This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics ...for thyroid nodule risk stratification.
A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself.
A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817,
< 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (
= 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS,
< 0.001).
The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.
Introduction:
Diabetes mellitus (DM) is a chronic disease with high morbidity and mortality, and glycemic control is key to avoiding complications. Technological innovations have led to the ...development of new tools to help patients with DM manage their condition.
Objective:
This consensus assesses the current perspective of physicians on the potential benefits of using smart insulin pens in the glycemic control of patients with type 1 diabetes (DM1) in Spain.
Methods:
The Delphi technique was used by 110 physicians who were experts in managing patients with DM1. The questionnaire consisted of 94 questions.
Results:
The consensus obtained was 95.74%. The experts recommended using the ambulatory glucose profile report and the different time-in-range (TIR) metrics to assess poor glycemic control. Between 31% and 65% of patients had TIR values less than 70% and were diagnosed based on glycosylated hemoglobin values. They believed that less than 10% of patients needed to remember to administer the basal insulin dose and between 10% and 30% needed to remember the prandial insulin dose.
Conclusions:
The perception of physicians in their usual practice leads them to recommend the use of ambulatory glucose profile and time in range for glycemic control. Forgetting to administer insulin is a very common problem and the actual occurrence rate does not correspond with clinicians’ perceptions. Technological improvements and the use of smart insulin pens can increase treatment adherence, strengthen the doctor–patient relationship, and help improve patients’ education and quality of life.
Objective
To evaluate the impact of glucose variability on the relationship between the GRI and other glycemic metrics in a cohort of pediatric and adult patients with type 1 diabetes (T1D) using ...intermittent scanning continuous glucose monitoring (isCGM).
Methods
We performed a cross-sectional study of 202 patients with T1D under intensive insulin treatment (25.2% CSII) using isCGM. Clinical, metabolic, and glycemic metrics were collected, and the GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. The correlation between the GRI and other classical glycometrics in relation to the coefficient of variation (CV) was evaluated.
Results
A total of 202 patients were included (53% male; 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution (TIR 59.0 ± 17.0%; CV 39.8 ± 8.0%; GMI 7.3 ± 1.1%). The mean GRI was 54.0 ± 23.3 with a CHypo and CHyper component of 5.7 ± 4.8 and 23.4 ± 14.3, respectively. A strong negative correlation was observed between the GRI and TIR (
R
= −0.917;
R
2
= 0.840;
p
< 0.001), showing differences when dividing patients with low glycemic variability (CV < 36%) (
R
= −0.974;
R
2
= 0.948;
p
< 0.001) compared to those with greater CV instability (≥36%) (
R
= −0.885;
R
2
= 0.784;
p
< 0.001). The relationship of GRI with its two components was strongly positive with CHyper (
R
= 0.801;
R
2
= 0.641;
p
< 0.001) and moderately positive with CHypo (
R
= 0.398;
R
2
= 0.158;
p
< 0.001). When the GRI was evaluated with the rest of the classic glycemic metrics, a strong positive correlation was observed with HbA1c (
R
= 0.617;
R
2
= 0.380;
p
< 0.001), mean glucose (
R
= 0.677;
R
2
= 0.458;
p
< 0.001), glucose standard deviation (
R
= 0.778;
R
2
= 0.605;
p
< 0.001), TAR > 250 (
R
= 0.801;
R
2
= 0.641;
p
< 0.001), and TBR < 54 (
R
= 0.481;
R
2
= 0.231;
p
< 0.001).
Conclusions
The GRI correlated significantly with all the glycemic metrics analyzed, especially with the TIR. Glycemic variability (GV) significantly affected the correlation of the GRI with other parameters and should be taken into consideration.
