Bolus guide: a novel insulin bolus dosing decision support tool based on selection of carbohydrate ranges

BACKGROUND

Optimal continuous subcutaneous insulin infusion (CSII) therapy emphasizes the relationship between insulin dose and carbohydrate consumption. One widely used tool (bolus calculator) requires the user to enter discrete carbohydrate values; however, many patients might not estimate carbohydrates accurately. This study assessed carbohydrate estimation accuracy in type 1 diabetes CSII users and compared simulated blood glucose (BG) outcomes using the bolus calculator and the "bolus guide," an alternative system based on ranges of carbohydrate load.

METHODS

Patients (n = 60) estimated the carbohydrate load of a representative sample of meals of known carbohydrate value. The estimated error distribution [coefficient of variation (CV)] was the basis for a computer simulation (n = 1.6 million observations) of insulin recommendations for the bolus guide and bolus calculator, translated into outcome blood glucose (OBG) ranges (< or =60, 61-200, >201 mg/dl). Patients (n = 30) completed questionnaires assessing satisfaction with the bolus guide.

RESULTS

The CV of typical meals ranged from 27.9% to 44.5%. The percentage of simulated OBG for the calculator and the bolus guide in the <60 mg/dl range were 20.8% and 17.2%, respectively, and 13.8% and 15.8%, respectively, in the >200 mg/dl range. The mean and median scores of all bolus guide satisfaction items and ease of learning and use were 4.17 and 4.2, respectively (of 5.0).

CONCLUSION

The bolus guide recommendation based on carbohydrate range selection is substantially similar to the calculator based on carbohydrate point estimation and appears to be highly accepted by type 1 diabetes insulin pump users.

An immune-protein score combining TRAIL, IP-10 and CRP for predicting severe COVID-19 disease

COVID-19 patients are oftentimes over- or under-treated due to a deficit in predictive management tools. This study reports derivation of an algorithm that integrates the host levels of TRAIL, IP-10, and CRP into a single numeric score that is an early indicator of severe outcome for COVID-19 patients and can identify patients at-risk to deteriorate. 394 COVID-19 patients were eligible; 29% meeting a severe outcome (intensive care unit admission/non-invasive or invasive ventilation/death). The score's area under the receiver operating characteristic curve (AUC) was 0.86, superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The score differentiated severe patients who further deteriorated from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001). The score accurately predicted COVID-19 patients at-risk for severe outcome, and therefore has potential to facilitate timely care escalation and de-escalation and appropriate resource allocation.

32. Host Immune-Protein Signature Combining TRAIL, IP-10 and CRP for Early and Accurate Prediction of Severe COVID-19 Outcome

Background

Accurately identifying COVID-19 patients at-risk to deteriorate remains challenging. Dysregulated immune responses impact disease progression and development of life-threatening complications. Tools integrating host immune-protein expression have proven useful in determining infection etiology and hold potential for prognosticating disease severity.

Methods

Adults with COVID-19 were enrolled at medical centers in Israel, Germany, and the United States (Figure 1). Severe outcome was defined as intensive care unit admission, non-invasive or invasive ventilation, or death. Tumor necrosis factor related apoptosis inducing ligand (TRAIL), interferon gamma inducible protein-10 (IP-10) and C-reactive protein (CRP) were measured using an analyzer providing values within 15 minutes (MeMed Key®). A signature indicating the likelihood of severe outcome was derived generating a score (0-100).

Description of derivation cohort

RT-PCR, reverse transcription polymerase chain reaction.

Results

Between March and November 2020, 518 COVID-19 patients were enrolled, of whom 394 were eligible, 29% meeting a severe outcome. Age ranged between 19-98 (median 61.5), with 59.1% male. Patients meeting severe outcomes exhibited higher levels of CRP and IP-10 and lower levels of TRAIL (Figure 2; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The signature’s area under the receiver operating characteristic curve (AUC) was 0.86 (95% confidence interval: 0.81-0.91). Performance was not confounded by age, sex, or comorbidities and was superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Clinical deterioration proximal to blood draw was associated with higher signature score. Scores of patients meeting a first outcome over 3 days after blood draw were significantly (p < 0.001) higher than scores of non-severe patients (Figure 3). Moreover, the signature differentiated patients who further deteriorated after meeting a severe outcome from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001; Figure 4).

TRAIL, IP-10, CRP and the severity signature score are differentially expressed in severe and non-severe COVID-19 infection

Dots represent patients and boxes denote median and interquartile range (IQR)

The signature score of patients meeting a severe outcome on or after the day of blood draw is significantly (p < 0.001) higher than the signature score of non-severe patients.

Dots represents patients and boxes denote median and IQR

Kaplan-Meier survival estimates for signature score bins

Conclusion

The derived signature combined with a rapid measurement platform has potential to serve as an accurate predictive tool for early detection of COVID-19 patients at risk for severe outcome, facilitating timely care escalation and de-escalation and appropriate resource allocation.

Disclosures

Alon Angel, n/a, MeMed (Employee, Shareholder) Niv Samuel Mastboim, BSc, MeMed (Employee, Shareholder) Oded Shaham, PhD, MeMed (Employee, Shareholder) Tahel Ilan Ber, MD, MeMed (Employee, Shareholder) Roy Navon, MSc, MeMed (Employee, Shareholder) Einav Simon, PhD, MeMed (Employee, Shareholder) Michal Rosenberg, PhD, MeMed (Employee) Yael Israeli, PhD, MeMed (Employee) Mary Hainrichson, PhD, MeMed (Employee, Shareholder) Noa Avni, PhD, MeMed (Employee) Eran Reiner, MD, MeMed (Employee) Kfir Oved, MD, PhD, MeMed (Board Member, Employee, Shareholder) Ilya Kagan, MD, MeMed (Scientific Research Study Investigator) Shaul Lev, M.D, MeMed (Scientific Research Study Investigator) Dror Diker, MD, MeMed (Scientific Research Study Investigator) Amir Jarjou’i, MD, MeMed (Scientific Research Study Investigator) Ramzi Kurd, MD, MeMed (Scientific Research Study Investigator) Guy Danziger, MD, MeMed (Scientific Research Study Investigator) Cihan Papan, MD, MeMed (Scientific Research Study Investigator) Sergey Motov, MD, MeMed (Scientific Research Study Investigator) Maanit Shapira, Ph.D, MeMed (Scientific Research Study Investigator) Tanya Gottlieb, PhD, MeMed (Employee, Shareholder) Eran Eden, PhD, MeMed (Board Member, Employee, Shareholder)