Clinical outcome predictors for metastatic renal cell carcinoma: a retrospective multicenter real-life case series.

Over the last decades, the therapeutic armamentarium of metastatic renal cell carcinoma (mRCC) has been revolutionized by the advent of tyrosin-kinase inhibitors (TKI), immune-checkpoint inhibitors (ICI), and immune-combinations. RCC is heterogeneous, and even the most used validated prognostic systems, fail to describe its evolution in real-life scenarios. Our aim is to identify potential easily-accessible clinical factors and design a disease course prediction system. Medical records of 453 patients with mRCC receiving sequential systemic therapy in two high-volume oncological centres were reviewed. The Kaplan-Meier method and Cox proportional hazard model were used to estimate and compare survival between groups. As first-line treatment 366 patients received TKI monotherapy and 64 patients received ICI, alone or in combination. The mean number of therapy lines was 2.5. A high Systemic Inflammation Index, a BMI under 25 Kg/m2, the presence of bone metastases before systemic therapy start, age over 65 years at the first diagnosis, non-clear-cell histology and sarcomatoid component were correlated with a worse OS. No significant OS difference was observed between patients receiving combination therapies and those receiving exclusively monotherapies in the treatment sequence. Our relapse prediction system based on pathological stage and histological grade was effective in predicting the time between nephrectomy and systemic treatment. Our multicentric retrospective analysis reveals additional potential prognostic factors for mRCC, not included in current validated prognostic systems, suggests a model for disease course prediction and describes the outcomes of the most common therapeutic strategies currently available.

Corrigendum to "Effect of Sustained Clinical Remission on the Risk of Lupus Flares and Impaired Kidney Function in Patients With Lupus Nephritis" [Kidney International Reports Volume 9, Issue 4, April 2024, Pages 1047-1056].

[This corrects the article DOI: 10.1016/j.ekir.2024.01.016.].

Effect of Sustained Clinical Remission on the Risk of Lupus Flares and Impaired Kidney Function in Patients With Lupus Nephritis.

Introduction: This retrospective study on patients with biopsy-proven lupus nephritis (LN) aimed to assess the probability of sustained clinical remission (sCR) and to investigate sCR effects on disease flares and impaired kidney function (IKF). Methods: sCR was defined as clinical-Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) = 0 and estimated glomerular filtration rate (eGFR) >60 ml/min per 1.73 m2 lasting ≥1 year; IKF: eGFR <60 ml/min per 1.73 m2 for >3 months. We analyzed the probability of achieving and maintaining sCR, and the yearly risk of flare. Cox models were used to identify predictors of sCR and IKF with variables analyzed as time-dependent covariates when appropriate. Results: Of 303 patients followed-up with for 14.8 (interquartile range: 9.8-22) years, 257 (84.8%) achieved sCR. The probability of achieving sCR progressively increased over time reaching 90% at 15 years. Baseline age (hazard ratio [HR]: 1.017; 95% confidence interval [CI]: 0.005-1.029; P = 0.004), hydroxychloroquine intake (HR: 1.385; 95% CI: 1.051-1.825; P = 0.021), and absence of arterial hypertension (HR: 0.699; 95% CI: 0.532-0.921; P = 0.011) were independent predictors of sCR. Among patients who achieved sCR, 142 (55.3%) developed a lupus flare after a median time of 3.6 (2.3-5.9) years. In the remaining 115 patients, sCR persisted for 9.5 (5.8-14.5) years. The probability of sCR to persist at 15 years was 38%. SLE flare risk decreased to 10%, 5%, and 2% in patients with sCR lasting <5, 5 to 10, and >10 years, respectively. At the last observation, 57 patients (18.81%) had IKF. sCR achievement (HR: 0.18, P < 0.001) and its duration (HR: 0.83, P < 0.001) were protective against IKF. Conclusion: sCR is an achievable target in LN management and protects against IKF. The longer the sCR, the higher the chance of its persistence and the lower the risk of SLE flares.

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities.

INTRODUCTION: A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments. METHODS: We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps. RESULTS: Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found. CONCLUSION: The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.

Azathioprine vs methotrexate in eosinophilic granulomatosis with polyangiitis: a monocentric retrospective study.

