ill patients admitted to ICU is nowadays most encountered in non-HIV patients. Serum BDG when used in combination with respiratory Pneumocystis PCR could help improve the certainty of PJP diagnosis.

Mortality in KPC-producing Klebsiella pneumoniae bloodstream infections: a changing landscape.

OBJECTIVES: To assess the impact of carbapenem resistance on mortality in Klebsiella pneumoniae bloodstream infection (BSI) in the era of novel ß-lactam/ß-lactamase inhibitor combinations. MATERIAL AND METHODS: Retrospective study of patients with K. pneumoniae BSI between January and August 2020 in 16 centres (CARBANEW study within the MULTI-SITA project). RESULTS: Overall, 426 patients were included: 107/426 (25%) had carbapenem-resistant K. pneumoniae (CR-Kp) BSI and 319/426 (75%) had carbapenem-susceptible K. pneumoniae (CS-Kp) BSI. Crude cumulative 30 day mortality was 33.8% and 20.7% in patients with, respectively, CR-Kp BSI and CS-Kp BSI (P = 0.027). Carbapenemase production or carbapenemase-encoding genes were detected in 84/98 tested CR-Kp isolates (85.7%), mainly KPC (78/84; 92.9%). Ceftazidime/avibactam was the most frequently used appropriate therapy for CR-Kp BSI (80/107; 74.7%). In multivariable analyses, variables showing an unfavourable association with mortality after correction for multiple testing were age-adjusted Charlson comorbidity index (HR 1.20; 95% CI 1.10-1.31, P < 0.001) and Pitt score (HR 1.33; 95% CI 1.15-1.55, P < 0.001), but not carbapenem resistance (HR 1.28, 95% CI 0.74-2.22, P = 0.410). In a propensity score-matched analysis, there was no difference in mortality between patients appropriately treated with ceftazidime/avibactam for CR-Kp BSI and patients appropriately treated with other agents (mainly meropenem monotherapy or piperacillin/tazobactam monotherapy) for CS-Kp BSI (HR 1.07; 95% CI 0.50-2.29, P = 0.866). CONCLUSIONS: Our results suggest that the increased mortality in CR-Kp BSI compared with CS-Kp BSI is not (or no longer) dependent on the type of therapy in areas where ceftazidime/avibactam-susceptible KPC-producing isolates are the most prevalent type of CR-Kp.

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact.

BACKGROUND: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. METHODS: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. RESULTS: Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81-5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50-3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92-2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. CONCLUSIONS: The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study.

Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype's development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics.

Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.

There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia. The manual validation of the random extraction of 381 episodes of candidemia and/or bacteremia, with automated organization in structured features of laboratory and microbiological data resulted in ≥99% correct extractions (with confidence interval < ±1%) for all variables. The final automatically extracted dataset consisted of 1338 episodes of candidemia (8%), 14,112 episodes of bacteremia (90%), and 302 episodes of mixed candidemia/bacteremia (2%). The final dataset will serve to assess the performance of different ML models for the early diagnosis of candidemia in the second phase of the AUTO-CAND project.

Increased Mortality Associated with Higher Pre-Dialysis Serum Sodium Variability: Results of the International MONitoring Dialysis Outcome Initiative.

BACKGROUND: Low serum sodium (SNa) is associated with an increased mortality in chronic hemodialysis (HD) patients. Dialysis patients are thought to have an individual pre-dialysis SNa set-point, yet there is evidence for variability of pre-dialysis SNa in individual patient. In this study, we explored the association of several SNa variability metrics with all-cause mortality in a large patient population from the international MONitoring Dialysis Outcomes (MONDO) Initiative. METHODS: All adult incident patients from the MONDO database with more than 5 SNa measurements during the first year on HD were included. All patients were required to survive the first year on HD (defined as the baseline). During the subsequent 2 years of follow-up, all-cause mortality was recorded. The following variability indicators were calculated during baseline: mean SNa and its SD; average real variability (ARV, average the absolute distance of the 2 consecutive SNa measurements), and average directional range (DR, the difference between minimum and maximum values). We used Cox Proportional hazard model with bivariate spline terms to analyze the joint association of SNa and SD, ARV and DR, respectively, with all-cause mortality. While conducting the multivariate Cox regression analyses, patients were stratified into 3 groups of DR (Negative DR: -20≤ DR ≤ -6, Null DR: -6< DR < 6 and Positive DR: 6≤ DR ≤20) with the Null DR as the reference group. RESULTS: We included 20,216 patients in the study. A SNa ≤135 mEq/L was observed to be the strongest predictor of evaluated mortality risk. Higher SNa variability (quantified as SD, ARV, and DR) was also associated with an increased mortality irrespective of SNa levels. When compared with higher SD or ARV, greater DR showed a stronger association with an elevated risk of death. Controlling the Cox Proportional hazard models for additional parameters showed consistent results. CONCLUSION: Higher SNa variability associated with increased all-cause mortality at all levels of SNa. DR of SNa showed the strongest association with mortality and may constitute a Simple and novel prognostic indicator, easily applicable at the bedside.

