Impact of a pharmacist-led oral chemotherapy monitoring clinic at the South Texas Veterans Health Care System.

As cancer treatments shift from traditional intravenous chemotherapy to inclusion of oral oncolytics, there is a critical need for structured oral chemotherapy monitoring and follow-up programs. To provide continuous care and minimize clinical gaps to Veterans receiving oral chemotherapy, the hematology/oncology clinical pharmacy practitioners designed and initiated a pilot, pharmacist-driven, Oral Chemotherapy Monitoring Clinic at the South Texas Veterans Health Care System supported by an oral chemotherapy certified pharmacy technician. A retrospective evaluation of patients receiving oral chemotherapy at the South Texas Veterans Health Care System was performed before (Phase I) and after (Phase II) pilot implementation to assess the impact of an Oral Chemotherapy Monitoring Clinic on compliance with drug-specific lab and symptom monitoring. Complete monitoring was defined as 100% of recommended labs and symptoms assessed per cycle, partial monitoring was <100%, but >0%, and incomplete monitoring was defined as 0%. The primary outcome assessed the proportion of patients receiving complete monitoring in Phase II compared to Phase I. Most patients were male (94%), with a median age of 72 years. The most common oncolytic was abiraterone acetate. Overall, drug-specific baseline and follow-up laboratory and symptom monitoring was complete at a statistically significantly higher rate in Phase II compared with Phase I (p-value < 0.01). A significantly higher portion of patients in the Phase II cohort had a clinical pharmacy practitioner intervention (44% vs. 90%; p < 0.01). Monitoring for Veterans receiving oral chemotherapy was optimized with clinical pharmacy practitioner and certified pharmacy technician involvement while simultaneously alleviating Oncologist and nurse oral chemotherapy workload.

Frequency of prothrombin time-international normalized ratio monitoring and the long-term prognosis in patients with mechanical valve replacement.

BACKGROUND: The study aimed to assess the correlation between the monitoring frequency of PT-INR and the long-term prognosis in patients with mechanical heart valve (MHV) replacement after discharge. METHODS: This single-center, observational study enrolled patients who underwent MHV replacement and discharged from June 2015 to May 2018. Patients or their corresponding family members were followed with a telephone questionnaire survey in July-October 2020. Based on monitoring intervals, patients were divided into frequent monitoring (FM) group (≤ 1 month) and less frequent monitoring (LFM) group (> 1 month). The primary endpoint was the composite of thromboembolic event, major bleeding or all-cause death. The secondary endpoints were thromboembolic event, major bleeding or all-cause death, respectively. RESULTS: A total of 188 patients were included in the final analysis. The median follow-up duration was 3.6 years (Interquartile range: 2.6 to 4.4 years). 104 (55.3%) patients and 84 (44.7%) patients were classified into the FM group and the LFM group, respectively. The FM group had a significantly lower incidence of the primary endpoint than the LFM group (3.74 vs. 1.16 per 100 patient-years, adjusted HR: 3.31 [95% CI 1.05-10.42, P = 0.041]). Secondary analysis revealed that the risk of thromboembolic events and all-cause death were also reduced in the FM group. CONCLUSIONS: The management of warfarin treatment in patients after MHV replacement remains challenging. Patients with less frequent monitoring of PT-INR might have worse clinical prognosis than those with frequent PT-INR monitoring.

Six Months of Remote Patient Monitoring Is Associated with Blood Pressure Reduction in Hypertensive Patients: An Uncontrolled Observational Study.

