Nota: Estas diretrizes se prestam a informar e não a substituir o julgamento clínico do médico que, em última análise, deve determinar o tratamento apropriado para seus pacientes.

BackgroundThe chronic kidney disease (CKD) is a worldwide critical problem, especially in developing countries. CKD patients usually begin their treatment in advanced stages, which requires dialysis and kidney transplantation, and consequently, affects mortality rates. This issue is faced by a mobile health (mHealth) application (app) that aims to assist the early diagnosis and self-monitoring of the disease progression.MethodsA user-centered design (UCD) approach involving health professionals (nurse and nephrologists) and target users guided the development process of the app between 2012 and 2016. In-depth interviews and prototyping were conducted along with healthcare professionals throughout the requirements elicitation process. Elicited requirements were translated into a native mHealth app targeting the Android platform. Afterward, the Cohen’s Kappa coefficient statistics was applied to evaluate the agreement between the app and three nephrologists who analyzed test results collected from 60 medical records. Finally, eight users tested the app and were interviewed about usability and user perceptions.ResultsA mHealth app was designed to assist the CKD early diagnosis and self-monitoring considering quality attributes such as safety, effectiveness, and usability. A global Kappa value of 0.7119 showed a substantial degree of agreement between the app and three nephrologists. Results of face-to-face interviews with target users indicated a good user satisfaction. However, the task of CKD self-monitoring proved difficult because most of the users did not fully understand the meaning of specific biomarkers (e.g., creatinine).ConclusionThe UCD approach provided mechanisms to develop the app based on the real needs of users. Even with no perfect Kappa degree of agreement, results are satisfactory because it aims to refer patients to nephrologists in early stages, where they may confirm the CKD diagnosis.

In order to determine metabolic disorders in children with urolithiasis, 50 patients with urinary calculi were studied. Abdominal pain and/or haematuria were the most predominant symptoms. Surgical procedures were required in 22% of these children and urinary tract infection was observed in 34% of this group. Only 2 children had anatomical malformations of the urinary tract. Absorptive hypercalciuria (32%), renal hypercalciuria (34%) and uric acid hyperexcretion (24%) were the most common metabolic abnormalities in these children. We were unable to find an underlying metabolic abnormality in only 14% of the patients. These data suggest that appropriate metabolic study will allow rational management of children with urinary stones.

The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist's opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results. INDEX TERMS Reviews, machine learning, medical diagnosis.

BackgroundRenal involvement in Schistosoma mansoni infection is not well studied. The aim of this study is to investigate the occurrence of renal abnormalities in patients with hepatosplenic schistosomiasis (HSS), especially renal tubular disorders.MethodsThis is a cross-sectional study with 20 consecutive patients with HSS followed in a medical center in Maceió, Alagoas, Brazil. Urinary acidification and concentration tests were performed using calcium chloride (CaCl2) after a 12-h period of water and food deprivation. The biomarker monocyte chemoattractant protein 1 (MCP-1) was quantified in urine. Fractional excretion of sodium (FENa+), transtubular potassium gradient (TTKG) and solute-free water reabsorption (TcH2O) were calculated. The HSS group was compared to a group of 17 healthy volunteers.ResultsPatients' mean age and gender were similar to controls. Urinary acidification deficit was found in 45% of HSS patients. Urinary osmolality was significantly lower in HSS patients (588±112 vs. 764±165 mOsm/kg, p = 0,001) after a 12-h period of water deprivation. TcH2O was lower in HSS patients (0.72±0.5 vs. 1.1±0.3, p = 0.04). Urinary concentration deficit was found in 85% of HSS patients. The values of MCP-1 were higher in HSS group than in control group (122±134 vs. 40±28 pg/mg-Cr, p = 0.01) and positively correlated with the values of microalbuminuria and proteinuria.ConclusionsHSS is associated with important kidney dysfunction. The main abnormalities found were urinary concentrating ability and incomplete distal acidification defect, demonstrating the occurrence of tubular dysfunction. There was also an increase in urinary MCP-1, which appears to be a more sensitive marker of renal damage than urinary albumin excretion rate.