Microbiota and metabolic diseases.

The microbiota is a complex ecosystem of microorganisms consisting of bacteria, viruses, protozoa, and fungi, living in different districts of the human body, such as the gastro-enteric tube, skin, mouth, respiratory system, and the vagina. Over 70% of the microbiota lives in the gastrointestinal tract in a mutually beneficial relationship with its host. The microbiota plays a major role in many metabolic functions, including modulation of glucose and lipid homeostasis, regulation of satiety, production of energy and vitamins. It exerts a role in the regulation of several biochemical and physiological mechanisms through the production of metabolites and substances. In addition, the microbiota has important anti-carcinogenetic and anti-inflammatory actions. There is growing evidence that any modification in the microbiota composition can lead to several diseases, including metabolic diseases, such as obesity and diabetes, and cardiovascular diseases. This is because alterations in the microbiota composition can cause insulin resistance, inflammation, vascular, and metabolic disorders. The causes of the microbiota alterations and the mechanisms by which microbiota modifications can act on the development of metabolic and cardiovascular diseases have been reported. Current and future preventive and therapeutic strategies to prevent these diseases by an adequate modulation of the microbiota have been also discussed.

Association between Heights of Dialysis Patients and Outcomes: Results from a Retrospective Cohort Study of the International MONitoring Dialysis Outcomes (MONDO) Database Initiative.

BACKGROUND: Tall people have improved metabolic profiles and better cardiovascular outcomes, a relationship inverted in dialysis patients. We investigated the relationship between height and outcomes in incident hemodialysis (HD) patients commencing treatment in an analysis of the international Monitoring Dialysis Outcomes (MONDO) database. METHODS: In this retrospective cohort study, we included incident HD patients commencing treatment between -January 1, 2006 and December 31, 2010 and investigated the association between height and mortality using the MONDO database. A 6-months baseline period preceded 2.5 years of follow-up, during which we recorded patient mortality. Patients were stratified in region-specific deciles of the respective database's population (Asia Pacific, North and South America, and Europe) and we developed Cox-proportional hazard models (additionally adjusted for age, gender, post-dialysis weight, eKt/V, albumin, interdialytic weight gain, phosphorus, and predialysis systolic blood pressure) for each database. RESULTS: We studied 23,353 patients (62 ± 15 years old, 42% female, body mass index 26 ± 6 kg/m2, height 165 ± 10 cm). We found a trend of increasing hazard ratio of death (HR) with increasing height for Asia Pacific, Europe, and South America. In the fully adjusted models, for South America, we found a trend of increasing HR without significance among deciles >5. In Europe, deciles 8-10 had significantly increased HR. No clear trend was found in North America. CONCLUSION: We found an increasing risk of death with increasing height in all regions, except North America. While the reasons remain unclear, further research may be warranted.

Dynamics of the erythropoiesis stimulating agent resistance index in incident hemodiafiltration and high-flux hemodialysis patients.