Background: Remote physiological monitoring (RPM) is a form of telehealth that measures vital signs at home and automatically reports the results to providers, thereby possibly improving chronic disease management. Medicare payment for RPM began in 2019. Two potential obstacles to RPM growth are the paucity of published clinical outcomes data and the Medicare requirement that monitoring be done at least 16 days per month to bill for the service. To help address these issues, we report the following uncontrolled observational study. Methods: A total of 1,102 consecutive patients enrolled in RPM were divided into four groups based on initial average mean arterial pressure (MAP) and into six groups based on the number of days per month MAP was measured. We report changes in MAP after 6 months of RPM as a function of initial MAP, and number of days per month MAP was monitored. Results: After 6 months of RPM, average MAP dropped from 97 to 93 (p < 0.01). This drop was greatest in the 50% of patients initially hypertensive. These patients saw average MAP reductions from 106 to 97 (p < 0.001) and became normotensive. Although MAP reduction was greatest the more frequently patients measured, significant reduction occurred in the hypertensive patients whether they measured more or less than 16 days per month (p < 0.001). No minimum threshold of measurements was found that predicted failure of RPM to lower MAP. Conclusions: RPM is associated with clinically and statistically significant reductions in average MAP in patients who were initially hypertensive. This benefit occurred irrespective of the number of days per month patients measured MAP.

Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models.

Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country.

Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes.

Background and Objectives: The purpose of this study is to determine the optimal number of scans per day required for attaining good glycemic regulation. Materials and Methods: The association of scanning frequency and glucometrics was analyzed according to bins of scanning frequency and bins of time in range (TIR) in the Croatian population of type 1 diabetes (T1DM) patients. Results: Intermittently scanned continuous glucose monitoring (isCGM) Libre users in Croatia performed on average 13 ± 7.4 scans per day. According to bins of scanning frequency, bin 5 with 11.2 ± 02 daily scans was sufficient for achieving meaningful improvements in glycemic regulation, while decreasing severe hypoglycemia required an increasing number of scans up to bin 10 (31 ± 0.9), yet with no effect on TIR improvement. When data were analyzed according to bins of TIR, an average of 16.3 ± 10.5 scans daily was associated with a TIR of 94.09 ± 3.49% and a coefficient of variation (CV) of 22.97 ± 4.94%. Improvement was shown between each successive bin of TIR but, of notice, the number of scans performed per day was 16.3 ± 10.5 according to TIR-based analysis and 31.9 ± 13.5 in bin 10 according to scan frequency analysis. Conclusions: In conclusion, an optimal average number of scans per day is 16.3 in order to achieve glucose stability and to minimize the burden associated with over-scanning.

Frequent monitoring of mineral metabolism in hemodialysis patients with secondary hyperparathyroidism: associations with achievement of treatment goals and with adjustments in therapy.

Background: Dialysis guidelines in Japan recommend more frequent measurement of mineral metabolism markers than the Kidney Disease: Improving Global Outcomes guidelines. However, the extent to which frequent marker measurement contributes to achievement of target ranges and to therapy adjustment is unknown. Methods: This multicenter cohort study involved 3276 hemodialysis patients with secondary hyperparathyroidism. Data on laboratory measurements and drug prescriptions were collected every 3 months. Main exposures were frequencies of measuring serum calcium and phosphorus [weekly/biweekly/monthly (reference)] and serum parathyroid hormone (PTH) [monthly/bimonthly/trimonthly (reference)] levels. Outcomes were achievement of guideline-specified ranges of mineral metabolism markers when serum levels were over, and maintenance of ranges when levels were already within, respective specified ranges, use of intravenous vitamin D receptor activator (VDRA) and initiation of cinacalcet use. Associations were examined via generalized estimating equations. Results: When serum marker levels exceeded the target range, weekly measurement of calcium and phosphorus was positively associated with achievement of the guideline-specified calcium range [adjusted odds ratio (AOR): 1.57, 95% confidence interval (CI) 1.09-2.26] but not phosphorus range (AOR: 0.99, 95% CI 0.74-1.33). Monthly measurement of PTH was positively associated with achievement of the guideline-specified PTH range (AOR: 1.14, 95% CI 1.01-1.27). When serum marker levels were within the guideline-specified range, increased frequency of measurements was not associated with in-range maintenance of marker levels for any of the three mineral markers assessed. Regarding treatment regimen, relatively frequent measurement of serum calcium and phosphorus was positively associated with cinacalcet initiation and relatively frequent measurement of serum PTH with cinacalcet initiation and intravenous VDRA use. Conclusions: Our results suggest that increasing frequency of measurements is helpful when serum marker levels exceed the target range, partially via adjustment in the therapeutic regimen. We found no evidence that frequent measurements are helpful when mineral levels are already within target ranges.

