Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

BACKGROUND: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. OBJECTIVE: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. METHODS: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. RESULTS: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. CONCLUSIONS: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

[This corrects the article DOI: 10.2196/27990.].

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

BACKGROUND: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE: The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS: A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS: This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS: An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.

Affordable Three-Dimensional Printed Heart Models.

This is a 7-years single institution study on low-cost cardiac three-dimensional (3D) printing based on the use of free open-source programs and affordable printers and materials. The process of 3D printing is based on several steps (image acquisition, segmentation, mesh optimization, slicing, and three-dimensional printing). The necessary technology and the processes to set up an affordable three-dimensional printing laboratory are hereby described in detail. Their impact on surgical and interventional planning, medical training, communication with patients and relatives, patients' perception on care, and new cardiac device development was analyzed. A total of 138 low-cost heart models were designed and printed from 2013 to 2020. All of them were from different congenital heart disease patients. The average time for segmentation and design of the hearts was 136 min; the average time for printing and cleaning the models was 13.5 h. The average production cost of the models was €85.7 per model. This is the most extensive series of 3D printed cardiac models published to date. In this study, the possibility of manufacturing three-dimensional printed heart models in a low-cost facility fulfilling the highest requirements from a technical and clinical point of view is demonstrated.

How to obtain a 3D printed model of renal cell carcinoma (RCC) with venous tumor thrombus extension (VTE) for surgical simulation (phase I NCT03738488).

This is the phase 1 of a multicenter clinical trial (NCT03738488), which aims to assess the efficacy and efficiency of surgery planning with 3D models of renal cell carcinoma (RCC) with venous tumor thrombus extension (VTE) compared to the standard images (CT). The objective of this phase is to obtain a 3D printed model of RCC with VTE that is feasible, accurate, reproducible, suitable for surgical simulation, and affordable. A specific protocol was developed to obtain the computed tomography (CT) image: early arterial and nephrogenic phase. ITK-snap® and VirSSPA Software® were used to segment the areas of interest. The resulting 3D mesh was processed with MeshMixer® and Cura®. Ten models from seven different cases were segmented and printed using different 3D printers and materials. We evaluated the material, scale, wall thickness, anatomy printed, 3D conformation, accuracy compared to the CT, suitability to perform the surgery, material, cost, and time (segmentation + design + fabrication + finishing). The four selected models were printed with a BQ Witbox FDM printer in polyurethane filament with a 0.8 mm wall thickness and 100% scale. All the relevant anatomical structures could be correctly identified, the 3D conformation was maintained with good accuracy compared to the CT and the surgery could be performed on them. Mean design time, model cost and printing time were 8.3 h, 33.4 €, and 38.5 h respectively. Various feasible 3D models of RCC with VTE were obtained after a few attempts. The final models were proved to be reproducible, accurate compared to the CT, and suitable for surgery simulation. The printing process was standardized making it possible to manufacture affordable 3D printed models.