Hyporesponsiveness to erythropoiesis-stimulating agent therapy in dialysis patients is poorly understood. Some studies report an improvement in the erythropoiesis-stimulating agent resistance index (ERI) with hemodiafiltration (HDF) versus high-flux hemodialysis (HD). We explored ERI dynamics in 38,340 incident HDF and HD patients treated in 22 countries over a 7-year period. Groups were matched by propensity score at baseline (6 months after dialysis initiation). The follow-up period (mean of 1.31 years) was stratified into 1 month intervals with delta analyses performed for key ERI-related parameters. Dialysis modality, time interval, and polycystic kidney disease were included in a linear mixed model with the outcome ERI. Baseline ERI was nonsignificantly higher in HDF versus HD treatment. ERI decreased significantly faster in HDF-treated patients than in HD-treated patients, was decreased in both HD and HDF when patients were treated with intravenous darbepoetin alfa, but only in HDF when treated with intravenous recombinant human erythropoietin (rHuEPO). A clear difference between HD- and HDF-treated patients could only be found for patients with high baseline ERI and assigned to intravenous rHuEPO treatment. A significant advantage in terms of lower ERI for patients treated by HDF was found. Sensitivity analysis limited this advantage for HDF to those patients treated with intravenous rHuEPO (not darbepoetin alfa or subcutaneous rHuEPO) and to patients with a high baseline ERI. Thus, our results allow more accurate planning for future clinical trials addressing anemia management in dialysis patients.

Hemodialysis outcomes in a global sample of children and young adult hemodialysis patients: the PICCOLO MONDO cohort.

BACKGROUND: The aim of this study was to describe the experience of pediatric and young adult hemodialysis (HD) patients from a global cohort. METHODS: The Pediatric Investigation and Close Collaborative Consortium for Ongoing Life Outcomes for MONitoring Dialysis Outcomes (PICCOLO MONDO) study provided de-identified electronic information of 3244 patients, ages 0-30 years from 2000 to 2012 in four regions: Asia, Europe, North America and South America. The study sample was categorized into pediatric (≤18 years old) and young adult (19-30 years old) groups based on the age at dialysis initiation. RESULTS: For those with known end-stage renal disease etiology, glomerular disease was the most common diagnosis in children and young adults. Using Europe as a reference group, North America [odds ratio (OR) 2.69; CI 1.29, 5.63] and South America (OR 4.21; CI 2.32, 7.63) had the greatest mortality among young adults. North America also had higher rates of overweight, obesity, hypertension, cardiovascular disease, hospitalizations and secondary diabetes compared with all other regions. Initial catheter use was greater for North American (86.4% in pediatric patients and 75.2% in young adults) and South America (80.6% in pediatric patients and 75.9% in young adults). Catheter use at 1-year follow-up was most common in North American children (77.3%) and young adults (62.9%). Asia had the lowest rate of catheter use. For both age groups, dialysis adequacy (equilibrated Kt/V) ranged between 1.4 and 1.5. In Asia, patients in both age groups had significantly longer treatment times than in any other region. CONCLUSIONS: The PICCOLO MONDO study has provided unique baseline and 1-year follow-up information on children and young adults receiving HD around the globe. This cohort has brought to light aspects of care in these age groups that warrant further investigation.

Longitudinal Changes in Body Composition in Patients After Initiation of Hemodialysis Therapy: Results From an International Cohort.

OBJECTIVE: In patients with advanced kidney disease, metabolic and nutritional derangements induced by uremia interact and reinforce each other in a deleterious vicious circle. Literature addressing the effect of dialysis initiation on changes in body composition (BC) is limited and contradictory. The aim of this study was to evaluate changes in BC in a large international cohort of incident hemodialysis patients. METHODS: A total of 8,227 incident adult end-stage renal disease patients with BC evaluation within the initial first 6 months of baseline, defined as 6 months after renal replacement therapy initiation, were considered. BC, including fat tissue index (FTI) and lean tissue index (LTI), were evaluated by Body Composition Monitor (BCM, Fresenius Medical Care, Bad Homburg, Germany). Exclusion criteria at baseline were lack of a BCM measurement before or after baseline, body mass index (BMI) < 18.5 kg/m(2), presence of metastatic solid tumors, treatment with a catheter, and prescription of less or more than 3 treatments per week. Maximum follow-up was 2 years. Descriptive analysis was performed comparing current values with the baseline in each interval (delta analysis). Linear mixed models considering the correlation structure of the repeated measurements were used to evaluate factors associated with different trends in FTI and LTI. RESULTS: BMI increased about 0.6 kg/m(2) over 24 months from baseline. This was associated with increase in FTI of about 0.95 kg/m(2) and a decrease in LTI of about 0.4 kg/m(2). Female gender, diabetic status, and low baseline FTI were associated with a significant greater increase of FTI. Age > 67 years, diabetes, male gender, high baseline LTI, and low baseline FTI were associated with a significant greater decrease of LTI. CONCLUSIONS: With the transition to hemodialysis, end-stage renal disease patients presented with distinctive changes in BC. These were mainly associated with gender, older age, presence of diabetes, low baseline FTI, and high baseline LTI. BMI increases did not fully represent the changes in BC.