The Effect of Time Monitoring on the Development of Time-Based Prospective Memory among Children Aged 7-11 Years Old.

Time-based prospective memory (TBPM) refers to the ability of an individual to successfully execute an expected plan in the future at a certain time point or after a definite period of time. This study investigated the potential developmental mechanism of school-age children's TBPM ability from the perspective of time monitoring. Experiment 1 used a between-subjects design of three ages (7, 9, 11) × two TBPM types (time point, time period) to investigate the trends and characteristics of two types of TBPM in children aged 7-11 years old. Experiment 2 used a between-subjects design of three ages (7, 9, 11) × two monitoring conditions (free monitoring, fixed monitoring) to investigate differences in two types of TBPM and monitoring behavior among school-age children under different monitoring conditions. These results showed that the age effect of TBPM was affected by the type of prospective memory (PM) and that time-point PM performance was significantly better than time-period PM performance among school-age children. These findings indicate that clear and definite external cues are helpful for school-age children in performing TBPM tasks. Moreover, there were significant differences found in the TBPM performance of school-age children under different time monitoring conditions. The performance of older children was significantly better than that of younger children. This indicates that older children can better allocate their attentional resources and use time monitoring strategies to improve their performance in PM tasks. Accordingly, this study showed that the TBPM ability of 7- to 11-year-old children is in continuous development and that the time monitoring behavior in the TBPM task is affected by task type and monitoring conditions.

Interpretation of river water quality data is strongly controlled by measurement time and frequency.

Water quality monitoring at high temporal frequency provides a detailed picture of environmental stressors and ecosystem response, which is essential to protect and restore lake and river health. An effective monitoring network requires knowledge on optimal monitoring frequency and data variability. Here, high-frequency hydrochemical datasets (dissolved oxygen, pH, electrical conductivity, turbidity, water temperature, total reactive phosphorus, total phosphorus and nitrate) from six UK catchments were analysed to 1) understand the lowest measurement frequency needed to fully capture the variation in the datasets; and 2) investigate bias caused by sampling at different times of the day. The study found that reducing the measurement frequency increasingly changed the interpretation of the data by altering the calculated median and data range. From 45 individual parameter-catchment combinations (six to eight parameters in six catchments), four-hourly data captured most of the hourly range (>90 %) for 37 combinations, whilst 41 had limited impact on the median (<0.5 % change). Twelve-hourly and daily data captured >90 % of the range with limited impact on the median in approximately half of the combinations, whereas weekly and monthly data captured this in <6 combinations. Generally, reducing sampling frequency had most impact on the median for parameters showing strong diurnal cycles, whilst parameters showing rapid responses to extreme flow conditions had most impact on the range. Diurnal cycles resulted in year-round intra-daily variation in most of the parameters, apart from nutrient concentrations, where daily variation depended on both seasonal flow patterns and anthropogenic influences. To design an optimised monitoring programme, key catchment characteristics and required data resolution for the monitoring purpose should be considered. Ideally a pilot study with high-frequency monitoring, at least four-hourly, should be used to determine the minimum frequency regime needed to capture temporal behaviours in the intended focus water quality parameters by revealing their biogeochemical response patterns.

Association of scan frequency with CGM-derived metrics and influential factors in adults with type 1 diabetes mellitus.

