Learning from Adaptations to the COVID-19 Pandemic: How Teleconsultation Supported Cancer Care Pathways at a Comprehensive Cancer Center in Northern Italy.

Multidisciplinary team (MDT) meetings are recognized as the gold standard for care management of cancer patients, and during the COVID-19 pandemic they were considered a priority to be maintained. Due to pandemic-related restrictions, MDT meetings were forcibly converted from in-person to telematic format. This retrospective study evaluated the annual performance of four MDT meeting indicators (MDT members' attendance, number of discussed cases, frequency of MDT meetings, and duration) between 2019 and 2022 to report on the implementation of teleconsultation in MDT meetings related to 10 cancer care pathways (CCPs). Over the study period, MDT member participation and the number of discussed cases improved or did not change in 90% (9/10) and 80% (8/10) of the CCPs, respectively. We did not observe significant differences in any of the CCPs included in the study regarding the annual frequency and duration of MDT meeting. Considering the rapidity, extent, and intensity with which telematic tools were adopted due to the COVID-19 pandemic, the results of this study showed that MDT teleconsultation supported the CCPs, and consequently, the delivery of cancer care in COVID-19 times, helping to understand the effects of telematic tools on health care performance and the parties involved.

Search for familial hypercholesterolemia patients in an Italian community: A real-life retrospective study.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a common inherited disorder of low-density lipoprotein (LDL) catabolism that causes elevated LDL-cholesterol (LDL-C) and premature atherosclerotic cardiovascular disease (ASCVD). Despite the availability of effective treatments, FH remains underdiagnosed and undertreated. The aims of the study were to identify putative FH subjects using data from laboratory and cardiology databases, genetically characterize suspected FH patients referred to the Lipid Clinic and monitor attainment of treatment goals in identified patients. METHODS AND RESULTS: We retrieved the electronic health records of 221,644 individuals referred to laboratory for routine assessment and of 583 ASCVD patients (age ≤65) who underwent percutaneous transluminal coronary angioplasty (PTCA). We monitored the lipid profiles of subjects with LDL-C ≥ 250 mg/dl identified by laboratory survey (LS-P), PTCA patients and patients from the Lipid Clinic (LC-P). The laboratory survey identified 1.46% of subjects with LDL-C ≥ 190 mg/dl and 0.08% with LDL-C ≥ 250 mg/dl. Probable/definite FH was suspected in 3% of PTCA patients. Molecularly-confirmed FH was found in 44% of LC-P subjects. Five new LDLR mutations were identified. The 50% LDL-C reduction target was achieved by 70.6% of LC-P patients. Only 18.5% of PTCA patients reached the LDL-C < 55 mg/dl target. CONCLUSION: By using a combined approach based on laboratory lipid profiles, documented ASCVD and Lipid Clinic data, we were able to identify subjects with a high probability of being FH. Attainment of LDL-C goals was largely suboptimal. Efforts are needed to improve FH detection and achievement of lipid targets.

Pregnancy-related reference intervals for serum thyrotropin based on real-life clinical data.

AIM: During pregnancy, thyroid homeostasis is physiologically modified, leading to altered levels of thyrotropin (TSH): hence, the adoption of pregnancy-related, population- and method-specific reference ranges is recommended. This monocentric and retrospective study was conducted to establish local pregnancy-related reference intervals for serum TSH in singleton pregnant women using real-life clinical data. METHODS: We included women who measured serum TSH during pregnancy at our Laboratory over six years, excluding pregnant women with current or past history of thyroid disease, pituitary or autoimmune diseases, use of medications known to influence thyroid function, multiple and/or pathological pregnancies, BMI >30 Kg/m2. RESULTS: We retrieved a total of 3744 TSH results. Reference limits (90% confidence intervals) for TSH (in mIU/L) are: first trimester 0.09 (0.06-0.12) - 3.16 (3.05-3.29); second trimester 0.25 (0.11-0.30) - 3.55 (3.34-3.73); third trimester 0.42 (0.15-0.48) - 3.93 (3.80-4.08). CONCLUSION: In conclusion, real-life clinical data could be used to establish or verify local reference intervals for TSH in pregnant women: this may reduce the risk of misclassification of pregnant women undergoing thyroid function testing.

SARS-CoV-2 Positive Hospitalized Cancer Patients during the Italian Outbreak: The Cohort Study in Reggio Emilia.