ObjectiveMost cases of familial isolated pituitary adenomas with mutated aryl hydrocarbon receptor-interacting protein (AIP:HGNC:358) gene develop somatotropinomas. They are characterised by an ...aggressive clinical phenotype including early age at diagnosis, large tumours and frequent invasiveness. There is little information on AIP gene mutations' prevalence in isolated somatotropinomas characterised by poor response to somatostatin analogue treatment. The aim of this study was to investigate the prevalence of AIP mutations in non-familial cases of somatotropinomas with poor response to conventional treatment.Design and methodsFifty patients with acromegaly (22 males/28 females, age 51±18 years) and 60 controls were included in this study performed at eight University Hospitals in Spain. None had family history of pituitary adenomas or other endocrine tumors. All patients failed to respond to conventional treatment including surgery and somatostatin analogues. Some patients received adjuvant radiotherapy and most cases required pegvisomant (PEG) treatment for normalisation of IGF1. AIP analysis was performed in DNA extracted from peripheral leucocytes, using standardised PCR protocol in which the coding regions of exons 1, 2, 3, 4, 5 and 6 were amplified. Possible deletions/duplications were studied using multiplex ligation-dependent probe amplification.ResultsSequence changes of potential different significance that could be considered as mutations or variations of unknown significance (VUS) of the AIP gene were found in four patients (8%). In two cases, two different mutations previously described were found: p.Arg9Gln and p.Phe269Phe. Two other VUS were also found: c.787+24C>T in intron 5 and c.100-18C>T in intron 1. Age at diagnosis ranged from 21 to 50 years old, and in all patients, the tumor was a macroadenoma depicting IGF1 normalisation under PEG treatment.ConclusionsAIP germline mutations show a low, but non-negligible, prevalence in non-familial acromegaly patients with tumors resistant to treatment with somatostatin analogues.
Purpose
To evaluate the predictive value of the rhTSH thyroglobulin stimulation test (rhTSH-Tg) compared to basal high-sensitive thyroglobulin (hs-Tg) under TSH suppressive therapy at 12 months after ...the completion of initial treatment to predict the long-term response and Dynamic Risk Stratification (DRS) at the last follow-up visit in a long-term DTC cohort.
Methods
Prospective study in 114 DTC patients (77.2% women, mean age 46.4 ± 14.1 years old, median/IQR evolution 6.73.1–8.0 years) from 2013 to 2020 undergoing total thyroidectomy and radioiodine ablation in whom hs-Tg and rhTSH-Tg was performed 12 months after completing initial treatment. Pearson correlation, receiving operating characteristics (ROC) and DRS at initial and last follow-up visit were analyzed.
Results
hs-Tg and rhTSH-Tg show a strong positive linear correlation (
r
= 0.864,
p
< 0.001). The diagnostic performance of initial hs-Tg and rhTSH-Tg levels were evaluated via ROC-AUC as a predictor of excellent response (ER) in the last follow-up visit. Hs-Tg showed a better AUC (0.969, 95%CI = 0.941–0.997) than rhTSH-Tg (0.944, 95%IC = 0.905–0.984;
p
< 0.001). The hs-Tg and rhTSH-Tg cutoff point of highest sensitivity (S) and specificity (E) was 0.110 and 0.815 ng/dl, respectively. Hs-Tg showed a higher diagnostic accuracy than rhTSH-Tg (S = 100% vs 96.8%, E = 84.3% vs 84.3%, NPV = 100% vs 98.6%, PPV = 70.5% vs 69.7%;
p
< 0.05). The DRS based on initial hs-Tg showed better ability to predict ER (93.3% vs 86.7%) and biochemical incomplete response (53.3%vs13.3%) in the last follow-up visit compared to rhTSH-Tg.
Conclusions
Both initial hs-Th and rhTSH-Tg were good predictors of long-term ER. In patients with hs-Tg, the rhTSH-test did not provide relevant prognosis information. An ER after initial treatment was associated with a very high NPV at subsequent follow-up.
To analyze the time in tight range (TITR), and its relationship with other glucometric parameters in patients with type 1 diabetes (T1D) treated with advanced hybrid closed-loop (AHCL) systems.
A ...prospective observational study was conducted on pediatric and adult patients with T1D undergoing treatment with AHCL systems for at least 3 months. Clinical variables and glucometric parameters before and after AHCL initiation were collected.
A total of 117 patients were evaluated. Comparison of metabolic control after AHCL initiation showed significant improvements in HbA1c (6.9 ± 0.9 vs. 6.6 ± 0.5%, p < 0.001), time in range (TIR) (68.2 ± 11.5 vs. 82.5 ± 6.9%, p < 0.001), TITR (43.7 ± 10.8 vs. 57.3 ± 9.7%, p < 0.001), glucose management indicator (GMI) (6.9 ± 0.4 vs. 6.6 ± 0.3%, p < 0.001), time below range (TBR) 70-54 mg/dl (4.3 ± 4.5 vs. 2.0 ± 1.4%, p < 0.001), and time above range (TAR) > 180 mg/dl (36.0 ± 7.6 vs. 15.1 ± 6.4%, p < 0.001). Coefficient of variation (CV) also improved (36.3 ± 5.7 vs. 30.6 ± 3.7, p < 0.001), while time between 140-180 mg/dl remained unchanged. In total, 76.3% achieved TITR > 50% (100% pediatric). Correlation analysis between TITR and TIR and GRI showed a strong positive correlation, modified by glycemic variability.