Introduction: This study aimed to investigate the association between scan frequency and intermittently scanned continuous glucose monitoring (isCGM) metrics and to clarify the factors affecting scan frequency in adults with type 1 diabetes mellitus (T1D). Methods: We enrolled adults with T1D who used FreeStyle® Libre. Scan and self-monitoring of blood glucose (SMBG) frequency and CGM metrics from the past 90-day glucose data were collected. The receiver operating characteristic curve was plotted to obtain the optimal cutoff values of scan frequency for the target values of time in range (TIR), time above range (TAR), and time below range (TBR). Results: The study was conducted on 211 adults with T1D (mean age, 50.9 ± 15.2 years; male, 40.8%; diabetes duration, 16.4 ± 11.9 years; duration of CGM use, 2.1 ± 1.0 years; and mean HbA1c, 7.6 ± 0.9%). The average scan frequency was 10.5 ± 3.3 scan/day. Scan frequency was positively correlated with TIR and negatively correlated with TAR, although it was not significantly correlated with TBR. Scan frequency was positively correlated with the hypoglycemia fear survey-behavior score, while it was negatively correlated with some glycemic variability metrics. Adult patients with T1D and good exercise habits had a higher scan frequency than those without exercise habits. The AUC for > 70% of the TIR was 0.653, with an optimal cutoff of 11 scan/day. Conclusions: In real-world conditions, frequent scans were linked to improved CGM metrics, including increased TIR, reduced TAR, and some glycemic variability metrics. Exercise habits and hypoglycemia fear-related behavior might affect scan frequency. Our findings could help healthcare professionals use isCGM to support adults with T1D.Clinical Trial Registry No. UMIN000039376.

To Use or Not to Use a Self-monitoring of Blood Glucose System? Real-world Flash Glucose Monitoring Patterns Using a Cluster Analysis of the FGM-Japan Study.

Objective This study investigated self-monitoring of blood glucose (SMBG) adherence and flash glucose monitoring patterns using a cluster analysis in Japanese type 1 diabetes (T1D) patients with intermittently scanned continuous glucose monitoring (isCGM). Methods We measured SMBG adherence and performed a data-driven cluster analysis using a hierarchical clustering in T1D patients from Japan using the FreeStyle Libre system. Clusters were based on three variables (testing glucose frequency and referred Libre data for hyperglycemia or hypoglycemia). Patients We enrolled 209 participants. Inclusion criteria were patients with T1D, duration of isCGM use ≥3 months, age ≥20 years old, and regular attendance at the collaborating center. Results The rate of good adherence to SMBG recommended by a doctor was 85.0%. We identified three clusters: cluster 1 (low SMBG test frequency but high reference to Libre data, 17.7%), cluster 2 (high SMBG test frequency but low reference to Libre data, 34.0%), and cluster 3 (high SMBG test frequency and high reference to Libra data, 48.3%). Compared with other clusters, individuals in cluster 1 were younger, those in cluster 2 had a shorter Libre duration, and individuals in cluster 3 had lower time-in-range, higher severe diabetic distress, and high intake of snacks and sweetened beverages. There were no marked differences in the incidence of diabetic complications and rate of wearing the Libre sensor among the clusters. Conclusion We stratified the patients into three subgroups with varied clinical characteristics and CGM metrics. This new substratification might help tailor diabetes management of patients with T1D using isCGM.

Adherence as a Predictor of Glycemic Control Among Adolescents With Type 1 Diabetes: A Retrospective Study Using Real-world Evidence.