OBJECTIVES: To analyse the effectiveness, safety and steroid-sparing effect of AZA and MTX as induction of remission and maintenance treatment in eosinophilic granulomatosis with polyangiitis. METHODS: We retrospectively collected data from 57 patients divided into four groups according to treatment: MTX/AZA as first-line agents (MTX1/AZA1) in non-severe disease or as second-line maintenance therapy (MTX2/AZA2) in severe disease previously treated with CYC/rituximab. During the first 5 years of treatment with AZA/MTX we compared the groups according to: remission rate [defined as R1: BVAS = 0; R2: BVAS = 0 with prednisone ≤5 mg/day; R3 (MIRRA definition): BVAS = 0 with prednisone ≤3.75 mg/day], persistence on therapy, cumulative glucocorticoid (GC) dose, relapse and adverse events (AEs). RESULTS: There were no significant differences in remission rates (R1) in each group (63% in MTX1 vs 75% in AZA1, P = 0.53; 91% in MTX2 vs 71% in AZA2, P = 0.23). MTX1 allowed R2 more frequently in the first 6 months compared with AZA1 (54% vs 12%, P = 0.04); no patients receiving AZA1 achieved R3 up to the first 18 months (vs 35% in MTX1, P = 0.07). The cumulative GC dose was lower for MTX2 vs AZA2 (6 g vs 10.7 g at 5 years, P = 0.03). MTX caused more AEs compared with AZA (66% vs 30%, P = 0.004), without affecting the suspension rate. No differences emerged in time-to-first relapse, although fewer patients treated with AZA2 had asthma/ENT relapses (23% vs 64%, P = 0.04). CONCLUSION: A significant proportion of patients achieved remission with both MTX and AZA. MTX1 had an earlier remission on a lower GC dose but MTX2 had a better steroid-sparing effect.

In person versus remote cognitive rehabilitation in patients with subjective cognitive decline or neurocognitive disorders: what factors drive patient's preference?

Background: To date, there is still a lack of consensus for identifying the ideal candidate for cognitive telerehabilitation (TR). The main goal of the present study is to identify the factors associated to the preference for either TR or in-person cognitive training (CT) programs in older adults at risk of dementia or with early cognitive impairment. Methods: A sample of 56 participants with subjective cognitive decline or neurocognitive disorders eligible for CT were enrolled at the Dementia Research Center and Neurorehabilitation Unit of IRCCS Mondino Foundation. All individuals underwent a baseline assessment to capture their complete profile, including cognitive reserve and lifestyle habits, sociodemographic characteristics, cognitive functioning, and mental health. Patients were then asked their preference for TR or in-person CT, before being randomized to either treatment as per protocol procedures. Statistical analyses included explorative descriptive approach, logistic regression, and non-parametric models to explore the overall contribution of each variable. Results: The two (TR and in-person) preference groups were similar for cognitive functioning and mental health status. Socio-demographic and lifestyle profiles seem to be the most important factors to influence the preference in terms of the area under the curve (AUC) of the models. The two preference groups differed in terms of socio-demographic characteristics (e.g., level of technological skills, age, and distance from the clinic). Furthermore, participants who selected the TR modality of CT had significantly higher levels of cognitive reserve and adopted more protective lifestyle habits (e.g., regular physical activity, Mediterranean diet) when compared to those who preferred in-person CT. Discussion: These findings highlight that the preference to receive CT delivered by TR or in person is a complex issue and is influenced by a variety of factors, mostly related to lifestyle habits and sociodemographic characteristics. Availability of profiles of patients that may be more attracted to one or the other modality of TR may help promote shared decision-making to enhance patient experience and outcomes.

An eHealth App (CAPABLE) Providing Symptom Monitoring, Well-Being Interventions, and Educational Material for Patients With Melanoma Treated With Immune Checkpoint Inhibitors: Protocol for an Exploratory Intervention Trial.