AHCL systems achieve significant improvements in metabolic control (TIR > 70% in 93.9% patients). The increase in TIR was not related to an increase in TIR 140-180 mg/dl. Despite being closely related to TIR, TITR allows for a more adequate discrimination of the achieved control level, especially in a population with good initial metabolic control. The correlation between TIR and TITR is directly influenced by the degree of glycemic variability.
To evaluate the relationship between the GRI -component of hypoglycemia (CHypo) and hyperglycemia (CHyper)- with diabetes quality of life (DQoL), diabetes-related stress (DDS), perception of ...hypoglycemia (Clarke Test), visual analogic scale (VAS) and diabetes-knowledge (DKQ2) in T1D.
Cross-sectional study in 92 patients with T1D under intensive insulin treatment (21.7% CSII) and flash glucose monitoring (isCGM). Clinical, metabolic and glycometric parameters and quality of life/satisfaction questionnaires were analyzed.
92 patients (54.3% male, BMI 25.4 ± 4.5 kg/m
, HbA1c 7.5 ± 1.0%, TIR 53.9 ± 15.9%) with mean age 36.1 ± 12.6years and 17.8 ± 11.3 T1D duration. The mean GRI was 60.6 ± 22.2 with a CHypo and CHyper of 5.9 ± 4.8 and 27.3 ± 14.4, respectively. 19.1% presented a pathological Clarke's test. Patients with TIR > 70% and GRI < 40 showed better VAS (8.8 ± 1.3 vs 9.3 ± 0.9, p < 0.05) and DDS (46.4 ± 22.1 vs 36.7 ± 16.6, p < 0.05) scores, showing no differences between groups. CHyper > 15 and Chypo > 3.4 were related to worse levels of DQoL (91.1 ± 23.9 vs 76.6 ± 18.6 and 94.6 ± 24.8 vs 79.8 ± 20.1, p < 0.01), DDS(49.8 ± 22.4 vs 35.7 ± 16.5 and 49.8 ± 22.4 vs 35.7 ± 16.5, p < 0.01),and DKQ2 (24.4 ± 4.3 vs 26.8 ± 5.2 and 24.1 ± 4.8 vs 26.0 ± 4.6, p < 0.05), respectively. Worse metabolic control defined by GRI correlated with worse scores in VAS (r = -0.209, p < 0.05), DQoL (r = 0.205, p < 0.05), and DDS (r = 0.205, p < 0.05). No difference was observed in knowledge´s scale. CHyper correlated with worse scores in VAS (r = -0.231, p < 0.05), DQoL (r = 0.422, p < 0.01), and DDS (r = 0.341, p < 0.01) and lower degree of knowledge DKQ2 (r = -0.231, p < 0.05). When analyzing DQoL as a dependent variable in a multiple lineal regression, only age (β = 0.747; p < 0.001) and CHyper (β = 0.717; p < 0.001) maintained statistical significance.
Higher GRI was related to worse quality of life, diabetes-related stress and satisfaction with treatment, analogous to the TIR results.CHyper an Chypo were related to a greater decline in quality of life, diabetes-related stress, and lower satisfaction with treatment.However, in a multiple linear regression, only CHyper maintained statistical significance.
Abstract Aims To evaluate the effects of liraglutide after 14 weeks of treatment on serum adipokines, insulin resistance index and cardiovascular risk biomarkers in overweight or obese T2DM patients ...unable to achieve glycemic control with metformin alone or in association with a sulfonylurea in daily clinical practice. Methods Prospective study in 59 consecutive overweight or obese (BMI ≥ 25 kg/m2 ) T2DM patients unable to achieve glycemic control (HbA1c > 7%, 53 mmol/mol) with metformin alone or in association with sulfonylurea that require initiation of liraglutide in progressive dose increase up to 1.8 mg/day subcutaneously. Weight, body composition, blood pressure, glucose, HbA1c, C-peptide, insulin, plasma lipids, adipokines (leptin, adiponectin, resistin and visfatin) as well as cardiovascular biomarkers (IL-6 and TNF-a) levels were measured fasting at baseline and 14 weeks after liraglutide initiation. Results 14 weeks of liraglutide treatment significantly reduced HbA1c, BMI and total body fat mass by 0.9%, 1.4 kg/m2 and 0.5% respectively. Statistically significant lower insulin resistance and higher insulin secretion was found by HOMA-IR 8.4 (1.6) vs 4.6 (0.9) mol m IU/L2 and HOMA-B 48.2 (9.0) vs 87.6 (16.3) μIU/mmol. Statistically significantly higher levels of visfatin 6.3 (2.1) vs 6.8 (2.1) ng/ml and resistin 3.6 (2.0) vs 4.3 (2.3) ng/ml were also observed after treatment. Baseline visfatin was negatively correlated with basal fasting plasma glucose r = −0.360 ( p < 0.05). Conclusions Liraglutide treatment for 14 weeks in daily clinical practice led to reduction of BMI and improvement of glucose control and insulin sensitivity and resistance parameters. Additionally, circulating levels of adipokines and pro-inflammatory factors could play an important role in GLP-1 treatment response.