In the coronavirus disease (COVID-19) pandemic, cancer patients could be a high-risk group due to their immunosuppressed status; therefore, data on cancer patients must be available in order to consider the most adequate strategy of care. We carried out a cohort study on the risk of hospitalization for COVID-19, oncological history, and outcomes on COVID-19 infected cancer patients admitted to the Hospital of Reggio Emilia. Between 1 February and 3 April 2020, a total of 1226 COVID-19 infected patients were hospitalized. The number of cancer patients hospitalized with COVID-19 infection was 138 (11.3%). The median age was slightly higher in patients with cancers than in those without (76.5 vs. 73.0). The risk of intensive care unit (ICU) admission (10.1% vs. 6.7%; RR 1.23, 95% Confidence Interval (CI) 0.63-2.41) and risk of death (34.1% vs. 26.0%; RR 1.07, 95% CI 0.61-1.71) were similar in cancer and non-cancer patients. In the cancer patients group, 89/138 (64.5%) patients had a time interval >5 years between the diagnosis of the tumor and hospitalization. Male gender, age > 74 years, metastatic disease, bladder cancer, and cardiovascular disease were associated with mortality risk in cancer patients. In the Reggio Emilia Study, the incidence of hospitalization for COVID-19 in people with previous diagnosis of cancer is similar to that in the general population (standardized incidence ratio 98; 95% CI 73-131), and it does not appear to have a more severe course or a higher mortality rate than patients without cancer. The phase II of the COVID-19 epidemic in cancer patients needs a strategy to reduce the likelihood of infection and identify the vulnerable population, both in patients with active antineoplastic treatment and in survivors with frequently different coexisting medical conditions.

Describing the Process and Tools Adopted to Cocreate a Smartphone App for Obesity Prevention in Childhood: Mixed Method Study.

BACKGROUND: Childhood obesity prevention is a public health priority in industrialized countries. The Reggio Emilia Local Health Authority has implemented a program involving primary and secondary prevention as well as the care of obese children. There are many health-promoting mobile apps, but few are targeted to children and very few are sponsored by public health agencies. OBJECTIVE: The goal of the research was to describe the process and tools adopted to cocreate a mobile app sponsored by the Reggio Emilia Local Health Authority to be installed in parents' phones aimed at promoting child health and preventing obesity. METHODS: After stakeholder mapping, a consulting committee including relevant actors, stakeholders, and users was formed. Key persons for childhood obesity prevention were interviewed, focus groups with parents and pediatricians were conducted, and community reporting storytelling was collected. The results of these activities were presented to the consulting committee in order to define the functionalities and contents of the mobile app. RESULTS: Three key trends emerged from community reporting: being active, playing, and being outdoors; time for oneself, family, and friends; and the pressures of life and work and not having time to be active and socialize. In focus groups, interviews, and labs, mothers showed a positive attitude toward using an app to manage their children's weight, while pediatricians expressed concerns that the app could increase their workload. When these findings were explored by the consulting committee, four key themes were extracted: strong relationships with peers, family members, and the community; access to safe outdoor spaces; children's need for age-appropriate independence; and professional support should be nonjudgmental and stigma-free. It should be a dialogue that promotes family autonomy. The app functions related to these needs include the following: (1) newsletter with anticipatory guidance, recipes, and vaccination and well-child visit reminders; (2) regional map indicating where physical activity can be done; (3) information on how to manage emergencies (eg, falls, burns, fever); (4) module for reinforcing the counseling intervention conducted by pediatricians for overweight children; and (5) a function to build a balanced daily diet. CONCLUSIONS: The pilot study we conducted showed that cocreation in health promotion is feasible, with the consulting committee being the key co-governance and cocreation tool. The involvement of stakeholders in this committee made it possible to expand the number of persons and institutions actively contributing to the project.

[Planning With Nanda, Noc, Nic Taxonomies In Neurologic Rehabilitation. A clinical study]. / La progettazione con tassonomie NANDA, NOC,NIC in riabilitazione neurologica.Uno studio clinico.

INTRODUCTION: Nursing classifications identify a specific professional responsibility, increase nursing visibility, according with nursing evolution of these last years. OBJECTIVE: To evaluate care planning with NANDA taxonomy in neurologic rehabilitation context. METHOD: Care plan managing with NANDA taxonomy, regarding diagnosis of constipation and impaired skin integrity, using a computerized tool for systematically observation, organized in check list. RESULTS: Registered data with taxonomy planning are higher in quantitative and qualitative terms. For most of patients (87%) one diagnosis has been opened, both diagnosis for 60% of them. CONCLUSION: Nursing care plan with NANDA taxonomy can be considered a valid methodology of care for neurologic patient, this since it requests a deep and complete registration of first assessment a systematically registration of each monitoring, it increases visibility of nursing job, and it underlines specific autonomy and responsibility in prevention and management of problems.

A novel method for the automatic grading of retinal vessel tortuosity.

