Factors that influence parathyroid hormone half-life: Are new intraoperative criteria needed?

Abstract

Hypothesis

Patient characteristics, such as age, gender, race, body mass index (BMI), and renal function may affect existing criteria for intraoperative parathyroid hormone (IOPTH) during minimally invasive parathyroidectomy.

Design

Retrospective review of a prospectively-collected parathyroid database.

Setting

Academic medical center.

Patients

306 patients with sporadic primary hyperparathyroidism (pHPT) who underwent initial parathyroidectomy between August 2005 and April 2011.

Interventions

All patients underwent MIP with complete IOPTH information.

Outcome measures

Individual IOPTH kinetic profiles were fitted with an exponential decay curve and individual IOPTH half-lives were determined. Univariate and multivariate analyses were performed to determine the association between patient demographics or laboratory data and IOPTH half-life.

Results

Mean age of the cohort was 60 years, 78% were female, 90% were White, and median BMI was 28.3 kg/m². Overall, median IOPTH half-life was 3 minutes, 9 seconds. On univariate analysis, there was no association between IOPTH half-life and patient age, renal function, preoperative serum calcium or PTH levels. Age, BMI, and age-BMI interaction were included in the final multivariate median regression analysis; race, gender, and GFR were not predictors of IOPTH half-life. IOPTH half-life increased with increasing BMI, an effect that diminished with increasing age and was negligible after age 55 (p=0.0014).

Conclusion

BMI, especially in younger patients, may have a role in the IOPTH half-life of patients undergoing parathyroidectomy. However, the differences in half-life are relatively small and the clinical implication are likely not significant. Current IOPTH criteria can continue to be applied to all patients undergoing parathyroidectomy for sporadic primary hyperparathyroidism.

Introduction

Primary hyperparathyroidism (pHPT) is defined as elevated serum calcium levels with inappropriate elevation of parathyroid hormone (PTH) levels. In the majority (75–85%) of patients, pHPT is the result of a single parathyroid adenoma; the remaining patients have involvement of more than one parathyroid gland (multigland hyperplasia [MGD]).¹ Parathyroidectomy is currently the only cure for primary hyperparathyroidism. Historically, the gold standard for parathyroidectomy involved bilateral cervical exploration and identification of all parathyroid glands, with removal of only enlarged glands. With the advent of intraoperative PTH (IOPTH) monitoring and improved radiographic techniques for preoperative gland localization imaging, minimally invasive parathyroidectomy (MIP) has become the favored approach. MIP has been demonstrated to have similar success rates as bilateral exploration, with lower rates of endocrine-specific complications, such as recurrent laryngeal nerve injury and permanent hypoparathyroidism.²

IOPTH monitoring involves sampling of PTH values preoperatively and intraoperatively, after resection of abnormal parathyroid glands. The definition of a successful parathyroidectomy utilizing IOPTH data varies by institution. Two common criteria include 1) a decrease of >50% from the highest pre-excision value (the “Miami criterion”) or 2) a decrease of >50% from the highest pre-excision value and into the normal range of the PTH assay.^3–5 Despite the various institutional differences, all current criteria rely solely on absolute intraoperative PTH values and few studies have investigated the effect of patient factors on IOPTH elimination that could alter these values.^3,5–10 Therefore, the purpose of this study was to identify patient characteristics (age, gender, race, body mass index [BMI], and/or renal function) that may affect the half-life of PTH and, if so, to determine if new criteria are needed for IOPTH monitoring during parathyroidectomy.

Methods

Patients

Following institutional review board approval, patient data was retrospectively collected from a single institution’s prospectively collected parathyroidectomy database populated from August 2005 to April 2011. Inclusion criteria for the study were patients with sporadic pHPT due to a single adenoma who underwent initial parathyroidectomy with a complete IOPTH profile, consisting of IOPTH levels assayed at 0–4 min, 5–9 min, 10–14 min, and 15–40 min post adenoma resection. Of the initial 314 patients, 8 patients were excluded due to non-standard IOPTH profiles. The final study cohort consisted of 306 patients.

