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An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment.

ABSTRACT: BACKGROUND:We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome. METHODS:Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII. RESULTS:Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients. CONCLUSION:Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia.

SUBMITTER: Park SW

PROVIDER: S-EPMC7043972 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Algorithms for intravenous insulin infusion may assign the infusion rate (IR) by a two-step process. First, the previous insulin infusion rate (IR(previous)) and the rate of change of blood glucose (BG) from the previous iteration of the algorithm are used to estimate the maintenance rate (MR) of insulin infusion. Second, the insulin IR for the next iteration (IR(next)) is assigned to be commensurate with the MR and the distance of the current blood glucose (BG(current)) from target. With use of a specific set of algorithm parameter values, a family of iso-MR curves is created, each giving IR as a function of MR and BG.To test the feasibility of estimating MR from the IR(previous) and the previous rate of change of BG, historical hyperglycemic data points were used to compute the "maintenance rate cross step next estimate" (MR(csne)). Historical cases had been treated with intravenous insulin infusion using a tabular protocol that estimated MR according to column-change rules. The mean IR on historical stable intervals (MR(true)), an estimate of the biologic value of MR, was compared to MR(csne) during the hyperglycemic iteration immediately preceding the stable interval. Hypothetically calculated MR(csne)-dependent IR(next) was compared to IR(next) assigned historically. An expanded theory of an algorithm is developed mathematically. Practical recommendations for computerization are proposed.The MR(true) determined on each of 30 stable intervals and the MR(csne) during the immediately preceding hyperglycemic iteration differed, having medians with interquartile ranges 2.7 (1.2-3.7) and 3.2 (1.5-4.6) units/h, respectively. However, these estimates of MR were strongly correlated (R(2) = 0.88). During hyperglycemia at 941 time points the IR(next) assigned historically and the hypothetically calculated MR(csne)-dependent IR(next) differed, having medians with interquartile ranges 4.0 (3.0-6.0) and 4.6 (3.0-6.8) units/h, respectively, but these paired values again were correlated (R(2) = 0.87). This article describes a programmable algorithm for intravenous insulin infusion. The fundamental equation of the algorithm gives the relationship among IR; the biologic parameter MR; and two variables expressing an instantaneous rate of change of BG, one of which must be zero at any given point in time and the other positive, negative, or zero, namely the rate of change of BG from below target (rate of ascent) and the rate of change of BG from above target (rate of descent). In addition to user-definable parameters, three special algorithm parameters discoverable in nature are described: the maximum rate of the spontaneous ascent of blood glucose during nonhypoglycemia, the glucose per daily dose of insulin exogenously mediated, and the MR at given patient time points. User-assignable parameters will facilitate adaptation to different patient populations.An algorithm is described that estimates MR prior to the attainment of euglycemia and computes MR-dependent values for IR(next). Design features address glycemic variability, promote safety with respect to hypoglycemia, and define a method for specifying glycemic targets that are allowed to differ according to patient condition.

Project description:Type 2 diabetes (T2D) is a pathophysiology that is characterized by insulin resistance, beta- and alpha-cell dysfunction. Mathematical models of various glucose challenge experiments have been developed to quantify the contribution of insulin and beta-cell dysfunction to the pathophysiology of T2D. There is a need for effective extended models that also capture the impact of alpha-cell dysregulation on T2D. In this paper a delay differential equation-based model is developed to describe the coupled glucose-insulin-glucagon dynamics in the isoglycemic intravenous glucose infusion (IIGI) experiment. As the glucose profile in IIGI is tailored to match that of a corresponding oral glucose tolerance test (OGTT), it provides a perfect method for studying hormone responses that are in the normal physiological domain and without the confounding effect of incretins and other gut mediated factors. The model was fit to IIGI data from individuals with and without T2D. Parameters related to glucagon action, suppression, and secretion as well as measures of insulin sensitivity, and glucose stimulated response were determined simultaneously. Significant impairment in glucose dependent glucagon suppression was observed in patients with T2D (duration of T2D: 8 (6-36) months) relative to weight matched control subjects (CS) without diabetes (k1 (mM)-1: 0.16 ± 0.015 (T2D, n = 7); 0.26 ± 0.047 (CS, n = 7)). Insulin action was significantly lower in patients with T2D (a1 (10 pM min)-1: 0.000084 ± 0.0000075 (T2D); 0.00052 ± 0.00015 (CS)) and the Hill coefficient in the equation for glucose dependent insulin response was found to be significantly different in T2D patients relative to CS (h: 1.4 ± 0.15; 1.9 ± 0.14). Trends in parameters with respect to fasting plasma glucose, HbA1c and 2-h glucose values are also presented. Significantly, a negative linear relationship is observed between the glucagon suppression parameter, k1, and the three markers for diabetes and is thus indicative of the role of glucagon in exacerbating the pathophysiology of diabetes (Spearman Rank Correlation: (n = 12; (-0.79, 0.002), (-0.73,.007), (-0.86,.0003)) respectively).

