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Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network.

ABSTRACT: To develop glycaemic goal individualization algorithms and assess potential impact on a healthcare system and different segments of the population with diabetes.A cross-sectional observational study of patients with diabetes in a primary care network age > 18 years with an HbA1c measured between 1 January and 31 December 2011. We applied diabetes guidelines to create targeted algorithms 1 and 2, which assigned HbA1c goals based on age, duration of diabetes (< 15 years or < 10 years), diabetes complications and Charlson co-morbidity score (< 6 or < 4) [targeted algorithm 2 was designed to assign more patients a goal < 64 mmol/mol (8.0%) than targeted algorithm 1]. Each patient's HbA1c was compared with these targeted goals and to the 'standard' goal < 53 mmol/mol (7.0%). Agreement was tested using McNemar's test.Overall, 55.7% of 12 199 patients would be considered controlled under the 'standard' approach, 61.2% under targeted algorithm 1 and 67.5% under targeted algorithm 2. Targeted algorithm 1 reclassified 1213 (23.6%) patients considered uncontrolled under the standard approach to controlled, P < 0.001. Targeted algorithm 2 reclassified 1844 (35.2%) patients, P < 0.001. Compared with those controlled under the standard goal, there was no significant difference in the proportion of those controlled using targeted goals who had Medicaid, had less than a high school diploma or received primary care in a federally qualified health centre.Two automated targeted algorithms would reclassify one quarter to one third of patients from uncontrolled to controlled within a primary care network without differentially affecting vulnerable patient subgroups.

SUBMITTER: Berkowitz SA

PROVIDER: S-EPMC4425735 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network.

Berkowitz S A SA Atlas S J SJ Grant R W RW Wexler D J DJ

Diabetic medicine : a journal of the British Diabetic Association 20140411 7

<h4>Aims</h4>To develop glycaemic goal individualization algorithms and assess potential impact on a healthcare system and different segments of the population with diabetes.<h4>Methods</h4>A cross-sectional observational study of patients with diabetes in a primary care network age > 18 years with an HbA1c measured between 1 January and 31 December 2011. We applied diabetes guidelines to create targeted algorithms 1 and 2, which assigned HbA1c goals based on age, duration of diabetes (< 15 year ...[more]

PMID: 24606323

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Project description:Importance:Diabetes guidelines recommend considering specific factors, such as diabetes duration and life expectancy, to individualize treatment in older adults. These individualized glycemic targets inform decisions on whether to intensify or deintensify medication treatment plans. How older adults with diabetes perceive these factors used to individualize glycemic targets is unknown. Objectives:To examine how older adults perceive factors used in diabetes guidelines for individualizing glycemic targets. Design, Setting, and Participants:A cross-sectional national survey was conducted from December 13, 2018, to January 3, 2019, of a nationally representative, probability-based online survey panel (KnowledgePanel). A total of 1364 KnowledgePanel members who were 65 years or older and had type 2 diabetes were invited to participate in the survey; 836 (61.3%) responded, and 818 (60.0%) completed the survey. Main Outcomes and Measures:The study randomized participants to 2 vignettes: one about adding and the other about removing diabetes medications from treatment plans. Participants rated the importance of 7 factors (diabetes duration, established diabetes complications, other health conditions, life expectancy, risk of adverse effects, cost, and treatment effort) in these treatment decisions using binary (yes/no) responses and the best-worst scaling method to quantify the factors' relative importance. All participants then answered questions on how different levels of each factor were associated with aggressiveness of diabetes treatment. Results:The sample included 818 participants (mean [SD] age, 74.0 [6.8] years; 469 [53.7%] male; and 668 [67.7%] white). A total of 410 participants answered questions about adding medicine, whereas 408 participants answered questions about stopping medicine. Of the 7 factors to consider for adding a diabetes medication to the treatment plan, the number who deemed each factor important ranged from 197 (45.6%) to 263 (62.8%). In contrast, these same factors were considered important by only 29 (8.4%) to 146 (37.7%) of participants when deciding to stop use of a diabetes medication. In both decisions, participants perceived the risk of adverse effects as the most important factor (relative importance was 22.8 for adding a medicine and 25.0 for stopping a medicine on a ratio scale in which, for each decision, the relative importance of the 7 factors adds up to 100, with 0 indicating complete indifference and 100 complete priority). In contrast to current guideline recommendations, most participants believed that patients with longer disease duration (498 [60.1%]), more established complications (632 [75.6%]), and greater number of other health conditions (545 [67.5%]) should receive more aggressive diabetes treatment. Conclusions and Relevance:Many older adults do not place high importance on factors recommended by guidelines to individualize diabetes treatment, especially when deciding to stop use of diabetes medications. Moreover, when considering treatment aggressiveness, many older adults weighted several factors in the opposite direction than suggested by the guidelines. Individualizing diabetes care in older adults will require effective communication regarding the benefits and consequences of making changes to treatment plans.

