Predicting diabetes clinical outcomes using longitudinal risk factor trajectories

A novel score for mobile health applications to predict and prevent mortality: Further validation and adaptation to US population using the US NHANES dataset

Background

The C-Score, which is an individual health score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized point-in-time health assessment tool that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death.

Objective

Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices.

Methods

We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test.

Results

Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs from 0.72 (95% CI 0.71-0.73) to 0.87 (95% CI 0.85-0.88).

Conclusions

Our study shows that this digital biomarker, the C-Score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk and as a counseling tool to motivate behavior changes and lifestyle modifications.

Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years' Data

PURPOSE

To evaluate the performance of machine-learning models based on multiple years of continuous data to predict incident diabetes among patients with metabolic syndrome.

PATIENTS AND METHODS

The dataset comprises the health records from 2008 to 2020 including 4510 nondiabetic participants with metabolic syndrome (MetS) at baseline and with at least 6 years of records. MetS was defined according to the International Diabetes Federation (IDF) criteria. Overall, 332 patients developed incident diabetes during the 7±1.4 years of follow-up. Three popular classification algorithms were evaluated on the dataset: logistic regression, random forest, and Xgboost. Five models including single-year models (year 1, year 2, and year 3) and multiple-year models (year 1-2 and year 1-3) were developed for each algorithm.

RESULTS

The model performances improved with the increasing longitudinal dataset as the area under the receiver operating characteristic curve (AUROC) was boosted for both random forest (year 1-3: AUROC=0.893; year 3: AUROC=0.862; year 1-2: AUROC=0.847; year 2: AUROC=0.838) and Xgboost (year 1-3: AUROC=0.897; year 3: AUROC=0.833; year 1-2: AUROC=0.856; year 2: AUROC=0.823) model. In the multiple-year models, the highest fasting plasma glucose, followed by the mean or lowest level of HbA1c and BMI had the most important predictive value for the onset of diabetes. In the "1-3" year model, "delta weight" which reflects the fluctuations of yearly change of weight was the fourth-most important feature.

CONCLUSION

This study demonstrated improved performance with the accumulation of longitudinal data when using machine learning for diabetes prediction in MetS patients. For individuals with similar clinical parameters, the variation trends of these parameters could change the risk of future diabetes. This result indicated that models based on longitudinal multiple years' data may provide more personalized assessment tools for risk evaluation.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study

BACKGROUND

Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes.

METHODS

A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review.

RESULTS

Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time.

CONCLUSIONS

Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Longevity leap: mind the healthspan gap

Life expectancy has increased by three decades since the mid-twentieth century. Parallel healthspan expansion has however not followed, largely impeded by the pandemic of chronic diseases afflicting a growing older population. The lag in quality of life is a recognized challenge that calls for prioritization of disease-free longevity. Contemporary communal, clinical and research trends aspiring to extend the health horizon are here outlined in the context of an evolving epidemiology. A shared action integrating public and societal endeavors with emerging interventions that target age-related multimorbidity and frailty is needed. A multidimensional buildout of a curative perspective, boosted by modern anti-senescent and regenerative technology with augmented decision making, would require dedicated resources and cost-effective validation to responsibly bridge the healthspan-lifespan gap for a future of equitable global wellbeing.

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Integrated quality assessment for diabetes care in Portuguese primary health care using prevention quality indicators

AIMS

This study evaluates the prevention quality indicators (PQI) for Diabetes Mellitus (DM) in Portugal using contemporary data and explores their variability according to Primary Health Care (PHC) quality indicators.

METHODS

We conducted a retrospective observational analysis of secondary data comprising Portuguese PHC indicators by health centres group (ACES) and the National Hospital Morbidity Database. We calculated and analysed age-sex-adjusted rates for each PQI. Worse-performing ACES were identified using the 2017 median PQI values as an assessment cut-off. A multivariate logistic analysis was carried to find variables associated with the likelihood of being a worse-performing ACES for the biennium.

RESULTS

The median values of the indicator PQI93 - Prevention Quality Diabetes Composite were 79 and 65.2 hospitalizations per 100 000 pop, in 2016 and 2017 respectively. Diabetes long term complications (PQI 03) accounted for most of the hospitalizations. The quality indicator in PHC with greater influence on PQI93 was the proportion of DM patients with <65 years with test results for HbA1c < = 6.5%.

CONCLUSIONS

This study shows that some PHC quality indicators are closely related to DM care, and so their monitoring is of high importance. Diabetes long term complications (PQI 03) demand greater attention from PHC professionals.