Queried patient data consisted of age, gender, race, BMI, preoperative serum calcium, preoperative PTH, glomerular filtration rate (Modification of Diet in Renal Disease formula [GFR]), and IOPTH concentrations with corresponding time values. All 306 patients underwent preoperative localization using cervical ultrasound and 99m technetium-labeled sestamibi scans.¹¹ Since 2009, sestamibi was routinely performed with single photon emission computed tomography (SPECT); patients with discordant ultrasound and sestamibi-SPECT underwent a 4-dimensional CT scan.

Intraoperative PTH Monitoring

All patients underwent MIP with IOPTH monitoring. Our institution’s IOPTH monitoring protocol consists of obtaining intraoperative PTH levels from a peripheral blood sample following anesthesia induction and prior to neck incision (baseline), at adenoma resection (zero value), and subsequently at 5 minute intervals (5, 10, and 15 min) post adenoma resection. Our institution’s criteria for intraoperative biochemical cure at 10 minutes post adenoma resection is defined as: (1) PTH decline of at least 50% from either the baseline or the zero value, whichever is higher, and (2) within normal immunoassay reference range. Intraoperative PTH levels were measured by the Immulite 1000 Turbo Intact PTH system (Diagnostics Products Corporation, Los Angeles, CA) from August 2005 to August 2009, and by the Roche Cobas e411 (Roche Diagnostics, Indianapolis, IN) from September 2009 to the present.

Kinetic Analyses

Individual intraoperative PTH data sets were modeled using an ideal single phase exponential decay with an additive constant function, C (pg/mL). The model equation can be expressed as, where A₀ = constant term (pg/mL), k = decay constant (1/min.), and t = time (min):

$$[\mathit{\text{IOPTH}}](t)=A_0{e}^{-kt}+C$$ (1)

For the analysis, it was assumed that the contribution of the non-adenomatous parathyroid glands to the IOPTH profile was constant.¹² Least squares nonlinear regression modeling of the IOPTH data sets, based on equation 1, was performed utilizing an open source implementation, ALGLIB, of the Levenberg-Marquardt algorithm in Visual Studio C++ (Microsoft, Redmond, WA).¹³ The program yielded the kinetic decay constant, k, constant term, A₀, and constant function, C, for each patient. Correlation coefficients were calculated to verify model conformity and IOPTH half-life values, ln(2)/k, were then determined (Microsoft Excel, Redmond, WA).

Statistical Analyses

Nonparametric statistical analyses were performed since the IOPTH half-life values were non-normally distributed in a positively-skewed fashion. Nonparametric Wilcoxon rank sum test and Spearman rank correlation tests were used for univariate tests of association between patient demographics or laboratory data and IOPTH half-life. Median regression was used in multivariate analysis of IOPTH half-life. Variables in the model included those with p <0.1 on univariate analysis and a priori hypothesized risk factors. Median regression model selection was performed manually with a significance level of <0.05. All statistical analysis was performed in SAS version 9.2 (The SAS Institute, Cary, NC).

Results

Cohort Summary Statistics

Table 1 summarizes the characteristics of the study cohort. Median age of the cohort was 60 years (interquartile range [IQR]: 53–69) and 78% were female. The majority of patients (90%) were white, 9% were black, and 1% were Hispanic. The median BMI of the cohort was 28.3 kg/m² (IQR: 25.0–32.7); 25% were normal or underweight (BMI <25), 34% were overweight (BMI 25 – 30), and 42% were obese (BMI >30). Cohort median GFR was 75.2 mL/min/1.73m² (IQR: 65.5–85.8); 18% had a GFR <60 mL/min/1.73m². The median preoperative serum calcium was 10.7 mg/dL (IQR: 10.3–11.3) and the median baseline preoperative PTH was 169.5 pg/mL (IQR: 118.0–169.5). No patient was on cinacelcet at the time of surgery; 39 (13%) patients were on a bisphosphonate at the time of surgery.

Table I.