Project description:Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

Project description:BackgroundPrevalence of prediabetes and type 2 diabetes mellitus (T2DM) is increasing worldwide. The objective of this study was to determine the proportion of people in Northern Iceland with prediabetes, at risk of developing T2DM or with manifest undiagnosed T2DM, as this information is lacking in Iceland.MethodsA cross-sectional study. Clients of the three largest primary health care centres in the Health Care Institution of North Iceland (HSN) were invited to participate if fulfilling the following inclusion criteria: a) aged between 18 and 75 years, b) not diagnosed with diabetes, c) speaking and understanding Icelandic or English fluently and d) living in the included service area. Data collection took place via face-to-face interviews between 1 March 2020 and 15 May 2021. Participation included answering the Finnish Diabetes Risk Score (FINDRISC), measuring the HbA1c levels and background information.ResultsOf the 220 participants, 65.9% were women. The mean age was 52.1 years (SD ± 14.1) and FINDRISC scores were as follows: 47.3% scored ≤8 points, 37.2% scored between 9 and 14 points, and 15.5% scored between 15 and 26 points. The mean HbA1c levels in mmol/mol, were 35.5 (SD ± 3.9) for men and 34.4 (SD ± 3.4) for women, ranging from 24 to 47. Body mass index ≥30 kg/m2 was found in 32% of men and 35.9% of women. Prevalence of prediabetes in this cohort was 13.2%. None of the participants had undiagnosed T2DM. Best sensitivity and specificity for finding prediabetes was by using cut-off points of ≥11 on FINDRISC, which gave a ROC curve of 0.814.ConclusionsThe FINDRISC is a non-invasive and easily applied screening instrument for prediabetes. Used in advance of other more expensive and invasive testing, it can enable earlier intervention by assisting decision making, health promotion actions and prevention of the disease burden within primary health care.Trial registrationThis study is a pre-phase of the registered study "Effectiveness of Nurse-coordinated Follow up Program in Primary Care for People at risk of T2DM" at www.Clinicaltrialsgov (NCT01688359). Registered 30 December 2020.

Project description:BackgroundAtrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection.ObjectiveThe objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis.MethodsThis is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review.ResultsAF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm.ConclusionsAn automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.

Project description:BackgroundClinical trials have demonstrated that initiating oral anti-diabetic drugs (OADs) significantly reduce glycated hemoglobin (HbA1c) levels. However, variability in lifestyle modifications and OAD adherence impact on their actual effect on glycemic control. Furthermore, evidence on dose adjustments and discontinuation of OAD on HbA1c is lacking. This study aims to use real-world data to determine the effect of OAD initiation, up-titration, down-titration, and discontinuation on HbA1c levels, among Asian patients managed in primary care.MethodsA retrospective cohort study over a 5-year period, from Jan 2015 to Dec 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of type 2 diabetes mellitus (T2DM) managed by a network of primary care clinics in Singapore. Nine OADs from five different classes (biguanides, sulphonyurea, dipeptidyl peptidase-4 [DPP-4] inhibitors, sodium-glucose cotransporter-2 [SGLT-2] inhibitors, and alpha-glucosidase inhibitors) were evaluated. Patients were grouped into "No OAD", "Non-titrators," and "Titrators" cohorts based on prescribing patterns. For the "Titrators" cohort, the various OAD titrations were identified. Subsequently, a descriptive analysis of HbA1c values before and after each titration was performed to compute a mean difference for each unique titration identified.ResultsAmong the cohort of 57,910 patients, 43,338 of them had at least one OAD titration, with a total of 76,990 pairs of HbA1c values associated with an OAD titration. There were a total of 206 unique OAD titrations. Overall, initiation of OADs resulted in a reduction of HbA1c by 3 to 12 mmol/mol (0.3 to 1.1%), respectively. These results were slightly lower than those reported in clinical trials of 6 to 14 mmol/mol (0.5 to 1.25%). The change of HbA1c levels due to up-titration, down-titration, and discontinuation were -1 to -8 mmol/mol (-0.1 to -0.7%), +1 to 7 mmol/mol (+0.1 to +0.6%), and +2 to 11 mmol/mol (+0.2 to +1.0%), respectively. The HbA1c lowering effect of initiating newer OADs, namely DPP-4 inhibitors and SGLT-2 inhibitors was 8 to 11 mmol/mol (0.7 to 0.9%) and 7 to 11 mmol/mol (0.6 to 1.0%), respectively.ConclusionThe real-world data on Asians with T2DM in this study show that the magnitudes of OAD initiation and dose titration are marginally lower than the results from clinical trials. During shared decision-making in selecting treatment options, the results enable physicians to communicate realistic expectation of the effect of oral medications on the glycemic control of their patients in primary care.

Dataset Information

Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network.

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Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network.

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