Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Martínez-Millana, Antonio; Bayo-Monton, Jose-Luis; Argente, María; Fernández-Llatas, Carlos; Merino-Torres, Juan Francisco; Salcedo, Vicente Traver

doi:10.3390/s18010079

Cited by 11 publications

(11 citation statements)

References 45 publications

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“…Our results were consistent with previous studies, showing that patients with diabetes were associated with more comorbidities than those without diabetes, including hypertension, ischemic heart disease, mental disorders, cardiac dysrhythmia, heart failure, chronic renal failure, cerebrovascular disease, renal dialysis, chronic obstructive pulmonary disease, asthma, and liver cirrhosis [19,20,21]. These comorbid conditions and basic characteristics might correlate with perioperative adverse outcomes.…”

Section: Discussionsupporting

confidence: 92%

Adverse Outcomes after Major Surgeries in Patients with Diabetes: A Multicenter Matched Study

Lin

Chang

Liu

et al. 2019

JCM

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The impact of diabetes on perioperative outcomes remains incompletely understood. Our purpose is to evaluate post-operative complications and mortality in patients with diabetes. Using the institutional and clinical databases of three university hospitals from 2009–2015, we conducted a matched study of 16,539 diabetes patients, aged >20 years, who underwent major surgery. Using a propensity score matching procedure, 16,539 surgical patients without diabetes who underwent surgery were also selected. Logistic regressions were used to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for post-operative complications and in-hospital mortality associated with diabetes. Patients with diabetes had a higher risk of postoperative septicemia (OR 1.33, 95% CI 1.01–1.74), necrotizing fasciitis (OR 3.98, 95% CI 1.12–14.2), cellulitis (OR 2.10, 95% CI 1.46–3.03), acute pyelonephritis (OR 1.86, 95% CI 1.01–3.41), infectious arthritis (OR 3.89, 95% CI 1.19–12.7), and in-hospital mortality (OR 1.51, 95% CI 1.07–2.13) compared to people without diabetes. Previous admission for diabetes (OR 2.33, 95% CI 1.85–2.93), HbA1c >8% (OR 1.96, 95% CI 1.64–2.33) and fasting glucose >180 mg/dL (OR 1.90, 95% CI 1.68–2.16) were predictors for post-operative adverse events. Diabetes patients who underwent surgery had higher risks of infectious complications and in-hospital mortality compared with patients without diabetes who underwent similar major surgeries.

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Section: Discussionsupporting

confidence: 92%

Adverse Outcomes after Major Surgeries in Patients with Diabetes: A Multicenter Matched Study

Lin

Chang

Liu

et al. 2019

JCM

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“…This finding should be further explored to know how many of these apps that also offer a dashboard for professional management. A recent study has discovered a decrease of the consultation time in type 2 diabetes management by using artificial intelligence and predictive modeling [27,28], and moreover, a review has proved these methods are being progressively established as suitable for use in daily clinical practice [29]. Future research should tap into the application of these methods for supporting both T1DM patients and healthcare professionals on the follow-up and control of this complex disease.…”

Section: Discussionmentioning

confidence: 99%

App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison

Martínez-Millana¹,

Jarones²,

Fernández-Llatas³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundResearch in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored.ObjectiveTo study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes.MethodsThe methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market.ResultsThe literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, χ2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution.ConclusionsThere are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.

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“…Patients were divided into two groups: cases for patients with confirmed T2DM based on the International Classification of Diseases-9 (ICD-9) code and controls for patients without diagnosis. A web-based system for executing the risk scores [21] was evaluated in the Endocrinology Department of University Hospital La Fe during a continuous 3-month period with the participation of endocrinologists and the head of department who used the tool for 2 hours per session (Figure 1). Three training sessions were planned with the participants prior to utilization of the web tool, which consisted of using the tool in two clinical scenarios (detailed in Section 2.5).…”

Section: Methodsmentioning

confidence: 99%

Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings

Martínez-Millana

Argente

Valdivieso

et al. 2019

JCM

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Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

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Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Cited by 11 publications

References 45 publications

Adverse Outcomes after Major Surgeries in Patients with Diabetes: A Multicenter Matched Study

Adverse Outcomes after Major Surgeries in Patients with Diabetes: A Multicenter Matched Study

App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison

Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings

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