Tortuosity is among the first alterations in the retinal vessel network to appear in many retinopathies, such as those due to hypertension. An automatic evaluation of retinal vessel tortuosity would help the early detection of such retinopathies. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. This justifies the need for a new definition, able to express in mathematical terms the tortuosity as perceived by ophthalmologists. We propose here a new algorithm for the evaluation of tortuosity in vessels recognized in digital fundus images. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. The algorithm has been compared with other available tortuosity measures on a set of 30 arteries and one of 30 veins from 60 different images. These vessels had been preliminarily ordered by a retina specialist by increasing perceived tortuosity. The proposed algorithm proved to be the best one in matching the clinically perceived vessel tortuosity.

Corneal endothelium cell field analysis by means of interacting Bayesian shape models.

A new method is proposed for the automatic detection and analysis of cell field contours in images of corneal endothelium. The algorithm is based on a set of single-cell contour models (a cell field), individually described statistically in term of shape a-priori information and a-posteriori image representation. Each cell can be individually identified (Maximum A Posteriori estimation) on the available image given a starting point and an appropriate optimization algorithm. Simulated Annealing has been adopted as the optimization algorithm to overcome the presence of several local minima in the resulting criterion function. When a cell field is considered, interaction between cell models can be used to introduce further information and improve the overall model identification. A statistical description of the cell field model is given by considering interaction between cell models. Preliminary results show that the extension from single cell models to field models improves the cell contours recognition. The developed theoretical framework is extremely flexible and can be easily adapted to different prior distributions or even to different object detection applications involving shape prior information.

Luminosity and contrast normalization in retinal images.

Retinal images are routinely acquired and assessed to provide diagnostic evidence for many important diseases, e.g. diabetes or hypertension. Because of the acquisition process, very often these images are non-uniformly illuminated and exhibit local luminosity and contrast variability. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. We propose here a new method to normalize luminosity and contrast in retinal images, both intra- and inter-image. The method is based on the estimation of the luminosity and contrast variability in the background part of the image and the subsequent compensation of this variability in the whole image. The application of this method on 33 fundus images showed an average 19% (max. 45%) reduction of luminosity variability and an average 34% (max. 85%) increment of image contrast, with a remarkable improvement, e.g., over low-pass correction. The proposed image normalization technique will definitely improve automatic fundus images analysis but will also be very useful to eye specialists in their visual examination of retinal images.

POPED, a software for optimal experiment design in population kinetics.

Population kinetic analysis is the methodology used to quantify inter-subject variability in kinetic studies. It entails the collection of (possibly sparse) data from dynamic experiments in a group of subjects and their quantitative interpretation by means of a mathematical model. This methodology is widely used in the pharmaceutical industry (where it is termed "pharmacokinetic population analysis") and recently it is becoming increasingly used in other areas of biomedical research. Unlike traditional kinetic studies, where the number of subjects can be quite small, population kinetic studies require large numbers of subjects. It is, therefore, of great interest to design these studies in the most efficient manner possible, to maximize the information content provided by the data. In this paper we propose an algorithm and a computer program, POPED, for the optimal design of a population kinetic experiment. In particular, the number of samples for each subject and the design of the individual sampling strategies, i.e. the number and location of the time points at which the output variable is sampled, will be considered. Among the various criteria proposed in the literature, D and ED optimality are the ones implemented in our software program, since they are the most widely used. A brief description of the techniques employed to perform design optimization is given, together with some details on their actual implementation. Some examples are then presented to show the program usage and the results provided.

A new tracking system for the robust extraction of retinal vessel structure.

Identification and measurement of blood vessels in retinal images could allow quantitative evaluation of clinical features, which may allow early diagnosis and effective monitoring of therapies in retinopathy. A new system is proposed for the automatic extraction of the vascular structure in retinal images, based on a sparse tracking technique. After processing pixels on a grid of rows and columns to determine a set of starting points (seeds), the tracking procedure starts. It moves along the vessel by analyzing subsequent vessel cross sections (lines perpendicular to the vessel direction), and extracting the vessel center, calibre and direction. Vessel points in a cross section are found by means of a fuzzy c-means classifier. When tracking stops because of a critical area, e.g. low contrast, bifurcation or crossing, a "bubble technique" module is run. It grows and analyzes circular scan lines around the critical points, allowing the exploration of the vessel structure beyond the critical areas. After tracking the vessels, identified segments are connected by a greedy connection algorithm. Finally bifurcations and crossings are identified analyzing vessel end points with respect to the vessel structure. Numerical evaluation of the performances of the system compared to human expert are reported.

An evaluation of population D-optimal designs via pharmacokinetic simulations.

One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.