Combination of pancreas volume and HbA1c level predicts islet yield in patients undergoing total pancreatectomy and islet autotransplantation

Islet Isolation Outcomes in Patients Undergoing Total Pancreatectomy With Islet Autotransplantation in the POST Consortium

BACKGROUND

In total pancreatectomy with islet autotransplantation (TPIAT), a greater number of islets transplanted produces more favorable outcomes. We aimed to determine predictors of islet isolation outcomes.

METHODS

We investigated factors associated with islet isolation outcomes expressed as islet number (IN), islet equivalents (IEQ; standardized to an islet with 150 μm diameter), IN/kg, or IEQ/kg using data from the multicenter Prospective Observational Study of TPIAT. Single-predictor linear regression was used to estimate the association of individual patient and disease characteristics with islet isolation outcomes, and augmented backward elimination was used to select variables to include in multivariable analyses.

RESULTS

In multivariable analyses, only elevated hemoglobin A1c was associated with worse outcomes for all measures ( P  < 0.001 for all). Total IEQ obtained for transplant was higher for participants with Hispanic ethnicity ( P  = 0.002) or overweight status pre-TPIAT ( P  < 0.001) and lower with non-White race ( P  = 0.03), genetic pancreatitis ( P  = 0.02), history of lateral pancreaticojejunostomy ( P  = 0.03), and presence of atrophy ( P  = 0.006) or ductal changes ( P  = 0.014) on imaging. IEQ/kg was higher in females ( P  = 0.01) and Hispanic participants ( P  = 0.046) and generally lower with older age (nonlinear association, P  < 0.001) and pancreatic atrophy ( P  < 0.001) on imaging. Total IN and IN/kg showed trends similar, but not identical, to IEQ and IEQ/kg, respectively.

CONCLUSIONS

Patient demographics and certain pancreatic disease features were associated with outcomes from islet isolation. Hemoglobin A1c before TPIAT was the metabolic testing measure most strongly associated with islet isolation results.

Cross-sectional imaging of the pancreas in diabetes

Diabetes mellitus presents a global health challenge characterized by dysregulated glucose metabolism and insulin resistance. Pancreas dysfunction contributes to the development and progression of diabetes. Cross-sectional imaging modalities have provided new insight into the structural and functional alterations of the pancreas in individuals with diabetes. This review summarizes MRI and CT studies that characterize pancreas alterations in both type 1 and type 2 diabetes and discusses future applications of these techniques.

Predicting endocrine function after total pancreatectomy and islet cell autotransplantation: A novel approach utilizing computed tomography texture analysis

BACKGROUND

Islet cell autotransplantation is an effective method to prevent morbidity associated with type IIIc diabetes after total pancreatectomy. However, there is no valid method to predict long-term endocrine function. Our aim was to assess computed tomography texture analysis as a strategy to predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation.

METHODS

All patients undergoing total pancreatectomy and islet cell autotransplantation from 2007 to 2020 who had high-quality preoperative computed tomography imaging available for texture analysis were included. The primary outcome was optimal long-term endocrine function, defined as stable glycemic control with <10 units of insulin/day.

RESULTS

Sixty-three patients met inclusion criteria. Median yield was 6,111 islet equivalent/kg body weight. At a median follow-up of 64.2 months, 12.7% (n = 8) of patients were insulin independent and 39.7% (n = 25) demonstrated optimal endocrine function. Neither total islet equivalent nor islet equivalent/kg body weight alone were associated with optimal endocrine function. To improve endocrine function prediction, computed tomography texture analysis parameters were analyzed, identifying an association between kurtosis (odds ratio, 2.32; 95% confidence interval, 1.08-4.80; P = .02) and optimal endocrine function. Sensitivity analysis discovered a cutoff for kurtosis = 0.60, with optimal endocrine function seen in 66.7% with kurtosis ≥0.60, compared with only 26.2% with kurtosis <0.60 (P < .01). On multivariate logistic regression including islet equivalent yield, only kurtosis ≥0.60 (odds ratio, 5.61; 95% confidence interval, 1.56-20.19; P = .01) and fewer small islet equivalent (odds ratio, 1.00; 95% confidence interval, 1.00-1.00; P = .02) were associated with optimal endocrine function, with the whole model demonstrating excellent prediction of long-term endocrine function (area under the curve, 0.775).

CONCLUSION

Computed tomography texture analysis can provide qualitative data, that when used in combination with quantitative islet equivalent yield, can accurately predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation.