BACKGROUND: Since treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools to provide either symptom monitoring or interventions to reduce treatment-related symptoms such as fatigue. However, an eHealth tool that facilitates the combination of both symptom monitoring and symptom management in patients with melanoma treated with ICIs is still needed. OBJECTIVE: In this pilot study, we will explore the use of the CAPABLE (Cancer Patients Better Life Experience) app in providing symptom monitoring, education, and well-being interventions on health-related quality of life (HRQoL) outcomes such as fatigue and physical functioning, as well as patients' acceptance and usability of using CAPABLE. METHODS: This prospective, exploratory pilot study will examine changes in fatigue over time in 36 patients with stage III or IV melanoma during treatment with ICI using CAPABLE (a smartphone app and multisensory smartwatch). This cohort will be compared to a prospectively collected cohort of patients with melanoma treated with standard ICI therapy. CAPABLE will be used for a minimum of 3 and a maximum of 6 months. The primary endpoint in this study is the change in fatigue between baseline and 3 and 6 months after the start of treatment. Secondary end points include HRQoL outcomes, usability, and feasibility parameters. RESULTS: Study inclusion started in April 2023 and is currently ongoing. CONCLUSIONS: This pilot study will explore the effect, usability, and feasibility of CAPABLE in patients with melanoma during treatment with ICI. Adding the CAPABLE system to active treatment is hypothesized to decrease fatigue in patients with high-risk and advanced melanoma during treatment with ICIs compared to a control group receiving standard care. The Medical Ethics Committee NedMec (Amsterdam, The Netherlands) granted ethical approval for this study (reference number 22-981/NL81970.000.22). TRIAL REGISTRATION: ClinicalTrials.gov NCT05827289; https://clinicaltrials.gov/study/NCT05827289. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49252.

V-care: An application to support lifestyle improvement in children with obesity.

BACKGROUND: Obesity is increasing in the pediatric population, and it represents an important risk factor for the life-long development of several diseases. Although health promotion represents the mainstay of obesity prevention and treatment, lifestyle modification programs are often unsuccessful. OBJECTIVES: The purpose of this article is to introduce the V-care app, a mobile health platform specifically developed to offer effective interaction and support young people in a long-term obesity treatment, combining different strategies to maximize the results of the lifestyle modification program and minimize the possibility of dropouts. METHODS: The V-care app is based on a conventional client-server architecture, but novelties of our approach are the involvement of families in the lifestyle modification program, and the design inspired to psychological/behavioral change theories, with the aim of raising the chance of patients' compliance to the program. V-care implements a goal-based behavioral intervention, providing specific feedbacks according to the patient's performance. A pilot usability and acceptability study was performed on a sample of thirteen children aged 6-12 years, using a questionnaire with a 5-points Likert scale to evaluate eight system features, identified as essential requirements based on the analysis of strengths and weaknesses of similar systems in literature. RESULTS: The pilot study highlighted very high rate of overall friendliness and perceived utility evaluation, while some critical issues emerged especially for the chatbot section, which may be due to the novelty of the technology. The positive evaluation of the design choices is confirmed by the average score greater than 3 for all the questions. CONCLUSIONS: The V-care app represents a digital innovation in the pediatric healthcare, and it could be introduced in children's primary healthcare nationwide, with the aim to offer an intervention program for controlling and preventing childhood obesity.

The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment.

BACKGROUND: Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines. OBJECTIVES: As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation. METHODS: The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent. RESULTS: The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production. CONCLUSION: The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.

V-care: An Application to Support Lifestyle Improvement in Children with Obesity.

Obesity is increasing in the pediatric population and it represents an important risk factor for the life-long development of several diseases. The aim of this work is to reduce children obesity through an educational program delivered through a mobile application. Novelties of our approach are the involvement of the families in the program and a design inspired to psychological/behavioral change theories, with the aim of maximizing the chance of patients' compliance to the program. A pilot usability and acceptability study has been performed on ten children aged 6-12 years using a questionnaire to evaluate eight system features on a Likert scale from 1 to 5. Encouraging results were obtained: mean scores were all above 3.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms.

BACKGROUND AND OBJECTIVE: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. RESULTS: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. CONCLUSION: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

HomeCoRe system for telerehabilitation in individuals at risk of dementia: A usability and user experience study.