Project description:BackgroundHypertension is a major contributor to various adverse health outcomes. Although previous studies have shown the benefits of home blood pressure (BP) monitoring over office-based measurements, there is limited evidence comparing the effectiveness of whether a BP monitor integrated into the electronic health record is superior to a nonintegrated BP monitor.ObjectiveIn this paper, we describe the protocol for a pragmatic multisite implementation of a quality improvement initiative directly comparing integrated to nonintegrated BP monitors for hypertension improvement.MethodsWe will conduct a randomized, comparative effectiveness trial at 3 large academic health centers across California. The 3 sites will enroll a total of 660 participants (approximately n=220 per site), with 330 in the integrated BP monitor arm and 330 in the nonintegrated BP control arm. The primary outcome of this study will be the absolute difference in systolic BP in mm Hg from enrollment to 6 months. Secondary outcome measures include binary measures of hypertension (controlled vs uncontrolled), hypertension-related health complications, hospitalizations, and death. The list of possible participants will be generated from a central data warehouse. Randomization will occur after enrollment in the study. Participants will use their assigned BP monitor and join site-specific hypertension interventions. Cross-site learning will occur at regular all-site meetings facilitated by the University of California, Los Angeles Value-Based Care Research Consortium. A pre- and poststudy questionnaire will be conducted to further evaluate participants' perspectives regarding their BP monitor. Linear mixed effects models will be used to compare the primary outcome measure between study arms. Mixed effects logistic regression models will be used to compare secondary outcome measures between study arms.ResultsThe study will start enrolling participants in the second quarter of 2023 and will be completed by the first half of 2024. Results will be published by the end of 2024.ConclusionsThis pragmatic trial will contribute to the growing field of chronic care management using remote monitoring by answering whether a hypertension intervention coupled with an electronic health record integrated home BP monitor improves patients' hypertension better than a hypertension intervention with a nonintegrated BP monitor. The outcomes of this study may help health system decision makers determine whether to invest in integrated BP monitors for vulnerable patient populations.Trial registrationClinicalTrials.gov NCT05390502; clinicaltrials.gov/study/NCT05390502.International registered report identifier (irrid)PRR1-10.2196/45915.

Project description:BackgroundClinical prediction rules (CPRs) represent well-validated but underutilized evidence-based medicine tools at the point-of-care. To date, an inability to integrate these rules into an electronic health record (EHR) has been a major limitation and we are not aware of a study demonstrating the use of CPR's in an ambulatory EHR setting. The integrated clinical prediction rule (iCPR) trial integrates two CPR's in an EHR and assesses both the usability and the effect on evidence-based practice in the primary care setting.MethodsA multi-disciplinary design team was assembled to develop a prototype iCPR for validated streptococcal pharyngitis and bacterial pneumonia CPRs. The iCPR tool was built as an active Clinical Decision Support (CDS) tool that can be triggered by user action during typical workflow. Using the EHR CDS toolkit, the iCPR risk score calculator was linked to tailored ordered sets, documentation, and patient instructions. The team subsequently conducted two levels of 'real world' usability testing with eight providers per group. Usability data were used to refine and create a production tool. Participating primary care providers (n = 149) were randomized and intervention providers were trained in the use of the new iCPR tool. Rates of iCPR tool triggering in the intervention and control (simulated) groups are monitored and subsequent use of the various components of the iCPR tool among intervention encounters is also tracked. The primary outcome is the difference in antibiotic prescribing rates (strep and pneumonia iCPR's encounters) and chest x-rays (pneumonia iCPR only) between intervention and control providers.DiscussionUsing iterative usability testing and development paired with provider training, the iCPR CDS tool leverages user-centered design principles to overcome pervasive underutilization of EBM and support evidence-based practice at the point-of-care. The ongoing trial will determine if this collaborative process will lead to higher rates of utilization and EBM guided use of antibiotics and chest x-ray's in primary care.Trial registrationClinicalTrials.gov Identifier NCT01386047.

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An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment.

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