PURPOSE: Metabolic control among adolescents with type 1 diabetes mellitus (T1DM) is generally poor. Nonadherence is a contributor to this poor glycemic control, leading to adverse outcomes. The findings of studies reporting the association between adherence and glycemic control are conflicting. This study aimed to assess the level of adherence among adolescents with T1DM and its relationship with glycemic control. METHODS: This was a retrospective, cross-sectional study that was conducted at Sidra Medicine, a state-of-the-art tertiary health care facility for women and children in Qatar. Mean blood or interstitial glucose monitoring frequency (BGMF) was used to assess adherence level among adolescents with T1DM, whereas glycemic control was assessed via documented glycated hemoglobin A1c (HbA1c). Adolescents who had a mean BGMF of ≥4 checks per day were considered adherent, and those who had an HbA1c level of <7% were considered as having controlled diabetes. Correlational and logistic regression analyses were performed to assess the relationship between adherence and glycemic control, incorporating other covariates into the model. FINDINGS: The rate of adherence among adolescents with T1DM in Qatar was 40.9%. Adherent adolescents had significantly lower median HbA1c levels compared with nonadherent adolescents (9.0% vs. 9.7%; P = 0.002). A significant negative correlation was found between BGMF and HbA1c level (correlation coefficient rs = -0.325; P < .001). Approximately 97% of nonadherent adolescents compared with 87% of adherent adolescents had suboptimal diabetes control (HbA1c ≥7%) (P = .016). Furthermore, nonadherent adolescents were 78% less likely to have controlled diabetes compared with adherent adolescents (adjusted odds ratio = 0.221; 95% CI, 0.063-0.778; P = 0.019). The combined effect of the determinants of glycemic control among adolescents with T1DM that were included in the multiple regression model was able to explain approximately 9% of the variances in glycemic control (Cox and Snell R2 = 0.092). IMPLICATIONS: The current findings suggest that nonadherence was highly prevalent among adolescents with T1DM and was a significant independent predictor of glycemic control, explaining 9% of the variability. This finding warrants further exploration of other possible predictors of poor glycemic control among the adolescent population. Comprehensive interventions, including educational, technological, and health service delivery aspects, aimed at improving adherence and ultimately optimizing glycemic control are warranted in adolescents with T1DM.

Real-world flash glucose monitoring in Brazil: can sensors make a difference in diabetes management in developing countries?

BACKGROUND: New technologies are changing diabetes treatment and contributing better outcomes in developed countries. To our knowledge, no previous studies have investigated the comparative effect of sensor-based monitoring on glycemic markers in developing countries like Brazil. The present study aims to evaluate the use of intermittent Continuous Glucose Measurements (iCGM) in a developing country, Brazil, regarding (i) frequency of glucose scans, (ii) its association with glycemic markers and (iii) comparison with these findings to those observed in global population data. METHODS: Glucose results were de-identified and uploaded to a dedicated database when Freestyle Libre™ readers were connected to an internet-ready computer. Data between September 2014 and Dec 2018, comprising 688,640 readers and 7,329,052 sensors worldwide, were analysed (including 17,691 readers and 147,166 sensors from Brazil). Scan rate per reader was determined and each reader was sorted into 20 equally-sized rank ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated A1c, time above, below and within range identified as 70-180 mg/dL. RESULTS: In Brazil, reader users performed an average of 14 scans per day, while around the world, reader users performed an average of 12 scans per day (p < 0.01). In Brazil dataset, those in the lowest and in the highest groups scanned on average 3.6 and 43.1 times per day had an estimated A1c of 7.56% (59 mmol/mol) and 6.71% (50 mmol/mol), respectively (p < 0.01). Worldwide, the lowest group and the highest groups scanned 3.4 times/day and 37.8 times/day and had an eA1c of 8.14% (65 mmol/mol) and 6.70% (50 mmol/mol), respectively (p < 0.01). For the scan groups in both populations, the time spent above 180 mg/dL decreased as the scan frequency increased. In both Brazil and around the world, as scan frequency increased, time in range (TIR) increased. In Brazil, TIR increased from 14.15 to 16.62 h/day (p < 0.01). Worldwide, TIR increased from 12.06 to 16.97 h/day (p < 0.01). CONCLUSIONS: We conclude that Brazilian users have a high frequency of scans, more frequent than global data. Similarly to the world findings, increased scan frequency is associated with better glycemic control.

Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests.