Purpose
Flash glucose monitoring (FGM) in patients with type 1 diabetes (DM1) provides glucometric data that allow assessing glycemic control beyond HbA1c. The objective of this study was to evaluate ...metabolic control and use of FGM in a cohort of the pediatric and adult population with DM1.
Material and methods
A cross-sectional study of patients with DM1 and FGM. Data on the use of the system and metabolic control were evaluated, carrying out a comparative study between different age ranges, ≤12 years; 13–19 years, 20–25 years, and ≥26 years.
Results
One hundred and ninety-five patients have included: 35.9% children and adolescents (≤19 years), 42.6% female, 26.2% in treatment with an insulin pump. Mean age was 28.5 ± 15.9 years, mean duration of diabetes 13.7 ± 11.0 years, and mean HbA1c 7.1 ± 0.9% (54 ± 6 mmol/l). Average daily FGM scans were 11.1 ± 6.7. Mean glucose was 162 ± 35 mg/dl, mean standard deviation (SD) 66.1 ± 20.4 mg/dl, mean coefficient of variation 41.4 ± 7.9%, mean time in range (TIR) 58.8 ± 17.0%, mean time above range 33.7 ± 17.6% and mean time below range 7.5 ± 5.8%. The pediatric group showed higher TIR, lower HbA1c, lower glycemic variability, lower mean glucose, and higher use of the device than the adult population. In the entire cohort, the device scans showed a negative quadratic correlation with HbA1c, mean glucose, SD, and age and a positive quadratic correlation with TIR.
Conclusions
Children under 12 years showed the best metabolic control and the most frequent use of the device. Metabolic control deteriorates with age. The greater number of device scans was in correlation with better metabolic control in all age groups.
Background:
To evaluate the glycemia risk index (GRI) as a new glucometry in pediatric and adult populations with type 1 diabetes (T1D) in clinical practice.
Methods:
A cross-sectional study of 202 ...patients with T1D receiving intensive treatment with insulin (25.2% continuous subcutaneous insulin infusion CSII) and intermittent scanning (flash) glucose monitoring (isCGM). Clinical and glucometric isCGM data were collected, as well as the component of hypoglycemia (CHypo) and component of hyperglycemia (CHyper) of the GRI.
Results:
A total of 202 patients (53% males and 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution were evaluated.
Adult patients (>19 years) presented higher glycated hemoglobin (HbA1c) (7.4 ± 1.1 vs 6.7 ± 0.6%; P < .01) and lower time in range (TIR) (55.4 ± 17.5 vs 66.5 ± 13.1%; P < .01) values than the pediatric population, with lower coefficient of variation (CV) (38.6 ± 7.2 vs 42.4 ± 8.9%; P < .05). The GRI was significantly lower in pediatric patients (48.0 ± 22.2 vs 56.8 ± 23.4; P < .05) associated with higher CHypo (7.1 ± 5.1 vs 5.0 ± 4.5; P < .01) and lower CHyper (16.8 ± 9.8 vs 26.5 ± 15.1; P < .01) than in adults.
When analyzing treatment with CSII compared with multiple doses of insulin (MDI), a nonsignificant trend to a lower GRI was observed in CSII (51.0 ± 15.3 vs 55.0 ± 25.4; P= .162), with higher levels of CHypo (6.5 ± 4.1 vs 5.4 ± 5.0; P < .01) and lower CHyper (19.6 ± 10.6 vs 24.6 ± 15.2; P < .05) compared with MDI.
Conclusions:
In pediatric patients and in those with CSII treatment, despite a better control by classical and GRI parameters, higher overall CHypo was observed than in adults and MDI, respectively. The present study supports the usefulness of the GRI as a new glucometric parameter to evaluate the global risk of hypoglycemia-hyperglycemia in both pediatric and adult patients with T1D.
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