Search

BackgroundMachine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. ObjectiveThe objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). MethodsElectronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. ResultsA total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. ConclusionsCurrent ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

Aims/Introduction: History of coronary artery disease (CAD), cerebrovascular disease (CeVD), type 2 diabetes and their combined effect on cardiovascular disease are essential for cardiovascular risk management. We investigated the association of prior CAD, prior CeVD, type 2 diabetes and their combination with the risk of cardiovascular disease. Materials and Methods: This is a historical cohort study including 342,033 participants (aged 18–72 years) followed up for ≥5 years between 2008 and 2016. Participants were classified into eight groups (with or without prior CAD, prior CeVD and type 2 diabetes). Type 2 Diabetes was defined by fasting plasma glucose and glycated hemoglobin levels, and antidiabetic drug prescription. Prior and subsequent CAD and CeVD were identified according to claims using International Classification of Diseases 10th Revision codes, medical procedures and questionnaires. Cox regression models were used to evaluate the risk of cardiovascular events. Results: The median follow‐up period was 6.4 years. The incidence of composite cardiovascular events of CAD and CeVD in the CAD−/CeVD−, CAD+/CeVD−, CAD−/CeVD+ and CAD+/CeVD+ groups were 1.92 and 6.94, 25.14 and 31.98 per 1,000 person‐years in non‐diabetes participants, and 8.66, 18.04, 39.98 and 60.72 in type 2 diabetes patients, respectively. Hazard ratios of cardiovascular events compared with CAD−/CeVD−/non‐diabetes were 1.66 (95% confidence interval 1.55–1.78) in CAD−/CeVD−/type 2 diabetes and 1.84 (1.56–2.18) in CAD+/CeVD−/non‐diabetes. CeVD+ was linked to a 4‐7‐fold increase in the risk of cardiovascular events regardless of CAD+ or type 2 diabetes. Conclusions: Type 2 diabetes increased the risk of cardiovascular disease as high as a history of CAD, whereas prior CeVD alone increased the risk of future CeVD without additional effects by type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background and objectives To clarify whether the presence or absence of fast walking and habitual physical activity are independently associated with the incidence of functional disability. Methods This historical cohort study was comprised of 9,652 (4,412 men, mean age 65 years) individuals aged 39–98 years without functional disability at baseline. Functional disability was determined based on the Japanese long-term care insurance system, which specified requirements for assistance in the activities of daily living. The impact of fast walking and habitual physical activity on the incidence of functional disability was analysed by Cox proportional hazards models. Results The follow-up period was a median of 3.7 years during which 165 patients were newly certified as having functional disability. In the multivariate analysis, baseline age in 5-year increments (hazard ratio 2.42 [95% confidence interval 2.18–2.69]), no habitual physical activity (1.56 [1.07–2.27]), and not fast walking (1.89 [1.32–2.69]) significantly increased the risk of functional disability after adjustment for covariates. The stratified analysis showed that compared with physical activity (+), the impact of physical activity (−) on the incidence of functional disability was observed in those aged ≥75 years regardless of fast walking (+). Fast walking (−) significantly increased the risk of disability compared with fast walking (+) in those aged <75 years regardless of a physical activity habit. Conclusion In Japanese, slow walking speed and lack of a physical activity habit were shown to be independent risk factors for incident functional disability, with their impact differing according to age. [ABSTRACT FROM AUTHOR]

To determine associations between severity of hypertension and risk of starting dialysis in the presence or absence of diabetes mellitus (DM). A nationwide database with claims data on 258 874 people with and without DM aged 19-72 years in Japan was used to elucidate the impact of severity of hypertension on starting dialysis. Initiation of dialysis was determined from claims using International Classification of Diseases-10 codes and medical procedures. Using multivariate Cox modeling, we investigated the severity of hypertension to predict the initiation of dialysis with and without DM. Hypertension was significantly associated with the initiation of dialysis regardless of DM. The incidence of starting dialysis in those with systolic blood pressure (SBP) ≤119 mm Hg and DM (DM+) was almost the same as in those with SBP ≥150 mm Hg and absence of DM (DM-). In comparison with SBP ≤119 mm Hg, SBP ≥150 mm Hg significantly increased the risk of the initiation of dialysis about 2.5 times regardless of DM+ or DM-. Compared with DM- and SBP ≤119 mm Hg, the HR for DM+ and SBP ≥150 mm Hg was 6.88 (95% CI 3.66 to 12.9). Although the risks of hypertension differed only slightly regardless of the presence or absence of DM, risks for starting dialysis with DM+ and SBP ≤119 mm Hg were equivalent to DM- and SBP ≥150 mm Hg, indicating more strict blood pressure interventions in DM+ are needed to avoid dialysis. Future studies are required to clarify the cut-off SBP level to avoid initiation of dialysis considering the risks of strict control of blood pressure. [ABSTRACT FROM AUTHOR]

Background: Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings., Methods: We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression., Results: The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m 2 ) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m 2 ). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 ( p = 0.323)., Conclusion: The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management., Competing Interests: HT, HN, YS, and KT were employed by NEC Solution Innovators, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fujihara, Yamada Harada, Horikawa, Iwanaga, Tanaka, Nomura, Sui, Tanabe, Yamada, Kodama, Kato and Sone.)