AIMS: Randomised controlled trials demonstrate that using flash glucose monitoring improves glycaemic control but it is unclear whether this applies outside trial conditions. We investigated glucose testing patterns in users worldwide under real life settings to establish testing frequency and association with glycaemic parameters. METHODS: Glucose results were de-identified and uploaded onto a dedicated database once readers were connected to an internet-ready computer. Data between September 2014 and May 2016, comprising 50,831 readers and 279,446 sensors worldwide, were analysed. Scan rate per reader was determined and each reader was sorted into twenty equally-sized rank-ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated HbA1c, time above, below and within range identified as 3.9-10.0 mmol/L. RESULTS: Users performed a mean of 16.3 scans/day [median (IQR): 14 (10-20)] with 86.4 million hours of readings and 63.8 million scans. Estimated HbA1c gradually reduced from 8.0% to 6.7% (64 to 50 mmol/mol) as scan rate increased from lowest to highest scan groups (4.4 and 48.1 scans/day, respectively; p < .001). Simultaneously, time below 3.9, 3.1 and 2.5 mmol/L decreased by 15%, 40% and 49%, respectively (all p < .001). Time above 10.0 mmol/L decreased from 10.4 to 5.7 h/day (44%, p < .001) while time in range increased from 12.0 to 16.8 h/day (40%, p < .001). These patterns were consistent across different countries. CONCLUSIONS: In real-world conditions, flash glucose monitoring allows frequent glucose checks with higher rates of scanning linked to improved glycaemic markers, including increased time in range and reduced time in hyper and hypoglycaemia.

Observaciones en el mundo real sobre la efectividad del sistema flash de monitorización de glucosa en la práctica clínica en Chile / Real-world observations of the effectiveness of the flash glucose monitoring in clinical practice in Chile

OBJETIVO: Evaluar el tiempo en rango de glucosa y su asociación con otras medidas del control glicémico establecidas por el consenso internacional del tiempo en rango en usuarios de vida real del sistema flash de monitorización de glucosa FreeStyle LibreTM en Chile. MÉTODOS: Se analizaron los datos provenientes de la base de datos Freestyle Libre™ entre diciembre de 2014 y enero de 2022. Las lecturas se dividieron en 10 grupos (deciles) del mismo tamaño (cada decil contenía aproximadamente 498 usuarios) en función del tiempo en rango. Para cada decil se calculó la media de determinaciones diarias, el promedio de glucosa, la HbA1c, la desviación estándar de glucosa, el coeficiente de variación de la glucosa, el tiempo en rango, el tiempo de glucosa (porcentaje) por encima de 250 mg/dL (TA250), el tiempo de glucosa (porcentaje) por encima de 180 mg/dL (TA180), el tiempo por debajo (porcentaje) de 70 mg/dL (TB70) y el tiempo por debajo (porcentaje) de 54 mg/dL (TB54). RESULTADOS: Desde diciembre de 2014 hasta enero de 2022 hubo 4984 lectores. El grupo con el mayor tiempo en rango mostró significativamente una menor glucosa promedio que el grupo con el tiempo en rango más bajo (decil 1: media 248,3 mg/dL, decil 10: media 113,2 mg/L, diferencia ­135,1 mg/dL, p<0.05). Asimismo, el mayor tiempo en rango se asoció con una menor desviación estándar (decil 1: media 93,7mg/dL, decil 10: media 26,7mg/L, diferencia: -67,0 mg/ dL, p<0,05), menor coeficiente de variación (decil 1: media 37,8%, decil 10: media 23,3%, diferencia: -14,5%, p<0,05), menor TA250 (decil 1: media 46,5%, decil 10: media 0,2%, diferencia: -46,3%, p<0.05), menor TA180 (decil 1: media 73,9%, decil 10: media 3,8%, diferencia: -70,1%, p<0.05), menor TB70 (decil 5: mediana 6,13%, decil 10: mediana 1,70%, diferencia: -4,43%, p<0.05) y menor TB54 (decil 5: mediana 1,79%, decil 10: mediana 0,12%, diferencia: -1,67%, p<0.05). El mayor tiempo en rango se asoció también significativamente con más determinaciones diarias (decil 1: media 11,4, decil 10: media 16,6, diferencia: 5,2, p<0,05). La frecuencia media de las determinaciones entre todos los lectores fue de 14,7 determinaciones diarias. CONCLUSIONES: En los pacientes con diabetes en Chile, el empleo del sistema flash de monitorización demuestra la asociación entre el mayor tiempo en rango, la reducción de la variabilidad de la glucosa y un menor riesgo de hiperglucemias e hipoglicemias y también con un mayor compromiso.