Clinical and demographic factors and IOPTH half-life for 306 pHPT patients

Variables	Mean (SD)	Median Half-Life (min.)	P-value
Age (yrs)	60 (13)	---	0.26†
Gender (N,%))	---	---	0.06*
Female (240, 78.4%)	---	3m 8s	---
Male (66, 21.6%)	---	3m 14s	---
Race (N, %)	---	---	<0.01*
White (276, 90.2%)	---	3m 7s	---
Black (26, 8.5%)	---	3m 50s	---
Hispanic (4, 1.3%)	---	2m 50s	---
BMI (kg/m2)	29.50 (6.33)	---	0.06*
< 25.00 (75, 24.5%)	22.65 (1.68)	3m	---
25.00–29.99 (103, 33.7%)	27.19 (1.37)	3m 8s	---
≥30.00 (128, 41.8%)	35.38 (5.11)	3m 23s	---
GFR (mL/min. per 1.73m2)	76.46 (19.84)	---	0.39†
Preoperative Serum [Ca2+] (mg/dL)	10.80 (.71)	---	0.19†
Baseline Preoperative [PTH] (pg/mL)	217.38 (186.63)	---	0.09†

^†P-value determined by Spearman rank correlation test

^*P-value determined by Wilcoxon rank sum test

Kinetic Analyses

On kinetic analysis, equation 1 was shown to fit individual IOPTH data with high conformity, as validated by a nonlinear regression correlation coefficient interquartile range of 0.9978–0.9999. The median calculated IOPTH half-life for the entire cohort was 3 minutes and 9 seconds (IQR: 2m37s–4m; mean = 3m28s). Calculated IOPTH half-life values were independent of the immunoassay implemented (p=0.11).

Statistical Analyses

There was an association between race and IOPTH half-life (Table 1); blacks demonstrated a longer IOPTH half-life than whites (3m50s vs. 3m7s; P<0.01). Hispanics had a shorter IOPTH half-life (2m50s) but the small Hispanic sample size (n=4) limited our ability to make any inferences for this subset of the population. There was a trend towards an association between BMI and IOPTH half-life (P=0.06); patients with a higher BMI had a longer IOPTH half-life. Similarly, there was a trend towards an association between gender and IOPTH half-life; males had a median IOPTH half-life that was 6 seconds longer than females (3m14s vs. 3m8s; P=0.06). There was no association between IOPTH half-life and patient age, renal function, preoperative serum calcium or PTH levels, or preoperative use of bisphosphonates.

Race, gender, and BMI along with a priori variables GFR and age plus an Age-BMI interaction factor were then considered in multivariate analysis. Controlling for these factors, race, gender, and GFR were not significant. Significant predictors of IOPTH half-life were BMI (P=0.04) and age-BMI interaction factor (P<0.001) with the final median IOPTH half-life model as given below, where A_c = (age-60) and B_c = (BMI-30):

$$\mathit{\text{half}}-\mathit{\text{life}}(A_c,B_c)=3.21-0.0025\times A_c+0.0020\times B_c-0.0021\times A_c\times B_c$$ (2)

Median IOPTH half-life increases with increasing BMI, but the predictive value of BMI declines with increasing age (Equation 2 and Figure 1). Therefore, the effect of BMI is negligible once a patient is older than 55 years, as the effect of BMI was not statistically significant until the age-BMI interaction was considered.

Figure 1.

Body mass index vs. predicted median IOPTH half-life stratified on the basis of age, with approximate curve fits.

Postoperative Follow-up

At the time of last follow-up, the median calcium for the study cohort was 9.4 mg/dL (range, 7.4–11.3) and median PTH was 22.6 (range 2.5–200.1). No patient had persistent disease and only one patient (0.3%) had a known recurrence, defined as both a serum calcium and PTH level above the upper limit of normal, at last follow-up, with a serum calcium level of 11.3 and PTH of 75 (IOPTH half-life, 2m40s). Median follow-up was 835 days (2.3 years, median 6 – 2193 days [.02 – 6.0 years]).

Discussion

Since the introduction of MIP, various criteria for IOPTH monitoring have been proposed. The two most commonly utilized are 1) the ‘Miami criterion’ (decrease of >50% from the highest pre-excision value at 10 minutes following parathyroid removal) and 2) a 10 minute post-excision decrease of >50% and into the normal range.^3–5 However, none of the previously published criteria account for patient characteristics, such as GFR or BMI, which might affect PTH elimination. One recent study by Cisco et al. examined the impact of race on IOPTH kinetics and found that African-American patients had higher initial pre-excision and 5-minute post-excision values, with similar 10-minute post-excision values; suggesting altered IOPTH kinetics, although no formal modeling was performed.¹⁰ In this study, the PTH elimination profiles of 306 patients that underwent MIP with IOPTH monitoring for primary HPT were analyzed to potentially identify preoperative predictors of IOPTH elimination that might affect intraoperative biochemical determination of cure. On multivariate analysis, statistically significant predictors of IOPTH half-life included BMI and an age-BMI interaction factor.

