Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment

Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform

Data collection on adverse reactions in recipients after vaccination is vital to evaluate potential health issues, but health observation diaries are onerous for participants. Here, we present a protocol to collect time series information using a smartphone or web-based platform, thus eliminating the need for paperwork and data submission. We describe steps for setting up the platform using the Model-View-Controller web framework, uploading lists of recipients, sending notifications, and managing respondent data. For complete details on the use and execution of this protocol, please refer to Ikeda et al. (2022).¹.

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

Background and Objective  Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine.

Methods  We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools.

Results  The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians.

Conclusion  This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder

OBJECTIVE

Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD.

METHODS

Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs.

RESULTS

Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs (n = 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used (n = 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups.

CONCLUSION

Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.

Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy

Purpose

Prostate cancer has a low mortality rate and requires persistent treatment; however, treatment decisions are challenging. Because prostate cancer is complex, the outcomes warrant thorough follow-up evaluation for appropriate treatment. Electronic health records (EHRs) do not present intuitive information. This study aimed to develop a Clinical Decision Support System (CDSS) for prognosis management of radical prostatectomy.

Methods

We used data from 5,199 prostate cancer patients from three hospitals' EHRs in South Korea, comprising laboratory results, surgery, medication, and radiation therapy. We used open source R for data preprocessing and development of web-based visualization system. We also used R for automatic calculation functionalities of two factors to visualize the data, e.g., Prostate-Specific Antigen Doubling Time (PSADT), and four Biochemical Recurrence (BCR) definitions: American Society of Therapeutic Radiology and Oncology (ASTRO), Phoenix, consecutive PSA > 0.2 ng/mL, and PSA > 0.2 ng/mL.

Results

We developed the Prostate Cancer Trajectory Map (PCT-Map) as a CDSS for intuitive visualization of serial data of PSA, testosterone, surgery, medication, radiation therapy, BCR, and PSADT.

Conclusions

The PCT-Map comprises functionalities for BCR and PSADT and calculates and visualizes the newly added patient data automatically in a PCT-Map data format, thus optimizing the visualization of patient data and allowing clinicians to promptly access patient data to decide the appropriate treatment.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Background

Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.

Results

In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.

Conclusions

By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

Development of Decision Support System to Predict Neurofeedback Response in ADHD: an Artificial Neural Network Approach

INTRODUCTION

Clinical decision support system (CDSS) is an analytical tool that converts raw data into useful information to help clinicians make better decisions for patients.

AIM

The purpose of this study was to investigate the efficacy of neurofeedback (NF), in Attention Deficit Hyperactivity Disorder (ADHD) by the development of CDSS based on artificial neural network (ANN).

METHODS

This study analyzed 122 patients with ADHD who underwent NF in the Parand-Human Potential Empowerment Institute in Tehran. The patients were divided into two groups according to the effects of NF: effective and non-effective groups. The patients' record information was mined by data mining techniques to identify effective features. Based on unsaturated condition of data and imbalanced classes between the patient groups (patients with successful NF response and those without it), the SMOTE technique was applied on dataset. Using MATLAB 2014a, a modular program was designed to test both multiple architectures of neural networks and their performance. Selected architecture of the neural networks was then applied in the procedure.

RESULTS

Eleven features from 28 features of the initial dataset were selected as effective features. Using the SMOTE technique, number of the samples rose to around 300 samples. Based on the multiple neural networks architecture testing, a network by 11-20-16-2 neurons was selected (specify>00.91%, sensivity=100%) and applied in the software.

CONCLUSION

The ANN used in this study has led to good results in sensivity, specificity, and AUC. The ANN and other intelligent techniques can be used as supportive tools for decision making by healthcare providers.

Efficacy of CPP-ACP and CPP-ACPF for Prevention and Remineralization of White Spot Lesions in Orthodontic Patients: a Systematic Review of Randomized Controlled Clinical Trials

INTRODUCTION

Enamel subsurface lesions or white spot lesions (WSLs) are commonly found in orthodontic patients with a prevalence of 5% to 97%.

AIM

This systematic review aimed to evaluate the efficacy of casein phosphopeptide amorphous calcium phosphate (CPP-ACP) and casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) for prevention and remineralization of WSLs in orthodontic patients in human randomized controlled clinical trials (RCTs).

METHODS

Relevant articles were retrieved by searching the Web of Science, Scopus, PubMed, and Cochrane Library databases up to November 2018 with no language or date restriction. The collected data included examination method, groups included in each study with number of patients in each group, study design, follow-up period and summary of important findings of each study. The risk of bias of each study was assessed according to the guidelines of the Cochrane Collaboration's tool.

RESULTS

Of 213 articles retrieved, 13 RCTs were included in this systematic review (none of them were included in the meta-analysis). Three articles showed superior efficacy of CPP-ACP for remineralization of WSLs while four studies reported the superior clinical efficacy of CPP-ACPF for this purpose.

CONCLUSION

Both CPP-ACP and CPP-ACPF can decrease the prevalence and increase the remineralization of WSLs during/after orthodontic treatment.

Attitudes toward inter-hospital electronic patient record exchange: discrepancies among physicians, medical record staff, and patients

Background

In this era of ubiquitous information, patient record exchange among hospitals still has technological and individual barriers including resistance to information sharing. Most research on user attitudes has been limited to one type of user or aspect. Because few analyses of attitudes toward electronic patient records (EPRs) have been conducted, understanding the attitudes among different users in multiple aspects is crucial to user acceptance. This proof-of-concept study investigated the attitudes of users toward the inter-hospital EPR exchange system implemented nationwide and focused on discrepant behavioral intentions among three user groups.