Seasonal variations in mortality and clinical indicators in international hemodialysis populations from the MONDO registry.

BACKGROUND: Seasonal mortality differences have been reported in US hemodialysis (HD) patients. Here we examine the effect of seasons on mortality, clinical and laboratory parameters on a global scale. METHODS: Databases from the international Monitoring Dialysis Outcomes (MONDO) consortium were queried to identify patients who received in-center HD for at least 1 year. Clinics were stratified by hemisphere and climate zone (tropical or temperate). We recorded mortality and computed averages of pre-dialysis systolic blood pressure (pre-SBP), interdialytic weight gain (IDWG), serum albumin, and log C-reactive protein (CRP). We explored seasonal effects using cosinor analysis and adjusted linear mixed models globally, and after stratification. RESULTS: Data from 87,399 patients were included (northern temperate: 63,671; northern tropical: 7,159; southern temperate: 13,917; southern tropical: 2,652 patients). Globally, mortality was highest in winter. Following stratification, mortality was significantly lower in spring and summer compared to winter in temperate, but not in tropical zones. Globally, pre-SBP and IDWG were lower in summer and spring as compared to winter, although less pronounced in tropical zones. Except for southern temperate zone, serum albumin levels were higher in winter. CRP levels were highest in winter. CONCLUSION: Significant global seasonal variations in mortality, pre-SBP, IDWG, albumin and CRP were observed. Seasonal variations in mortality were most pronounced in temperate climate zones.

Relationship of Neutrophil-to-Lymphocyte Ratio and Serum Albumin Levels with C-Reactive Protein in Hemodialysis Patients: Results from 2 International Cohort Studies.

BACKGROUND/AIM: The neutrophil-to-lymphocyte ratio (NLR), defined as the neutrophil count divided by lymphocyte count, is an inexpensive and readily available parameter, which may serve as a surrogate for inflammation markers, such as C-reactive protein (CRP). The aim of this study was to determine the utility of NLR in the prediction of elevated CRP levels in hemodialysis (HD) patients. METHODS: We analyzed 43,272 HD patients from 2 distinct cohorts within the Monitoring Dialysis Outcomes research collaboration in whom contemporaneous measurements of neutrophil and lymphocyte counts, serum albumin and CRP levels were available. Logistic regression was used to determine the relationship of trichotomized NLR (<2.5, 2.5-5 and >5.0) and albumin levels (<3.1, 3.1-4.0 and >4.0 g/dl) with elevated CRP levels (>10.0, >20.0 and >30.0 mg/l). Congruence of the prediction models was examined by comparing the regression parameters and by cross-validating each regression equation within the other cohort. RESULTS: We found that NLR >5.0 vs. <2.5 (cohort 1: OR 2.3; p < 0.0001 and cohort 2: OR 2.0; p < 0.0001) was associated with CRP levels >10.0 mg/l. Stepwise increase in odds ratio for CRP >10.0 mg/l was observed with the combination of high NLR and low albumin levels (NLR >5.0 and albumin <3.1) (cohort 1: OR 7.6; p < 0.0001 and cohort 2: OR 11.9; p < 0.0001). Cross-validation of the 2 regression models revealed a predictive accuracy of 0.68 and 0.69 in the respective cohorts. CONCLUSION: This study suggests that NLR could serve as a potential surrogate marker for CRP. Our results may add to diagnostic abilities in settings where CRP is not measured routinely in HD patients. NLR is easy to integrate into daily practice and may be used as a marker of systemic inflammation.