OBJECTIVE: To evaluate glucose time in range and its association with other metrics of glucose control established by the International Consensus on TIR amongst real-life patients using the Flash Glucose Monitoring system FreeStyle LibreTM in Chile. METHODS: Data from the Freestyle Libre™ database between December 2014 and January 2022 were analyzed. Readers were divided into 10 groups (deciles) of the same size (each decile had approximately 498 users) according to time in range. For each decile of time in range, the mean of daily scans, average glucose, estimated HbA1c, glucose standard deviation, glucose coefficient of variation, time in range, glucose time (percentage) above 250 mg/dL (TA250), and glucose time (percentage) above 180 mg/dL (TA180), and the median of glucose time (percentage) below 70 mg/dL (TB70) and glucose time (percentage) below 54 mg/dL (TB54), were calculated. RESULTS: From December 2014 to January 2022, there were 4984 readers. The group with the highest TIR showed significantly lower average glucose than the group with the lowest TIR (decile 1: mean 248.3 mg/dL, decile 10: mean 113.2 mg/L, difference: ­135.1 mg/dL, p<0.05). In addition, more time in range was associated with a lower glucose standard deviation (decile 1: mean 93.7 mg/dL, decile 10: mean 26.7 mg/L, difference: -67.0 mg/dL, p<0.05), lower glucose coefficient of variation (decile 1: mean 37.8%, decile 10: mean 23.3%, difference: -14.5%, p<0.05), lower TA250 (decile 1: mean 46.5%, decile 10: mean 0.2%, difference: -46.3%, p<0.05),lower TA180 (decile 1: mean 73.9%, decile 10: mean 3.8%, difference: -70.1%, p<0.05), lower TB70 (decile 5: median 6.13%, decile 10: median 1.70%, difference: -4.43%, p<0.05) and lower TB54 (decile 5: median 1.79%, decile 10: median 0.12%, difference: -1.67%, p<0.05). Greater TIR was also associated with significantly more daily scans (decile 1: mean 11.4, decile 10: mean 16.6, difference: 5.2, p<0.05). Mean scan frequency amongst all readers was 14.7 daily scans. CONCLUSIONS: In patients with diabetes from Chile, the use of the flash glucose monitoring system demonstrates the association between greater TIR, reduced glucose variability, and reduced risk of hyperglycemia and hypoglycemia, and also its association with greater engagement.

The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.

The role of blood glucose (BG) measurement frequency on the domains of glycemic control is not well defined. This Monte Carlo mathematical simulation of glycemic control in a cohort of critically ill patients modeled sets of 100 patients with simulated BG-measuring devices having 5 levels of measurement imprecision, using 2 published insulin infusion protocols, for 200 hours, with 3 different BG-measurement intervals-15 minutes (Q15'), 1 hour (Q1h), and 2 hours (Q2h)-resulting in 1,100,000 BG measurements for 3000 simulated patients. The model varied insulin sensitivity, initial BG value and rate of gluconeogenesis. The primary outcomes included rates of hyperglycemia (BG > 180 mg/dL), hypoglycemia (BG < 70 and 40 mg/dL), proportion of patients with elevated glucose variability (within-patient coefficient of variation [CV] > 20%), and time in range (BG ranges 80-150 mg/dL and 80-180 mg/dL). Percentages of hyperglycemia, hypoglycemia at both thresholds, and patients with elevated glucose variability as well as time outside glycemic targets were substantially higher in simulations with measurement interval Q2h compared to those with measurement interval Q1h and moderately higher in simulations with Q1h than in those with Q15'. Higher measurement frequency mitigated the deleterious effect of high measurement imprecision, defined as CV ≥ 15%. This Monte Carlo simulation suggests that glycemic control in critically ill patients is more optimal with a BG measurement interval no longer than 1h, with further benefit obtained with use of measurement interval of 15'. These findings have important implications for the development of glycemic control standards.