To our knowledge, this study of kinetic modeling of each individual patient’s IOPTH elimination during parathyroidectomy is the largest of its kind in the published literature.^12,14–17 Our finding of a mean IOPTH half-life of 3m28s is consistent with previous studies. In a two-phase model, Maier et al. reported a first phase mean IOPTH half-life of 3m24s (Nichols Institute Diagnostics assay) and in a single phase model, similar to the model used in this study, Bieglmayer et al. reported mean IOPTH half-life values ranging from 3m18s to 3m42s (Nichols Laboratories, Diagnostic Products Corporation, and Roche-Diagnostics assays respectively).^14,16

Selection of a kinetic model that most closely approximated the IOPTH elimination data of the cohort was paramount to quantifying IOPTH elimination in this study. Previous IOPTH kinetic studies have implemented either a multi-exponential decay model (multi-phase) or a single exponential decay model.^12,14,17 Maier et al. used a two phase exponential decay model; however, this required multiple IOPTH data points that were not obtained in our cohort.¹⁴ Two single phase models dependent on first order kinetics have previously been described. Libutti et al. utilized the pre-excision PTH as the initial data point, required three intraoperative data points, and assumed that the non-adenomatous glands’ contribution to serum PTH immediately increased as a function of the intraoperative decay constant, k, and time post-adenoma resection.¹⁷ In contrast, in the model utilized by Bieglmayer et al., the initial data point was the PTH level at the time of adenoma resection, required four intraoperative data points, and assumed that the non-adenomatous glands’ contribution to serum PTH was constant post-adenoma resection.¹⁶ Both of these models yielded identical IOPTH half-lives in an ideal setting.^16,17 However, in the operative setting, potential parathyroid gland manipulation during parathyroidectomy likely alters the true IOPTH profile and would thus exclude use of the preoperative PTH as the initial data point in attaining the most accurate IOPTH elimination profile. Therefore, this study utilized a model similar to Bieglmayer et al. in the analysis of IOPTH kinetics.

Only one previous study has investigated the predictors of IOPTH elimination. Gannago-Yared et al. evaluated 108 patients with primary HPT who underwent minimally invasive parathyroidectomy and determined that elderly age and reduced renal function (MDRD GFR <60 ml/min/1.73m²) were independent preoperative predictors of a slower decrease in IOPTH between the PTH values at baseline and at 10 minutes post-adenoma resection.⁹ In this study, however, GFR was not found to be an independent predictor of IOPTH half-life on univariate or multivariate analysis. The results of this study do show BMI and an age-BMI interaction factor to be predictors of IOPTH half-life on multivariate analysis. The relationship between BMI and PTH metabolism is unclear. Animal models have shown that the hepatic Kupffer cell is a major determinant of the first phase of PTH elimination, which corresponds to the elimination phase of our model.^18,19 Therefore, it is possible that patients with higher BMI, perhaps due to a higher likelihood of steatohepatitis, may have reduced hepatic metabolism of PTH.²⁰

There are several limitations to this study, including those inherent to a retrospective chart review. Furthermore, recorded times for IOPTH blood draws were approximate, and may not reflect exact time intervals; therefore, the calculated IOPTH half-life may be inaccurate. However, since the predicted half-life differences were short (seconds), this limitation was not likely to affect the outcomes of this study.

In conclusion, patient race, gender and renal function were not independent predictors of IOPTH half-life in patients with pHPT undergoing parathyroidectomy. Interestingly, BMI and an age-BMI interaction factor may predict IOPTH half-life. However, predicted IOPTH half-life differences based on BMI are small, on the order of seconds, thus limiting the translation of the findings into clinical practice. Furthermore, the overall success rate in this cohort of patients was >99%, providing further evidence that these relatively small differences in IOPTH half-life are not clinically significant. Given these findings, it is recommended that current IOPTH criteria should continue to be applied to all patients undergoing parathyroidectomy for sporadic primary hyperparathyroidism.