Methods

The system was designed by combining a Health Level 7-based protocol, object-relational mapping, and other medical informatics techniques to ensure interoperability in realizing patient-centered practices. After implementation, three user-specific questionnaires for physicians, medical record staff, and patients were administered, with a 70 % response rate. The instrument showed favorable convergent construct validity and internal consistency reliability. Two dependent variables were applied: the attitudes toward privacy and support. Independent variables comprised personal characteristics, work characteristics, human aspects, and technology aspects. Major statistical methods included exploratory factor analysis and general linear model.

Results

The results from 379 respondents indicated that the patients highly agreed with privacy protection by their consent and support for EPRs, whereas the physicians remained conservative toward both. Medical record staff was ranked in the middle among the three groups. The three user groups demonstrated discrepant intentions toward privacy protection and support. Experience of computer use, level of concerns, usefulness of functions, and specifically, reason to use electronic medical records and number of outpatient visits were significantly associated with the perceptions. Overall, four categories of independent variables were associated with the mean difference in the perceptions.

Conclusions

Discrepant attitudes toward privacy and support among the three user groups are identified. Patients may require further education and communication regarding the system. Culturally fit e-Consent should be incorporated into the system to fully utilize the computing power of the Internet when also considering workload. The concern for misuse of EPRs might lead to low support among physicians. Highly readable EPR documents and managerial incentives for information exchange may improve system use.

An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

Measurement equivalence and feasibility of the EORTC QLQ-PR25: paper-and-pencil versus touch-screen administration

OBJECTIVE

We assessed the measurement equivalence and feasibility of the paper-and-pencil and touch-screen modes of administration of the Taiwan Chinese version of the EORTC QLQ-PR25, a commonly used questionnaire to evaluate the health-related quality of life (HRQOL) in patients with prostate cancer in Taiwan.

METHODS

A cross-over design study was conducted in 99 prostate cancer patients at an urology outpatient clinic. Descriptive exact and global agreement percentages, intraclass correlation, and equivalence test based on minimal clinically important difference (MCID) approach were used to examine the equity of HRQOL scores between these two modes of administration. We also evaluated the feasibility of computerized assessment based on patients' acceptability and preference. Additionally, we used Rasch rating scale model to assess differential item functioning (DIF) between the two modes of administration.

RESULTS

The percentages of global agreement in all domains were greater than 85% in the EORTC QLQ-PR25. All results from equivalence tests were significant, except for Sexual functioning, indicating good equivalence. Only one item exhibited DIF between the two modes. Although nearly 80% of the study patients had no prior computer-use experience, the overall proportion of acceptance and preference for the touch-screen mode were quite high and there was no significant difference across age groups or between computer-use experience groups.

CONCLUSIONS

The study results showed that the data obtained from the modes of administration were equivalent. The touch-screen mode of administration can be a feasible and suitable alternative to the paper-and-pencil mode for assessment of patient-reported outcomes in patients with prostate cancer.

Interdisciplinary decision making in prostate cancer therapy - 5-years' time trends at the Interdisciplinary Prostate Cancer Center (IPC) of the Charité Berlin

Background

Patients with prostate cancer face the difficult decision between a wide range of therapeutic options. These men require elaborate information about their individual risk profile and the therapeutic strategies´ risks and benefits to choose the best possible option. In order to detect time trends and quality improvements between an early patient population (2003/2004) and a later reference group (2007/2008) data was analysed with regards to epidemiologic parameters, differences in diagnostics and the type and ranking of the recommended therapies taking into account changes to Gleason Grading System and implementation of new therapeutic strategies, particularly Active surveillance, in 2005.

Methods

Data from all 496 consecutive patients who received consultation in 2003/2004 (n = 280) and 2007/2008 (n = 216) was retrospectively evaluated. Categorical variables were compared using the Chi-square test. Dependent variables were analysed using the unpaired Students´ t-test and the Mann–Whitney U-test.

Results

The cohorts were comparable concerning clinical stage, initial PSA, prostate volume, comorbidities and organ confined disease. Patients in Cohort I were younger (66.44 vs. 69.31y; p < .001) and had a longer life expectancy (17.22 vs. 14.75y; p < .001). 50.9%, 28.2% and 20.9% in Cohort I and 37.2%, 39.6% and 23.2% in Cohort II showed low-, intermediate- and high-risk disease (D´Amico) with a trend towards an increased risk profile in Cohort II (p = .066). The risk-adapted therapy recommended as first option was radical prostatectomy for 91.5% in Cohort I and 69.7% in Cohort II, radiation therapy for 83.7% in Cohort I and 50.7% in Cohort II, and other therapies (brachytherapy, Active surveillance, Watchful waiting, high-intensity focused ultrasound) for 6.5% in Cohort I and 6.9% in Cohort II (p < .001). Radiation therapy was predominant in both cohorts as second treatment option (p < .001). Time trends showing quality improvement involved an increase in biopsy cores (9.95 ± 2.38 vs. 8.43 ± 2.29; p < .001) and an increased recommendation for bilateral nerve sparing (p < .001).

Conclusion

In the earlier years, younger patients with a more favourable risk profile presented for interdisciplinary consultation. A unilateral recommendation for radical prostatectomy and radiation therapy was predominant. In the later years, the patient population was considerably older. However, this group may have benefitted from optimised diagnostic possibilities and a wider range of treatment options.