Metabolic measures before surgery and long-term diabetes outcomes in recipients of total pancreatectomy and islet autotransplantation

In this single-center, retrospective cohort study, we aimed to elucidate simple metabolic markers or surrogate indices of β-cell function that best predict long-term insulin independence and goal glycemic HbA1c control (HbA1c ≤ 6.5%) after total pancreatectomy with islet autotransplantation (TP-IAT). Patients who underwent TP-IAT (n = 371) were reviewed for metabolic measures before TP-IAT and for insulin independence and glycemic control at 1, 3, and 5 years after TP-IAT. Insulin independence and goal glycemic control were achieved in 33% and 68% at 1 year, respectively. Although the groups who were insulin independent and dependent overlap substantially on baseline measures, an individual who has abnormal glycemia (prediabetes HbA1c or fasting glucose) or estimated IEQs/kg < 2500 has a very high likelihood of remaining insulin dependent after surgery. In multivariate logistic regression modelling, metabolic measures correctly predicted insulin independence in about 70% of patients at 1, 3, and 5 years after TP-IAT. In conclusion, metabolic testing measures before surgery are highly associated with diabetes outcomes after TP-IAT at a population level and correctly predict outcomes in approximately two out of three patients. These findings may aid in prognostic counseling for chronic pancreatitis patients who are likely to eventually need TP-IAT.

Total pancreatectomy sequelae and quality of life: results of islet autotransplantation as a possible mitigation strategy

Total pancreatectomy (TP) is a procedure weighed down not only by postoperative morbidity and mortality but also by long-term effects as a consequence of endocrine and exocrine pancreatic insufficiency. While the latter is now managed quite effectively with pancreatic enzyme replacement therapy, the former remains a challenge. The diabetes resulting after TP, with the complete loss of endogenous insulin and contraregulatory hormones, is characterized by important glycemic variations and is, therefore, frequently referred to as "brittle diabetes". One method to reduce the impact of brittle diabetes in patients undergoing TP is the re-infusion of autologous pancreatic islets isolated from the resected pancreas. Indications to islet autotransplantation (IAT), originally described for patients undergoing TP for chronic pancreatitis, have since been extended to selected patients with other benign and malignant diseases of pancreas. This review recaps on the literature regarding long-term postoperative complications, their impact on quality of life after TP and the role of IAT.

Imaging prediction of islet yield and post-operative insulin requirement in children undergoing total pancreatectomy with islet autotransplantation

BACKGROUND

Predicting post-operative glycemic control in children undergoing total pancreatectomy with islet autotransplantation (TPIAT) remains difficult. The purpose of our study was to explore preoperative imaging as a marker for islet yield and insulin need in pediatric patients undergoing TPIAT.

METHODS

This was a retrospective study of children (≤18 years) who had undergone TPIAT between April 2015 and December 2018 and had 6 or more months of post-TPIAT follow-up. Patient specific factors (height, weight, body mass index [BMI], body surface area [BSA]) and pancreas volume segmented from the most recent pre-operative cross-sectional imaging were explored as predictors of islet yield (total islet counts [TIC], total islet equivalents [TIE], islet equivalents per kilogram body weight [IEQ/kg]) and glycemic control (total daily dose of insulin per kilogram body weight [TDD/kg], insulin independence) using Pearson correlation and univariate and multiple regression.

RESULTS

Thirty-three patients, median age 13 years (IQR: 10-15 years), 64% female (21/33) met inclusion criteria. Nine patients (27%) achieved insulin independence at six months. Median TIE isolated was 310,000 (IQR: 200,000-460,000). Segmented pancreas volume was moderately associated with TIE (coefficient estimate = 0.34, p = 0.034). On multiple regression analysis, there was no significant predictor of insulin independence but number of attacks of pancreatitis (estimate = 0.024; p = 0.018) and segmented pancreas volume by body weight (estimate = -0.71; p < 0.001) were significant predictors of insulin TDD/kg.

CONCLUSION

Pancreas volume segmented from pre-TPIAT imaging has predictive performance for post-TPIAT insulin need in children.

Quantitative Magnetic Resonance Imaging of the Pancreas of Individuals With Diabetes

Magnetic resonance imaging (MRI) has the potential to improve our understanding of diabetes and improve both diagnosis and monitoring of the disease. Although the spatial resolution of MRI is insufficient to directly image the endocrine pancreas in people, the increasing awareness that the exocrine pancreas is also involved in diabetes pathogenesis has spurred new MRI applications. These techniques build upon studies of exocrine pancreatic diseases, for which MRI has already developed into a routine clinical tool for diagnosis and monitoring of pancreatic cancer and pancreatitis. By adjusting the imaging contrast and carefully controlling image acquisition and processing, MRI can quantify a variety of tissue pathologies. This review introduces a number of quantitative MRI techniques that have been applied to study the diabetic pancreas, summarizes progress in validating and standardizing each technique, and discusses the need for image analyses that account for spatial heterogeneity in the pancreas.