Background: Telerehabilitation has enabled a broader application of cognitive rehabilitation programs. We have recently developed HomeCoRe, a system for supporting cognitive intervention remotely with the assistance of a family member. The main goal of the present study was to determine usability and user experience of HomeCoRe in individuals at risk of dementia and in their family members. The association between subjects' technological skills and main outcome measures was evaluated as well. Methods: Fourteen individuals with subjective cognitive decline (SCD) or mild neurocognitive disorder (mNCD) were recruited to participate in this pilot study. All participants received a touch-screen laptop implemented with the HomeCoRe software. The intervention consisted of 18 sessions and included a patient-tailored adaptive protocol of cognitive exercises. Usability was assessed in terms of treatment adherence and participants' performance across sessions; user experience via self-reported questionnaires and a descriptive diary. Results: Usability and user experience were overall satisfactory and suggested usability, pleasantness, and high motivation while using HomeCoRe. Technological skills correlated only with the perceived ability to start and/or perform exercises autonomously. Discussion: These results, although preliminary, suggest that the usability and user experience of HomeCoRe are satisfactory and independent of technological skills. These findings encourage wider and more systematic use of HomeCoRe to overcome the current limitations of in-person cognitive rehabilitation programs and to reach more individuals at risk of dementia.

Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.

Background and objectives: Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment. Patients and methods: The retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems. Results: The median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses. Conclusions: Our AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.

A comparative study of the definitions of intradialytic hypotension correlated with increased mortality to identify universal predictors.

Intradialytic hypotension (IDH) is a common complication in patients undergoing hemodialysis therapy. No consensus on the definition of intradialytic hypotension has been established so far. As a result, coherent and consistent evaluation of its effects and causes is difficult. Some studies have highlighted existing correlations between certain definitions of IDH and the risk of mortality for the patients. This work is mainly focused on these definitions. Our aim is to understand if different IDH definitions, all correlated with increased mortality risk, catch the same onset mechanisms or dynamics. To check whether the dynamics captured by these definitions are similar, we performed analyses of the incidence, of the IDH event onset timing, and checked whether there were similarities between the definitions in those aspects. We evaluated how these definitions overlap with each other and we evaluated which common factors could allow identifying patients at risk of IDH at the beginning of a dialysis session. The definitions of IDH we analyzed through statistical and machine learning approaches, showed a variable incidence on the HD sessions and had different onset time. We found that the set of parameters relevant for the prediction of the IDH was not always the same for the definitions considered. However, it can be observed that some predictors, such as the presence of comorbidities such as diabetes or heart disease, and a low pre-dialysis diastolic blood pressure, have shown universal relevance in highlighting an increased risk of IDH during the treatment. Among those parameters, the one that showed a major importance is the diabetes status of the patients. Diabetes or heart disease presence are permanent risk factors pointing out an increased IDH risk during the treatments, while, pre-dialysis diastolic blood pressure is instead a parameter that can change at every session and should be used to evaluate the specific risk to develop IDH for each session. The identified parameters could be used in the future to train more complex prediction models.

Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: a data-driven cluster analysis.

The present study aimed to identify clusters of cognitive profiles as well as to explore the effects of these clusters on demographic/individual characteristics and on improvements after a computer-based cognitive training (CCT) in early cognitive impairment. Fifty-seven subjects underwent to an adaptive CCT for 3 weeks (4 individual face-to-face sessions/week of 45 min) and were evaluated at baseline (T0), post-intervention (T1), and after 6 (T2) and 12 (T3) months. Clusters of cognitive profiles were explored with k-means analysis. The analysis revealed two clusters, which were composed by 27 and 30 patients characterized by lower (Cluster 1) and higher (Cluster 2) cognitive functioning. At T1, cognitive performance improved in both groups, but Cluster 1 gained more benefits in global cognitive functioning than Cluster 2. However, at T3, Cluster 2 remained stable in its clinical condition, whereas Cluster 1 showed a pronounced worsening. In conclusion, Cluster 1 profile was associated with a more marked but also short-lasting responsiveness to CCT, whereas patients fitting with Cluster 2 characteristics seemed to obtain more CCT benefits in terms of stability or even delay of cognitive/functional decline. These findings may have relevant implications in informing the timing and modality of delivery of CCT.

Prognostic Value of Combined Radiomic Features from Follow-Up DWI and T2-FLAIR in Acute Ischemic Stroke.

The biological pathways involved in lesion formation after an acute ischemic stroke (AIS) are poorly understood. Despite successful reperfusion treatment, up to two thirds of patients with large vessel occlusion remain functionally dependent. Imaging characteristics extracted from DWI and T2-FLAIR follow-up MR sequences could aid in providing a better understanding of the lesion constituents. We built a fully automated pipeline based on a tree ensemble machine learning model to predict poor long-term functional outcome in patients from the MR CLEAN-NO IV trial. Several feature sets were compared, considering only imaging, only clinical, or both types of features. Nested cross-validation with grid search and a feature selection procedure based on SHapley Additive exPlanations (SHAP) was used to train and validate the models. Considering features from both imaging modalities in combination with clinical characteristics led to the best prognostic model (AUC = 0.85, 95%CI [0.81, 0.89]). Moreover, SHAP values showed that imaging features from both sequences have a relevant impact on the final classification, with texture heterogeneity being the most predictive imaging biomarker. This study suggests the prognostic value of both DWI and T2-FLAIR follow-up sequences for AIS patients. If combined with clinical characteristics, they could lead to better understanding of lesion pathophysiology and improved long-term functional outcome prediction.

Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device.

Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.

Personalising Symptoms Reporting in Telemonitoring Applications for Cancer Patients.

Patient reported outcomes have been shown to be predictive of cancer patients' prognosis, and their monitoring through electronic applications have been shown to positively impact survival. On the other hand, patient apps in general show a number of criticalities that often lead patients to abandon their use. One of them is usability. A scarce attention to usability during app development leads to unsatisfactory user experience. In this work, we present an algorithm to facilitate patient symptoms reporting, by personalising the list of symptoms according to their probability of occurrence in the specific patient. This avoids searching long lists of items, thus decreasing the patients' burden in symptom reporting.

Beyond ISN/RPS Lupus Nephritis Classification: Adding Chronicity Index to Clinical Variables Predicts Kidney Survival.

Background: A renewed interest for activity and chronicity indices as predictors of lupus nephritis (LN) outcome has emerged. Revised National Institutes of Health activity and chronicity indices have been proposed to classify LN lesions, but they should be validated by future studies. The aims of this study were (1) to detect the histologic features associated with the development of kidney function impairment (KFI), and (2) to identify the best clinical-histologic model to predict KFI at time of kidney biopsy. Methods: Patients with LN who had more than ten glomeruli per kidney biopsy specimen were admitted to the study. Univariate and multivariate logistic regression and Cox proportional hazards models were used to investigate whether activity and chronicity indices could predict KFI development. Results: Among 203 participants with LN followed for 14 years, correlations were found between the activity index, and its components, and clinical-laboratory signs of active LN at baseline. The chronicity index was correlated with serum creatinine. Thus, serum creatinine was significantly and directly correlated with both activity and chronicity indices. In the multivariate analysis, glomerulosclerosis (OR, 3.05; 95% CI, 1.17 to 7.91; P=0.02) and fibrous crescents (OR, 6.84; 95% CI, 3.22 to 14.52; P<0.001) associated with either moderate/severe tubular atrophy (OR, 3.17; 95% CI, 1.04 to 9.64; P=0.04), or with interstitial fibrosis (OR, 2.36; 95% CI, 1.05 to 5.32; P=0.04), predicted KFI. Considering both clinical and histologic features, serum creatinine (OR, 1.68; 95% CI, 1.31 to 2.15; P<0.001), arterial hypertension (OR, 4.64; 95% CI, 1.90 to 11.32; P<0.001), glomerulosclerosis (OR, 2.12; 95% CI, 1.00 to 4.50; P=0.05), and fibrous crescents (OR, 5.18; 95% CI, 2.43 to 11.04; P<0.001) independently predicted KFI. Older age (P<0.001) and longer delay between clinical onset of LN and kidney biopsy (P<0.001) were significantly correlated with baseline chronicity index. Conclusions: The chronicity index and its components, but not the activity index, were significantly associated with an impairment of kidney function. The Cox model showed that serum creatinine, arterial hypertension, chronic glomerular lesions, and delay in kidney biopsy predicted KFI. These data reinforce the importance of timely kidney biopsy in LN.

Corrigendum: Diagnostic Delay of Pulmonary Embolism in COVID-19 Patients.

[This corrects the article DOI: 10.3389/fmed.2021.637375.].